WO2021081051A1

WO2021081051A1 - Predictive powertrain control for a multi-mode hybrid electric vehicle

Info

Publication number: WO2021081051A1
Application number: PCT/US2020/056600
Authority: WO
Inventors: Darrell ROBINETTE; Josh ORLANDO; Joseph ONCKEN; Bo Chen; Kovid SACHDEVA; Huanqing WANG
Original assignee: Michigan Technological University; University of Michigan Ann Arbor
Current assignee: Michigan Technological University; University of Michigan Ann Arbor
Priority date: 2019-10-21
Filing date: 2020-10-21
Publication date: 2021-04-29
Anticipated expiration: 2022-04-21

Abstract

One example control system for a hybrid electric vehicle (HEV) includes an electronic processor configured to retrieve future path condition data indicative of a future path condition of a path on which the HEV is traveling. The electronic processor may determine a desired operating mode of the HEV based on the future path condition data and current operating characteristic data of the HEV. The electronic processor may control a mode switching actuator to be configured such that the powertrain of the HEV operates in the desired operating mode. The electronic processor may be further configured to control at least one driving component of a powertrain of the HEV based on the operating characteristic data, the future path condition data, and the desired operating mode.

Description

PREDICTIVE POWERTRAIN CONTROL FOR A MULTI-MODE HYBRID ELECTRIC VEHICLE

RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 62/924,086, filed on October 21, 2019; to U.S. Provisional Application No. 62/962,141, filed on January 16, 2020; and to U.S. Provisional Application No. 63/049,686, filed July 9, 2020, the entire contents of all of which are hereby incorporated by reference.

STATEMENT OF GOVERNMENT INTEREST

[0002] This disclosure was made with government support under DE-AR0000788 awarded by the U.S. Department of Energy. The government has certain rights in the disclosure.

FIELD

[0003] The present disclosure relates to powertrain control for multi-mode hybrid electric vehicles, and more particularly to powertrain control that takes into account predictions of future vehicle velocity demand and future vehicle torque demand.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0004] The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts included in the disclosure, and explain various principles and advantages of those embodiments.

[0005] FIG. 1 is a diagram of a communication system including a hybrid electric vehicle (HEV) according to one example embodiment.

[0006] FIG. 2 is a simplified block diagram of the HEV of the communication system of FIG. 1 according to one example embodiment.

[0007] FIG. 3 is a simplified block diagram of control, communication, and user interface components of the HEV of FIG. 2 according to one example embodiment. [0008] FIG. 4 illustrates a high level flow diagram of functional features implemented by an electronic processor of the HEV of FIGS. 2 and 3 according to one example embodiment.

[0009] FIG. 5 illustrates a flow diagram of an optimal mode path planning algorithm of FIG. 4 that is executed by the electronic processor of the HEV according to one example embodiment.

[0010] FIG. 6 illustrates a flow diagram of a nonlinear model predictive control (NMPC) powertrain controller 420 of FIG. 4 that is implemented by the electronic processor of the HEV according to one example embodiment.

[0011] FIG. 7 illustrates an example mode path implementation made by the electronic processor of the HEV in accordance with the optimal mode path planning algorithm of FIG. 5 according to one example embodiment.

[0012] FIG. 8 illustrates a flow chart of a method of controlling driving components of the HEV by the electronic processor of the HEV according to one example embodiment.

[0013] Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments.

[0014] The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

DETAILED DESCRIPTION

[0015] Hybrid electric vehicles (HEVs), including plug-in hybrid electric vehicles (PHEVs), include multiple propulsion systems (e.g., an engine and one or more electric motors) that are controlled by a control system that determines which of the propulsion systems is active, inactive, or reactive to drive the wheels of the HEV or charge a battery of the HEV. Current control systems for HEVs use rule-based methods that are configured for real-time implementation based on real-time information detected by onboard sensors. However, current control systems for HEVs lack the ability to predictively control and automatically adapt to driving condition changes that will be experienced by the HEV in the near future (e.g., driving condition changes caused by traffic conditions, path/road conditions, etc.). By only controlling the multiple propulsion systems based on real-time information, current control systems may control the multiple propulsion systems in manners that waste energy (e.g., stored energy from a battery of the HEV and/or fuel used to power the engine of the HEV). Accordingly, there is a technological problem with the control systems of multi- mode HEVs that causes the HEVs to waste energy in at least some operating situations.

[0016] For example, a control system of an HEV may turn off the engine due to real-time information indicating that the HEV’s torque demand is low only to realize a few seconds later that the HEV’s torque demand has become high as the HEV begins to travel up a hill. Thus, the current control system may restart the engine only seconds after turning the engine off. In some instances, restarting of the engine may consume more energy than leaving the engine running during this time period. As another example, a control system of an HEV may leave the engine running longer than necessary in some situations because the control system may be programmed to attempt to avoid frequent stopping and restarting of the engine. However, when the HEV is traveling on a path of prolonged downhill grade, the HEV may be able to operate purely using the electric motor(s) because only low torque demand may be needed. Thus, any extended operation of the engine during the time that the HEV is traveling on the path of prolonged downhill grade may cause the HEV to waste energy in the form of excess fuel consumption.

[0017] To address the technological problem of wasted energy in multi-mode HEVs, disclosed are, among other things, predictive control systems and methods for controlling the powertrain of a multi-mode HEV using future path condition data indicative of a future path condition of a path on which the HEV is traveling. The disclosed control systems and methods provide powertrain mode selection and control (i.e., power split optimization of the propulsion systems of the HEV) that result in energy savings (e.g., energy savings of the battery of the HEV and/or savings in fuel used to power the engine of the HEV) compared to current control systems and methods that do not taking future path condition data into account. In other words, the disclosed control systems and methods provide an optimal mode selection and operating strategy for the current instant of HEV operation that is considerate of future driving conditions of the path on which the HEV is traveling. Accordingly, the disclosed control systems and methods allow the HEV to operate more efficiently by reducing energy consumption with little to no reduction in operating performance of the HEV as perceived by the user.

[0018] Continuing the first example above, the disclosed control system and method may determine that the HEV is approaching a hill in a road in the next three seconds and may keep the engine running despite real-time information indicating that the torque demand is low. Continuing the second example above, the disclosed control system and method may determine that the HEV is currently traveling on a path with a prolonged downhill grade (e.g., for the next ten seconds of travel). Accordingly, the engine may be turned off and the HEV may be switched to electric-only mode due to the HEV likely remaining in a low torque demand state for at least the next ten seconds.

[0019] One embodiment provides a control system for a hybrid electric vehicle. The control system may include a memory configured to store future path condition data indicative of a future path condition of a path on which the hybrid electric vehicle is traveling. The control system may also include a plurality of sensors, each of which is configured to monitor an operating characteristic of the hybrid electric vehicle. The control system may also include an electronic processor communicatively coupled to the memory, to the plurality of sensors, to a mode switching actuator of a powertrain of the hybrid electric vehicle, and to driving components of the powertrain of the hybrid electric vehicle. The electronic processor may be configured to receive, from the plurality of sensors, operating characteristic data of the plurality of operating characteristics. The electronic processor may be further configured to retrieve, from the memory, the future path condition data of the path on which the hybrid electric vehicle is traveling. The electronic processor may be further configured to determine a desired operating mode of the hybrid electric vehicle based on the operating characteristic data and the future path condition data. The electronic processor may be further configured to control the mode switching actuator to be configured such that the powertrain of the hybrid electric vehicle operates in the desired operating mode. The electronic processor may be further configured to determine a control action for controlling at least one of the driving components of the powertrain based on the operating characteristic data, the future path condition data, and the desired operating mode. The electronic processor may be further configured to control the at least one of the driving components of the powertrain in accordance with the control action.

[0020] Another embodiment provides a method of controlling a hybrid electric vehicle. The method may include receiving, from each of a plurality of sensors of the hybrid electric vehicle, operating characteristic data of an operating characteristic of the hybrid electric vehicle. The method also may include retrieving, from a memory of the hybrid electric vehicle, future path condition data of a path on which the hybrid electric vehicle is traveling. The future path condition data may be indicative of a future path condition of the path on which the hybrid electric vehicle is traveling. The method also may include determining, with an electronic processor of the hybrid electric vehicle, a desired operating mode of the hybrid electric vehicle based on the operating characteristic data and the future path condition data. The method also may include controlling, with the electronic processor, a mode switching actuator of a powertrain of the hybrid electric vehicle to be configured such that the powertrain of the hybrid electric vehicle operates in the desired operating mode. The method also may include determining, with the electronic processor, a control action for controlling at least one driving component of the powertrain based on the operating characteristic data, the future path condition data, and the desired operating mode. The method also may include controlling, with the electronic processor, the at least one driving component of the powertrain in accordance with the control action.

[0021] For ease of description, some or all of the example systems and devices presented herein are illustrated with a single example of each of its component parts. Some examples may not describe or illustrate all components of the systems or devices. Other example embodiments may include more or fewer of each of the illustrated components, may combine some components, or may include additional or alternative components.

