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WO2022039866A1 - Procédé et système utilisant un algorithme d'apprentissage machine destiné à réguler le confort thermique - Google Patents

Procédé et système utilisant un algorithme d'apprentissage machine destiné à réguler le confort thermique Download PDF

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Publication number
WO2022039866A1
WO2022039866A1 PCT/US2021/042320 US2021042320W WO2022039866A1 WO 2022039866 A1 WO2022039866 A1 WO 2022039866A1 US 2021042320 W US2021042320 W US 2021042320W WO 2022039866 A1 WO2022039866 A1 WO 2022039866A1
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Prior art keywords
comfort
occupant
probability
cabin
temperature
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Ceased
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PCT/US2021/042320
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English (en)
Inventor
Ioannis Androulakis
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Gentherm Inc
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Gentherm Inc
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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/0073—Control systems or circuits characterised by particular algorithms or computational models, e.g. fuzzy logic or dynamic models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/00735—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models
    • B60H1/00742—Control systems or circuits characterised by their input, i.e. by the detection, measurement or calculation of particular conditions, e.g. signal treatment, dynamic models by detection of the vehicle occupants' presence; by detection of conditions relating to the body of occupants, e.g. using radiant heat detectors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
    • B60H1/00—Heating, cooling or ventilating [HVAC] devices
    • B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
    • B60H1/0073—Control systems or circuits characterised by particular algorithms or computational models, e.g. fuzzy logic or dynamic models
    • B60H2001/00733—Computational models modifying user-set values
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2120/00—Control inputs relating to users or occupants

