Range-extending type automobile energy management method combining driving style and hot preference

Abstract

The invention discloses a range-extending type automobile energy management method combining a driving style and a heat preference, and relates to the technical field of range-extending type electric automobile heat management. Through the method design, the method has better individuation, fully considers the individuation demand of each driver, improves the riding comfort, has better adaptability, can process the condition of combination of various driving styles and hot preferences, is strong in adaptability, has better intelligence, adopts big data analysis and machine learning technologies, and has good application prospects. Intelligentization of passenger compartment temperature control is achieved, better energy-saving capacity is achieved, waste heat of an electric drive loop and an engine loop and external air volume can be utilized, energy consumption of a vehicle environment control system is remarkably reduced, and under different driving styles and heat preferences and under the low-temperature range extending working condition, the electric drive loop, the engine loop and external cold air are combined to be utilized, so that the vehicle environment control system is more energy-saving and environment-friendly. And the temperature of the passenger compartment is intelligently controlled, so that the energy consumption is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management of extended-range electric vehicles, and in particular to an energy management method for extended-range electric vehicles that combines driving style and thermal preference. Background Art

[0002] The thermal environment inside the vehicle has a direct impact on the driver's mental health and physiological condition. A pleasant in-vehicle space environment can not only ensure that the driver feels comfortable and happy, but also promote their physical and mental health development and effectively reduce fatigue. On the contrary, a bad in-vehicle environment will not only cause emotional unhappiness, but may also aggravate fatigue, thereby increasing the probability of traffic accidents. It can be seen that creating a good in-vehicle environment is crucial.

[0003] 1. Limitations of the existing system

[0004] In the current passenger cabin temperature control system, it is common to rely on a preset temperature adjustment method. This method lacks flexibility and cannot meet the changing environmental needs and personalized preferences of passengers during driving.

[0005] 2. Ignoring personalized needs

[0006] At present, most vehicle temperature control systems fail to fully consider the driver's personalized needs, resulting in the inability to achieve dynamic and real-time temperature adjustment, which greatly reduces the comfort experience of drivers and passengers.

[0007] 3. High-end vehicles lack personalized features

[0008] Although some high-end vehicles have begun to try to provide personalized settings, these functions are often not perfect enough to fully cover the diverse needs of drivers.

[0009] 4. Ignoring the impact of driving style on energy consumption

[0010] Existing vehicle temperature control systems largely ignore the impact of driving style on energy consumption. Different driving styles, such as aggressive or conservative, have a significant impact on vehicle energy consumption, which is not fully addressed in existing systems.

[0011] 5. Lack of energy consumption optimization strategy

[0012] In the face of complex situations with multiple driving styles and thermal preferences, existing systems lack effective energy consumption optimization strategies, namely, the use of waste heat from the electric drive circuit in aggressive situations and the use of cold air from the outside in cool situations, which not only leads to energy waste, but also may increase the operating costs of the vehicle and have an adverse impact on the environment.

[0013] Therefore, a new solution to the above problems needs to be proposed. Summary of the invention

[0014] The purpose of the present invention is to provide an energy management method for extended-range vehicles that combines driving style and thermal preference, so as to achieve intelligent control of the temperature of the passenger compartment under different driving styles and thermal preferences by combining the use of electric drive circuits, engine circuits and external cold air under low-temperature extended-range conditions, so as to effectively reduce energy consumption and thereby solve the technical problems raised in the background technology.

[0015] To achieve the above object, the present invention provides the following technical solution: an extended-range vehicle energy management method combining driving style and thermal preference, comprising at least the following steps:

[0016] S1: Collect driving condition data for training and identification of driving conditions and subsequent driver style classification;

[0017] S2: Kinematically divide the working condition data through the LSTM neural network, and further divide the working condition data again with the deep learning neural network model;

[0018] S3: classifying the driving styles of the drivers to obtain the driving styles of the drivers;

[0019] S4: Build a data-driven comfort adaptation model to adapt the comfortable temperature of the passenger compartment. That is, combined with big data analysis, the data-driven comfort adaptation model first extracts and learns the driver's temperature adjustment habits, so as to accurately identify and output the driver's heat preference;

[0020] S5: Build a specific electric drive circuit and engine circuit coupling, use the heat of the electric drive circuit and the heat of the engine circuit, and maintain the outside air volume at the driver's preferred temperature through the heater core;

[0021] S6: The driving style and temperature preference identified by S3 and S4 are used as inputs to the MPC controller to control the electronic three-way valve opening of the electric drive circuit and the engine circuit, the blower speed and the external air intake under different driving styles and thermal preferences to achieve energy regulation management.