[0022] FIG. 1 is a diagram of a communication system 100 according to one example embodiment. In the example illustrated, the communication system 100 includes a hybrid electric vehicle (HEV) 105 and a communication network 110. In some embodiments, the communication system 100 also includes at least one additional connected vehicle 115 that may be any type of vehicle (e.g., a hybrid vehicle, an electric vehicle, an engine-propelled vehicle, etc.). The communication system 100 may also include a database 120 connected to the communication network 110 and accessible by other devices in the system 100 (e.g.,

HEV 105) via the network 110. Although FIG. 1 shows a single HEV 105, a single connected vehicle 115, and a single database 120, in some embodiments, the system 100 may include fewer or additional components. For example, the system 100 may include multiple HEVs 105, connected vehicles 115, and/or databases 120. As another example, the system 100 may not include the network 110, the connected vehicle 115, and/or the database 120. In embodiments, the HEV 105 may function as a stand-alone device with built-in components that allow for performance of the methods described herein.

[0023] In some embodiments, the HEV 105 communicates with the connected vehicle 115 and/or the database 120 over the communication network 110 (for example, by sending and receiving radio signals to and from network infrastructure of the network 110 such as base stations). The communication network 110 may include wireless and wired portions. All or parts of the communication network 110 may be implemented using various existing specifications or protocols. In some embodiments, the network 110 is a wide area network (“WAN”) (e.g., a TCP/IP based network), a local area network (“LAN”), a neighborhood area network (“NAN”), a home area network (“HAN”), or personal area network (“PAN”) employing any of a variety of communications protocols, such as Wi-Fi, Bluetooth, ZigBee, etc. In some embodiments, the network 110 is a cellular network, such as, for example, a Global System for Mobile Communications (“GSM”) network, a General Packet Radio Service (“GPRS”) network, a Code Division Multiple Access (“CDMA”) network, an Evolution-Data Optimized (“EV-DO”) network, an Enhanced Data Rates for GSM Evolution (“EDGE”) network, a 3GSM network, a 4GSM network, a 4G LTE network, a 5GNew Radio network, a Digital Enhanced Cordless Telecommunications (“DECT”) network, a Digital AMPS (“IS-136/TDMA”) network, or an Integrated Digital Enhanced Network (“iDEN”) network, etc. The connections between the devices 105, 115, 120 and the network 110 are, for example, wired connections, wireless connections, or a combination of wireless and wired connections.

[0024] In some embodiments, the devices 105, 115, 120 may communicate directly with each other using a communication channel or connection that is outside of the communication network 110. For example, as indicated in FIG. 1, the HEV 105 and the connected vehicle 115 may communicate directly with each other when they are within a predetermined distance from each other.

[0025] In some embodiments, the HEV 105 and the connected vehicle 115 include built- in hardware components that allow the HEV 105 and the connected vehicle 115 to communicate with other devices. For example, the built-in hardware may be included in the HEV 105 and/or the connected vehicle 115 at the time of manufacturing (e.g., connected to a control system of the vehicle 105, 115; integrated into a dashboard; etc.). In other embodiments, a device separate from the connected vehicle 115 (e.g., a smart phone) may be used inside the connected vehicle 115 as the communication means for the connected vehicle 115. In some embodiments, the separate device may be able to communicate with the control system of the connected vehicle 115 (e.g., to receive data from sensors of the connected vehicle 115) for transfer to other devices (e.g., the HEV 105). In some embodiments, the separate device may include its own sensors to determine information regarding the connected vehicle 115. For example, the separate device may be able to determine a speed at which the connected vehicle is traveling by periodically monitoring a location of the separate device over a time period.

[0026] FIG. 2 is a simplified block diagram of the HEV 105 of the communication system 100 of FIG. 1 according to one embodiment. In the example shown, the HEV 105 includes control, communication, and user interface components 205 as shown in further detail in FIG. 3. The HEV 105 also includes a mode switching actuator 210 and a battery 215 that are both coupled to the control, communication and user interface components 205 (e.g., coupled to one or more electronic processors 305 shown in FIG. 3). The HEV 105 also includes driving components 220 (also referred to as propulsion systems or a powertrain) that are configured to drive wheels of the HEV 105 to cause the HEV 105 to move. The driving components 220 may include an internal combustion engine 225 that uses fuel such as gasoline to generate power. The driving components 220 may also include one or more electric motors 230 configured to use power from the battery 215 to generate power. In some embodiments, the battery 215 also provides power to the control, communication, and user interface components 205.

[0027] In some embodiments, the mode switching actuator 210 is controlled by an electronic processor 305 of the control, communication, and user interface components 205 to configure certain driving components 220 to connect with each other and/or axles of the HEV 105 to operate in a desired or requested drive mode of a plurality of possible drive modes. In some embodiments, the mode switching actuator 210 includes one or more of a clutch and a gearset. For example, the mode switching actuator 210 may include one or more of a planetary gearset, a passive one way clutch, a hydraulic rotating clutch, and a hydraulic braking clutch that are configurable to connect different driving components 220 of the HEV 105 with each other. Specific examples of mode switching actuators 210 and their functions are disclosed in U.S. Provisional Application No. 62/924,086 and U.S. Provisional Application No. 63/049,686, to which this application claims priority and which are incorporated by reference.

[0028] In some embodiments, the HEV 105 may be configurable to operate in four possible modes that include charge depleting (CD) modes and charge sustaining (CS) modes. In some embodiments, charge depleting operation is a default mode of operation for the HEV 105. Charge depleting operation may be utilized by the HEV 105 until one of two events occurs at which point charge saving operation may begin. The first event may be that a state of charge of the battery 215 reaches a lower limit (e.g., approximately 16%). The second event may be the operator of the HEV 105 enters a user input requesting that the HEV 105 operate in charge saving mode.

[0029] In some embodiments, the charge depleting mode(s) is an electric-only mode in which only the one or more electric motors 230 provide power to drive the wheels of the HEV 105. In some embodiments, the HEV 105 may include multiple charge depleting modes. For example, power may be provided by only a first electric motor 230 in a first charge depleting mode, by only a second electric motor 230 in a second charge depleting mode, and by both electric motors 230 in a third charge depleting mode. In other embodiments, the HEV 105 may include a single electric motor 230 and may include a single charge depleting mode during which power is provided by the single electric motor 230. In high torque demand situations of the HEV 105 (e.g., vehicle launch and high acceleration events), multiple electric motors 230 may be used instead of single electric motor 230. In low torque demand situations (e.g., after the vehicle has reached a desired travel speed and desires to maintain the travel speed), a single electric motor 230 may be used to maintain rotation of the wheels of the HEV 105. [0030] In some embodiments, charge saving operation utilizes one of three modes (i.e., hybrid modes) to maintain battery state of charge within approximately ±1% of either the lower limit state of charge in the case of a depleted battery 215 or, in the case of a non- depleted battery, the state of charge of the battery 215 at the time of the in-vehicle charge saving command received from the operator of the HEV 105. The below examples of these three modes refer to an embodiment of the HEV 105 that includes two electric motors 230.

[0031] In some embodiments, one charge saving mode includes a low extended range (LER) mode that is an input powersplit hybrid arrangement. In the LER mode, the engine speed is decoupled from the wheels of the HEV 105. The power produced by the engine 225 is split between a carrier of the HEV 105, which serves as the output, and a first electric motor 230, which acts as a generator and speed controller for the engine 225. Depending on the amount of power produced by the first electric motor 230, power is sent to a second electric motor 230, which also provides power to the output, and, in the case of the power produced by the first electric motor 230 being in excess of that required by the second electric motor 230, to the battery 215. The LER mode is most useful during high torque, low speed events. For example, use of the LER mode is prominent in scenarios such as city driving where frequent stop and starts occur and overall vehicle speeds are relatively low.

[0032] In some embodiments, a second charge saving mode includes a fixed ratio extended range (FER) mode that is a parallel hybrid arrangement. The mode switching actuator(s) 210 is controlled such that the first electric motor 230 is grounded and the engine speed is directly coupled to wheel speed. This allows the engine 225 to deliver power directly to the output carrier of the HEV 105 while the second electric motor 230 is able to either provide or absorb power from the output carrier depending on the current output power demand of the HEV 105. The FER mode is useful during acceleration events and can provide efficient charging of the battery 215 when overall output power demand of the HEV 105 is low.

[0033] In some embodiments, a third charge saving mode includes a high extended range (HER) mode that is a compound powersplit hybrid arrangement. The mode switching actuator(s) 210 is controlled such that, similar to the LER mode, the engine speed is decoupled from wheel speed and is controlled by the electric motors 230. Because this mode switching actuator arrangement allows the engine 225 to be run at low speeds with high drive unit output speeds, the HER mode is most useful when used in low-torque demand situations such as highway driving.