Definitions

  • HVAC heating, ventilation and air conditioning
  • a typical modern vehicle also includes seats having thermal effectors that are controlled to achieve occupant thermal comfort.
  • the thermal effectors may include heating and/or cooling elements that further heat or cool the occupant through the seat support surfaces.
  • Thermal comfort is usually associated with one simple parameter such as the mean temperature. Although temperature is a major driver of thermal comfort it does a poor job in reflecting the perception of pleasantness/unpleasantness in people. This perception is a regulated by multiple environmental parameters on one hand (temperature stratification, humidity, and radiation) and personal characteristics on the other (clothing level, height, weight, age, gender etc). Therefore, the driver of an automobile has to frequently regulate HVAC controls to account for the dynamic environment of the car cabin. The problem is aggravated in case of multiple occupancy where multiple opinions come at play.
  • EHT equivalent homogenous temperature
  • An exemplary method of controlling an occupant microclimate system comprising the steps of determining vehicle environmental conditions, determining occupant personal parameters, determining cabin conditions, predicting a probability of comfort at a first portion of an occupant and a probability of comfort at a second portion of the occupant based upon the environmental conditions, cabin conditions, and occupant personal parameters, the predicting step performed using one of a machine learned algorithm and a machine learning algorithm, combining the predicted probability of comfort at the first portion and the probability of comfort at the second portion with the vehicle environmental conditions and the occupant personal parameters according to a relationship determined by the one of the machine learned algorithm and the machine learning algorithm, outputting at least one of a binary thermal comfort determination and a probability of overall thermal comfort, and regulating at least one thermal effector based upon the one of the binary thermal comfort determination and the probability of overall thermal comfort.
  • the vehicle environmental conditions include at least one of vehicle exterior temperature and vehicle exterior humidity.
  • the cabin conditions include at least two of a cabin temperature data, a cabin humidity and a cabin solar radiation.
  • the cabin conditions include at least three of mean temperature at the cabin floor, mean temperature at the occupant belt line or waist, mean temperature at a breath level or face, temperature of a cushion between knees at a surface of the cushion, an internal NTC temperature of the cushion, a temperature of a seat back at a surface of the seat back, and internal NTC temperature of the seat back, and a difference between the temperatures at the breath level and at a cabin floor.
  • the occupant personal parameters include at least two of occupant weight, occupant height, occupant gender, and occupant clothing.
  • the one of the machine learned algorithm and the machine learning algorithm is an Extremely Gradient Boosted Trees (XGBoost) machine learned system.
  • XGBoost Extremely Gradient Boosted Trees
  • the at least one additional predicted probability of comfort parameter has a lower weight in the combination than the predicted probability of comfort at the first portion and the probability of comfort at the second portion.
  • the thermal effectors are selected from the group comprising a climate-controlled seat, a head rest/neck conditioner, a climate-controlled headliner, a steering wheel, a heated gear shifter, a heater mat, and a mini-compressor system.
  • Another example of any of the above described methods of controlling an occupant microclimate system further includes predicting a probability of comfort at a third portion of the occupant based upon the environmental conditions, cabin temperature data, and occupant personal parameters, and wherein combining the predicted probability of comfort at the first portion and the probability of comfort at the second portion includes combining the predicted probability of comfort at the third portion.
  • the third portion is a portion of the occupant contacting a steering wheel.
  • the first portion is a portion of the occupant contacting a seat cushion and the second portion is a portion of the occupant contacting a seat back.
  • Another example of any of the above described methods of controlling an occupant microclimate system further includes predicting a probability of a whole-body comfort of the occupant based upon the environmental conditions, cabin temperature data, and occupant personal parameters, and wherein combining the predicted probability of comfort at the first portion and the probability of comfort at the second portion includes combining the probability of the whole-body comfort.
  • combining the predicted probability of comfort at the first portion, the probability of comfort at the second portion, and the probability of the whole-body comfort further includes providing the probabilities and the environmental conditions, the cabin conditions, and the occupant personal parameters to a deep learning multi output (DLMO) function and combining using the DLMO function.
  • DLMO deep learning multi output