[0022] Furthermore, the driving condition data collected in S1 is obtained by collecting signals from sensors on the test vehicle, and the condition data at least includes the vehicle speed per unit time, the power battery SOC, the driver's accelerator pedal opening, the brake signal, the steering wheel angle signal and the intelligent driving signal.

[0023] Furthermore, the S2 at least includes the following steps:

[0024] The input is a velocity fragment of time under multiple units of time;

[0025] The output is the kinematic division obtained by the LSTM neural network, the kinematic division is to obtain the driving state, and the kinematic division at least includes starting, acceleration, deceleration, constant speed and idling;

[0026] The kinematically divided driving state and operating condition data are input into a deep learning neural network model, and then the operating condition data is divided again. The suburban operating conditions include urban operating conditions, suburban operating conditions and high-speed operating conditions.

[0027] Furthermore, the S3 at least includes the following steps:

[0028] The kinematic partitioning obtained by S2 is used as input to the deep learning neural network model;

[0029] The driving data, the driver's accelerator pedal opening, the steering wheel angle signal and the driver's brake signal are synchronously input into the deep learning neural network model;

[0030] Further classification is performed through deep learning neural network model to obtain the driver's driving style;

[0031] The driver's driving style includes at least an aggressive style, a smooth style and a conservative style.

[0032] Furthermore, the building of the data-driven comfort adaptation model in S4 at least includes the following steps:

[0033] Using multiple sets of data as input, the multiple sets of data at least include individual historical automobile air conditioning usage data, external weather conditions and driver clothing conditions, and the multiple sets of data are all obtained based on big data;

[0034] The PMV model is built through the pre-set PMV (Predicted Mean Vote) algorithm. Multiple groups of data are input into the PMV model. The PMV model can convert the preferences in the input data into specific numerical indicators to form a personalized calorie preference benchmark.

[0035] Further setting a personalized thermal habit adaptation algorithm, and importing the results obtained by the PMV model into the personalized thermal habit adaptation algorithm;

[0036] Set up a comfortable temperature model that adapts to the thermal habits of different drivers and output the results.

[0037] At the same time, the input data will continue to record and update each driver's preference changes to ensure the timeliness and accuracy of the data;

[0038] On this basis, we make in-depth use of data analysis technology to dynamically adjust the PMV value to achieve accurate calculation of the most comfortable temperature for an individual.

[0039] Furthermore, the PMV algorithm is as follows:

[0040]

[0041] in:

[0042] T=0.303*e -0.036*M +0.028

[0043]

[0044] M is the passenger's metabolic rate; Pw is the partial pressure of water vapor in the passenger compartment; Ta is the air temperature in the passenger compartment; Tr is the average radiation temperature in the cab; Td is the surface temperature of the clothes; hc is the convection heat transfer coefficient of the human body surface; Icl is the thermal resistance of the passenger's clothes; Va is the air flow rate in the passenger compartment; e is a natural constant; for is or; and It is composed of M, P w and T a Composition parameters; f cl is the heat transfer coefficient of the clothing.

[0045] Furthermore, the personalized thermal habit adaptation algorithm is as follows:

[0046] PMVa=f a (data)

[0047] Where: PMVa is personalized thermal habit; f a is the calculation formula of the personalized thermal habit model; date is a large amount of thermal habit data.

[0048] Furthermore, the comfortable temperature model adapted to the thermal habits of different drivers is as follows:

[0049] T comfort =f2(PMVa)

[0050] Where: T comfort is the comfortable temperature; f2 is the calculation formula of the comfortable temperature model.

[0051] Further, the engine circuit is sequentially connected by a high-temperature radiator, a second physical three-way valve, a third physical three-way valve, a second electronic water pump, an engine body, a thermostat, and a first physical three-way valve, and a fan is provided behind the high-temperature radiator;

[0052] The electric drive circuit starts from the low-temperature radiator, passes through the sixth physical three-way valve, the first electronic water pump and the third electronic three-way valve, passes through the generator controller, the generator, the motor controller and the motor after passing through the third electronic three-way valve, and is sequentially connected to the fourth physical three-way valve and the fifth physical three-way valve, and the generator controller, the generator, the motor controller and the motor are connected in parallel;

[0053] The electric drive circuit and the engine circuit are both connected to a warm air circuit;