[0034] The block diagram of FIG. 2 is a simplified block diagram of the HEV 105 that may not show all components of the HEV 105 and/or all connections between different components of the HEV 105 in some embodiments. In other words, the HEV 105 may include fewer or additional components in different arrangements in other embodiments. For example, the control, communication, and user interface 205 may include hardware to control the electric motors 230 (e.g., see control circuitry 370 of FIG. 3) despite FIG. 2 not showing a connection between the electric motors 230 and the control, communication, and user interface components 205.

[0035] FIG. 3 is a simplified block diagram of the control, communication, and user interface components 205 of the HEV 105 according to one embodiment. In the example shown, the HEV 105 includes an electronic processor 305 (for example, a microprocessor or another electronic device). The electronic processor 305 may include input and output interfaces (not shown) and may be electrically connected to a memory 310, a transceiver 315 including or connected to an antenna 316 (the combination of which may be referred to as a first network interface), a display 320, a microphone 325, and a speaker 330. The electronic processor 305 may receive data from any one or a combination of sensors 335 included in the HEV 105. The sensors 335 may include a battery sensor(s) 340, a velocity sensor 345, a torque sensor 350, an accelerometer(s)/gyroscope(s) 355, a global positioning system (GPS) sensor 360, a laser imaging, detection, and ranging (LiDAR) system 365, and/or the like. In some embodiments, the HEV 105 may include fewer or additional components in configurations different from that illustrated in FIG. 3. For example, in some embodiments, the HEV 105 also includes a camera that captures images/video of the surroundings of the HEV 105. As another example, the HEV 105 may not include the display 320 or the microphone 325 in some embodiments. In some embodiments, the HEV 105 performs additional functionality than the functionality described below.

[0036] The memory 310 may include read only memory (ROM), random access memory (RAM), other non-transitory computer-readable media, or a combination thereof. The electronic processor 305 is configured to receive instructions and data from the memory 310 and execute, among other things, the instructions. In particular, the electronic processor 305 executes instructions stored in the memory 310 to perform the methods described herein.

[0037] The combination of the transceiver 315 and the antenna 316 sends and receives data to and from the network 110 and/or the connected vehicle 115. For example, the transceiver 315 is a wireless communication transceiver for wirelessly communicating with the network 110 and/or the connected vehicle 115. The transceiver 315 may be a cellular transceiver, a WiFi transceiver, and/or another kind of transceiver that allows for wireless communication with external devices when operated in conjunction with the antenna 316. In some embodiments, the antenna 316 is integrated into the transceiver 315. In some embodiments, the electronic processor 305 receives information from external devices such as the connected vehicle 115 and/or the database 120 via wireless communication that occurs through the transceiver 315 and the antenna 316. For example, the connected vehicle 115 may have recently traveled on the same path on which the HEV 105 currently traveling and may have monitored road conditions and/or traffic conditions at the point on the path that the HEV 105 is approaching. Accordingly, the connected vehicle 115 may send information regarding the monitored road condition and/or traffic conditions for the point(s) on the path that the HEV 105 is approaching to the HEV 105. In some embodiments, the database 120 may store similar information from one or more connected vehicles 115 and may provide this similar information to the HEV 105 when the HEV 105 is approaching a corresponding point(s) of the path that was previously and recently traveled by the connected vehicle(s) 115. Additionally, the database 120 may store status information about the path on which the HEV 105 is currently traveling by communicating with other external devices (e.g., traffic cameras, etc.). The database 120 may also be pre-programmed with map data that indicates a plurality of properties of paths on which vehicles travel (e.g., location of traffic signs, locations of turns, curves, etc.; an amount of incline/grade/steepness of the path at different points in the path; etc.). In some embodiments, the information regarding the monitored road condition and/or traffic conditions for the point(s) on the path that the HEV 105 is approaching may be referred to as future path condition data.

[0038] In some embodiments, the electronic processor 305 receives electrical signals representing sound from the microphone 325 such as instructions from the user regarding operation of the HEV 105. The electronic processor 305 may output data received from the network 110 via the transceiver 315 and the antenna 316, for example from external devices such as the connected vehicle 115 or the database 120, through the speaker 330, the display 320, or a combination thereof. In some embodiments, the electronic processor 305 may not output received data from external devices to the operator of the HEV 105. In some embodiments, the electronic processor 305 may additionally or alternatively use the received data to control operation of the HEV 105, for example by determining a desired operating mode and/or a control action of the driving components 220. In some embodiments, the display 320 may include a touchscreen display configured to receive a user input. In some embodiments, the HEV 105 includes a separate input device (i.e., a keyboard, touchpad or the like) to receive user input.

[0039] As shown in FIG. 3, the electronic processor 305 may be coupled to driving components control circuitry 370 to control operation of the driving components 220 and/or the mode switching actuator 210. In some embodiments, the driving components control circuitry 370 includes field-effect transistors (FETs) between the battery 215 and the electric motor(s) 230 that can be controlled to be a conductive or non-conductive state to control whether the battery 215 provides power to the electric motor(s) 230. In some embodiments, the driving components control circuitry 370 includes an engine starter configured to start the engine 225. The driving components control circuitry 370 may include other circuitry in some embodiments.

[0040] In some embodiments, the battery sensor(s) 340 include a voltage sensor, a current sensor, and/or the like that provide data that may allow the electronic processor 305 to determine a state of charge of the battery 215. For example, the voltage sensor may measure an open circuit voltage of the battery 215. The memory 310 may store other information about the battery 215 that may be assumed to remain constant when calculating the state of charge of the battery 215 in some embodiments. For example, the memory 310 may store an internal resistance of the battery 215 and/or a total battery capacity of the battery 215. The combination of stored information about the battery 215 and monitored data of the battery 215 may be used by the electronic processor 305 to determine a state of charge of the battery 215 as explained in greater detail herein.

[0041] In some embodiments, the velocity sensor 345 provides data to the electronic processor 305 to allow the electronic processor 305 to determine a speed/velocity at which the HEV 105 is traveling. For example, the velocity sensor 345 may be a speedometer. The velocity sensor 345 may include one or more sensors to allow the electronic processor 305 to determine a speed/velocity of the engine 225 and/or the electric motor(s) 230. In some embodiments, the torque sensor 350 provides data to the electronic processor 305 to allow the electronic processor 305 to determine a torque supplied by the engine 225 (e.g., a torque being applied to a wheel axle of the HEV 105). For example, the torque sensor 350 may include a strain gauge. In some embodiments, the accelerometer(s)/gyroscope(s) provide data to the electronic processor 305 to allow the electronic processor 305 to determine an orientation of the HEV 105 (e.g., whether the HEV 105 is traveling on an inclined surface, traveling on a declined surface, traveling on a curve in a road, or the like). In some embodiments, the GPS sensor 360 allows the electronic processor 305 to determine a location of the HEV 105 as the HEV 105 moves along a path. For example, the memory 310 may store map information and the electronic processor 305 may determine a location of the HEV 105 (e.g., a specific point on a road) using the GPS sensor 360 and the map information stored in the memory 310.

[0042] In some embodiments, the HEV 105 includes the LiDAR system 365, a camera, and/or another system configured to monitor the surroundings of the HEV 105. The LiDAR system 365 and/or these other surrounding monitoring devices may provide data to the electronic processor 305 to allow the electronic processor 305 to determine characteristics of the surroundings of the HEV 105. For example, the LiDAR system 365 and/or these other surrounding monitoring devices may detect an object in front of the HEV 105 (e.g., an obstruction in the road, another vehicle a certain distance in front of the HEV 105, a traffic signal in front of the HEV 105, and the like).

[0043] In some embodiments, the driving components control circuitry 370 includes additional sensors to monitor the operation of one or more of the driving components 220 of the HEV 105. For example, the HEV 105 may include one or more Hall sensors to monitor the speed of the electric motor(s) 230 and/or the engine 225. The electronic processor 305 may use the monitored speed of the electric motor(s) 230 and/or the engine 225 to control the driving components control circuitry 370 to control the driving components 220 to operate, for example, at a desired speed. [0044] Although FIG. 3 illustrates a block diagram of the control, communication, and user interface components 205 of the HEV 105, in some embodiments, the connected vehicle 115 may include similar components that perform similar functions as those shown in FIG. 3 and as explained above with respect to FIG. 3. In some embodiments, the connected vehicle 115 may include fewer or additional components in configurations different from that illustrated in FIG. 3. In some embodiments, the connected vehicle 115 may have similar driving components 220 as the HEV 105. However, in other embodiments, the connected vehicle 115 may include different driving components 220 than the HEV 105. For example, the connected vehicle 115 may not include a battery 215 configured to drive the wheels, which may be configured to be driven solely by an internal combustion engine. As explained previously herein, in some embodiments, the components of FIG. 3 may be included in a separate external device (e.g., a smart phone) that is located inside the connected vehicle 115 without being physically integrated within the connected vehicle. Nevertheless, the combination of the vehicle and the separate external device that is located inside the vehicle may be referred to as the connected vehicle 115.

