Heating method and apparatus for battery in vehicle, processor, and vehicle

Abstract

A heating method for a battery in a vehicle, comprising: step S101, when a vehicle is being driven, obtaining driving condition information of the vehicle and state information of a battery in the vehicle, wherein the state information is used for representing the operating state of the battery as the vehicle is being driven; step S102, determining a temperature change model of the battery on the basis of the driving condition information and the state information, wherein the temperature change model is used for representing the heat production and / or the heat dissipation of the battery; step S103, establishing, on the basis of the temperature change model, a battery consumption model for the vehicle and corresponding constraints, wherein the battery consumption model is used for representing the energy required by the battery and / or for driving the vehicle; step S104, determining target heating data of the battery on the basis of the battery consumption model and the constraints; and step S105, executing a heating operation on the battery on the basis of the target heating data. Further disclosed are a heating apparatus for a battery in a vehicle, a processor, a vehicle, a computer-readable storage medium, an electronic device, and a computer program product. The present method as a whole satisfies the power demand of a user and can further prolong the low-temperature mileage of a vehicle; consequently, the technical problem of heating having a poor effect on a battery is solved, and the effect of heating on the battery is improved.

Description

Method, device, processor and vehicle for heating battery in vehicle

[0001] Cross-reference

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on September 15, 2023, with application number 202311195682.1 and invention name “Method, device, processor and vehicle for heating batteries in vehicles”, the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0003] The embodiments of the present disclosure relate to the field of vehicles, and in particular, to a method, device, processor, and vehicle for heating a battery in a vehicle. Background Art

[0004] In related technologies, if battery heating is required, the current battery temperature is detected. If the battery temperature reaches a certain threshold, the battery heating device is controlled to heat the battery. However, since it is impossible to calculate the heat generation caused by heating and the heat dissipation caused by air cooling in real time according to different operating conditions to heat the battery, and since heating consumes the battery's own electrical energy, there is a possibility of reducing vehicle range. Therefore, the technical problem of poor battery heating effect still exists.

[0005] With respect to the technical problem of poor heating effect of the battery in the above-mentioned related technologies, no effective solution has been proposed so far.

[0006] Summary of the Invention

[0007] The embodiments of the present disclosure provide a method, device, processor, and vehicle for heating a battery in a vehicle, so as to at least solve the technical problem of poor heating effect of the battery.

[0008] According to one aspect of an embodiment of the present disclosure, a method for heating a battery in a vehicle is provided. The method may include: obtaining driving condition information and battery status information of the vehicle while the vehicle is driving, wherein the status information is used to represent the operating state of the battery during driving; determining a battery temperature variation model based on the driving condition information and the status information, wherein the temperature variation model is used to represent the heat generation and / or heat dissipation of the battery; establishing a battery consumption model and corresponding constraints for the vehicle based on the temperature variation model, wherein the battery consumption model is used to represent the energy required to drive the vehicle and / or the battery; determining target heating data for the battery based on the battery consumption model and the constraints; and performing a heating operation on the battery based on the target heating data, wherein the target heating data is used to represent the operating state of the battery after the heating operation is performed.

[0009] Optionally, based on the driving condition information and status information, a temperature change model of the battery is determined, including: based on the driving condition information and status information, establishing a heat generation model of the battery and a heat dissipation model of the battery, wherein the heat generation model is used to represent the heat generation condition, and the heat dissipation model is used to represent the heat dissipation condition; based on the heat generation model and the heat dissipation model, determining the temperature change model of the battery.

[0010] Optionally, a battery heat generation model and a battery heat dissipation model are established based on the driving condition information and the status information, including: under the driving condition corresponding to the driving condition information, a heat generation model is established based on the battery heat generation data in the status information, the battery volume, the battery specific heat capacity, the battery mass and the temperature rise data caused by the battery heat generation.

[0011] Optionally, a heat dissipation model of the battery is established based on driving condition information and state information, including: under driving conditions, establishing a heat dissipation model based on battery air cooling data, battery temperature, vehicle ambient temperature, specific heat capacity and mass.

[0012] Optionally, based on the heat generation model and the heat dissipation model, a temperature change model of the battery is determined, including: determining temperature rise data based on the heat generation model, and determining temperature drop data based on the heat dissipation model; based on the temperature rise data and the temperature drop data, determining a temperature change model, wherein the temperature change model is used to predict the temperature change conditions of the battery at future moments.

[0013] Optionally, a battery consumption model of the vehicle is established based on the temperature change model, including: determining the battery's braking recovery power and battery consumption data based on the temperature change model; and establishing a battery consumption model based on the braking recovery power and consumption data.

[0014] Optionally, before determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model, the method also includes: determining the battery's operating power based on the vehicle's weight, vehicle's driving speed, vehicle's acceleration and vehicle's frontal area in the driving condition information, wherein the operating power is used to indicate the battery's discharge condition or charging condition; determining the battery's state of charge model based on the state of charge, battery voltage, battery capacity and operating power; determining the battery's braking recovery power based on the operating power; determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model, including: determining the braking recovery power based on the temperature change model, the state of charge model and the braking recovery power; and determining the power consumption data based on the relationship between the battery's temperature rise data and the battery's power consumption in the temperature change model.

[0015] Optionally, before establishing a battery consumption model based on the braking recovery power and power consumption data, the method also includes: determining the internal resistance power consumption of the battery based on the battery voltage and the battery operating power; determining the vehicle's power consumption based on driving condition information, wherein the power consumption is the power consumed by driving the vehicle; establishing a battery consumption model based on the braking recovery power and power consumption data, including: determining the battery consumption model based on the internal resistance power consumption, power consumption data, braking recovery power and vehicle power consumption.

[0016] Optionally, based on the temperature change model, the constraint conditions of the vehicle are established, including: establishing the constraint conditions based on the temperature of the battery, the state of charge of the battery, and the temperature rise data of the battery.

[0017] Optionally, while the vehicle is traveling, driving condition information of the vehicle is obtained, including: obtaining navigation information during the vehicle's driving process; and predicting the driving condition information based on historical driving data and navigation information of the vehicle.

[0018] Optionally, based on the vehicle's historical driving data and navigation information, the driving condition information is predicted, including: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determining the vehicle's state transition probability based on the discrete results, wherein the state transition probability is used to represent the transition probability from the vehicle's current speed state to the vehicle's speed state at the next moment; generating a vehicle's state transition probability matrix based on the state transition probability; determining the vehicle's initial speed state vector and the target speed state vector after the state transition occurs based on the state transition probability matrix; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0019] According to another aspect of the embodiment of the present disclosure, a heating device for a battery in a vehicle is also provided. The device may include: an acquisition component configured to acquire driving condition information of the vehicle and status information of the battery in the vehicle during driving, wherein the status information is used to characterize the working state of the battery during driving; a first determination component configured to determine a temperature change model of the battery based on the driving condition information and the status information, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation of the battery; an establishment component configured to establish a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; a second determination component configured to determine target heating data of the battery based on the battery consumption model and the constraints; and an execution component configured to perform a heating operation on the battery based on the target heating data, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed.

[0020] According to another aspect of an embodiment of the present disclosure, a processor is provided, which is configured to run a program, wherein when the program is run, the method for heating a battery in a vehicle according to an embodiment of the present disclosure is executed.

[0021] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device containing the computer-readable storage medium is controlled to execute the method for heating a battery in a vehicle according to the present disclosure.

[0022] According to another aspect of the present disclosure, an electronic device is provided. The electronic device includes one or more processors and a memory, wherein the memory is configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle battery heating method according to the present disclosure.

[0023] According to another aspect of an embodiment of the present disclosure, a computer program product is also provided, including a non-volatile computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it performs the steps of the method for heating a battery in a vehicle of the embodiment of the present disclosure.

[0024] According to another aspect of the embodiments of the present disclosure, a vehicle is provided, which is used to perform the method for heating a battery in a vehicle according to the embodiments of the present disclosure.