[0054] The heating circuit is composed of a first electronic three-way valve, a heating core and a second electronic three-way valve, and the first physical three-way valve, the second physical three-way valve, the fifth physical three-way valve and the sixth physical three-way valve are connected to the engine circuit and the electric drive circuit;

[0055] A blower and an external air volume opening are also provided between the engine circuit, the electric drive circuit and the heater circuit, and the blower and the external air volume opening are used to control the air volume entering the passenger compartment and the heater core to heat the passenger compartment;

[0056] The high-temperature radiator and the fan are used to heat the engine body, and the low-temperature radiator is used to dissipate heat from the electric drive circuit.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. The present invention has better personalization through method design, fully considers the personalized needs of each driver, and improves riding comfort;

[0059] 2. The present invention has better adaptability through method design, can handle various driving styles and thermal preference combinations, and has strong adaptability;

[0060] 3. The present invention has better intelligence through method design, and adopts big data analysis and machine learning technology to realize intelligent control of passenger cabin temperature;

[0061] 4. The present invention has better energy-saving capability through method design, and can utilize waste heat of electric drive circuit and engine circuit and external air volume to significantly reduce the energy consumption of vehicle environmental control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 The LSTM neural network of the present invention performs kinematic division on the driving condition and divides it into a schematic diagram of driving states such as starting, acceleration, deceleration, constant speed, and idling;

[0064] Figure 2 A schematic diagram of the deep learning neural network model of the present invention for classifying driving conditions into urban conditions, suburban conditions, and high-speed conditions;

[0065] Figure 3 A schematic diagram of classifying driver styles using the deep learning neural network model of the present invention;

[0066] Figure 4 This is a schematic diagram of the passenger compartment comfort temperature adaptation principle of the present invention;

[0067] Figure 5 The schematic diagram of the coupling between the electric drive circuit and the engine circuit of the present invention;

[0068] Figure 6 It is a schematic diagram of the model predictive control MPC of the present invention;

[0069] Figure 7 It is the overall flow chart of the present invention.

[0070] In the figure: 1. high-temperature radiator; 2. low-temperature radiator; 3. fan; 4. engine; 5. physical three-way valve; 6. thermostat; 7. first electronic three-way valve; 8. second electronic three-way valve; 9. blower; 10. external air volume opening; 11. first electronic water pump; 12. second electronic water pump; 13. generator controller; 14. generator; 15. motor controller; 16. motor; 17. heater core. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0072] An energy management method for extended-range vehicles that combines driving style and thermal preference, especially intelligent control technology for passenger compartment temperature under different driving styles and thermal preferences, to achieve effective reduction in energy consumption;

[0073] At least the following steps are included:

[0074] S1: Collect driving condition data for training and identification of driving conditions and subsequent driver style classification;

[0075] The driving condition data collected in S1 is obtained by collecting signals from sensors on the test vehicle. The driving condition data at least includes the vehicle speed per unit time, the power battery SOC, the driver's accelerator pedal opening, the brake signal, the steering wheel angle signal and the intelligent driving signal.

[0076] S2: Kinematic division of working condition data through LSTM neural network, see Figure 1 , and further use the deep learning neural network model to divide the working condition data again, see Figure 2 ;

[0077] The input is a velocity fragment of time under multiple units of time;

[0078] The output is the kinematic division obtained by the LSTM neural network. The kinematic division is to obtain the driving state. The kinematic division at least includes starting, acceleration, deceleration, constant speed and idling.

[0079] The kinematically divided driving state and working condition data are input into the deep learning neural network model, and then the working condition data is divided again. The working conditions are divided into urban conditions, suburban conditions and high-speed conditions.

[0080] S3: Classify the driver's driving style and obtain the driver's driving style. Figure 3 ;

[0081] The kinematic division obtained by S2 is used as input to the deep learning neural network model;

[0082] The driving data, the driver's accelerator pedal opening, the steering wheel angle signal and the driver's brake signal are synchronously input into the deep learning neural network model;

[0083] Further classification is performed through deep learning neural network model to obtain the driver's driving style;

[0084] The driver's driving style includes at least an aggressive style, a smooth style and a conservative style.