[0045] FIGS. 4-6 are functional flow diagrams illustrating control features implemented by the electronic processor 305 to control operation of the driving components 220 of the HEV 105 according to one some example embodiments. The flow diagrams of FIGS. 4-6 are implemented by one or more of the components of the HEV 105 shown in FIGS. 2 and 3.

[0046] FIG. 4 illustrates a high level flow diagram 400 that includes a velocity trajectory generator 405 implemented by the electronic processor 305. The electronic processor 305 also acts an integrated predictive powertrain controller 410 that executes optimal mode path planning 415 and that includes a nonlinear model predictive control (NMPC) powertrain controller 420. In some embodiments, each of the optimal mode path planning 415 and the NMPC powertrain controller 420 receive information from a simplified gearbox model 425 in order to make calculations regarding mode selection and control actions for the driving components 220 (i.e., powertrain) of the HEV 105 as described herein.

[0047] In some embodiments, the electronic processor 305 acts as a velocity trajectory generator 405 by determining a future vehicle velocity demand and a future vehicle torque demand for the HEV 105. The electronic processor 305 may determine the future vehicle velocity demand and the future vehicle torque demand in a number of different manners. For example, the memory 310 may store future path condition data indicative of a future path condition of a path on which the hybrid electric vehicle is traveling. Continuing this example, this future path condition data may be pre-programmed values of vehicle velocity demand and vehicle torque demand for each point on a plurality of paths (e.g., roads), that are stored in a look-up table. As the HEV 105 travels along a path, the electronic processor 305 may determine a location of the HEV 105 on the path using the GPS sensor 360 and may use its determined location to retrieve a vehicle velocity demand and a vehicle torque demand corresponding to upcoming portions of the path from the memory 310.

[0048] As another example, the electronic processor 305 may dynamically determine the future vehicle velocity demand and the future vehicle torque demand based on at least one of a traffic condition, a traffic sign location, an elevation of the path, a change of direction of the path, and a weather condition. For example, any of this information may be detected by one or more of the sensors 335 of the HEV 105. Additionally or alternatively, any of this information may be received from the connected vehicle 115 (e.g., that has recently traveled on the path in front of the HEV 105), the database 120, and/or another external device over the network 110. Upon sensing or receiving future path condition data indicative of a future path condition of a path on which the HEV 105 is traveling, the HEV 105 may determine further future path condition data such as the future vehicle velocity demand and the future vehicle torque demand. For example, upon receiving information indicating that a stalled vehicle is located five hundred feet ahead of the HEV 105, the electronic processor 305 may determine that a braking condition is likely to occur followed by slowed traveling speeds and eventual reacceleration after the HEV 105 has passed the stalled vehicle. As another example, the electronic processor 305 may determine that future torque demand is likely to decrease if the road on which the HEV 105 is traveling decreases in incline/pitch in the near future. On the other hand, the electronic processor 305 may determine that future torque demand is likely to increase if the road on which the HEV 105 is traveling increases in incline/pitch in the near future. Regardless of how the HEV 105 obtains or calculates the future path condition data, the future path condition data may be stored in the memory 310 for later retrieval and use by the electronic processor 305.

[0049] In some embodiments, the electronic processor 305 determines a future vehicle velocity demand and a future vehicle torque demand for the HEV 105 for each of a plurality of time intervals (e.g., one second time intervals) of a future time period (e.g., ten seconds). Continuing the first above example with respect to the stalled vehicle, the electronic processor 305 may determine that the velocity and torque demand are likely to remain constant for the next two seconds before the HEV 105 decelerates as the HEV approaches the stalled vehicle. The electronic processor 305 may determine that the speed demand for future seconds three through six is likely to remain constant while the torque demand is likely to decrease as the HEV 105 maintains a low speed when passing the stalled vehicle. The electronic processor 305 may also determine that the speed demand and torque demand for future seconds seven through ten are likely to increase as the HEV 105 begins accelerating back up to full traveling speed after the HEV 105 passes the stalled vehicle. By having the electronic processor 305 predict the speed and torque demands for each time interval in a future time period, the electronic processor 305 may efficiently control the driving components 220 at the current time to save energy when compared to known HEV control methods. For example, an HEV using a known HEV control method may be operating in electric-motor-only (EV) mode and may receive real-time torque sensor data indicating a high torque demand as the EHV is approaching the stalled vehicle. The known HEV control method may instruct the engine to start (i.e., switch to a hybrid mode of operation) in response to the real-time torque sensor data indicating a high torque demand only to realize that the torque demand will decrease as the HEV decelerates when approaching the stalled vehicle. On the other hand, in the same situation in which real-time torque sensor data indicates a high torque demand, the electronic processor 305 implementing the optimal mode path planning algorithm 415 using future path condition data may remain in EV mode until the HEV 105 passes the stalled vehicle with the engine remaining off, thus saving energy.

[0050] As another example, the electronic processor 305 may determine that the future path condition data indicates that the path on which the HEV 105 is traveling includes an extended segment of slight downhill grading/pitch. Accordingly, the electronic processor 305 may determine that there is likely to be a low torque demand for the near future and that a mode switch from a hybrid operating mode to the EV mode will not result in a significant state of charge penalty (i.e., a significant amount of energy consumed that would undesirably reduce the state of charge of the battery 215). Thus, the electronic processor 305 may turn off the engine 225 to operate in the EV mode. [0051] While the examples discussed herein utilize ten one second time intervals over a ten second time period in the future, in other embodiments, other time interval values and other time period values may be used. In some embodiments, the time interval may be selected based on an amount of time that it takes for the mode switching actuator 210 to switch the driving components 220 from one operation mode to another operation mode. For example, the time interval may be selected to be longer than the longest amount of time that it takes for the mode switching actuator 210 to switch the driving components 220 from one operation mode to another operation mode.

[0052] In some embodiments, the electronic processor 305 takes into account current operating characteristics of the HEV 105 when determining the future vehicle velocity demand and the future vehicle torque demand. Again continuing the previous example with respect to the stalled vehicle, a current velocity of the HEV 105 may indicate whether the HEV 105 is likely to slow down when approaching the stalled vehicle. For example, if the HEV 105 is already traveling at a reduced speed (e.g., ten miles per hour on a highway with a speed limit of seventy miles per hour), the electronic processor 305 may determine that the HEV 105 is likely to maintain a similar speed until the HEV 105 passes the stalled vehicle (i.e., until the traffic jam ends). However, if the HEV 105 is traveling at seventy miles per hour when approaching the stalled vehicle, the electronic processor 305 may determine that some type of reduction in speed is likely to occur as the HEV 105 approaches the stalled vehicle.

[0053] Once the electronic processor 305 determines the future vehicle velocity demand and the future vehicle torque demand (i.e., a predicted vehicle trajectory), the electronic processor 305 uses the determined demand information along with current operating characteristics of the HEV 105 to determine one or more control actions to control the driving components 220 (i.e., the powertrain). To do so, the electronic processor 305 implements the integrated predictive powertrain controller 410. The powertrain controller 410 is shown as including three functional components 415, 420, and 425 that may overlap with each other in some embodiments. In other words, while three separate components 415, 420, and 425 are shown in FIG. 4, the electronic processor 305 and other components of the HEV 105 serve to implement these components 415, 420, and 425 together in some embodiments. [0054] As indicated by the feedback arrow in FIG. 4, the electronic processor 305 is configured to receive, from one or more sensors 335, operating characteristic data of a plurality of operating characteristics of the HEV 105, particularly the driving components 220 and the battery 215. From the received data, the electronic processor 305 may calculate a current velocity of the HEV 105 and a state of charge of the battery 215, among other operating characteristics.

[0055] In some embodiments, the electronic processor 305 is programmed according to a simplified gearbox model 425 (i.e., a simplified powertrain model) that reduces a plurality of kinematics of the driving components 220 from a first amount of degrees of freedom to a lesser amount of degrees of freedom as a function of commanded torques and desired accelerations of at least one of an input shaft and an output shaft of a gearbox of the driving components 220. In some embodiments, the simplified gearbox model 425 makes computations of the electronic processor 305 more efficient by allowing the electronic processor 305 to more quickly perform calculations regarding current operational characteristics of the driving components 220 and predicted future velocity and torque demands of the HEV 105. For example, the simplified gearbox model 425 may allow the electronic processor 305 to reduce the amount of variables used during calculations regarding current operational characteristics of the driving components 220 and predicted future velocity and torque demands of the HEV 105. In some embodiments, simplified gearbox model 425 is used to provide feedback information from the sensors 335 for use during optimal mode path planning 415 and/or for use by the NMPC powertrain controller 420. Additional details of the simplified gearbox model 425 are explained in U.S. Provisional Application No. 62,924,086, to which this application claims priority and which is incorporated by reference.