[0025] In an embodiment of the present disclosure, while a vehicle is in motion, driving condition information and battery status information are obtained, wherein the status information is used to characterize the operating state of the battery during the vehicle's operation. A battery temperature variation model is determined based on the driving condition information and the status information, wherein the temperature variation model is used to characterize the heat generation and / or heat dissipation of the battery. A battery consumption model and corresponding constraints are established for the vehicle based on the temperature variation model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery. Target heating data for the battery is determined based on the battery consumption model and the constraints. A heating operation is performed on the battery based on the target heating data, wherein the target heating data is used to represent the operating state of the battery after the heating operation is performed. In other words, during operation of the vehicle, the embodiment of the present disclosure can obtain driving condition information and battery status information of the vehicle, determine the battery temperature variation model based on the driving condition information and the status information, thereby establishing the battery consumption model and corresponding constraints for the vehicle, and determine the target heating data for the battery based on the obtained battery consumption model and constraints, thereby performing a heating operation on the battery. By taking into account the heat generation conditions after heating the battery and the heat dissipation conditions brought about by air cooling under different driving conditions of the vehicle, the battery heating operation is comprehensively considered, thereby ensuring that not only the user's power needs are met as a whole, but also the vehicle's low-temperature cruising range is extended, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the embodiments of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the embodiments of the present disclosure and do not constitute an improper limitation on the embodiments of the present disclosure. In the drawings:

[0027] FIG1 is a flow chart of a method for heating a battery in a vehicle according to an embodiment of the present disclosure;

[0028] FIG2 is a schematic diagram of a heating control system for a power battery of a pure electric vehicle in a navigation system according to an embodiment of the present disclosure;

[0029] FIG3 is a schematic diagram of a heating system principle diagram according to an embodiment of the present disclosure;

[0030] FIG4 is a flow chart of a power battery heating method according to an embodiment of the present disclosure;

[0031] FIG5 is a schematic diagram of a battery heating device in a vehicle according to an embodiment of the present disclosure;

[0032] FIG6 is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present disclosure;

[0033] FIG7 is a schematic structural diagram of a processor according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present disclosure.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or components is not necessarily limited to those steps or components clearly listed, but may include other steps or components that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] According to an embodiment of the present disclosure, an embodiment of a method for heating a battery in a vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in certain conditions, the steps shown or described may be executed in an order different from that shown here.

[0037] FIG1 is a flow chart of a method for heating a battery in a vehicle according to an embodiment of the present disclosure. As shown in FIG1 , the method may include the following steps:

[0038] Step S101 , while the vehicle is traveling, obtain the vehicle's driving condition information and the status information of the battery in the vehicle, wherein the status information is used to represent the working state of the battery during the vehicle's driving.

[0039] In the technical solution provided in the above step S101 of the embodiment of the present disclosure, the vehicle's driving condition information and the status information of the battery in the vehicle can be obtained to achieve the purpose of indicating the working status of the battery during the vehicle's driving. The battery can be a power battery or a battery pack. The vehicle can be a pure electric vehicle. The driving condition information can be the road condition information ahead collected by the navigation system, or it can be predicted information of the driving condition, such as the distance to the end point, the estimated driving time to the end point, the average speed of the traffic flow, etc. It should be noted that the predicted information in the above driving conditions is only for illustration and is not specifically limited here.

[0040] Optionally, the battery status information may be battery information sent by a battery management system (BMS), such as battery temperature, battery state of charge (SOC), and battery allowable charge and discharge power. It should be noted that this is only a preferred implementation for obtaining battery status information and does not specifically limit the method and process for obtaining battery status information, which are not listed here.

[0041] In the disclosed embodiments, the vehicle's starting position, interruption position, average speed, total distance, average acceleration, and other data can be detected in real time during operation, and the aforementioned data can be stored in the cloud. The cloud can also predict the vehicle's driving condition information, wherein the data stored in the cloud can become historical big data. For example, historical big data can be used to predict the vehicle's driving condition during the current driving process. In this case, by taking into account the vehicle's driving condition information at different times, the technical effect of improving the accuracy of the vehicle's driving condition information during operation is achieved.

[0042] Step S102 : determining a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation condition of the battery.

[0043] In the technical solution provided in the above step S102 of the embodiment of the present disclosure, the temperature change model of the battery can be determined based on the driving condition information and status information of the vehicle obtained in step S101, so as to achieve the purpose of representing the heat generation condition and / or heat dissipation condition of the battery. Among them, the temperature change model can be established by the battery temperature change trajectory, for example, it can be a battery temperature rise trajectory. The temperature change model can be used to characterize the change condition of the battery temperature, and can be used to predict the battery temperature at the next moment in the future based on the battery temperature at the previous moment. It should be noted that the determination of the temperature change model is only illustrated here by way of example, and the process and method of determining the temperature change model are not specifically limited.

[0044] Optionally, based on the obtained driving condition information and state information of the vehicle, a battery heat generation model and a battery heat dissipation model can be established respectively, and the battery temperature change model can be determined based on the obtained battery heat generation model and heat dissipation model.

[0045] In the embodiment of the present disclosure, a battery temperature change model is determined based on driving condition information such as the vehicle's starting position, interruption position, average speed, total distance, average acceleration, and battery status information such as battery temperature, battery state of charge, and battery allowable charge and discharge power. This not only achieves the purpose of accurately characterizing the heat generation and heat dissipation conditions of the battery, but also achieves the purpose of accurately obtaining the temperature change conditions of the battery, thereby realizing the technical effect of improving the accuracy of determining the temperature change of the battery.

[0046] Step S103 : establishing a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery.

[0047] In the technical solution provided in step S103 of the disclosed embodiment, a vehicle battery consumption model and corresponding constraints can be established based on the temperature variation model obtained in step S102 to characterize the energy required to drive the vehicle and / or the battery. The battery consumption model can be established by calculating the energy consumed by the battery during the entire operating cycle as it supplies power to various devices in the vehicle.

[0048] Optionally, the battery consumption model is determined based on the power consumed by the battery's internal resistance, the power consumed by battery heating, the battery's reclaimed power, and the power consumed by driving the vehicle. Constraints include constraints on battery temperature, constraints on the battery's state of charge, and constraints on temperature rise caused by battery heating.

[0049] In the disclosed embodiment, a battery consumption model for the vehicle can be determined based on the amount of electricity consumed by the battery's internal resistance, the amount of electricity consumed after the battery is heated, the amount of electricity recovered by the battery, and the amount of electricity consumed to drive the vehicle, as well as corresponding constraints on the battery temperature, constraints on the battery's state of charge, and constraints on the temperature rise caused by battery heating, so as to achieve the purpose of accurately characterizing the energy required to drive the vehicle and / or the battery.

[0050] Step S104 : determining target heating data for the battery based on the battery consumption model and constraints.

[0051] In the technical solution provided in step S104 of the embodiment of the present disclosure, after establishing the vehicle battery consumption model and corresponding constraints, target heating data for the battery can be determined to achieve the purpose of heating the battery. The target heating data can be an optimal target temperature rise.

[0052] In the embodiment of the present disclosure, the battery consumption model of the vehicle is determined based on the power consumed by the internal resistance of the battery, the power consumed after the battery is heated, the power recovered by the battery, and the power consumed by driving the vehicle, as well as the corresponding constraints on the battery temperature, the constraints on the battery state of charge, and the constraints on the temperature rise caused by battery heating, to determine the optimal target temperature rise of the battery. For example, the battery heating temperature rise is increased from 0 degrees Celsius (°C) to 40 degrees Celsius, with an interval of 1°C, and based on the principle of optimal energy consumption, the target heating data of the battery can be obtained. It should be noted that this is only an example of determining the target heating data of the battery, and does not specifically limit the method and process for determining the target heating data of the battery.

[0053] Step S105 : performing a heating operation on the battery based on the target heating data.

[0054] In the technical solution provided in step S105 of the embodiment of the present disclosure, after determining the target heating data of the battery, a heating operation can be performed on the battery to achieve the purpose of characterizing the working state of the battery. The target heating data is used to represent the working state of the battery after the heating operation is performed.

[0055] Optionally, based on the target heating data, a heating command is sent through the vehicle control unit (VCU) of the vehicle, so that the heating device works at maximum load, and then heats the battery until the battery temperature rises to the target heating data, and then stops heating.

[0056] In the above steps S101 to S105 of the embodiment of the present disclosure, while the vehicle is in motion, the vehicle's driving condition information and the status information of the battery in the vehicle are obtained, wherein the status information is used to characterize the operating state of the battery in the vehicle during the driving process; based on the driving condition information and the status information, a battery temperature change model is determined, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation of the battery; based on the temperature change model, a battery consumption model and corresponding constraints are established for the vehicle, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; based on the battery consumption model and the constraints, target heating data for the battery is determined; and based on the target heating data, a heating operation is performed on the battery, wherein the target heating data is used to represent the operating state of the battery after the heating operation is performed. In other words, during the operation of the vehicle, the embodiment of the present disclosure can obtain the vehicle's driving condition information and the status information of the battery in the vehicle, determine the battery temperature change model based on the driving condition information and the status information, thereby establishing the battery consumption model and corresponding constraints for the vehicle, and determine the target heating data for the battery based on the obtained battery consumption model and constraints, thereby performing a heating operation on the battery. By taking into account the heat generation conditions after heating the battery and the heat dissipation conditions brought about by air cooling under different driving conditions of the vehicle, the battery heating operation is comprehensively considered, thereby ensuring that not only the user's power needs are met as a whole, but also the vehicle's low-temperature cruising range is extended, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect.

[0057] The above method of this embodiment is further introduced below.

[0058] As an optional embodiment, step S102 determines the temperature change model of the battery based on the driving condition information and the status information, including: establishing a heat generation model and a heat dissipation model of the battery based on the driving condition information and the status information, wherein the heat generation model is used to represent the heat generation condition, and the heat dissipation model is used to represent the heat dissipation condition; and determining the temperature change model of the battery based on the heat generation model and the heat dissipation model.