[0085] S4: Build a data-driven comfort adaptation model to adapt the comfortable temperature of the passenger compartment. That is, combined with big data analysis, the data-driven comfort adaptation model first extracts and learns the driver's temperature adjustment habits, so as to accurately identify and output the driver's heat preference. See Figure 4 ;

[0086] Building a data-driven comfort adaptation model includes at least the following steps:

[0087] Using multiple sets of data as input, the multiple sets of data at least include individual historical automobile air conditioning usage data, external weather conditions and driver clothing conditions, and the multiple sets of data are all obtained based on big data;

[0088] The PMV model is built through the pre-set PMV (Predicted Mean Vote) algorithm. Multiple groups of data are input into the PMV model. The PMV model can convert the preferences in the input data into specific numerical indicators to form a personalized calorie preference benchmark.

[0089] Further setting a personalized thermal habit adaptation algorithm, and importing the results obtained by the PMV model into the personalized thermal habit adaptation algorithm;

[0090] Set up a comfortable temperature model that adapts to the thermal habits of different drivers and output the results.

[0091] At the same time, the input data will continue to record and update each driver's preference changes to ensure the timeliness and accuracy of the data;

[0092] On this basis, we make in-depth use of data analysis technology to dynamically adjust the PMV value to achieve accurate calculation of the most comfortable temperature for an individual.

[0093] The PMV algorithm is as follows:

[0094]

[0095] in:

[0096] T=0.303*e -0.036*M +0.028

[0097]

[0098] M is the passenger's metabolic rate; Pw is the partial pressure of water vapor in the passenger compartment; Ta is the air temperature in the passenger compartment; Tr is the average radiation temperature in the cab; Tcl is the surface temperature of the clothes; Hc is the convection heat transfer coefficient of the human body surface; Icl is the thermal resistance of the passenger's clothes; Va is the air flow rate in the passenger compartment; =e is a natural constant; for is or; and It is composed of M, P w and T a Composition parameters; f cl is the heat transfer coefficient of the clothing.

[0099] The personalized thermal habit adaptation algorithm is as follows:

[0100] PMVa=f a (data)

[0101] Where: PMVa is personalized thermal habit; f a is the calculation formula of the personalized thermal habit model; date is a large amount of thermal habit data.

[0102] The comfortable temperature model adapted to different drivers' thermal habits is as follows:

[0103] T comfort =f2(PMVa)

[0104] Where: T comfort is the comfortable temperature; f2 is the calculation formula of the comfortable temperature model.

[0105] S5: Build a specific electric drive circuit and engine circuit coupling, use the heat of the electric drive circuit and the heat of the engine circuit, and maintain the outside air volume at the driver's preferred temperature through the heater core. Figure 5 ;

[0106] The engine circuit is sequentially connected by a high-temperature radiator 1, a second physical three-way valve 18, a third physical three-way valve 19, a second electronic water pump 12, an engine body 4, a thermostat 6, and a first physical three-way valve 5. A fan 3 is provided behind the high-temperature radiator 1.

[0107] The electric drive circuit starts from the low-temperature radiator 2, passes through the sixth physical three-way valve 22, the first electronic water pump 11 and the third electronic three-way valve 23, passes through the generator controller 13, the generator 14, the motor controller 15 and the motor 16 after passing through the third electronic three-way valve 23, and is connected to the fourth physical three-way valve 20 and the fifth physical three-way valve 21 in sequence, and the generator controller 13, the generator 14, the motor controller 15 and the motor 16 are connected in parallel;

[0108] The electric drive circuit and the engine circuit are both connected to a warm air circuit;

[0109] The warm air circuit is composed of a first electronic three-way valve 7, a warm air core 17, and a second electronic three-way valve 8. The first physical three-way valve 5, the second physical three-way valve 18, the fifth physical three-way valve 21, and the sixth physical three-way valve 22 are connected to the engine circuit and the electric drive circuit;

[0110] A blower 9 and an outside air volume opening 10 are also provided between the engine circuit, the electric drive circuit and the heating circuit. The blower 9 and the outside air volume opening 10 are used to control the air volume entering the passenger compartment and the heating core 17 to heat the passenger compartment.

[0111] The high-temperature radiator 1 and the fan 3 are used to heat the engine body 4, and the low-temperature radiator 2 is used to dissipate heat from the electric drive circuit.

[0112] S6: The driving style and temperature preference identified by S3 and S4 are used as inputs to the MPC controller to control the electronic three-way valve opening of the electric drive circuit and the engine circuit, the blower speed and the external air intake under different driving styles and thermal preferences to achieve energy regulation management.

