Battery temperature control method and device

Abstract

The invention provides a battery temperature control method and device. The method can be applied to the field of intelligent vehicles. The method comprises the following steps: acquiring working condition characteristic information and state information of a battery, wherein the working condition characteristic information comprises at least one of vehicle speed information, power information and navigation information of a vehicle; a battery temperature optimization strategy is determined according to the working condition feature information, the state information and a mapping relation, and the mapping relation comprises working condition data, the state information of the battery and the mapping relation between the battery temperature optimization strategies determined by the working condition data and the state information of the battery; and controlling the temperature of the battery according to the battery temperature optimization strategy. The embodiment of the invention can be applied to an intelligent vehicle or an electric vehicle, and the endurance mileage of the vehicle can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent vehicles, and more particularly, to a battery temperature control method and device. Background Art

[0002] The annual sales volume of electric vehicles is increasing year by year and is gradually replacing fuel vehicles. However, the endurance retention rate of electric vehicles at low temperatures remains a pain point. There are mainly two reasons for the decrease in the endurance of electric vehicles at low temperatures: one is the increase in the energy consumption of the passenger compartment, and the other is the decrease in battery performance at low temperatures. The first point mainly depends on user habits, and there is little room for optimization of the electric vehicle control strategy.

[0003] Therefore, how to optimize the battery heating strategy at low temperatures has become an urgent problem to be solved. Summary of the Invention

[0004] The present application provides a battery temperature control method and device, which helps to improve the endurance mileage of the vehicle.

[0005] In a first aspect, a battery temperature control method is provided. The method includes: obtaining first operating condition characteristic information and first state information of the battery, where the first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information; determining a first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and a mapping relationship, where the mapping relationship includes a mapping relationship between operating condition data, state information of the battery, and a plurality of battery temperature optimization strategies determined by the operating condition data and the state information of the battery, and the plurality of battery temperature optimization strategies includes the first battery temperature optimization strategy; and controlling the temperature of the battery according to the first battery temperature optimization strategy.

[0006] Based on the above technical solution, the vehicle can select a suitable battery temperature optimization strategy from a plurality of battery temperature optimization strategies determined by operating condition data and state information of the battery according to the operating condition characteristic information and state information of the battery obtained in real time, so as to control the temperature of the battery. Since the plurality of battery temperature optimization strategies are determined by a plurality of factors (operating condition data and state information of the battery) that affect the power performance and economy of the vehicle at low temperatures, it can be ensured that the control method of the battery temperature is more in line with the current operating conditions, which helps to reduce the energy consumption of the vehicle, and thus helps to improve the endurance mileage of the vehicle.

[0007] In some possible implementations, the first state information may be data collected by a battery management system (BMS). For example, the first state information includes at least one of the state of charge (SOC) of the battery, temperature, state of health (SOH) of the battery, battery capacity, and state of power (SOP) of the battery.

[0008] In some possible implementations, the speed information of the vehicle may be determined from data collected by a speed sensor of the vehicle. For example, the speed information may be a speed characteristic, or a curve of speed changing over time, etc.

[0009] In some possible implementations, the power information may be the total power change information of the vehicle.

[0010] In some possible implementations, the mapping relationship may include the correspondence between operating condition data, the state information of the battery, and the battery temperature optimization strategy. The battery temperature optimization strategy in the mapping relationship may be determined by the operating condition data and the state information of the battery. The operating condition data may be data under different operating conditions (such as commuting conditions, long-distance conditions, etc.) collected based on a big data platform.

[0011] Exemplarily, the data under different operating conditions may include speed change information under different operating conditions (such as speed change characteristics or speed change curves of the vehicle speed). For example, the commuting condition may include the speed characteristics or speed change curve of the vehicle during the driving process from home to the company, and the long-distance condition may include the speed characteristics or speed change curve of the vehicle during the driving process from home to scenic spot A.

[0012] Exemplarily, the data under different operating conditions may include the total power change information of the vehicle under different operating conditions (such as the total power change characteristics or total power change curves of the vehicle). For example, the commuting condition may include the total power change curve of the vehicle during the driving process from home to the company, and the long-distance condition may include the total power change curve of the vehicle during the driving process from home to scenic spot A.

[0013] Exemplarily, the state information of the battery in the mapping relationship may include the initial SOC and initial temperature of the vehicle, or the SOC and temperature after a preset duration of power-on of the vehicle.

[0014] In some possible implementations, the operating condition data may further include the ambient temperature where the vehicle is located.

[0015] In some possible implementation manners, the battery temperature optimization strategy in the mapping relationship can be determined by the operating condition data, the state information of the battery, and the objective function. For example, the objective function can include a vehicle energy flow model.

[0016] In some possible implementation manners, the method further includes: obtaining the mapping relationship from a cloud server. Exemplarily, the cloud server can determine multiple battery temperature optimization strategies based on the operating condition data and the state information of the battery, and establish a mapping relationship among the operating condition data, the state information of the battery, and the battery temperature optimization strategy. The cloud server can send the mapping relationship to the vehicle.