[0059] In this embodiment, based on the driving condition information and state information, a battery heat generation model and a battery heat dissipation model can be established, so that the battery temperature change model can be determined to achieve the purpose of accurately characterizing the battery's heat generation and heat dissipation conditions.

[0060] Alternatively, the heat generation model of the battery can be expressed by the following formula:

[0061] Among them, ΔT can be used to represent the temperature rise of the battery, q can be used to represent the heat generation rate of the battery, V can be used to represent the battery volume, c can be used to represent the specific heat capacity of the battery, m batt Can be used to express battery quality, ΔT heat It can be used to indicate the temperature rise caused by battery heating.

[0062] Alternatively, the heat dissipation model of the battery can be expressed by the following formula:

[0063] Among them, ΔT′ is used to represent the temperature drop of the battery, h is used to represent the air cooling convection heat transfer coefficient, and A batt It is used to represent the convection heat transfer area of ​​air cooling, T(i) is used to represent the battery temperature at the i-th moment, T envir It is used to indicate the ambient temperature, c is used to indicate the specific heat capacity of the battery, m batt Used to indicate battery quality.

[0064] As an optional embodiment, step S102 establishes a heat generation model and a heat dissipation model of the battery based on the driving condition information and the status information, including: under the driving condition corresponding to the driving condition information, establishing a heat generation model based on the heat generation data of the battery in the status information, the volume of the battery, the specific heat capacity of the battery, the mass of the battery, and the temperature rise data caused by the heat generation of the battery; under the driving condition, establishing a heat dissipation model based on the air cooling data of the battery, the temperature of the battery, the ambient temperature, specific heat capacity and mass of the vehicle; and determining a temperature change model based on the temperature rise data determined in the heat generation model and the temperature drop data determined in the heat dissipation model, wherein the temperature change model is used to predict the temperature change condition of the battery at a future moment.

[0065] In this embodiment, under driving conditions corresponding to driving condition information, a heat generation model can be established based on the battery's heat generation data, battery volume, battery specific heat capacity, battery mass, and temperature rise data caused by battery heat generation, as contained in the status information. A heat dissipation model can be established based on the battery's air cooling data, battery temperature, vehicle ambient temperature, battery specific heat capacity, and battery mass. Based on the temperature rise data determined in the heat generation model and the temperature rise data determined in the heat dissipation model, a temperature variation model is determined to predict the battery's temperature variation at future times, thereby achieving the technical effect of accurately determining the battery's temperature variation at future times.

[0066] Alternatively, the temperature change model can be expressed by the following formula:

[0067] The heat generation data of the battery can be the heat generation rate of the battery, which can be represented by q. The volume of the battery can be represented by V. The specific heat capacity of the battery can be represented by c, m batt Used to indicate the quality of the battery. The temperature rise data caused by the heat generated by the battery can be the temperature rise of the battery or the temperature rise caused by the heating of the battery. It can be expressed by ΔT heat The air cooling data of the battery can be determined by multiplying the air cooling convection heat transfer coefficient by the air cooling convection heat transfer area. The air cooling convection heat transfer coefficient can be expressed by h, A batt It is used to represent the convection heat transfer area of ​​air cooling. The vehicle's ambient temperature can be expressed by T envir T(i) is used to represent the battery temperature at time i, and T(i+1) is used to represent the battery temperature at time i+1.

[0068] As an optional implementation method, step S103 establishes a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, including: determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model; establishing a battery consumption model based on the braking recovery power and the power consumption data; and establishing constraints based on the battery temperature, the battery's state of charge, and the battery's temperature rise data.

[0069] In the disclosed embodiment, after determining the temperature variation model, the battery's regenerative braking capacity and battery power consumption data are determined to establish a battery consumption model. Constraints can be established based on the battery temperature, battery state of charge, and battery temperature rise data.

[0070] Optionally, the braking regenerative power can be expressed by the following formula:

[0071] P′(i)=P(i)|P(i)>0 (8)

[0072] P batt (i) = f2(SOC(i), T(i)) (10)

[0073] According to the above formula, the corresponding power P′(i) can be obtained by dividing P(i) by the positive power corresponding to the drive. The battery allowable charging power P is obtained according to the battery SOC and temperature. batt (i). Combine the above battery recovery power P′(i) with the battery allowable charging power P batt (i) Take the smaller one, then add up the integral to get the recovered energy E 回收 .

[0074] Optionally, based on the battery temperature, the battery state of charge, and the battery temperature rise data, the established constraint condition may be expressed by the following formula:

[0075] SOC min ≤SOC≤SOC max

[0076] Among them, T represents the battery temperature, Indicates the maximum temperature of the battery. It indicates the minimum temperature of the battery, SOC indicates the state of charge of the battery, SOC max Indicates the maximum value of the battery state of charge, SOC min It represents the minimum value of the battery state of charge. It represents the maximum temperature rise caused by battery heating. It indicates the minimum temperature rise caused by battery heating.

[0077] As an optional embodiment, step S103, before determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model, the method also includes: determining the battery's operating power based on the vehicle's weight, the vehicle's driving speed, the vehicle's acceleration, and the vehicle's frontal area in the driving condition information, wherein the operating power is used to indicate the battery's discharge condition or charging condition; determining the battery's state of charge model based on the state of charge, the battery's voltage, the battery's capacity, and the operating power; determining the battery's braking recovery power based on the operating power; determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model, including: determining the braking recovery power based on the temperature change model, the state of charge model, and the braking recovery power; and determining the power consumption data based on the relationship between the battery's temperature rise data and the battery's power consumption in the temperature change model.

[0078] In this embodiment, the battery's operating power can be determined based on the vehicle's weight, speed, acceleration, and frontal area in the driving condition information, thereby indicating the battery's discharge or charge status. A battery state-of-charge model can be determined based on the state of charge, battery voltage, battery capacity, and operating power. Based on the obtained operating power, the battery's regenerative braking power can be determined. After determining the temperature change model, the battery's regenerative braking power and battery power consumption data can be determined.

[0079] Optionally, based on the obtained temperature change model, state of charge model and braking recovery power, the braking recovery power can be determined, and the consumed power data can be determined based on the relationship between the battery temperature rise data and the battery power consumption in the temperature change model.

[0080] Alternatively, the operating power of the battery may be the battery discharge (charge) power. The battery state of charge model may be established based on the SOC trajectory or may be expressed by the following formula:

[0081] Among them, SOC(i+1) represents the SOC at the i+1th moment, SOC(i) represents the SOC at the i-th moment, P(i) represents the battery discharge (charge) power at the i-th moment, U(i) represents the battery voltage at the i-th moment, C batt It represents the total capacity of the battery, and Δt represents the interval time from the i-th moment to the i+1-th moment.

[0082] Alternatively, the braking regenerative power can be expressed by the following formula:

[0083] P′(i)=P(i)|P(i)>0 (8)

[0084] From the above formula, we can get the corresponding recovery power P'(i) by dividing P(i) by the positive power corresponding to the drive. The battery power consumption data can be expressed by the following formula:

[0085] E heat =f3(ΔT heat ) (11)

[0086] It can be seen from the above formula that the power consumption of the battery can be determined by the battery temperature within a time interval, and the above formula is used to represent the corresponding functional relationship between the power consumption and the battery temperature rise.

[0087] As an optional embodiment, in step S103, before establishing a battery consumption model based on the braking recovery power and the power consumption data, the method also includes: determining the internal resistance power consumption of the battery based on the battery voltage and the operating power of the battery; determining the vehicle's power consumption based on the driving condition information, wherein the power consumption is the power consumed by driving the vehicle; establishing a battery consumption model based on the braking recovery power and the power consumption data, including: determining the battery consumption model based on the internal resistance power consumption, the power consumption data, the braking recovery power and the power consumption.

[0088] In this embodiment, the battery's internal resistance power consumption can be determined based on the battery voltage and the battery's operating power. The vehicle's power consumption can also be determined based on the vehicle's driving condition information, accurately representing the power consumed to drive the vehicle. A battery consumption model can be determined based on the obtained internal resistance power consumption, power consumption data, regenerative braking power, and vehicle power consumption.

[0089] Alternatively, the power consumption of the battery's internal resistance can be calculated using the following formula:

[0090] Among them, the battery internal resistance can be calculated by the following formula:

[0091] R batt (i) = f1(SOC(i), T(i)) (4)

[0092] From the above formula, we can determine the internal resistance of the battery by the battery state of charge and battery temperature. P(i) can be calculated and determined based on the predicted operating conditions using the vehicle dynamics formula. The specific formula is as follows:

[0093] In the above formula, m is used to represent the vehicle weight, g is used to represent the acceleration due to gravity, f is used to represent the rolling resistance coefficient, Cd is used to represent the drag coefficient, A is used to represent the frontal area, V(i) is used to represent the vehicle speed, δ is used to represent the vehicle rotation conversion coefficient, and a(i) is used to represent the vehicle acceleration at the i-th moment.