[0113] For different combinations of driving styles (aggressive, smooth, conservative) and thermal preferences (hot, moderate, cold), the specific control strategies are as follows:

[0114] Strategies for different combinations:

[0115] 1. When driving aggressively and liking heat, the first electronic three-way valve (7) and the second electronic three-way valve (8) are controlled, and the heat of the electric drive circuit is connected to the heater core. The blower brings the warm air into the passenger compartment with a large air volume, which can simultaneously maintain the heat of the passenger compartment and the electric drive circuit within a reasonable range;

[0116] 2. When driving aggressively or moderately, the first electronic three-way valve (7) and the second electronic three-way valve (8) are controlled so that the electric drive circuit and the engine circuit are connected to the heater core in a certain proportion, and the blower brings the warm air into the passenger compartment with a moderate air volume;

[0117] 3. When driving aggressively and liking cold, the first electronic three-way valve (7) and the second electronic three-way valve (8) are controlled to connect the engine circuit to the heater core, and the blower blows in a small air volume. The opening of the outside air volume is adjusted to introduce cold air, and the two air volumes are mixed to maintain the temperature of the passenger compartment;

[0118] 4. Stable driving + heat preference, control the first electronic three-way valve (7) and the second electronic three-way valve (8), use the heat of the engine circuit to access the heater core, and the blower brings the warm air into the passenger compartment with a large air volume;

[0119] 5. Stable driving + moderate joy, control the first electronic three-way valve (7) and the second electronic three-way valve (8) so that the electric drive circuit is connected to the heater core, and the blower brings warm air into the passenger compartment with a moderate air volume;

[0120] 6. Stable driving + cold weather, control the first electronic three-way valve (7) and the second electronic three-way valve (8), use the heat of the electric drive circuit to access the heater core, the blower blows in a small air volume, adjusts the opening of the external air volume, introduces cold air, and mixes the two air volumes to maintain the temperature of the passenger compartment;

[0121] 7. When driving conservatively and liking heat, the first electronic three-way valve (7) and the second electronic three-way valve (8) are controlled, the heat of the engine circuit is used to access the heater core, and the blower brings the warm air into the passenger compartment with a large air volume;

[0122] 8. Driving conservatively + moderately, the first electronic three-way valve (7) and the second electronic three-way valve (8) are controlled, the heat of the engine circuit is used to access the heater core, and the blower brings the warm air into the passenger compartment with a moderate air volume;

[0123] 9. When driving conservatively and preferring cold weather, the first electronic three-way valve (7) and the second electronic three-way valve (8) are controlled, the heat of the engine circuit is used to access the heater core, the blower blows in a smaller air volume, the opening of the outside air volume is adjusted, cold air is introduced, and the two air volumes are mixed to maintain the temperature of the passenger compartment.

[0124] For specific strategies, please refer to the following table, which is a schematic diagram of the control strategy of the electric drive circuit, the engine circuit and the external air volume opening under different driving style and thermal preference combinations of the present invention;

[0125]

[0126] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for energy management of an extended-range vehicle combining driving style and thermal preference, characterized in that: At least the following steps are included: S1: Collect driving condition data for training and identification of driving conditions and subsequent driver style classification; S2: Kinematically divide the working condition data through the LSTM neural network, and further divide the working condition data again with the deep learning neural network model; S3: classifying the driving styles of the drivers to obtain the driving styles of the drivers; S4: Build a data-driven comfort adaptation model to adapt the comfortable temperature of the passenger compartment. That is, combined with big data analysis, the data-driven comfort adaptation model first extracts and learns the driver's temperature adjustment habits, so as to accurately identify and output the driver's heat preference; S5: Build a specific electric drive circuit and engine circuit coupling, use the heat of the electric drive circuit and the heat of the engine circuit, and maintain the outside air volume at the driver's preferred temperature through the heater core; S6: The driving style and temperature preference identified by S3 and S4 are used as inputs to the MPC controller to control the electronic three-way valve opening of the electric drive circuit and the engine circuit, the blower speed and the external air intake under different driving styles and thermal preferences to achieve energy regulation management.

2. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 1, characterized in that: The driving condition data collected in S1 is obtained by collecting signals from sensors on the test vehicle, and the condition data at least includes vehicle speed per unit time, power battery SOC, driver's accelerator pedal opening, brake signal, steering wheel angle signal and intelligent driving signal.

3. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 2, characterized in that: The S2 at least comprises the following steps: The input is a velocity fragment of time under multiple units of time; The output is the kinematic division obtained by the LSTM neural network, the kinematic division is to obtain the driving state, and the kinematic division at least includes starting, acceleration, deceleration, constant speed and idling; The kinematically divided driving state and operating condition data are input into a deep learning neural network model, and then the operating condition data is divided again. The suburban operating conditions include urban operating conditions, suburban operating conditions and high-speed operating conditions.

