Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
  • B60L53/60—Monitoring or controlling charging stations
  • B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
  • B60L53/60—Monitoring or controlling charging stations
  • B60L53/66—Data transfer between charging stations and vehicles
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
  • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
  • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
  • B60L58/13—Maintaining the SoC within a determined range
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
  • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
  • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
  • B60L58/15—Preventing overcharging
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/80—Time limits
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/12—Electric charging stations
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Definitions

• the invention relates to a method for controlling the charging operation of an electric motor vehicle with a battery associated with an electric motor, wherein the motor vehicle is connected via an energy connection with at least one electrical energy source.
• Electric motor vehicles are already well known in the art.
• This motor vehicles are called, the drive has an electric motor, which is powered by a battery, in particular a high voltage battery.
• the electric motor vehicle can be moved, therefore, electrical energy must be stored in the battery.
• an electrical energy source such as a power grid
• special charging devices or charging stations may be provided.
• the data storage and organization on the Internet for example, with electronic calendars or in so-called social networks, such as "Facebook” or “Xing", is used more and more often. This is mainly due to the simplicity and proliferation of mobile Internet devices, such as smartphones, promoted.
• the invention has for its object to provide a charging control system, which allows using available data improved, in particular automated control of the charging operation of an electric motor vehicle.
• At least one charging parameter determining the temporal course of a charging process is determined as a function of at least one predictive, time-related usage information for the motor vehicle.
• the present invention therefore proposes to use available data of the at least one user in order to determine use of the motor vehicle in the future, in particular for an operating section following an actual charging process, as predictive, time-related utilization information for the electric motor vehicle. If such a prediction is known, it can be concluded, in particular taking into account further criteria, how optimally the shop has to go, in particular when and how far the electric motor vehicle, ie its battery to be charged. Thus, it is thus possible that the electric motor vehicle is always or at least in many cases predictive of the next phase of operation, in particular special the next drive, required state of charge of the battery at the appropriate time provides. In this way, load times and manual intervention by the user can be minimized.
• the invention is based on the finding that the electric motor vehicle, which is parked, for example, at the place of residence of the user, at the user's workplace or in public space, is turned off at loading places where it lingers for a long time.
• a user can establish the energy connection at the current parking space of the electric motor vehicle, for example by means of a connecting cable or parking on a contactless charging device. If one now uses data which will be discussed in more detail below, it may be attempted to determine what the next operating section of the electric motor vehicle looks like and what charge the battery will be required when.
• At least one usage information by an evaluation or retrieval of related to the at least one user of the electric motor vehicle, retrievable on the Internet planning data is determined, in particular from an electronic calendar and / or a social network , In particular, all electronic calendars and all social networks of a user can be viewed. It is therefore provided according to the invention, in particular via an evaluation program means, which can be advantageously implemented as an online service, to analyze activities of the users of the motor vehicle in social networks and electronic calendars. For example, depending on the current vehicle location and the participation in events or appointments that the user has specified, for example, in a social network or maintained in an electronic calendar, a pending ride be predicted.
• From the corresponding data can then be determined, for example, using a navigation system and / or route planner also how much electrical energy is required, so depending on the charging parameters can be adjusted, in such a way that the information corresponding to the use of information carried out at the correct time can be.
• patterns can be inferred, which means that a pattern recognition can be carried out in order to be able to conclude regularly recurring appointments or journeys and operating sections. For example, from entries in a social network and / or entries in an electronic calendar, it can be concluded that the user goes to a gym every Wednesday and will drive there. It can therefore be found at regular intervals recurring requirements for the state of charge of the battery, which can result in a corresponding suitable control of the charging process.
• At least one usage information is determined by an evaluation of time-related operating data of the electric motor vehicle from the past, in particular with regard to regularly recurring journeys.
• time-related operating data of the electric motor vehicle is determined by an evaluation of time-related operating data of the electric motor vehicle from the past, in particular with regard to regularly recurring journeys.
• the planning data retrieved from the Internet it is possible to use operating data of the motor vehicle itself in order to conclude from the use of the motor vehicle in the past on the future use.
• This also makes it possible, in particular, to determine regularly recurring operating sections, in particular trips, which are carried out, for example, weekly, monthly or daily. In this way, predictions from planning data can be further improved particularly advantageous.
• pattern identifications are made which indicate recurrent operating sections of the motor vehicle, in particular regularly recurring journeys. This can be, for example, the daily commute to work or also weekly or monthly events in which the driver participates and to which he drives with the electric motor vehicle.
• Such operating patterns of the electric motor vehicle can be determined as usage information both from the planning data and from the operating data, preferably from both together.