  • a microclimate control system for an occupant includes a first input device configured to provide vehicle environmental conditions, a second input device configured to provide occupant personal parameters, a third input device configured to provide cabin conditions, at least one thermal effector configured to heat and/or cool an occupant, and a controller configured to predict a probability of comfort at a first position and a probability of comfort at a second position of at least one person based upon the environmental conditions, cabin conditions, and occupant personal parameters, the predicting step performed using one of a machine learning algorithm and a machine learned algorithm, combine the predicted probability of comfort at the first position and the probability of comfort at the second position with the vehicle environmental conditions and the occupant personal parameters according to a relationship determined by the one of the machine learned algorithm and the machine learning algorithm, outputting at least one of a binary thermal comfort determination and a probability of overall thermal comfort, and regulating at least one thermal effector based upon the one of the binary thermal comfort determination and the probability of overall thermal comfort.
  • the vehicle environmental conditions include at least one of vehicle exterior temperature and vehicle exterior humidity.
  • the cabin conditions include at least two of a cabin temperature data, a cabin humidity and a cabin solar radiation.
  • the cabin conditions include at least three of mean temperature at the cabin floor, mean temperature at the occupant belt line or waist, mean temperature at a breath level or face, temperature of a cushion between knees at a surface of a cushion, an internal NTC temperature of the cushion, a temperature of a seat back at a surface of the seat back, and internal NTC temperature of the seat back, and a difference between the temperatures at the breath level and at a cabin floor.
  • the second input device is at least one array of pressure sensors in a seat
  • the occupant personal parameters include at least two of occupant weight, occupant height, occupant gender, and occupant clothing.
  • the occupant personal parameters include at least two of occupant weight, occupant height, occupant gender, and occupant clothing.
  • the occupant personal parameters further includes at least one additional predicted probability of a comfort parameter.
  • the at least one additional predicted probability of comfort parameter has a lower weight in the combination than the predicted probability of comfort at the first position and the probability of comfort at the second position.
  • Another example of any of the above described microclimate control systems for an occupant further includes predicting a probability of a whole-body comfort of the occupant based upon the environmental conditions, cabin temperature data, and occupant personal parameters, and wherein combining the predicted probability of comfort at the first portion and the probability of comfort at the second portion includes combining the probability of the whole-body comfort.
  • any of the above described microclimate control systems for an occupant combining the predicted probability of comfort at the first portion, the probability of comfort at the second portion, and the probability of the whole-body comfort further includes providing the probabilities and the environmental conditions, the cabin conditions, and the occupant personal parameters to a deep learning multi output (DLMO) function and combining using the DLMO function.
  • DLMO deep learning multi output
  • Figure 1 is a block diagram of a neural network using inputs affecting occupant thermal comfort to establish a mapping between those inputs and an occupant thermal comfort output.
  • Figure 2 is a simplified block diagram illustrating neural network training using the inputs illustrating in Figure 1 to establish the mapping between the inputs and output.
  • Figure 3 is a flow chart depicting an example method of controlling an occupant microclimate system.
  • Figure 4 illustrates an exemplary system for effecting thermal controls within a vehicle.
  • Figure 5 schematically illustrates an alternate presentation of the system of Figures 1-3, with an added probability of whole-body comfort estimation.
  • This disclosure provides a method for capturing environmental and personal characteristics and making predictions of individual preferences of thermal satisfaction within the car cabin.
  • the disclosed system and method rely upon the readings from a grid of simple, inexpensive sensors, or inputs, and the output of transfer functions, where such sensors are lacking, to infer the thermal comfort state of one or more automobile passenger(s) according to a relationship f(x).
  • the prediction is based on a single machine learning algorithm, which is trained using a data set of the inputs and their associated occupant thermal comfort.
  • the machine learning algorithm may predict a different occupant thermal comfort for each person in the vehicle and the thermal effectors are controlled accordingly.
  • the algorithm used in the prediction of thermal comfort is very flexible in expanding to include other signals, such as heart rate variability parameters, and to make inferences or decisions on wellness preferences, and can be adapted by one skilled in the art to accommodate any other newly available input signal types.
  • Figure 1 is a highly simplified block diagram of a neural network.
  • the neural network (f( )) performs a non-linear multivariate mapping from one set of parameters (inputs 200) to another (outputs 300).
  • the outputs 300 are determined via a function (or set of functions) f(x) that is the result of the mapping performed on the inputs by the neural network.
  • inputs that affect occupant thermal comfort are mapped by the neural network (f( )) to provide an output corresponding to occupant thermal comfort.