[0094] Optionally, E drive It represents the amount of electricity consumed to drive the car. The battery consumption model can be calculated using the following formula:

[0095] E total =E 内阻 +E heat -E 回收 +E drive (12)

[0096] According to the above formula, the battery energy consumption of the vehicle in the entire cycle is related to the operating conditions and the power of the vehicle accessories. It can also be considered that it has little correlation with the battery temperature. Therefore, solving the minimum energy consumption in the entire cycle is to solve E 内阻 +E heat -E 回收 Minimum.

[0097] Optionally, according to the formulas (1) to (12) and the constraints mentioned in the embodiment of the present disclosure, the energy consumed in the entire cycle E can be determined as total Corresponding to the battery heating temperature rise, the battery heating temperature rise is (preferably 0°C) to (preferably 40°C), with 1°C as the interval, one by one is substituted into the above formula to conveniently solve n (for example, 41) heating schemes, corresponding to n total power consumption E total , find the smallest E total Corresponding That is the optimal target temperature rise.

[0098] Optionally, the VCU sends a heating command to the heating device to control the heating device to work at maximum load and heat the battery until the battery temperature rises to Then stop heating.

[0099] As an optional embodiment, step S101 obtains the vehicle's driving condition information during the vehicle's driving process, including: obtaining the vehicle's navigation information during the vehicle's driving process; and predicting the driving condition information based on the vehicle's historical driving data and navigation information.

[0100] In this embodiment, navigation information can be obtained while the vehicle is in motion. Based on the vehicle's historical driving data and navigation data, the vehicle's driving condition information can be predicted. The navigation information can be road traffic information transmitted by the navigation system, such as the average speed of traffic, the distance from the vehicle's starting point to the destination, or the estimated time of arrival of the vehicle at the destination. Driving condition information can be referred to as simply "condition information." The vehicle's historical driving data can be stored on the vehicle's cloud server or can be the vehicle's historical usage data, referred to as simply "historical big data."

[0101] Optionally, the vehicle's driving condition information can be predicted using the vehicle controller based on navigation information and historical big data stored in a cloud server.

[0102] Optionally, the vehicle's navigation controller can record and store the vehicle's starting and ending positions for each driving cycle. The VCU records and stores the vehicle's average speed, total distance traveled, average acceleration, and other information for each driving cycle. The air conditioning controller stores the average ambient temperature for each driving cycle. After each driving cycle, the navigation and VCU upload this information to a cloud server for storage.

[0103] As an optional embodiment, step S101 predicts driving condition information based on the vehicle's historical driving data and navigation information, including: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determining the vehicle's state transition probability based on the discrete results, wherein the state transition probability is used to represent the transition probability from the vehicle's current speed state to the vehicle's next speed state; generating a vehicle's state transition probability matrix based on the state transition probability; determining the vehicle's initial speed state vector and the target speed state vector after the state transition occurs based on the state transition probability matrix; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0104] In this embodiment, when predicting driving condition information based on the vehicle's historical driving data and navigation information, the speed data in the historical driving data and the speed data in the navigation information can first be discretized to obtain a discrete result, thereby determining the vehicle's state transition probability. Based on the obtained vehicle state transition probability, a vehicle state transition probability matrix is ​​generated, and then the vehicle's initial speed state vector and the target speed state vector after the state transition are determined. Based on the obtained initial speed state vector and target speed state vector, a prediction model can be generated to achieve the purpose of predicting the vehicle's driving condition information. The speed data in the historical driving data and the speed data in the navigation information can be collectively referred to as the driving speed.

[0105] Optionally, the nearest neighbor method can be used to discretize the speed data in the historical driving data and the speed data in the navigation information, so that the above vehicle speed can be discretized into finite values. The discretization of the vehicle speed into finite values ​​can be expressed as v s ∈{v1,v2,,v N}, where v1, v2, v N and v s It can be expressed as the speed of the vehicle at different times. Here, only an example is given to illustrate the result of the discretization process, and no specific limitation is imposed on the result of the discretization process.

[0106] Alternatively, the vehicle speed during driving can be divided into different states based on the discrete results. Based on the different states, the state transition probability of the vehicle can be determined. For example, the vehicle speed during driving can be divided into 100 possible states, with a discrete speed interval of 5 kilometers per hour (km / h). The driving speed state number is U = 1, 2, ..., 25. The vehicle speed is determined by the current speed state U i The vehicle speed state U at the next moment j The probability of state transition probability Pi,j At the current moment, the vehicle speed is v i At the next moment, the vehicle speed is v j The probability is:

[0107] P i,j =P(v(k+1)=v j |v(k)=v i )

[0108] Among them, P i,j It represents the element in row i and column j of the state transition probability matrix.

[0109] Optionally, P i,j The value of can be obtained by the maximum likelihood estimation method, that is, by the following formula:

[0110] Among them, F i,j It refers to the vehicle's speed from v i Transfer to v j The number of times, F i It refers to the vehicle's speed from v i The total number of transfers, where i, j = 0, 1, …, N.

[0111] Optionally, the state transition probability matrix may be a Markov transition probability matrix, which may be represented by P. The Markov transition probability matrix P may be generated by calculating the transition probability and number of times from the current driving speed to the next driving speed, and combining each state probability value. The matrix P is shown below:

[0112] Alternatively, the initial speed state vector may be an initial vehicle speed state vector. For example, assuming that the system has n independent driving speed states, the initial vehicle speed state vector of the system is:

[0113] S(0)=[S1(0),S2(0),…,S m (0),…,S n (0)], where S m (0) refers to the initial probability when the vehicle speed state is m.

[0114] Alternatively, the target speed state vector can be a state vector after state transition. For example, after k steps of state transition, the probability of the system being in speed state m is S m (k), the state vector after state transfer is: S(k)=[S1(k),S2(k),…,S m (k),…,S n (k)], where S m(k) refers to the probability that the system is in state m at time k.

[0115] Optionally, the prediction model can be a Markov prediction model, and the Markov prediction model can be expressed as Based on the prediction model of the Markov chain and the solution of the state transfer matrix, the predicted speed value is:

[0116] v(k)=[(U k -1)+r k ] d

[0117] Among them, v(k) represents the vehicle speed at time k, U k It represents the speed state of the vehicle at time k, d represents the division length of the speed state, and its value can be 5, and r represents a random number uniformly distributed at time k.

[0118] In the disclosed embodiment, during vehicle operation, vehicle driving condition information and battery status information can be obtained. Based on the driving condition information and status information, a battery temperature variation model is determined, thereby establishing a battery consumption model and corresponding constraints for the vehicle. Based on the obtained battery consumption model and constraints, target heating data for the battery is determined, thereby performing a heating operation on the battery. By comprehensively considering the heat generated by heating the battery and the heat dissipation caused by air cooling under different driving conditions, the battery heating operation is performed, thereby ensuring that not only the user's power requirements are met overall, but also the vehicle's low-temperature cruising range is extended, thereby solving the technical problem of poor battery heating effectiveness and achieving the technical effect of improving the battery heating effectiveness.

[0119] The technical solutions of the embodiments of the present disclosure are illustrated below with reference to preferred implementations.

[0120] At present, the internal resistance of current power batteries is relatively large at low temperatures, and both discharge performance and charging recovery performance are significantly reduced compared to normal temperature. The reduction in power battery discharge power will lead to poor acceleration of the entire vehicle. The reduction in power battery recovery power will lead to reduced vehicle braking energy recovery and increased power consumption, which directly affects the vehicle's power and range. In order to solve the problem of poor performance of power batteries at low temperatures, various power battery heating methods have been proposed in the prior art. Some use the battery's own power to heat with external heating equipment, some use motor pulses and batteries to form a self-discharge and self-charging circuit to heat the battery, and some use motor stalling for heating. However, since none of the above methods take into account the different driving conditions of the vehicle and the energy consumed by the battery during the entire cycle of the vehicle to comprehensively consider the battery heating operation, the technical problem of poor battery heating effect still exists.

[0121] In a related technology, a heating system for a power battery is proposed, which may include: a heat source device, a circulation pipeline and a controller; wherein the heat source device is used to provide heat energy, a circulation pump and a heating chamber are provided on the circulation pipeline, and the power battery pack of a pure electric vehicle is connected to the circulation pipeline, the circulation pump is used to drive the condenser to circulate in the circulation pipeline, and the condenser absorbs the heat energy provided by the heat source device when flowing through the heating chamber to be heated, and the heated condenser heats the power batteries in the power battery pack when circulated to the power battery pack; the controller controls the heat source device and the circulation pump according to the heating start signal.

[0122] In another related technology, a power battery self-heating control method, system and automobile are proposed. The method may include: when the real-time temperature of the power battery is lower than a preset low temperature threshold, switching to the self-heating mode of the power battery; after obtaining the current open-circuit voltage and AC internal resistance of the power battery according to the current SOC, determining the switching frequency and duty cycle required for the heating circuit to heat the power battery; controlling the on-off state of the transistors in the three-phase bridge structure component according to the determined switching frequency and duty cycle, so that the self-heating circuit between the three-phase bridge structure component and the power battery is turned on; performing self-heating operation on the power battery through the turned-on self-heating circuit until the real-time temperature of the power battery is higher than the preset target temperature threshold, disconnecting the self-heating circuit and exiting the self-heating mode of the power battery.