• loading operation may also take into account trips for which no such pattern can be recognized, where pattern recognition is "negative.”
• Periods for which there is no pattern can also be referred to as "spontaneous" periods, in which the user his electric motor vehicle if, then uses more spontaneously.
• At least one probability is determined with which the user spontaneously moves the electric motor vehicle.
• Such a likelihood may be lower for the night than, for example, for periods of the weekend when irregular, but frequent movement of the electric motor vehicle may occur.
• Such "spontaneous periods” or “spontaneous time windows” can also be further analyzed.
• a statistical analysis of the operating data of the past for the derivation of statistical usage information is performed, which are taken into account in determining the charging parameters for such periods.
• a minimum charge state derived in particular from the statistical usage information is realized via the charging parameters. For example, a division of the entire charging process can then be carried out, so that an attempt is first made in a timely manner to obtain the minimum charge state. range, while charging to a final destination state of charge can be continued at later times.
• a current position of the electric motor vehicle when determining the charging parameters. If the current position of the electric motor vehicle and a destination, for example, from a date entered in a calendar, known, it is namely possible to determine from such usage information a destination and therefore a route to the destination.
• a consumption of the electric motor vehicle which can be used to determine the charging parameters, in particular a destination charge state, can also be determined in a fundamentally known manner for such a route determined automatically. In this case, however, it is expedient to start from a value which is slightly higher than that calculated for the route, in order to realize a kind of "safety buffer".
• At least one time period parameter determining a charging period and / or at least one target charge parameter describing a charging load state can be determined as the charging parameters.
• the period parameters may define periods of time in which to charge, although, of course, other charging parameters may be taken into account, for example an actually consumed electrical energy during a charging period per unit time, for example a limitation of the charging current, and the like.
• the target charge parameter can be based on a calculation of a consumption for a route determined from the usage information, but it is also conceivable, in particular for a pattern recognition taking into account operating data of the past, for example a statistical analysis of past, same Operating sections, to achieve a Zielladelinger.
• the destination charge state parameter ensures that the use of the electric vehicle, inferred from past planning data and / or operating data, is also possible.
• the time period parameters determining at least one charging period several possibilities are conceivable which can also be used in combination.
• the charging period is determined to end at the predicted beginning of the next journey.
• a corresponding start time for the charging process is determined by a backward calculation at the determined point in time at which the journey should begin at the earliest, so that the battery has the required charge for departure without the user having to interact and immediately ready for use.
• the charging period is determined lying at a time as low as possible energy prices.
• billing models or contracts of the user or at the current charging location can be taken into account, since often, as already explained, a longer parking period is available than would be necessary for the charging process. Therefore, it is conceivable by the analysis of energy costs to make the charging process particularly favorable for the user.
• the charging period is determined lying in a time of low network utilization.
• power grids to which electric vehicles can be charged are not always or not able to accommodate the corresponding load.
• Such information may initially be generally used, for example, to place charging operations away from peak load times of the power grid, for example in night areas or the like.
• other vehicle information on the charging operation of at least one other, at the same energy source and / or spatially adjacent charged electric motor vehicle are taken into account, in particular such that a maximum utilization of the power source not is exceeded.
• a vehicle-external computing device for example a computing device of the Internet, where charge parameters and / or usage information are determined for a plurality of electric motor vehicles.
• a computing device then communicates with a plurality, preferably all, of electric vehicles of a specific area or branch of a power network and can monitor and tune their charging activity, although it is still taken into account which future use is predicted by the usage information for the respective electric motor vehicles.
• the criteria just described for the loading periods can of course be used additionally, possibly with different weighting and / or priority, in order to achieve an optimization across criteria.
• the destination load state parameter is determined descriptively as an optimum minimum load state for a next drive determined from the usage information.
• the method according to the invention thus makes it possible to limit the state of charge of the battery in such a way that the predicated ride is possible, without having to fully charge the battery, especially to its maximum state of charge. This eliminates "unnecessary" charging and increases battery life, especially when compared to previous concepts where the battery is always charged immediately as soon as the power connection is established, especially in light of the fact that battery life is often prolonged
• the state of charge of the battery is in an optimal state of charge state that does not include the extreme states, the use of an optimal minimum state of charge, which ideally still contains a safety value, results in distinct advantages.
• the determination of the usage information and / or the loading parameters can preferably be carried out by a computer program, in particular also a computer program comprising a plurality of components which can also run on different computing devices. It is also particularly advantageous to use artificial intelligence techniques. It can therefore be provided that the determination of the usage information and / or the loading parameters takes place using a neural network and / or a genetic algorithm. Of course, other variants of evolutionary algorithms can be used in addition to the genetic algorithm. In this way, a particularly profound analysis of the usage information underlying data, in particular planning data, is possible.