  • Inputs include, for example, estimated external temperature taken from the CAN bus of the vehicle 201, occupant weight 202, occupant height 203, occupant gender 204, mean air temperature at the cabin floor 205, mean air temperature at the occupant belt line or waist 206, mean air temperature at the breath level or face 207, air temperature measured at the cushion between the knees 208, temperature of the exterior surface of the seat back 209, internal NTC temperature of the seat back 211, temperature of the exterior surface of the seat cushion 210, internal NTC temperature of the cushion 212, cabin humidity 213, cabin solar radiation 214, and an on/off status of a heated steering wheel 215.
  • additional or different inputs may be used.
  • the actual temperature gradient from the internal heating element to the seat surface is determined. This allows the process to account for any thermal gradients that may differ from default gradients due to the compression from the person sitting on the seat, wear on the system, or any similar factor. This modification to the thermal gradient is dependent not only on the weight of the person, but also on how the person carries their weight, where in the seat the person is positioned, how worn the cushion is, and the like.
  • the determined temperature transfer gradient allows the relationship f(x) to account for the actual speed at which the heat from the thermal effectors (see Figure 4) within the cushions and seat back reaches the person, which further impacts the comfort of the person.
  • the relationship f(x) utilizes the inputs 200 to determine an output 300 that is one of a binary thermal comfort prediction 301 or a probability of thermal comfort 302 using a multi-step process.
  • the first step 400 receives the inputs 200 and generates a clothing insulation prediction 401 based on the estimated external temperature taken from the CAN bus of the vehicle 201, occupant weight 202, occupant height 203, and the occupant gender 204.
  • the first step estimates a temperature gradient 402 from the floor to a person’s head using the mean air temperature at the cabin floor 205, mean air temperature at the occupant belt line or waist 206, mean air temperature at the breath level or face 207, air temperature measured at the cushion between the knees 208.
  • the estimates 401, 402 are determined in the first step 400 and the inputs 200 are then used in a second step 500 to estimate a probability of comfort at the cushion 501, a probability of comfort at the back 502, and a probability of comfort at the hands 503 of the person.
  • Each of these probabilities is determined via a unique relationship generated by the single neural network during the neural network training process. While it is appreciated that comfort at the cushion, back and hands has the largest impact on a person’s overall comfort or discomfort, in alternative examples additional comfort locations (e.g. comfort at the body, comfort at the feet, a whole-body comfort metric, etc.) can be determined and utilized alongside the cushion, back and hands determinations. In such examples, the additional comfort determinations can be given a smaller weight in determining the output 300 for the overall comfort determination.
  • the output 300 takes the form of either a binary thermal comfort 301 output or a probability of thermal comfort 302 output depending on the configuration of the system.
  • the binary thermal comfort 301 output is a binary prediction of either “comfort” or “discomfort” and indicates whether the person is thermally comfortable or uncomfortable.
  • the probability of thermal comfort 302 output is a prediction of a probability that the person is comfortable. Utilization of the probability of thermal comfort can allow for further improvement of some systems by providing a targeted minimum probability of comfort and adjusting controls until the minimum probability is met.
  • the determinations made in the first step 400 and the second step 500 are made according to the relationship (f(x)) that is determined via the machine learning algorithm. While described herein using a machine learning algorithm that continuously iterates and develops, it is appreciated that alternative examples can utilize an algorithm that is learned via a machine learning process but remains static once implemented. Such a system is referred to as a machine learned algorithm. In one example, the machine learning system utilized is an Extreme Gradient Boost (XGBoost) system. It is appreciated that in alternative examples, alternative machine learning systems can be used to similar effect.
  • XGBoost Extreme Gradient Boost
  • the machine learning algorithm is trained using a data set.
  • the training 100 begins by providing a segment of a large data record for training purposes, indicated at block 102.
  • An algorithm is iteratively trained to a desired error (block 106), using additional data from the training data set (block 108), if necessary.
  • the training is complete (block 110) and the machine learning algorithm relationship f(x) has been sufficiently established for use in the vehicle climate control system.
  • the predicted relationship between the inputs and output for the given machine learning algorithm is established.
  • This training process is performed using a single machine learning algorithm to increase the speed at which the output 300 is determined, while the utilization of the predicted probability of cushion comfort 501, probability of back comfort 502 and probability of hand comfort 503 is heavily weighted to improve the accuracy of the output 300.
  • vehicle environmental conditions are determined, as indicated at block 12.