[0056] FIG. 5 illustrates a flow diagram 500 of the optimal mode path planning algorithm 415 implemented by the electronic processor 305 according to one example embodiment. In some embodiments, the optimal mode path planning algorithm is configured to select/determine a desired operating mode of the HEV 105 based on the current operating characteristic data of the HEV and based on the future path condition data regarding a future path condition of the path on which the HEV 105 is traveling. [0057] As shown in FIG. 5, as an input to the optimal mode path planning algorithm 415, the electronic processor 305 retrieves, from the memory 310, the future path condition data (e.g., predicted vehicle trajectory) that was previously determined by the electronic processor 305, received from an external device via wireless communication, and/or pre-programmed into the memory 310 as described previously herein with respect to the velocity trajectory generator 405. Although not shown in FIG. 5, the electronic processor 305 also receives operating characteristic data of the HEV 105 (e.g., as processed by the simplified gearbox model 425) as an input to the optimal mode path planning algorithm 415 as generally indicated in FIG. 4.

[0058] Using the future path condition data and the operating characteristic data, the electronic processor 305 determines a predicted operating mode (i.e., a desired operating mode) for the HEV 105 for each of a plurality of time intervals of a future time period. In some embodiments, at block 505, the electronic processor 305 determines, based on the operating characteristic data and the future path condition data, a first residing energy consumption cost of continuing to operate in a current operating mode (e.g., the LER mode) of the powertrain (i.e., driving components 220) during a next time interval. Also at block 505, the electronic processor 305 determines, based on the operating characteristic data and the future path condition data, a second residing energy consumption cost of operating in a different operating mode (e.g., the FER mode and/or an electric- motor-only mode) of the powertrain during the next time interval. In other words, the electronic processor 305 determines a residing energy consumption cost for each of the possible operating modes that may be used in the next time interval.

[0059] At block 510, the electronic processor 305 determines a mode changing energy consumption cost of switching from the current operating mode to the different operating mode based at least partially on an amount of energy consumed by the mode switching actuator 210 to switch from the current operating mode to the different operating mode.

[0060] In some embodiments, the electronic processor 305 calculates the energy consumption costs (i.e., the amount of energy estimated to be consumed by the HEV 105 in a given mode or when switching from one mode to another mode) using the simplified gearbox model 425. In some embodiments, for charge depleting modes, inputs to the model 425 include vehicle speed and axle torque as sensed by the sensors 335. In some embodiments, for charge saving modes, inputs to the model 425 include vehicle speed, axle torque, engine speed, and engine torque as sensed by the sensors 335. In some embodiments, for switching between different modes, the memory 310 may store an estimated amount of energy required to switch from any one mode to any other mode. In some embodiments, the consumption costs include at least one of estimated fuel consumed and estimated electrical energy consumed. In some embodiments, the model 425 normalizes the calculated energy consumption costs to a common unit of energy (e.g., megajoules, grams of fuel consumed, etc.) as explained in greater detail below.

[0061] As indicated by blocks 505 and 510 of FIG. 5, the cost function, shown in equation (1) below, includes two types of cost. At block 505, the first type of cost is the cost incurred by residing in a specific mode in a given time step. This cost includes terms for the fuel consumed during each time step,

and a term penalizing deviation in the actual state of charge from the reference state of charge, ( SOC_Re^_erence — SOC_(k)). At block 510, the second type of cost is the cost incurred by transitioning from one mode to another (i.e., a mode shift penalty term). The mode shift penalty term includes the kinetic energy change in rotating one or more of the mode switching actuators 210 due to a mode shift. For example the mode shift penalty includes the electrical energy required by an electric hydraulic pump to execute the mode shift, and, in the case of a mode shift that requires an engine start, the additional fuel required to start the engine 225. The mode shift penalty term serves two purposes. One purpose is to prevent energy intensive mode transitions. The second purpose is to prevent frequent mode shifts that would result in poor perceived drive quality by the driver of the HEV 105. In other words, in some embodiments, the electronic processor 305 may be configured to determine the mode changing energy consumption cost based partially on an amount of mode changes of the powertrain (i.e., driving components 220) that are predicted to occur in previous time intervals of the future time period. The terms a, b, and g in equation (1) are cost function weight factors that may be adjusted to control how much each factor in the below equation affects the estimated energy consumption cost.

[0062] In some embodiments, the cost function weight factors are manually set based on experimental data in order to produce a desired output. However, in some embodiments, the cost function weight factors are automatically and dynamically selected by the electronic processor 305. The weighting process is started by introducing normalization terms. As all terms in the cost function are in a unit of energy, all terms may be converted to a common unit of energy. For example, the fuel consumption term may be left in its base units of grams of fuel consumed and the state of charge (SOC) and mode shift penalties may be normalized to equivalent grams of fuel consumed. These normalized terms are presented in equations (2) and (3) where l is the normalizing factor, Qeattery is the total capacity of the battery in kWh, N is the number of prediction horizon steps, and BSFC_Min is the minimum BSFC point of the engine in . the most efficient operating point to use the engine to replenish the charge used from the battery. Multiplying X_soc by the percentage the current prediction time interval’s SOC is below the reference SOC results in the grams of fuel required to raise the SOC back to the reference SOC level. This quantity is then divided by the number of time intervals in the prediction horizon (e.g., ten second time period) in order to determine the amount of fuel required to return SOC to the reference level at the end of the prediction horizon. Multiplying the energy required to execute a mode shift, the mode shift penalty, by ^_{mode shi}f_{t penalty} results in the equivalent grams of fuel required to execute the shift. Applying these normalizing factors places all three cost function terms in directly comparable units. This assists in the application of appropriate weighting terms.

[0063] At block 515, the electronic processor 305 compares the first residing energy consumption cost to a sum of the second residing energy consumption cost and the mode changing energy consumption cost to determine whether remaining in a current mode or switching to a different mode has the lowest estimated energy consumption cost. At block 520, the electronic processor 305 selects the operating mode with the lowest energy consumption cost as the desired operating mode. For example, the electronic processor 305 selects the current operating mode as the desired operating mode in response to determining that the first residing energy consumption cost is less than the sum of the second residing energy consumption cost and the mode changing energy consumption cost. On the other hand, the electronic processor 305 selects a different operating mode as the desired operating mode in response to determining that the first residing energy consumption cost is more than the sum of the second residing energy consumption cost and the mode changing energy consumption cost.

[0064] Once the electronic processor 305 has selected the mode associated with the least energy consumption cost, the electronic processor 305 is configured to control the mode switching actuator 210 to be configured such that the powertrain (i.e., driving components 220) of the HEV 105 operates in the desired operating mode. For example, if the mode is unchanged, then the electronic processor 305 may not provide any new instructions to the mode switching actuator 210. However, if the electronic processor 305 determines that there should be a mode change, the electronic processor 305 controls mode switching actuator 210 to operate and configure the driving components 220 according to the selected changed mode. As indicated in FIG. 5, the optimal mode path planning algorithm may also provided the desired operating mode to the NMPC powertrain controller 420 to be used as an input for the control calculations made by the NMPC powertrain controller 420 to control the driving components 220 in the desired operating mode.

[0065] The objective of the optimal mode path planning algorithm 415 may be to utilize a prediction of vehicle state, which includes future vehicle speed and torque demand, in order to plan a desired trajectory of drive unit operating mode over the next N seconds (e.g., ten seconds). Planning this trajectory of future modes may allow for the best possible mode command to be issued at the current time step. By repeating execution of the optimal mode path planning algorithm 415 at each execution step of the electronic processor 305 (e.g., for every future time interval of, for example, one second in a ten second time period), a near optimal mode selection strategy can be followed for the entire drive cycle of the HEV 105.

[0066] FIG. 7 illustrates an example mode path implementation 700 of the optimal mode path planning algorithm 415. As shown in FIG. 7, a future time period of ten seconds is shown with ten one second time intervals Ti through Tio. Also as shown in FIG. 7, the HEV 105 has four operational mode options during each time interval: electric-motor-only (EV) and the three hybrid modes LER, FER, and HER. The HEV 105 is currently in the FER mode at time To. Through execution of the optimal mode path planning algorithm 415, the electronic processor 305 may iterate through all possible paths of modes over the next ten one-second time intervals and select the path between each time interval (i.e., whether to switch modes and if so, which mode to switch to) that results in the least energy consumption cost for the HEV 105. In FIG. 7, the electronic processor 305 estimates that the HEV 105 will remain in the FER mode for the next four seconds before switching the LER mode for one second at time Ts. The electronic processor 305 estimates that the HEV 105 will then switch to the EV mode at time Tb and remain in the EV mode until the end of the ten second time period. Using the estimated mode switching path 700, the electronic processor 305 determines that the HEV 105 should remain in the FER mode for the next second (T I). At time Ti, the electronic processor 305 repeats the calculations explained above for the next ten second time window (e.g., T2 through T11) to determine the desired operating mode for the HEV 105 based at least partially on future path condition data.

[0067] In some embodiments, mode switching of the HEV 105 may be limited. For example, the HEV 105 may only allow for direct mode shifts due to physical limitations of the mode switching actuator 210. For example, the HEV 105 may not be able to switch from the FER mode directly to the EV mode. Rather, the HEV 105 may have to first switch to the LER mode and then from the LER mode to the EV mode in some embodiments. When implementing the optimal mode path planning algorithm 415, the electronic processor 305 may take into account such physical limitations of mode switching of the HEV 105.