• the method is carried out by a computer-external, central computing device responsible for a plurality of electric motor vehicles, in particular a computing device of the Internet, for example a back-end system. It can therefore be provided that the usage information and / or at least partially the charging parameters are determined on a computer-external computing device and transmitted via a communication link, in particular the Internet, to the electric motor vehicle, in particular a battery control device.
• an online service for example in the sense of a "predictive charging" service, which determines suitable usage information and / or charging parameters for various electric motor vehicles and communicates these to the respective electric motor vehicles, where appropriate to send an informative message also to the at least one user of the electric motor vehicle
• the charging parameters can also already be specified by the computing device, it is of course also possible to determine them at least partially by the electric motor vehicle, in particular a battery control device, be it from the usage information or from a charging parameter
• the charging parameters are already complete, which indeed also involves data exchange with the user Rokraft poverty can be done, determined by the computing device and the Eiekraftkrafthus be specified. In this way, in particular, special cases can be realized, such as the consideration of energy prices and the charging of other electric vehicles, as already described.
• a message can be sent to a communication device, in particular a smartphone, of the user of the motor vehicle.
• a communication device in particular a smartphone
• the computing device can transmit the advantageously determined information which optimizes the charging process to the motor vehicle or if the advantageous automatic charging operation, which the method according to the invention makes possible, is not possible because of a particularly forgotten production of an energy connection, it can be advantageously provided in that the user is automatically informed about this maladministration. He can then take appropriate action.
• the user of the motor vehicle is sent at least one message about a planned charging operation and / or a more suitable for him due to the charging parameters used electricity tariff.
• it is extremely expedient if automatically charging parameters have been determined for a charging process, to also inform the at least one user of the motor vehicle about the thus planned and automatically set charging processes. The user can then react to this, if necessary, for example, if he plans a spontaneous journey or the like, and a corresponding manual change is made. take or transmit a corresponding usage information to the performing the inventive method performing computing device.
• the method according to the invention thus makes it possible to predictably provide the user of electric motor vehicles with the optimum amount of charge, to minimize the duration of charging processes by providing a preview and to increase acceptance.
• "unnecessary" charging processes can be avoided and charging processes can be optimally timed, especially with regard to the utilization of electricity networks. The customer satisfaction is increased by the predictive charging and the fear of a charging process forgotten and thus restricted in mobility to be diminished.
• 1 shows a system for carrying out the method according to the invention
• 2 shows a flow chart for the sequence of the method according to the invention.
• Fig. 1 shows a system in which the method according to the invention is carried out.
• An electric motor vehicle 1 is connected via an energy connection 2 to a charging device 3, in order to be able to charge a battery 5, in particular a high-voltage battery, assigned in particular to an electric motor 4.
• the operation of the battery 5 is controlled as well as the charging process on the basis of charging parameters by a battery control unit 6.
• the electric motor vehicle 1 also has a communication device 7, via which the electric motor vehicle 1, in the present case via the Internet, can communicate with a computer 8 (backend system).
• the communication may be at least partially wireless, but it is also possible to realize the communication via the charging device 3. Consequently, the computing device 8 is able to exchange data with the motor vehicle 1.
• the computing device 8 is also connected to a smartphone 9 of a user of the electric motor vehicle 1 as a communication device of the user.
• the computing device 8 can also access other data sources 10 of the Internet, in particular social networks 11 and electronic appointment calendars 12.
• the computing device 8 forms part of a central online service for a large number of electric motor vehicles 1, which are not shown in greater detail here, and optionally other motor vehicles of a manufacturer. It can be accessed via various online services, for example, using an app on the smartphone 9. Other access to the online services of the computing device 8 as a back-end system are common Internet browsers and the like. If necessary, some services can also be accessed from the electric motor vehicle 1. It should be noted at this point that the computing device 8 does not necessarily have to consist of a single computer, but is usually modular, that is, multiple computers used.
• a predictive charging management for controlling charging processes of the electric motor vehicle 1 is offered via the computing device 8 for the electric motor vehicles 1 communicating with it as part of the method according to the invention.
• a computer program 13 is provided on the computing device 8, possibly divided into several components if necessary, which is designed to evaluate from social networks 11 and electronic calendars 12 of the at least one user of the motor vehicle 1 retrieved planning data, in particular together with operating data of the electric motor vehicle 1 in order to determine predictive, time-related usage information for the electric motor vehicle 1 by data evaluation, for example information about the following operating sections and their beginning and / or duration.