[0123] However, the above method still has the technical problem of poor heating effect on the battery.

[0124] To address the aforementioned issues, embodiments of the present disclosure propose a method for heating a battery in a vehicle. The method may include: obtaining driving condition information and status information of the battery in the vehicle during vehicle operation; determining a battery temperature variation model based on the driving condition information and status information, thereby establishing a battery consumption model and corresponding constraints for the vehicle; determining target heating data for the battery based on the battery consumption model and constraints obtained above, thereby performing a heating operation on the battery. By comprehensively considering the different driving conditions of the vehicle and the energy consumed by the battery throughout the vehicle's operating cycle, the battery heating operation is comprehensively considered, thereby ensuring that not only the user's power requirements are met overall, but also the vehicle's low-temperature cruising range is extended, thereby resolving the technical problem of poor battery heating effectiveness and achieving the technical effect of improving the battery heating effectiveness.

[0125] The following is a further introduction to the embodiments of the present disclosure.

[0126] Figure 2 is a schematic diagram of a heating control system for a pure electric vehicle power battery of a navigation system according to an embodiment of the present disclosure. As shown in Figure 2, the heating control system may include: an on-board navigation system 201, a vehicle controller (VCU) 202, a heating device 203, a battery management system (BMS) 204 and a cloud server 205.

[0127] In this embodiment, the VCU, as the core of the entire control, can receive road traffic information sent by the navigation system, including the average speed of traffic, the distance from the starting point to the end point, the estimated time of arrival at the end point, etc. The VCU can receive battery information sent by the BMS, mainly including battery temperature, battery SOC (state of charge), battery allowable charge and discharge power, etc. The VCU can receive vehicle historical big data stored in the cloud server. The VCU controls the heating device to heat the battery, and the heating device reports its own working status (load).

[0128] FIG3 is a schematic diagram of a heating system principle diagram according to an embodiment of the present disclosure. As shown in FIG3 , the heating system may include: a power battery 301 , an electric water pump 302 , an expansion water tank 303 and a heating device 304 .

[0129] In this embodiment, a heating circuit can be formed by several components such as the power battery 301, the electric water pump 302, the expansion water tank 303 and the heating device 304. After the heating device generates heat, the heat can be transferred to the power battery through the operation of the electric water pump to heat the power battery.

[0130] FIG4 is a flow chart of a power battery heating method according to an embodiment of the present disclosure. As shown in FIG4 , the power battery heating method may include the following steps:

[0131] In step S401, the navigation system collects road condition information ahead and sends it to the vehicle controller.

[0132] In the technical solution provided in step S401 of the present disclosure, the navigation system collects road condition information ahead and sends it to the VCU. The specific road condition information includes the distance to the destination, the estimated travel time to the destination, the average speed of the traffic flow, etc. This is only for illustration and is not a specific limitation.

[0133] In step S402, the vehicle controller predicts the operating condition based on the navigation information and historical big data.

[0134] In the technical solution provided in the above step S402 of the present disclosure, the VCU performs operating condition prediction based on the navigation information and the historical big data stored in the cloud server.

[0135] Optionally, the navigation controller records and stores the vehicle's starting and ending positions for each drive cycle. The VCU records and stores the vehicle's average speed, total distance traveled, and average acceleration for each drive cycle. The air conditioning controller stores the average ambient temperature for each drive cycle. Table 1 shows the vehicle's historical usage data. The stored historical vehicle usage data obtained above is shown in Table 1. After each drive cycle, the navigation and VCU upload this information to a cloud server for storage.

[0136] Table 1 Historical usage data of vehicles

[0137] Optionally, the working condition prediction is performed according to the Markov theory, and the specific implementation and operation method are as follows: first calculate the state transition probability matrix, and use the nearest neighbor method to discretize the driving speed into a finite value: v s ∈{v1,v2,…,v N}, where v1, v2, v N and v s It can be expressed as the vehicle speed at different times.

[0138] Optionally, the vehicle speed during the driving process is divided into 100 possible states, with a discrete speed interval of 5 km / h. The driving speed state number is U = 1, 2, ..., 25. The vehicle speed is determined by the current speed state U i The vehicle speed state U at the next moment j The probability of state transition probability P i,j At the current moment, the vehicle speed is v i At the next moment, the vehicle speed is v j The probability is:

[0139] P i,j =P(v(k+1)=v j|v(k)=v i )

[0140] Among them, P i,j It represents the element of row i and column j of the state transition probability matrix. i,j The value of can be obtained by the maximum likelihood estimation method, that is, by the following formula: Among them, F i,j It refers to the vehicle's speed from v i Transfer to v j The number of times, F i It refers to the vehicle's speed from v i The total number of transfers, where i, j = 0, 1, …, N.

[0141] Alternatively, the transition probability and number of times from the current driving speed to the next driving speed can be calculated, and each state probability value can be combined to generate a Markov transition probability matrix P, and the matrix P is as follows:

[0142] Assuming that the system has n independent driving speed states, the initial speed state vector of the system is: S(0) = [S1(0), S2(0), ..., S m (0),…,S n (0)], where S m (0) refers to the initial probability when the vehicle speed state is m. After k steps of state transition, the probability of the system being in the vehicle speed state m is S m (k), the state vector after state transfer is: S(k)=[S1(k),S2(k),…,S m (k),…,S n (k)], where S m (k) refers to the probability that the system is in state m at time k. The Markov prediction model can be expressed as

[0143] Based on the prediction model of the Markov chain and the solution of the state transfer matrix, the predicted speed value is:

[0144] v(k)=[(U k -1)+r k ] d

[0145] Among them, v(k) represents the vehicle speed at time k, U k It represents the speed state of the vehicle at time k, d represents the division length of the speed state, and its value can be 5, and r represents a random number uniformly distributed at time k.

[0146] Step S403: establishing a heat generation and heat dissipation model for the battery, and predicting the battery temperature change trajectory.

[0147] In the technical solution provided in the above step S403 of the present disclosure, a battery heat generation and heat dissipation model can be established to predict the battery temperature change trajectory according to the working conditions, wherein the battery temperature change trajectory can be called the battery temperature rise trajectory.

[0148] Optionally, the heat generation model of the battery may be:

[0149] Among them, ΔT can be used to represent the temperature rise of the battery, q can be used to represent the heat generation rate of the battery, V can be used to represent the battery volume, c can be used to represent the specific heat capacity of the battery, m batt Can be used to express battery quality, ΔT heat It can be used to indicate the temperature rise caused by battery heating.

[0150] Optionally, the heat dissipation model of the battery can be:

[0151] Among them, ΔT′ is used to represent the temperature drop of the battery, h is used to represent the air cooling convection heat transfer coefficient, and A batt It is used to represent the convection heat transfer area of ​​air cooling, T(i) is used to represent the battery temperature at the i-th moment, T envir It is used to indicate the ambient temperature, c is used to indicate the specific heat capacity of the battery, m batt Used to indicate battery quality.

[0152] Optionally, the battery temperature change trajectory can be predicted based on the battery heat generation model and battery heat dissipation model established above, wherein the battery temperature rising trajectory can be:

[0153] Among them, T(i+1) is used to represent the battery temperature of the battery at time i+1. According to the above formula, based on the heat generation and heat dissipation conditions of the battery, and the battery temperature of the battery at time i, the battery temperature at the next moment can be predicted.

[0154] Step S404 , calculating the power consumed by the internal resistance according to the temperature change trajectory, and calculating the power recovered by braking according to the predicted working conditions.

[0155] In the technical solution provided in the above step S404 of the present disclosure, the internal resistance change trajectory is first determined. The internal resistance is related to the battery SOC and the battery temperature, that is,

[0156] R batt (i) = f1(SOC(i), T(i)) (4)

[0157] As can be seen from the above formula, after this functional relationship is determined in the battery pack, it can also be obtained through bench testing. Alternatively, the SOC trajectory can be determined according to the following formula:

[0158] Among them, SOC(i+1) is used to represent the SOC at the i+1th moment, SOC(i) is used to represent the SOC at the i-th moment, P(i) is used to represent the battery discharge (charge) power at the i-th moment, U(i) is used to represent the battery voltage at the i-th moment, C batt It is used to represent the total capacity of the battery, and Δt is used to represent the interval time from the i-th moment to the i+1-th moment.

[0159] Alternatively, P(i) can be calculated and determined based on the predicted working conditions using the vehicle dynamics formula. The specific formula is as follows:

[0160] Where m is the vehicle weight, g is the acceleration due to gravity, f is the rolling resistance coefficient, Cd is the drag coefficient, A is the frontal area, V(i) is the vehicle speed, δ is the vehicle rotation conversion coefficient, and a(i) is the vehicle acceleration at the i-th moment.