4. The method for managing energy of an extended-range vehicle combining driving style and thermal preference according to claim 3, characterized in that: The S3 at least includes the following steps: The kinematic partitioning obtained by S2 is used as input to the deep learning neural network model; The driving data, the driver's accelerator pedal opening, the steering wheel angle signal and the driver's brake signal are synchronously input into the deep learning neural network model; Further classification is performed through deep learning neural network model to obtain the driver's driving style; The driver's driving style includes at least an aggressive style, a smooth style and a conservative style.

5. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 1, characterized in that: The construction of the data-driven comfort adaptation model in S4 at least includes the following steps: Using multiple sets of data as input, the multiple sets of data at least include individual historical automobile air conditioning usage data, external weather conditions and driver clothing conditions, and the multiple sets of data are all obtained based on big data; The PMV model is built through the pre-set PMV (Predicted Mean Vote) algorithm. Multiple groups of data are input into the PMV model. The PMV model can convert the preferences in the input data into specific numerical indicators to form a personalized calorie preference benchmark. Further setting a personalized thermal habit adaptation algorithm, and importing the results obtained by the PMV model into the personalized thermal habit adaptation algorithm; Set up a comfortable temperature model that adapts to the thermal habits of different drivers and output the results. At the same time, the input data will continue to record and update each driver's preference changes to ensure the timeliness and accuracy of the data; On this basis, we make in-depth use of data analysis technology to dynamically adjust the PMV value to achieve accurate calculation of the most comfortable temperature for an individual.

6. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 5, characterized in that: The PMV algorithm is as follows: in: T=0.303*e -0.036*M +0.028 M is the passenger's metabolic rate; Pw is the partial pressure of water vapor in the passenger compartment; Ta is the air temperature in the passenger compartment; Tr is the average radiation temperature in the cab; Tcl is the surface temperature of the clothes; Hc is the convection heat transfer coefficient of the human body surface; Icl is the thermal resistance of the passenger's clothes; Va is the air flow rate in the passenger compartment; e is a natural constant; for is or; and It is composed of M, P w and T a Composition parameters; f cl is the heat transfer coefficient of the clothing.

7. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 5, characterized in that: The personalized thermal habit adaptation algorithm is as follows: PMVa=f a (data) Where: PMVa is personalized thermal habit; f a is the calculation formula of the personalized thermal habit model; date is a large amount of thermal habit data.

8. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 5, characterized in that: The comfortable temperature model adapted to the thermal habits of different drivers is as follows: T comfort =f2(PMVa) Where: T comfort is the comfortable temperature; f2 is the calculation formula of the comfortable temperature model.

9. The method for energy management of an extended-range vehicle combining driving style and thermal preference according to claim 1, characterized in that: The engine circuit is sequentially connected and composed of a high-temperature radiator (1), a second physical three-way valve (18), a third physical three-way valve (19), a second electronic water pump (12), an engine body (4), a thermostat (6), and a first physical three-way valve (5), and a fan (3) is provided behind the high-temperature radiator (1); The electric drive circuit starts from the low-temperature radiator (2), passes through the sixth physical three-way valve (22), the first electronic water pump (11) and the third electronic three-way valve (23), passes through the generator controller (13), the generator (14), the motor controller (15) and the motor (16) after passing through the third electronic three-way valve (23), and is sequentially connected to the fourth physical three-way valve (20) and the fifth physical three-way valve (21), wherein the generator controller (13), the generator (14), the motor controller (15) and the motor (16) are connected in parallel; The electric drive circuit and the engine circuit are both connected to a warm air circuit; The warm air circuit is composed of a first electronic three-way valve (7), a warm air core (17) and a second electronic three-way valve (8); the first physical three-way valve (5), the second physical three-way valve (18), the fifth physical three-way valve (21) and the sixth physical three-way valve (22) are connected to the engine circuit and the electric drive circuit; A blower (9) and an outside air volume opening (10) are also provided between the engine circuit, the electric drive circuit and the heating circuit, and the blower (9) and the outside air volume opening (10) are used to control the air volume entering the passenger compartment and the heating core (17) to heat the passenger compartment; The high-temperature radiator (1) and the fan (3) are used to heat the engine body (4), and the low-temperature radiator (2) is used to dissipate heat from the electric drive circuit.

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