[0068] With the desired operating mode of the HEV 105 selected, the electronic processor 305 proceeds to implement the functionality of the NMPC powertrain controller 420 to calculate and provide control actions of the driving components 220 to drive the wheels of the HEV 105. In some embodiments, a main functionality of the NMPC powertrain controller 420 implemented by the electronic processor 305 is to determine a control action for controlling at least one of the driving components 220 of the powertrain based on the operating characteristic data of the HEV 105, the future path condition data of the HEV 105, and the desired operating mode of the HEV 105 as determined by the optimal mode path planning algorithm 415.

[0069] FIG. 6 illustrates a flow diagram 600 of the NMPC powertrain controller 420 implemented by the electronic processor 305 according to one example embodiment. As shown in FIG. 6, the NMPC powertrain controller 420 receives numerous inputs from the velocity trajectory generator 405 (e.g., the future vehicle torque demand (Tout, Profile) and the future vehicle velocity demand (Vprofiie)), from the optimal mode path planning algorithm 415 (e.g., the desired operating mode), and from the sensors 335 of the HEV 105 (e.g., current HEV velocity (V vehicle), current state of charge of the battery 215 of the HEV 105 (SOCvehicie), current speed of the engine 225, current speed of the electric motor(s) 230, etc.)· In some embodiments, values measured by the sensors 335 may be processed by the simplified gearbox model 425 (see FIG. 4) before being provided as inputs to the NMPC powertrain controller 420. In some embodiments, at block 605, the electronic processor 305 determines a control action for controlling at least one of the driving components 220 of the powertrain based on the operating characteristic data, the future path condition data, and the desired operating mode.

[0070] In some embodiments, the electronic processor 305 determines the control action for controlling the at least one of the driving components 220 of the power train by iteratively predicting energy consumption costs over time intervals of a future time period. In some embodiments, the time intervals may be one second and the future time period may be ten seconds as explained previously herein with respect to the optimal mode path planning algorithm 415.

[0071] To make these iterative predictions, in some embodiments, at block 605 of FIG. 6, the electronic processor 305 is configured to determine a plurality of proposed first control actions for a first time interval of the future time period. Each of the proposed first control actions may be based on the operating characteristic data, the future path condition data, the desired operating mode, and operational constraints of the driving components of the powertrain (see block 607 of FIG. 6). As an example of operational constraints 607, the engine 225 and the electric motor(s) 230 may have a maximum speed value and/or maximum torque value that is stored in the memory 310. By referencing these stored maximum values, the electronic processor 305 may ensure that proposed control actions/operational values of the driving components 220 are within the operational constraints 607 of the driving components 220. In some embodiments, other components may have stored operational constraints. For example, the battery 215 may have a maximum charge limit and a maximum discharge limit that may depend on the predicted battery power (PBattery). As shown in FIG.

6, in some embodiments, at block 605, the electronic processor 605 also takes into account (i) an amount of fuel predicted to be consumed by the HEV 105 (represented by mj) for each set of proposed control actions and/or (ii) a predicted state of charge of the battery 215 (represented by SOCpredicted) for each set of proposed control actions.

[0072] Using each of the above-noted inputs as shown in FIG. 6, at block 605 the electronic processor 305 determines a proposed control action for one or more of the drive components 220 of the HEV 105. For example, similar to the calculations made by the optimal mode path planning algorithm 415, the electronic processor 305 may use a short, seconds-length horizon prediction (e.g., ten seconds) of future vehicle operating condition as determined by the velocity trajectory generator 405 to determine the proposed control action at the current time. In some embodiments, at block 605, the electronic processor 305 determines an energy consumption cost for operating the driving components 220 based on the inputs received by the NMPC powertrain controller 420. At block 610, the determined energy consumption cost dictates proposed torque commands and/or proposed speed commands (i.e., proposed control actions) for one or more of the driving components 220 of the HEV 105. A version of the simplified gearbox model 425 may be used by the electronic processor 305 at block 610 to determine estimated powertrain and motor losses if the proposed torque commands and/or proposed speed commands (i.e., proposed control actions) were to be used. For example, the simplified gearbox model 425 may receive the proposed control actions as inputs and may output predicted engine speed, engine torque, electric motor speed, and/or electric motor torque based on the proposed commands and the current operating characteristics of the HEV 105 as monitored by the sensors 335.

[0073] When making the energy consumption cost calculation at block 605, the NMPC powertrain controller 420 may be configured to perform one or more objectives. In some embodiments, different energy consumption cost functions are used depending on the desired operating mode of the HEV 105 (e.g., one cost function for electric- motor-only (EV) mode and a different cost function for the three hybrid modes). For example, the only objective of the electronic processor 305 in the EV mode may be to minimize overall battery power with little to no reduction in operational function of the HEV 105. On the other hand, in the hybrid modes, the cost function may include both state of charge of the battery 215 and fuel consumed by the engine 225. Accordingly, the objectives of the electronic processor 305 when the HEV 105 operates in the hybrid modes may be to minimize the amount of fuel consumed while maintaining the state of charge of the battery 215 at a certain level. In some embodiments, the cost functions may include inertia and acceleration terms to more accurately model the driving components 220 and to penalize rapid engine accelerations due to a large amount of torque and energy that may be required to perform a rapid engine acceleration. Accordingly, the NMPC powertrain controller 420 may provide energy savings versus current powertrain control methods by reducing an amount of rapid engine speed changes by taking into account future path condition data indicative of a future path condition of a path on which the HEV 105 is traveling. Additional details of these energy consumption cost functions for each mode of operation of the HEV 105 are explained in U.S. Provisional Application No. 62/962,141, to which this application claims priority and which is incorporated by reference.

[0074] Using the torque commands and/or speed commands generated at block 610, at block 615, the electronic processor 305 determines predicted losses of the driving components 220 under the assumption that the driving components 220 are driven according to the torque commands and/or speed commands (i.e., the proposed control actions) for the first time interval of the future time period. At block 620, the electronic processor 305 uses the predicted losses of the driving components 220 to predict an amount of energy that will be used by the HEV 105 during the first time interval of the future time period. In other words, the electronic processor 305 may be configured to determine a first predicted energy consumption cost for implementing each of the proposed first control actions during the first time interval of the future time period. For example, the electronic processor 305 determines a predicted state of charge level of the battery 215 and a predicted amount of fuel used by the HEV 105. These predicted energy levels are fed back to block 605 (as shown in FIG. 6) to allow the electronic processor 305 to select the proposed control action for the next time interval of the future time period that most closely aligns with the objective(s) of an energy cost consumption function of a given mode of operation of the HEV 105.

[0075] In other words, the electronic processor 305 may be configured to determine a plurality of proposed second control actions for a second time interval of the future time period immediately following the first time interval in a similar manner as was done for the first time interval. Each of the proposed second control actions may be based on the operating characteristic data, the future path condition data, the desired operating mode, the operational constraints of the driving components of the powertrain, and the first predicted energy consumption cost for implementing each of the proposed first control actions during the first time interval of the future time period. Also similar to the calculations made for the first time interval of the future time period, the electronic processor 305 may be configured to determine a second predicted energy consumption cost for implementing each of the second proposed control actions during the second time interval of the future time period.

[0076] By repeating this process for numerous values of proposed control actions and for numerous future time intervals (i.e., the third through nth time intervals) within the future time period, the electronic processor 305 determines the proposed control action(s) with the lowest overall energy consumption cost (i.e., optimized control action(s)) for the current time. For example, the electronic processor 305 may be configured to determine a predicted overall energy consumption cost for each combination of the proposed first control actions and the proposed second control actions (and the proposed third through nth control actions) by adding a corresponding first predicted energy consumption cost of each proposed first control action with a corresponding second predicted energy consumption cost of each proposed second control action. Repetition of this process for each future time interval of the future time period may be similar to the process explained previously herein with respect to the optimal mode path planning algorithm 415 (e.g., see FIG. 7).

[0077] The electronic processor 305 may be configured to then select one of the proposed first control actions as the control action for controlling the at least one of the driving components of the powertrain in response to determining that the selected one of the proposed first control actions results in the lowest predicted overall energy consumption cost from among the determined predicted overall energy consumption costs. In some embodiments, the electronic processor 305 is configured to then provide the selected proposed control action (represented by the “Optimized Control Actions” in FIG. 6) to the driving components control circuitry 370 to control at least one of the driving components 220 in accordance with the torque commands and/or speed commands (i.e., proposed control actions) associated with the lowest predicted overall energy consumption cost.

[0078] The above-explained process of iteratively predicting energy consumption costs of a plurality of proposed control actions over time intervals of a future time period may be repeated as each time interval passes and as the future time period continues to move into the future. In other words, the future time period may be a rolling ten-second window, and the electronic processor 305 may re-make the above-explained iterative predictions for each time interval of the ten-second window once per second to take into account changes in, for example, future path condition data of the most recent future ten-second window.