  • the vehicle environmental conditions include, for example, vehicle exterior temperature and vehicle exterior humidity.
  • Cabin conditions are also determined, as indicated at block 14.
  • the cabin conditions include at least one of cabin temperature data, cabin humidity and cabin solar radiation.
  • Occupant personal parameters are determined, as indicated at block 16.
  • Occupant personal parameters include, for example, occupant weight, occupant height and occupant gender, occupant age, occupant culture/region and/or occupant habit(s). These parameters may be sensed directly or indirectly, input manually or automatically from external devices (e.g., phones, watches or fitness trackers), or predicted using one or more algorithms.
  • the thermal comfort control method 10 utilizes the data provided from blocks 12, 14 and 16 to predict a multiple of occupant thermal comfort values of each occupant at the cushion, at the back and at the hands of the occupant, as indicated at block 18.
  • the prediction is performed using a single machine learning algorithm to provide the multiple occupant thermal comfort values.
  • Example machine learning algorithms that can be used as an alternative to XGBoost include LightGBM, Neural Nets, Random Forests, Extremely Randomized Trees, Adaptive boosting, Logistic Regression, Support Vector Machines, and/or Naive Bayes classifiers.
  • the multiple occupant thermal comfort values 501, 502, 503 are combined with the inputs 200 in the second step 500 according to the relationship f(x) to determine an overall thermal comfort prediction output 300 at block 20.
  • the overall thermal comfort prediction output 300 is, in one example, a binary comfort or discomfort determination.
  • the output 300 is provided to a controller that regulates a set of thermal effectors to create the defined conditions at block 22 to maintain the output.
  • the overall thermal comfort prediction output 300 is a probability of thermal comfort 302.
  • the method 10 includes a “probability threshold”. When the determined probability of thermal comfort meets or exceed the threshold, the method 10 outputs the settings to the controller that regulates the thermal effectors, as described in the binary comfort/discomfort output 301. Alternatively, when the probability of comfort is below the threshold, the method 10 can alter one or more parameters that defines the inputs and reiterate the method 10 the determine the new probability of comfort.
  • Figure 5 schematically illustrates an alternate example presentation the system of Figures 1-3, with an added consideration of the probability of whole-body comfort estimation 504.
  • the example of figure 5 operates substantially identically to the examples of Figures 1-3 to generate predictions of the probability of local comfort at the hands, back and cushion using prediction models 501, 052, 503.
  • the system of Figure 5 includes an estimation model 504 that estimates a probability of whole-body comfort using the f(x) relationship(s) described previously. This estimation is then stacked with each of the outputs from the local prediction estimation models 501, 502, 503 on top of the rest of the inputs and provided to a deep learning architecture (DLMO 601) that generates multiple outputs.
  • DLMO 601 deep learning architecture
  • the deep learning architecture accepts the stacked combination of each of the estimated probabilities from the estimation models 501, 502, 503, 504 and the inputs 201-215 to generate multiple outputs with one output corresponding to the binary thermal comfort proposition 301, and another output corresponding to probability of overall thermal comfort.
  • the multiple output deep learning algorithm is referred to as DLMO 601 , and provides a prediction of the overall thermal comfort while at the same time optimizing predictions for each of the local body comforts.
  • the example system includes a processor 630 that is configured to implement the relationships f(x) determined by the neural network 630 and generate the output(s) 300 described above.
  • the outputs are provided to a thermal effector controller 640.
  • the estimated occupant thermal comfort from the output 300 is then used by the thermal effector controller 640 to regulate the thermal effectors 602, 604, 606, 608, 610, 612, 614, 612 within the vehicle.
  • the thermal effectors include, for example, the seat 602, a steering wheel 604, a shifter 606, a mat 608 (such as a floor mat, a door panel, and/or a dash panel), a headliner 610, a mini-compressor system 612, a cushion thermal conditioner 614, and/or a back/neck/head thermal conditioner 616.
  • EHT equivalent homogeneous temperature
  • the predicted cushion, back, and hand probability of comfort 501, 502, 503 are provided with heavier weighting than other parameters as these portions of the person represent the dominant feeling of comfort for the person.
  • the occupant temperature stratification may be calculated using transfer functions based upon empirical data.
  • the occupant temperature stratification approximates the temperature at six different heights relative to the seated occupant. That is, the temperature vertical stratification adjusts the cabin air temperature for the level of stratification in that particular zone e.g. “breath level”.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Mechanical Engineering (AREA)
  • Thermal Sciences (AREA)
  • Mathematical Physics (AREA)
  • Fuzzy Systems (AREA)
  • Signal Processing (AREA)
  • Software Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • General Engineering & Computer Science (AREA)
  • Air Conditioning Control Device (AREA)
  • Air-Conditioning For Vehicles (AREA)