[0161] Optionally, the calculation method of the power consumption of the internal resistance can be implemented by the following formula:

[0162] The meanings of the parameters in the above formulas have been explained above and will not be repeated here.

[0163] Optionally, the braking regenerative power can be calculated using the following formula:

[0164] P′(i)=P(i)|P(i)>0 (8)

[0165] P batt (i) = f2(SOC(i), T(i)) (10)

[0166] According to the above formula, the corresponding power P′(i) can be obtained by dividing P(i) by the positive power corresponding to the drive. The battery allowable charging power P is obtained according to the battery SOC and temperature. batt (i). Combine the above battery recovery power P′(i) with the battery allowable charging power P batt (i) Take the smaller one, then add up the integral to get the recovered energy E 回收 .

[0167] Step S405 , determining the power consumed by the heating battery according to the test, and obtaining the power consumption corresponding to different temperature rises.

[0168] In the technical solution provided in step S405 of the present disclosure, a heating test can be conducted in advance on the power battery to statistically analyze the corresponding relationship between the battery temperature rise and power consumption, as shown in Table 2. Table 2 is a table showing the corresponding relationship between battery heating temperature rise and power consumption. Generally, the battery temperature rise and power consumption are approximately in a linear proportional relationship. According to Table 2, the corresponding functional relationship between power consumption and battery temperature rise can be:

[0169] E heat =f3(ΔT heat ) (11)

[0170] Among them, E heat Indicates the battery consumption.

[0171] Table 2 Correspondence between battery heating temperature rise and power consumption

[0172] Step S406: formulate constraint conditions based on the obtained state equation of power consumption under the entire working condition.

[0173] In the technical solution provided in step S406 of the present disclosure, the energy consumed by the battery in the entire cycle can be expressed by the following formula:

[0174] E total =E 内阻 +E heat -E 回收 +E drive (12)

[0175] Among them, E drive It represents the amount of electricity consumed to drive the car, and is related to the working conditions and the power of the car's accessories. It can also be considered that it has little correlation with the battery temperature. Therefore, the energy consumed in the entire cycle is minimized, that is, solving E 内阻 +E heat -E 回收 Reach minimum.

[0176] Alternatively, the minimum energy consumed in the entire cycle can be constrained by the following formula:

[0177] SOC min ≤SOC≤SOC max

[0178] Among them, T represents the battery temperature, Indicates the maximum temperature of the battery. It indicates the minimum temperature of the battery, SOC indicates the state of charge of the battery, SOC max Indicates the maximum value of the battery state of charge, SOC min It represents the minimum value of the battery state of charge. It represents the maximum temperature rise caused by battery heating. It indicates the minimum temperature rise caused by battery heating.

[0179] Step S407 : deriving an optimal target temperature rise for battery heating based on the optimal energy consumption solution.

[0180] In the technical solution provided in step S407 of the present disclosure, the energy consumed in the entire cycle, E, can be determined according to the above formulas (1) to (12) and the above constraints. total Corresponding to the battery heating temperature rise, the battery heating temperature rise is (preferably 0°C) to (preferably 40°C), with 1°C as the interval, one by one is substituted into the above formula to conveniently solve n (for example, 41) heating schemes, corresponding to n total power consumption E total , find the smallest E total Corresponding That is the optimal target temperature rise.

[0181] In step S408, the vehicle controller controls the heating device to heat the battery to a target temperature.

[0182] In the technical solution provided in step S408 of the present disclosure, the VCU sends a heating command, and the heating device works at maximum load to heat the battery until the battery temperature rises to Then stop heating.

[0183] In the disclosed embodiment, the road condition information ahead can be collected by the navigation system and sent to the VCU. The VCU predicts the operating condition based on the navigation information and historical big data stored in the cloud server, and then establishes a heat generation and heat dissipation model for the battery. The battery temperature change trajectory can be predicted based on the operating condition, and the internal resistance power consumption is calculated based on the obtained temperature change trajectory. The braking recovery power is calculated based on the predicted operating condition, so as to determine the power consumed by heating the battery based on the experiment, and obtain the power consumption corresponding to different temperature rises, so as to obtain the power consumption state equation under the entire operating condition, formulate corresponding constraints, and obtain the optimal target temperature rise for battery heating based on the optimal energy consumption solution. The VCU can then control the heating device to heat the battery until it is heated to the target temperature, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect.

[0184] According to an embodiment of the present disclosure, a heating device for a battery in a vehicle is also provided. It should be noted that the heating device for a battery in a vehicle can be used to implement the heating method for a battery in a vehicle in Example 1.

[0185] Figure 5 is a schematic diagram of a battery heating device in a vehicle according to an embodiment of the present disclosure. As shown in Figure 5, the battery heating device 500 in the vehicle may include: an acquisition component 501, a first determination component 502, an establishment component 503, a second determination component 504 and an execution component 505.

[0186] The acquisition component 501 is configured to acquire the vehicle's driving condition information and the status information of the battery in the vehicle during the vehicle's driving, wherein the status information is used to represent the working state of the battery during the vehicle's driving.

[0187] The first determining component 502 is configured to determine a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation condition of the battery.

[0188] The establishing component 503 is configured to establish a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery.

[0189] The second determining component 504 is configured to determine target heating data of the battery based on the battery consumption model and the constraint conditions.

[0190] The execution component 505 is configured to perform a heating operation on the battery based on the target heating data, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed.

[0191] Optionally, the first determination component 502 may include: a first establishment component, used to establish a heat generation model of the battery and a heat dissipation model of the battery based on driving condition information and status information, wherein the heat generation model is used to represent the heat generation condition and the heat dissipation model is used to represent the heat dissipation condition; a first determination component, used to determine the temperature change model of the battery based on the heat generation model and the heat dissipation model.

[0192] Optionally, the first establishing component may include: a first establishing sub-component, configured to establish a heat generation model under a driving condition corresponding to the driving condition information based on the heat generation data of the battery in the status information, the volume of the battery, the specific heat capacity of the battery, the mass of the battery and the temperature rise data caused by the heat generation of the battery; a second establishing sub-component, configured to establish a heat dissipation model under the driving condition based on the air cooling data of the battery, the temperature of the battery, the ambient temperature, specific heat capacity and mass of the vehicle; a first determining sub-component, configured to determine a temperature change model based on the temperature rise data determined in the heat generation model and the temperature drop data determined in the heat dissipation model, wherein the temperature change model is used to predict the temperature change condition of the battery at a future moment.

[0193] Optionally, the establishment component 503 may include: a second determination component, which is configured to determine the battery's braking recovery power and the battery's power consumption data based on a temperature change model; a second establishment component, which is configured to establish a battery consumption model based on the braking recovery power and the power consumption data; and a third establishment component, which is configured to establish constraints based on the battery's temperature, the battery's state of charge, and the battery's temperature rise data.

[0194] Optionally, the second determination component may include: a second determination subcomponent configured to determine the battery's operating power based on the vehicle's weight, driving speed, acceleration, and frontal area in the driving condition information, wherein the operating power is used to indicate the battery's discharge or charging status; a third determination subcomponent configured to determine the battery's state of charge model based on the state of charge, battery voltage, battery capacity, and operating power; a fourth determination subcomponent configured to determine the battery's braking regenerative power based on the operating power; and a fifth determination subcomponent configured to determine the battery's braking regenerative power and battery power consumption data based on a temperature change model. The fifth determination subcomponent may be configured to determine the braking regenerative power based on the temperature change model, the state of charge model, and the braking regenerative power; and determine the power consumption data based on the relationship between the battery's temperature rise data and the battery's power consumption in the temperature change model.

[0195] Optionally, the second establishing component may include: a sixth determining subcomponent configured to determine the battery's internal resistance power consumption based on the battery voltage and the battery's operating power; a seventh determining subcomponent configured to determine the vehicle's power consumption based on driving condition information, where the power consumption is the power consumed by driving the vehicle; and a third establishing subcomponent configured to establish a battery consumption model based on braking regenerative power and power consumption data. The third establishing subcomponent is configured to determine the battery consumption model based on the internal resistance power consumption, power consumption data, braking regenerative power, and power consumption.

[0196] Optionally, the acquisition component 501 may include: an acquisition component configured to acquire navigation information during vehicle driving; and a prediction component configured to predict driving condition information based on historical driving data and navigation information of the vehicle.

[0197] Optionally, the prediction component may include: an acquisition subcomponent, configured to discretize the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; an eighth determination subcomponent, configured to determine the state transition probability of the vehicle based on the discrete results, wherein the state transition probability is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; a first generation subcomponent, configured to generate a state transition probability matrix of the vehicle based on the state transition probability; a ninth determination subcomponent, configured to determine the initial speed state vector of the vehicle and the target speed state vector after the state transition occurs based on the state transition probability matrix; a second generation subcomponent, configured to generate a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; a prediction subcomponent, configured to predict the driving condition information based on the prediction model.