[0079] With reference to stalled vehicle example provided above with respect to the optimal mode path planning algorithm 415, the NMPC powertrain controller 420 may provide similar benefits by utilizing predicted future torque demand and predicted future speed demand of the HEV 105. Specifically, if real-time torque sensor data indicates a high torque demand of the HEV 105 but the predicted future torque demand is low due to the HEV 105 approaching the stalled vehicle, the electronic processor 305 may wait to provide a torque command that increases torque provided by the engine 225 until after the HEV 105 passes the stalled vehicle. Such control may prevent a large engine speed change due to rapid changes in torque demand of the HEV 105. As explained previously herein, preventing large engine speed changes reduces an amount of energy consumed by the HEV 105 compared to HEVs using known HEV control methods. For example, an HEV using a known HEV control method may have instructed the engine to increase its torque output in response to real-time torque sensor data indicating a high torque demand. However, the electronic processor 305 implementing the NMPC powertrain controller 420 using future path condition data may keep the torque output of the engine 225 relatively constant until the HEV 105 passes the stalled vehicle, thus saving energy.

[0080] As a more general example, the NMPC powertrain controller 420 may provide energy savings when the HEV 105 is traveling on a curvy and/or changing incline/pitch section of a path. For example, as soon as a curved portion of the path or an increase in incline/pitch of the path (i.e., a likely future deceleration) comes into the ten second future time window of the NMPC powertrain controller 420, the electronic processor 305 implementing the NMPC powertrain controller 420 is configured to alter its commands to use less energy in response to the predicted upcoming reduction in required power/torque.

[0081] While the NMPC powertrain controller 420 is shown in FIG. 6 as receiving a selection of the desired operating mode from the optimal mode path planning algorithm 415, in some embodiments, the NMPC powertrain controller 420 and the optimal mode path planning algorithm 415 may be independent of each other. In some embodiments, the electronic processor 305 of the HEV 105 may implement only one of the NMPC powertrain controller 420 and the optimal mode path planning algorithm 415 without implementing the other. For example, the electronic processor 305 may implement the optimal mode path planning algorithm 415 to select a desired operating mode of the HEV 105 but may use a known method of controlling operation of the driving components 220 within the desired mode of operation. Alternatively, the electronic processor 305 may implement the NMPC powertrain controller 420 to control operation of the driving components 220 while using a known method to determine a desired operating mode of the HEV 105. As yet another alternative, the electronic processor 305 may implement both the NMPC powertrain controller 420 and the optimal mode path planning algorithm 415, but the desired operating mode as determined by the optimal mode path planning algorithm 415 may not be provided to the NMPC powertrain controller 420 as an input.

[0082] FIG. 8 illustrates a flow chart of a method 800 of controlling the driving components 220 of the HEV 105 by the electronic processor 305 according to one example embodiment. In the embodiment shown, the method 800 includes the electronic processor 305 implementing the optimal mode path planning algorithm 415 and the NMPC powertrain controller 420 as described previously herein.

[0083] At block 805, the electronic processor 305, receives from a plurality of the sensors 335, operating characteristic data of a plurality of operating characteristics of the HEV 105 (e.g., current HEV speed, current HEV torque of a wheel axle, state of charge of the battery 215, engine speed, electric motor speed, etc.). At block 810, the electronic processor 305 retrieves, from the memory 310, future path condition data of the path on which the HEV 105 is traveling as described previously herein. At block 815, the electronic processor 305 determines a desired operating mode of the HEV 105 based on the operating characteristic data and the future path condition data. At block 815, the electronic processor 305 controls the mode switching actuator 210 to be configured such that the powertrain (i.e., driving components 220) of the HEV 105 operates in the desired operating mode. In some embodiments, blocks 815 and 820 represent execution of the optimal mode path planning algorithm 415 by the electronic processor 305.

[0084] At block 825, the electronic processor 305 determines a control action for controlling at least one of the driving components 220 of the powertrain based on the operating characteristic data, the future path condition data, and the desired operating mode. In some embodiments, the electronic processor 305 controls the at least one of the driving components 220 of the powertrain in accordance with the control action. In some embodiments, block 825 and 830 represent execution of the NMPC powertrain controller 420 by the electronic processor 305. As shown in FIG. 8, the method 800 may loop back to block 805 to repeat itself by evaluating new operating characteristic data and future path condition data as the HEV 105 continues to propel itself along a path of travel.

[0085] In some embodiments, the method 800 may end at block 820 and may loop back to block 805 to continue executing block 805 through 820. In such embodiments, the electronic processor 305 may execute only the optimal mode path planning algorithm 415 without executing the functionality of the NMPC powertrain controller 420. In other embodiments, the method 800 may not include block 815 and 820. In such embodiments, the electronic processor 305 may execute only the functionality of the NMPC powertrain controller 420 without executing the optimal mode path planning algorithm 415.

[0086] In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes may be made without departing from the scope of the disclosure as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present teachings.

[0087] The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential features or elements of any or all the claims.

The disclosure is defined solely by the appended claims including any amendments made during the pendency of this application and all equivalents of those claims as issued.

[0088] Moreover in this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “has,” “having,” “includes,” “including,” “contains,” “containing” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises ... a,” “has ... a,” “includes ... a,” or “contains ... a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms “a” and “an” are defined as one or more unless explicitly stated otherwise herein. The terms “substantially,” “essentially,” “approximately,” “about” or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art, and in one non-limiting embodiment the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1% and in another embodiment within 0.5%. The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed.

[0089] It will be appreciated that some embodiments may be comprised of one or more generic or specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the method and/or apparatus described herein. Alternatively, some or all functions could be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches could be used.

[0090] Moreover, an embodiment may be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (for example, comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a Flash memory. Further, it is expected that one of ordinary skill, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating such software instructions and programs and ICs with minimal experimentation.

[0091] The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it may be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.

Claims

CLAIMS We claim:

1. A control system for a hybrid electric vehicle, the control system comprising: a memory configured to store future path condition data indicative of a future path condition of a path on which the hybrid electric vehicle is traveling; a plurality of sensors, each of which is configured to monitor an operating characteristic of the hybrid electric vehicle; and an electronic processor communicatively coupled to the memory, to the plurality of sensors, to a mode switching actuator of a powertrain of the hybrid electric vehicle, and to driving components of the powertrain of the hybrid electric vehicle, the electronic processor configured to receive, from the plurality of sensors, operating characteristic data of the plurality of operating characteristics, retrieve, from the memory, the future path condition data of the path on which the hybrid electric vehicle is traveling, determine a desired operating mode of the hybrid electric vehicle based on the operating characteristic data and the future path condition data, control the mode switching actuator to be configured such that the powertrain of the hybrid electric vehicle operates in the desired operating mode, determine a control action for controlling at least one of the driving components of the powertrain based on the operating characteristic data, the future path condition data, and the desired operating mode, and control the at least one of the driving components of the powertrain in accordance with the control action.

2. The control system of claim 1, wherein the electronic processor is configured to determine the desired operating mode of the hybrid electric vehicle by determining a predicted operating mode for each of a plurality of time intervals of a future time period by: determining, based on the operating characteristic data and the future path condition data, a first residing energy consumption cost of continuing to operate in a current operating mode of the powertrain during a next time interval; determining, based on the operating characteristic data and the future path condition data, a second residing energy consumption cost of operating in a different operating mode of the powertrain during the next time interval; determining a mode changing energy consumption cost of switching from the current operating mode to the different operating mode based at least partially on an amount of energy consumed by the mode switching actuator to switch from the current operating mode to the different operating mode; comparing the first residing energy consumption cost to a sum of the second residing energy consumption cost and the mode changing energy consumption cost; selecting the current operating mode as the desired operating mode of the hybrid electric vehicle in response to determining that the first residing energy consumption cost is less than the sum of the second residing energy consumption cost and the mode changing energy consumption cost; and selecting the different operating mode as the desired operating mode of the hybrid electric vehicle in response to determining that the first residing energy consumption cost is more than the sum of the second residing energy consumption cost and the mode changing energy consumption cost.

3. The control system of claim 2, wherein the first residing energy consumption cost, the second residing energy consumption cost, and the mode changing energy consumption cost each include at least one of estimated fuel consumed and estimated electrical energy consumed; and wherein the first residing energy consumption cost, the second residing energy consumption cost, and the mode changing energy consumption cost are each normalized to a common unit of energy.

4. The control system of claim 2, wherein the electronic processor is further configured to determine the mode changing energy consumption cost based partially on a number of mode changes of the powertrain that are predicted to occur in previous time intervals of the future time period.