Abstract

Un procédé (10) de commande d'un système de microclimat d'occupant consiste à déterminer des conditions environnementales de véhicule (Bloc 12), à déterminer des paramètres personnels d'occupant (Bloc 16), à déterminer des conditions d'habitacle (Bloc 14), à prédire une probabilité de confort au niveau d'une première partie et d'une seconde partie d'un occupant sur la base des conditions environnementales, des conditions d'habitacle et des paramètres personnels d'occupant, à combiner des probabilités de confort prédites avec les conditions environnementales du véhicule et les paramètres personnels de l'occupant selon une relation déterminée par l'algorithme appris par machine ou l'algorithme d'apprentissage machine, à délivrer en sortie d'au moins l'une d'une détermination de confort thermique binaire et d'une probabilité de confort thermique global et à réguler au moins un effecteur thermique sur la base de la détermination de confort thermique binaire (Bloc 20) et de la probabilité de confort thermique global (Bloc 22). L'étape de prédiction est réalisée au moyen d'un algorithme appris par machine ou d'un algorithme d'apprentissage machine.
PCT/US2021/042320 2020-08-20 2021-07-20 Procédé et système utilisant un algorithme d'apprentissage machine destiné à réguler le confort thermique Ceased WO2022039866A1 (fr)

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US63/068,059 2020-08-20

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115828699A (zh) * 2022-12-19 2023-03-21 华中科技大学 功率半导体模块全生命周期结温预测方法、系统及终端
US20250074145A1 (en) * 2023-08-29 2025-03-06 Rivian Ip Holdings, Llc Personalizing climate conditions in a vehicle

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170078732A (ko) * 2014-10-31 2017-07-07 젠썸 인코포레이티드 차량 마이크로공조 시스템 및 이를 제어하는 방법
US20180251007A1 (en) * 2015-09-08 2018-09-06 Valeo Systemes Thermiques Thermal management system for a motor vehicle and corresponding thermal management method
US20180345753A1 (en) * 2016-01-12 2018-12-06 Jaguar Land Rover Limited System, method and computer program for thermal control
KR20190112681A (ko) * 2019-09-16 2019-10-07 엘지전자 주식회사 차량 공조 제어 장치 및 방법
US20200031195A1 (en) * 2018-07-24 2020-01-30 Saic Innovation Center Llc Personalized adaptive hvac system control methods and devices
US10583709B2 (en) * 2016-11-11 2020-03-10 International Business Machines Corporation Facilitating personalized vehicle occupant comfort
FR3086582A1 (fr) * 2018-09-27 2020-04-03 Valeo Systemes Thermiques Procede de gestion thermique pour dispositif de gestion thermique, en particulier pour un habitacle de vehicule

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20170078732A (ko) * 2014-10-31 2017-07-07 젠썸 인코포레이티드 차량 마이크로공조 시스템 및 이를 제어하는 방법
US20180251007A1 (en) * 2015-09-08 2018-09-06 Valeo Systemes Thermiques Thermal management system for a motor vehicle and corresponding thermal management method
US20180345753A1 (en) * 2016-01-12 2018-12-06 Jaguar Land Rover Limited System, method and computer program for thermal control
US10583709B2 (en) * 2016-11-11 2020-03-10 International Business Machines Corporation Facilitating personalized vehicle occupant comfort
US20200031195A1 (en) * 2018-07-24 2020-01-30 Saic Innovation Center Llc Personalized adaptive hvac system control methods and devices
FR3086582A1 (fr) * 2018-09-27 2020-04-03 Valeo Systemes Thermiques Procede de gestion thermique pour dispositif de gestion thermique, en particulier pour un habitacle de vehicule
KR20190112681A (ko) * 2019-09-16 2019-10-07 엘지전자 주식회사 차량 공조 제어 장치 및 방법

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HANTAEYOUNGHUANGLINJIE: "A Model for Relating a Thermal Comfort Scale to EHT Comfort Index", SAE TECHNICAL PAPER 2004-01-0919, 2004

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115828699A (zh) * 2022-12-19 2023-03-21 华中科技大学 功率半导体模块全生命周期结温预测方法、系统及终端
US20250074145A1 (en) * 2023-08-29 2025-03-06 Rivian Ip Holdings, Llc Personalizing climate conditions in a vehicle

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