[0198] In an embodiment of the present disclosure, the vehicle's driving condition information and status information of the battery in the vehicle are obtained by an acquisition component during the vehicle's driving, wherein the status information is used to characterize the working state of the battery during the vehicle's driving; a temperature change model of the battery is determined based on the driving condition information and the status information by a first determination component, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation condition of the battery; a battery consumption model of the vehicle and corresponding constraints are established based on the temperature change model by an establishment component, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; target heating data of the battery is determined based on the battery consumption model and constraints by a second determination component; a heating operation is performed on the battery based on the target heating data by an execution component, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed, thereby solving the technical problem of poor heating effect of the battery and achieving the technical effect of improving the heating effect of the battery.

[0199] The various functional components provided in the embodiments of the present disclosure may be operated in a battery heating method in a vehicle or a similar computing device, and may also be stored as part of a computer-readable storage medium.

[0200] FIG6 is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present disclosure. As shown in FIG6 , a program product 60 in a real-time manner according to the present disclosure is provided, on which a computer program is stored. When the computer program is executed by a processor, program code for implementing the following steps is implemented:

[0201] Acquiring the vehicle's driving condition information and the status information of the battery in the vehicle during driving, wherein the status information is used to represent the working state of the battery during driving;

[0202] Determining a battery temperature variation model based on the driving condition information and the state information, wherein the temperature variation model is used to characterize a heat generation condition and / or a heat dissipation condition of the battery;

[0203] Based on the temperature change model, establishing a battery consumption model for the vehicle and corresponding constraints, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery;

[0204] Determine the target heating data of the battery based on the battery consumption model and constraints;

[0205] A heating operation is performed on the battery based on the target heating data, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed.

[0206] Optionally, the computer program also implements program code for the following steps when executed by the processor: based on driving condition information and status information, establishing a heat generation model of the battery and a heat dissipation model of the battery, wherein the heat generation model is used to represent the heat generation condition and the heat dissipation model is used to represent the heat dissipation condition; based on the heat generation model and the heat dissipation model, determining a temperature change model of the battery.

[0207] Optionally, the computer program also implements program code of the following steps when executed by the processor: under the driving condition corresponding to the driving condition information, establishing a heat generation model based on the heat generation data of the battery in the status information, the volume of the battery, the specific heat capacity of the battery, the mass of the battery and the temperature rise data caused by the heat generation of the battery.

[0208] Optionally, the computer program also implements program codes for the following steps when executed by the processor: under driving conditions, establishing a heat dissipation model based on the air cooling data of the battery, the temperature of the battery, the ambient temperature, specific heat capacity and mass of the vehicle.

[0209] Optionally, the computer program is also a program code that implements the following steps when executed by the processor: determining temperature rise data based on the heat generation model, and determining temperature drop data based on the heat dissipation model; determining a temperature change model based on the temperature rise data and the temperature drop data, wherein the temperature change model is used to predict the temperature change conditions of the battery at future times.

[0210] Optionally, the computer program also implements program codes of the following steps when executed by the processor: determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model; and establishing a battery consumption model based on the braking recovery power and the power consumption data.

[0211] Optionally, the computer program is also executed by the processor to implement program code for the following steps: determining the operating power of the battery based on the vehicle weight, vehicle speed, vehicle acceleration and vehicle frontal area in the driving condition information, wherein the operating power is used to indicate the discharge condition or charging condition of the battery; determining the state of charge model of the battery based on the state of charge, battery voltage, battery capacity and operating power; determining the braking recovery power of the battery based on the operating power; determining the braking recovery power based on the temperature change model, the state of charge model and the braking recovery power; determining the power consumption data based on the relationship between the temperature rise data of the battery in the temperature change model and the power consumption of the battery.

[0212] Optionally, the computer program also implements program codes of the following steps when executed by the processor: determining the internal resistance power consumption of the battery based on the battery voltage and the operating power of the battery; determining the vehicle's power consumption based on driving condition information, wherein the power consumption is the power consumed by driving the vehicle; determining a battery consumption model based on the internal resistance power consumption, power consumption data, braking recovery power and power consumption.

[0213] Optionally, the computer program further implements program codes for the following steps when executed by the processor: establishing constraint conditions based on the temperature of the battery, the state of charge of the battery, and the temperature rise data of the battery.

[0214] Optionally, the computer program also implements program codes of the following steps when executed by the processor: obtaining navigation information during the vehicle's driving process; and predicting driving condition information based on the vehicle's historical driving data and navigation information.

[0215] Optionally, the computer program is also a program code that implements the following steps when executed by the processor: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determining the state transition probability of the vehicle based on the discrete results, wherein the state transition probability is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; generating a state transition probability matrix of the vehicle based on the state transition probability; determining the initial speed state vector of the vehicle and the target speed state vector after the state transition occurs based on the state transition probability matrix; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0216] Optionally, in the embodiment of the present disclosure, the computer-readable storage medium may also be configured as program codes for various preferred or optional method steps provided by the method for heating a battery in a vehicle.

[0217] Optionally, the specific examples in the embodiments of the present disclosure may refer to the examples described in the above embodiments, and this embodiment will not be repeated here.

[0218] Computer-readable storage media may include a data signal transmitted in baseband or as part of a carrier wave, carrying readable program code. Such a transmitted data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Non-volatile storage media may transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0219] The program code contained in the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the foregoing.

[0220] According to an embodiment of the present disclosure, a processor is also provided. FIG7 is a schematic diagram of the structure of a processor according to an embodiment of the present disclosure. As shown in FIG7 , the processor 70 is configured to run a program, wherein the program, when running, executes the vehicle battery heating method according to the embodiment of the present disclosure.

[0221] In the embodiment of the present disclosure, the processor 70 may execute an operating program of a vehicle fault information processing method.

[0222] Optionally, in the embodiment of the present disclosure, the processor 70 may be configured to perform the following steps:

[0223] Acquiring the vehicle's driving condition information and the status information of the battery in the vehicle during driving, wherein the status information is used to represent the working state of the battery during driving;

[0224] Determining a battery temperature variation model based on the driving condition information and the state information, wherein the temperature variation model is used to characterize a heat generation condition and / or a heat dissipation condition of the battery;

[0225] Based on the temperature change model, establishing a battery consumption model for the vehicle and corresponding constraints, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery;

[0226] Determine the target heating data of the battery based on the battery consumption model and constraints;

[0227] A heating operation is performed on the battery based on the target heating data, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed.

[0228] Optionally, the processor 70 can be further configured to perform the following steps: based on driving condition information and status information, establish a heat generation model of the battery and a heat dissipation model of the battery, wherein the heat generation model is used to represent the heat generation condition and the heat dissipation model is used to represent the heat dissipation condition; based on the heat generation model and the heat dissipation model, determine the temperature change model of the battery.

[0229] Optionally, the processor 70 can also be configured to perform the following steps: under the driving condition corresponding to the driving condition information, a heat generation model is established based on the heat generation data of the battery in the status information, the volume of the battery, the specific heat capacity of the battery, the mass of the battery and the temperature rise data caused by the heat generation of the battery.

[0230] Optionally, the processor 70 may be further configured to perform the following steps: under driving conditions, establishing a heat dissipation model based on the air cooling data of the battery, the temperature of the battery, the ambient temperature, specific heat capacity and mass of the vehicle.

[0231] Optionally, the processor 70 can also be configured to perform the following steps: determine temperature rise data based on the heat generation model, and determine temperature drop data based on the heat dissipation model; determine a temperature change model based on the temperature rise data and the temperature drop data, wherein the temperature change model is used to predict the temperature change conditions of the battery at future moments.

[0232] Optionally, the processor 70 may be further configured to perform the following steps: determining the battery's braking recovery power and the battery's power consumption data based on the temperature change model; and establishing a battery consumption model based on the braking recovery power and the power consumption data.

[0233] Optionally, the processor 70 may be further configured to perform the following steps: determining the operating power of the battery based on the vehicle weight, vehicle speed, vehicle acceleration and vehicle frontal area in the driving condition information, wherein the operating power is used to indicate the discharge condition or charging condition of the battery; determining the state of charge model of the battery based on the state of charge, battery voltage, battery capacity and operating power; determining the braking recovery power of the battery based on the operating power; determining the braking recovery power based on the temperature change model, the state of charge model and the braking recovery power; determining the power consumption data based on the relationship between the temperature rise data of the battery in the temperature change model and the power consumption of the battery.

[0234] Optionally, the processor 70 can also be configured to perform the following steps: determining the internal resistance power consumption of the battery based on the battery voltage and the operating power of the battery; determining the vehicle's power consumption based on driving condition information, wherein the power consumption is the power consumed to drive the vehicle; determining a battery consumption model based on the internal resistance power consumption, power consumption data, braking recovery power and power consumption.

[0235] Optionally, the processor 70 may be further configured to perform the following steps: establishing a constraint condition based on the battery temperature, the battery state of charge, and the battery temperature rise data.