5. The control system of claim 1, wherein the plurality of sensors include: a velocity sensor configured to measure one or more of a velocity at which the hybrid electric vehicle is currently traveling, a speed of an engine of the hybrid electric vehicle, a speed of an electric motor of the hybrid electric vehicle; and at least one battery sensor configured to measure at least one characteristic of a battery of the hybrid electric vehicle, wherein the at least one characteristic of the battery is usable by the electronic processor to determine a state of charge of the battery.

6. The control system of claim 1, wherein the mode switching actuator includes one or more of a clutch and a gear set; and wherein the driving components include an engine and one or more electrical motors.

7. The control system of claim 1, wherein the future path condition data includes a future vehicle velocity demand and a future vehicle torque demand, at least one of which is based on at least one of a traffic condition, a traffic sign location, an elevation of the path, a change of direction of the path, or a weather condition.

8. The control system of claim 1, further comprising a transceiver communicatively coupled to the memory and to the electronic processor, wherein the transceiver is configured to: receive the future path condition data via wireless communication with an external device; and store the future path condition data in the memory.

9. The control system of claim 1, wherein the electronic processor is configured to determine the control action via implementation of a simplified powertrain model that reduces a plurality of kinematics of the powertrain from a first amount of degrees of freedom to a lesser amount of degrees of freedom as a function of commanded torques of at least one of an input shaft and an output shaft of a gearbox of the powertrain and as a function of desired accelerations of at least one of the input shaft and the output shaft of the gearbox of the powertrain.

10. The control system of claim 1, wherein the electronic processor is configured to determine the control action for controlling the at least one of the driving components of the powertrain by: determining a plurality of proposed first control actions for a first time interval of a future time period, wherein each of the proposed first control actions is based on the operating characteristic data, the future path condition data, the desired operating mode, and operational constraints of the driving components of the powertrain; determining a first predicted energy consumption cost for implementing each of the proposed first control actions during the first time interval of the future time period; determining a plurality of proposed second control actions for a second time interval of the future time period immediately following the first time interval, wherein each of the proposed second control actions is based on the operating characteristic data, the future path condition data, the desired operating mode, the operational constraints of the driving components of the powertrain, and the first predicted energy consumption cost for implementing each of the proposed first control actions; determining a second predicted energy consumption cost for implementing each of the second proposed control actions during the second time interval of the future time period; determining a predicted overall energy consumption cost for each combination of the proposed first control actions and the proposed second control actions by adding a corresponding first predicted energy consumption cost of each proposed first control action with a corresponding second predicted energy consumption cost of each proposed second control action; and selecting one of the proposed first control actions as the control action for controlling the at least one of the driving components of the powertrain in response to determining that the selected one of the proposed first control actions results in the lowest predicted overall energy consumption cost from among the determined predicted overall energy consumption costs.

11. A method of controlling a hybrid electric vehicle, the method comprising: receiving, from each of a plurality of sensors of the hybrid electric vehicle, operating characteristic data of an operating characteristic of the hybrid electric vehicle; retrieving, from a memory of the hybrid electric vehicle, future path condition data of a path on which the hybrid electric vehicle is traveling, the future path condition data being indicative of a future path condition of the path on which the hybrid electric vehicle is traveling; determining, with an electronic processor of the hybrid electric vehicle, a desired operating mode of the hybrid electric vehicle based on the operating characteristic data and the future path condition data; controlling, with the electronic processor, a mode switching actuator of a powertrain of the hybrid electric vehicle to be configured such that the powertrain of the hybrid electric vehicle operates in the desired operating mode; determining, with the electronic processor, a control action for controlling at least one driving component of the powertrain based on the operating characteristic data, the future path condition data, and the desired operating mode; and controlling, with the electronic processor, the at least one driving component of the powertrain in accordance with the control action.

12. The method of claim 11, wherein determining the desired operating mode of the hybrid electric vehicle includes determining, with the electronic processor, a predicted operating mode for each of a plurality of time intervals of a future time period by: determining, with the electronic processor and based on the operating characteristic data and the future path condition data, a first residing energy consumption cost of continuing to operate in a current operating mode of the powertrain during a next time interval; determining, with the electronic processor and based on the operating characteristic data and the future path condition data, a second residing energy consumption cost of operating in a different operating mode of the powertrain during the next time interval; determining, with the electronic processor, a mode changing energy consumption cost of switching from the current operating mode to the different operating mode based at least partially on an amount of energy consumed by the mode switching actuator to switch from the current operating mode to the different operating mode; comparing, with the electronic processor, the first residing energy consumption cost to a sum of the second residing energy consumption cost and the mode changing energy consumption cost; selecting, with the electronic processor, the current operating mode as the desired operating mode of the hybrid electric vehicle in response to determining that the first residing energy consumption cost is less than the sum of the second residing energy consumption cost and the mode changing energy consumption cost; and selecting, with the electronic processor, the different operating mode as the desired operating mode of the hybrid electric vehicle in response to determining that the first residing energy consumption cost is more than the sum of the second residing energy consumption cost and the mode changing energy consumption cost.

13. The method of claim 12, wherein the first residing energy consumption cost, the second residing energy consumption cost, and the mode changing energy consumption cost each include at least one of estimated fuel consumed and estimated electrical energy consumed; and wherein the first residing energy consumption cost, the second residing energy consumption cost, and the mode changing energy consumption cost are each normalized to a common unit of energy.

14. The method of claim 12, wherein determining the mode changing energy consumption cost includes determining, with the electronic processor, the mode changing energy consumption cost based partially on a number of mode changes of the powertrain that are predicted to occur in previous time intervals of the future time period.

15. The method of claim 11, wherein receiving the operating characteristic data includes: receiving, with the electronic processor and from a velocity sensor, first data indicating one or more of a velocity at which the hybrid electric vehicle is currently traveling, a speed of an engine of the hybrid electric vehicle, a speed of an electric motor of the hybrid electric vehicle; and receiving, with the electronic processor and from at least one battery sensor, second data indicating at least one characteristic of a battery of the hybrid electric vehicle, wherein the at least one characteristic of the battery is usable by the electronic processor to determine a state of charge of the battery.

16. The method of claim 11, wherein the future path condition data includes a future vehicle velocity demand and a future vehicle torque demand, at least one of which is based on at least one of a traffic condition, a traffic sign location, an elevation of the path, a change of direction of the path, or a weather condition.

17. The method of claim 11, further comprising: receiving, with a transceiver of the hybrid electric vehicle, the future path condition data via wireless communication with an external device; and storing, with the transceiver, the future path condition data in the memory.

18. The method of claim 11, wherein determining the control action for controlling the at least one driving component includes implementing, with the electronic processor, a simplified powertrain model that reduces a plurality of kinematics of the powertrain from a first amount of degrees of freedom to a lesser amount of degrees of freedom as a function of commanded torques of at least one of an input shaft and an output shaft of a gearbox of the powertrain and as a function of desired accelerations of at least one of the input shaft and the output shaft of the gearbox of the powertrain.

19. The method of claim 11, wherein determining the control action for controlling the at least one of the driving components of the powertrain includes: determining, with the electronic processor, a plurality of proposed first control actions for a first time interval of a future time period, wherein each of the proposed first control actions is based on the operating characteristic data, the future path condition data, the desired operating mode, and operational constraints of the driving components of the powertrain; determining, with the electronic processor, a first predicted energy consumption cost for implementing each of the proposed first control actions during the first time interval of the future time period; determining, with the electronic processor, a plurality of proposed second control actions for a second time interval of the future time period immediately following the first time interval, wherein each of the proposed second control actions is based on the operating characteristic data, the future path condition data, the desired operating mode, the operational constraints of the driving components of the powertrain, and the first predicted energy consumption cost for implementing each of the proposed first control actions; determining, with the electronic processor, a second predicted energy consumption cost for implementing each of the second proposed control actions during the second time interval of the future time period; determining, with the electronic processor, a predicted overall energy consumption cost for each combination of the proposed first control actions and the proposed second control actions by adding a corresponding first predicted energy consumption cost of each proposed first control action with a corresponding second predicted energy consumption cost of each proposed second control action; and selecting, with the electronic processor, one of the proposed first control actions as the control action for controlling the at least one of the driving components of the powertrain in response to determining that the selected one of the proposed first control actions results in the lowest predicted overall energy consumption cost from among the determined predicted overall energy consumption costs.

20. A control system for a hybrid electric vehicle, the control system comprising: a memory configured to store future path condition data indicative of a future path condition of a path on which the hybrid electric vehicle is traveling; a plurality of sensors, each of which is configured to monitor an operating characteristic of the hybrid electric vehicle; and an electronic processor communicatively coupled to the memory, to the plurality of sensors, and to a mode switching actuator of a powertrain of the hybrid electric vehicle, the electronic processor configured to receive, from the plurality of sensors, operating characteristic data of the plurality of operating characteristics, retrieve, from the memory, the future path condition data of the path on which the hybrid electric vehicle is traveling, determine a desired operating mode of the hybrid electric vehicle based on the operating characteristic data and the future path condition data, and control the mode switching actuator to be configured such that the powertrain of the hybrid electric vehicle operates in the desired operating mode.