• Charging parameters are derived from these usage information, in particular also by the computer program 13, which are then transmitted to the battery control device 6 and used there to control the charging operation of the electric motor vehicle 1.
• FIG. 2 There are initially schematically input data, from which the usage information 14 are determined by an analysis module 15, shown schematically.
• the planning data 16, 17 and, if appropriate, the operating data 18 are evaluated. logical networks and / or evolutionary algorithms, in particular genetic algorithms used.
• the result is then as usage information 14 in particular statements about when the next operating section, in particular the next drive of the electric motor vehicle 1 is pending, and the required electrical energy for this trip or this operating section descriptive information.
• a location information 19 about the current position of the electric motor vehicle 1 can be taken into account at this point, for example, if it is to be determined whether it is necessary to drive to a certain date.
• the analysis module 15 As part of the analysis by the analysis module 15 not only registered or announced appointments and their location are derived from the planning data 16 and 17, but it is also for the planning data and, so used, the operating data 18, a pattern recognition performed. In this way, it can be determined when recurring trips of the at least one user of the motor vehicle 1 occur. Such trips may include, for example, the daily commute to work and back, but also findings that every Wednesday a gym is visited or every month a friend in a neighboring town is visited. Such information about regularly recurring operating sections, in particular journeys, can thus be used together with uniquely present appointments that can be derived from the planning data 16, 17 to predict specific journeys. However, the method according to the invention also makes it possible to provide for spontaneous trips by also providing statistical usage information.
• spontaneously occurring trips can also be viewed statistically in order to be able to derive, for example, a minimal load condition that is not available for a specific period of time for which pattern recognition and the scheduling analysis of the planning data 16, 17 are not actually following should stand. For example, it may be based on an average demand. However, it is also conceivable to make the minimum charge state that should be present in such periods dependent on an actually determined probability for spontaneous driving, after it is more likely at some points in time can be that a user takes a spontaneous ride than at other times. Various possibilities are conceivable here.
• a further calculation module 20 can then derive charging parameters 21 from the predictive, time-related usage information 14, which control the charging process until the next predicted journey.
• partial charging operations can certainly be provided, for example, when initially charging to a minimal charge state for spontaneous trips, but then an optimal Zielladeschreib for the battery 5, which is tuned to the next predicted ride until the earliest possible beginning of this Ride should be achieved.
• the last partial load for example, can result from a retroactive accounting starting from this earliest possible date.
• charging parameters are therefore determined which on the one hand specify the destination charge state at a specific time, but on the other hand define load periods.
• the criteria according to which these charging period parameters result for the charging periods can be varied, and not just the example of the simple recalculation just described starting from an earliest possible time for the predicated journey. Namely, it is particularly advantageous if the calculation module 20 takes into account data 22 of further electric motor vehicles 1 in a specific area, for example a branch of the power grid used as an energy source, ie a road, a settlement or a city. Then, the charging of the various electric vehicles 1 can be conveniently planned so that the utilization of the power grid is not too large, which means that a maximum load is not exceeded by the charging.
• further data 23 about the power grid and its prices can be taken into account, so that, for example, charging operations can be planned in such a way that the most economically obtainable power is used. If this does not permit a current tariff used by the user, special information can be sent to him as a message, in particular to the smartphone 9, after which he receives a customary information charging is recommended so that it can save money.
• neural networks and genetic algorithms can also be used in the calculation module 20.
• the optimal target load state for the predicted next trip can be determined in various ways. If, for example, information about a one-time appointment or a journey at a recurring appointment is given from another position, it is possible to derive from the location information 19 and the usage information 14 relating to the destination a route for which the consumption can then be determined , This is set, in particular with a safety margin, then as the optimal minimum battery charge. But it is also possible, especially for regularly recurring trips, to analyze previous trips to be able to conclude on the energy needs.
• the determined charging parameters 21 are sent to the battery control unit 6.
• a message informing the user about the planned charging process is also sent to the smartphone 9. So this can intervene in case of doubt, for example, if the prediction is wrong and the user needs more energy and the like.

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• Engineering & Computer Science (AREA)
• Power Engineering (AREA)
• Transportation (AREA)
• Mechanical Engineering (AREA)
• Life Sciences & Earth Sciences (AREA)
• Sustainable Development (AREA)
• Sustainable Energy (AREA)
• Electric Propulsion And Braking For Vehicles (AREA)

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• 2012
  • 2012-08-10 DE DE102012015949.8A patent/DE102012015949A1/de not_active Ceased
• 2013
  • 2013-06-18 WO PCT/EP2013/001801 patent/WO2014023369A2/fr not_active Ceased

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