[0236] Optionally, the processor 70 may be further configured to perform the following steps: obtaining navigation information during the vehicle's driving process; and predicting driving condition information based on the vehicle's historical driving data and navigation information.

[0237] Optionally, the processor 70 can also be configured to perform the following steps: discretize the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determine the state transition probability of the vehicle based on the discrete results, wherein the state transition probability is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; generate a state transition probability matrix of the vehicle based on the state transition probability; determine the initial speed state vector of the vehicle and the target speed state vector after the state transition occurs based on the state transition probability matrix; generate a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predict the driving condition information based on the prediction model.

[0238] The processor 70 can execute various functional applications and data processing by running software programs and components stored in the memory, that is, implement the above-mentioned method for heating the battery in the vehicle.

[0239] According to another aspect of the present disclosure, an electronic device is provided. The electronic device includes one or more processors and a memory, wherein the memory is configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle battery heating method according to the present disclosure.

[0240] According to another aspect of an embodiment of the present disclosure, a computer program product is also provided, including a non-volatile computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it performs the steps of the method for heating a battery in a vehicle of the embodiment of the present disclosure.

[0241] According to an embodiment of the present disclosure, a vehicle is further provided, which is used to execute the method for heating a battery in a vehicle in embodiment 1 of the present disclosure.

[0242] The serial numbers of the above-mentioned embodiments of the present disclosure are for description only and do not represent the advantages or disadvantages of the embodiments.

[0243] In the above embodiments of the present disclosure, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0244] In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the components can be a logical function division. In actual implementation, there may be other division methods, such as multiple components or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of components or components, which can be electrical or other forms.

[0245] The components described as separate parts may or may not be physically separate, and the components shown as components may or may not be physical components, that is, they may be located in one place or distributed across multiple components. Some or all of the components may be selected according to actual needs to achieve the purpose of the present embodiment.

[0246] In addition, the functional components in the various embodiments of the present disclosure may be integrated into a single processing component, each component may exist physically separately, or two or more components may be integrated into a single component. The aforementioned integrated components may be implemented in the form of hardware or software functional components.

[0247] If the integrated components are implemented in the form of software functional components and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0248] The above is only a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications should also be regarded as within the scope of protection of the present disclosure. Industrial Applicability

[0249] The solution provided in the embodiment of the present application can be applied to determine whether the battery needs to be heated. When heating the battery, the vehicle's driving condition information and the status information of the battery in the vehicle can be obtained. Based on the driving condition information and status information, the temperature change model of the battery is determined, thereby establishing a battery consumption model of the vehicle and corresponding constraints. Based on the battery consumption model and constraints obtained above, the target heating data of the battery is determined, thereby performing a heating operation on the battery. By taking into account the heat generation conditions after heating the battery and the heat dissipation conditions brought about by air cooling under different driving conditions of the vehicle, the heating operation of the battery is comprehensively considered, thereby ensuring that not only the user's power requirements are met as a whole, but also the low-temperature cruising range of the vehicle is extended, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect.

Claims

1. A method for heating a battery in a vehicle, comprising: During the driving of the vehicle, the driving condition information of the vehicle and the status information of the battery in the vehicle are obtained, wherein the status information is used to characterize the working state of the battery during the driving of the vehicle; Determining a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model is used to characterize a heat generation condition and / or a heat dissipation condition of the battery; Based on the temperature change model, establishing a battery consumption model of the vehicle and corresponding constraints, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; Determining target heating data for the battery based on the battery consumption model and the constraint condition; A heating operation is performed on the battery based on the target heating data, wherein the target heating data is used to indicate a working state of the battery after the heating operation is performed.

2. The method according to claim 1, wherein: Determining a temperature change model of the battery based on the driving condition information and the state information includes: Based on the driving condition information and the state information, establishing a heat generation model of the battery and a heat dissipation model of the battery, wherein the heat generation model is used to represent the heat generation condition, and the heat dissipation model is used to represent the heat dissipation condition; A temperature variation model of the battery is determined based on the heat generation model and the heat dissipation model.

3. The method according to claim 2, wherein: Based on the driving condition information and the state information, a heat generation model of the battery is established, including: Under the driving condition corresponding to the driving condition information, the heat generation model is established based on the heat generation data of the battery in the status information, the volume of the battery, the specific heat capacity of the battery, the mass of the battery and the temperature rise data caused by the heat generation of the battery.

4. The method according to claim 2, wherein: Based on the driving condition information and the state information, a heat dissipation model of the battery is established, including: Under the driving condition corresponding to the driving condition information, the heat dissipation model is established based on the air cooling data of the battery, the temperature of the battery, the ambient temperature of the vehicle, the specific heat capacity of the battery and the mass of the battery.

5. The method according to claim 2, wherein: Determining a temperature change model of the battery based on the heat generation model and the heat dissipation model includes: Determining temperature rise data based on the heat generation model, and determining temperature drop data based on the heat dissipation model; The temperature change model is determined based on the temperature rise data and the temperature drop data, wherein the temperature change model is used to predict the temperature change condition of the battery at a future moment.

6. The method according to claim 1, wherein: Based on the temperature change model, a battery consumption model of the vehicle is established, including: Determining the braking recovery power of the battery and the power consumption data of the battery based on the temperature change model; The battery consumption model is established based on the braking regenerative power and the consumed power data.

7. The method according to claim 6, wherein: Before determining the braking recovery power of the battery and the power consumption data of the battery based on the temperature change model, the method further includes: determining the operating power of the battery based on the weight of the vehicle, the driving speed of the vehicle, the acceleration of the vehicle, and the frontal area of ​​the vehicle in the driving condition information, wherein the operating power is used to indicate the discharge condition or the charging condition of the battery; determining a state-of-charge model of the battery based on the state-of-charge of the battery, the voltage of the battery, the capacity of the battery, and the operating power; determining a braking recovery power of the battery based on the operating power; Determining the braking recovery power of the battery and the power consumption data of the battery based on the temperature change model includes: Determining the braking regenerative power based on the temperature change model, the state of charge model and the braking regenerative power; The power consumption data is determined based on the relationship between the temperature rise data of the battery and the power consumption of the battery in the temperature change model.

8. The method according to claim 6, wherein: Before establishing the battery consumption model based on the braking power recovery and the power consumption data, the method further includes: Determining the power consumption of the internal resistance of the battery based on the voltage of the battery and the operating power of the battery; Based on the driving condition information, the power consumption of the vehicle is determined, wherein the power consumption is the amount of electricity consumed to drive the vehicle; The battery consumption model is established based on the braking recovery power and the consumed power data, including: The battery consumption model is determined based on the internal resistance power consumption, the power consumption data, the braking recovery power and the power consumption.

9. The method according to claim 1, wherein: Based on the temperature change model, establishing the constraint conditions of the vehicle includes: The constraint condition is established based on the temperature of the battery, the state of charge of the battery, and the temperature rise data of the battery.

10. The method according to claim 1, wherein: During the driving of the vehicle, the driving condition information of the vehicle is obtained, including: Obtaining navigation information during the driving of the vehicle; The driving condition information is predicted based on the historical driving data of the vehicle and the navigation information.

11. The method according to claim 10, wherein: Predicting the driving condition information based on the historical driving data of the vehicle and the navigation information includes: Discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain a discrete result; Based on the discrete result, determining a state transition probability of the vehicle, wherein the state transition probability is used to represent a transition probability from a speed state of the vehicle at a current moment to a speed state of the vehicle at a next moment; Generating a state transition probability matrix of the vehicle according to the state transition probability; Based on the state transition probability matrix, determining an initial speed state vector of the vehicle and a target speed state vector after state transition occurs; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; Based on the prediction model, the driving condition information is predicted.

12. A heating device for a battery in a vehicle, comprising: An acquisition component is configured to acquire the driving condition information of the vehicle during the driving of the vehicle and Status information of the battery in the vehicle, wherein the status information is used to characterize the working status of the battery during driving of the vehicle; a first determination component, configured to determine a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model is used to characterize a heat generation condition and / or a heat dissipation condition of the battery; An establishment component is configured to establish a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; a second determination component, configured to determine target heating data of the battery based on the battery consumption model and the constraint condition; An execution component is configured to perform a heating operation on the battery based on the target heating data, wherein the target heating data is used to indicate a working state of the battery after the heating operation is performed.

13. A processor, wherein: The processor is used to run a program, and when the program is run by the processor, the method for heating a battery in a vehicle according to any one of claims 1 to 11 is executed.

14. A vehicle, configured to execute the method for heating a battery in a vehicle according to any one of claims 1 to 11.

15. A computer-readable storage medium, wherein: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for heating a battery in a vehicle according to any one of claims 1 to 11.

16. An electronic device, wherein: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method for heating a battery in a vehicle according to any one of claims 1 to 11.

17. A computer program product, wherein: The invention comprises a computer program, which, when executed by a processor, implements the method for heating a battery in a vehicle according to any one of claims 1 to 11.

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