[0017] In some possible implementation manners, determining the first battery temperature optimization strategy according to the first operating condition feature information, the first state information, and the mapping relationship includes: determining the first operating condition (such as a commuting operating condition or a long-distance operating condition, etc.) in which the vehicle is located according to the first operating condition feature information (for example, the speed feature within a preset duration after the vehicle is powered on); determining the first battery temperature optimization strategy according to the first operating condition, the first state information, and the mapping relationship.

[0018] In some possible implementation manners, the first battery temperature optimization strategy includes a curve of the heating power output by the heating device of the battery changing with time, or a curve of the battery temperature changing with time.

[0019] Combined with the first aspect, in some implementation manners of the first aspect, the multiple battery temperature optimization strategies are determined by the operating condition data, the operating condition error, and the state information of the battery, and the operating condition error is used to correct the operating condition data.

[0020] Based on the above technical solution, the battery temperature optimization strategy in the mapping relationship can be determined based on the operating condition error. Taking the operating condition data including the vehicle speed change curve as an example, the operating condition error can be to correct the speed change curve. In this way, by introducing the operating condition error, it helps to improve the robustness of the vehicle's battery control and also helps to ensure the stability of the endurance benefit.

[0021] In some possible implementation manners, the operating condition data can include the total power change curve of the vehicle, and the operating condition error can be used to correct the total power change curve.

[0022] In combination with the first aspect, in some implementations of the first aspect, the multiple battery temperature optimization strategies are determined by the operating condition data, the state information of the battery, and an objective function, and the objective function is determined by at least one of the following parameters: the internal resistance of the battery, the open circuit voltage (OCV) of the battery, the power loss of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the loop heat exchange information of the battery, and the loop heat exchange information of the thermal management positive temperature coefficient (PTC).

[0023] Based on the above technical solution, when determining the battery temperature optimization strategy in the mapping relationship, the parameters of the battery can also be considered. In this way, the multiple battery temperature optimization strategies can comprehensively consider the relevant factors affecting battery heating, which helps to improve the accuracy of the battery temperature optimization strategy, and thus helps to improve the driving range of the vehicle.

[0024] In combination with the first aspect, in some implementations of the first aspect, the objective function is determined by an optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

[0025] Based on the above technical solution, when determining the battery temperature optimization strategy in the mapping relationship, the optimization objective can also be considered. In this way, the battery temperature optimization strategy in the mapping relationship can take into account different objectives, thus meeting the multi-objective needs of users.

[0026] In some possible implementations, determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and the mapping relationship includes: determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, the mapping relationship, and the input of the user, where the input of the user is used to indicate a first optimization objective, and the first optimization objective includes an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

[0027] In some possible implementations, the method includes: when determining the operating condition of the vehicle according to the first operating condition characteristic information, controlling a prompting device (for example, a display screen or a voice prompting device) to prompt the user to input an optimization objective; and determining the first optimization objective according to the input of the user.

[0028] In combination with the first aspect, in some implementations of the first aspect, the mapping relationship includes the mapping relationship between the operating condition data, the state information of the battery, the state data of the subsystems in the vehicle, and the multiple battery temperature optimization strategies determined by the operating condition data, the state information of the battery, and the state data of the subsystems. The subsystems include at least one of the cockpit system and the electric drive system. The method further includes: obtaining the first state data of the subsystem; wherein, determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and the mapping relationship includes: determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state data, the first state information, and the mapping relationship.

[0029] Based on the above technical solution, the battery temperature optimization strategy in the mapping relationship comprehensively considers the operating condition data, the state information of the battery, and the state of the cockpit system or the electric drive system, which can ensure that the control method of the battery temperature is more in line with the current operating conditions, helps to reduce the energy consumption of the vehicle, and thus helps to improve the cruising range of the vehicle.

[0030] In combination with the first aspect, in some implementations of the first aspect, the first operating condition characteristic information includes the first vehicle speed characteristic within a preset power-on duration of the vehicle. The mapping relationship includes the mapping relationship between the second vehicle speed characteristic from the first location to the second location, the second state information of the battery, and the first battery temperature optimization strategy determined by the second vehicle speed characteristic and the second state information. Determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and the mapping relationship includes: when the first vehicle speed characteristic matches the second vehicle speed characteristic and the first state information matches the second state information, determining the first battery temperature optimization strategy.

[0031] Based on the above technical solution, when the speed characteristic within the preset power-on duration of the vehicle matches the speed characteristic from the first location to the second location and the first state information and the second state information match, the vehicle can obtain the first battery temperature optimization strategy through the mapping relationship. In this way, the vehicle can consider the impact of future operating conditions on the battery performance in advance based on the first battery temperature optimization strategy, which helps to improve the accuracy of battery temperature control and thus helps to improve the cruising range of the vehicle.

[0032] In some possible implementation manners, the mapping relationship includes the correspondence between a first working condition (for example, a commuting working condition from location A to location B), second state information of the battery, and a first battery temperature optimization strategy determined by the working condition data and the second state information under the first working condition. Determining the first battery temperature optimization strategy according to the first working condition feature information, the first state information, and the mapping relationship includes: determining that the vehicle is in the first working condition according to the first working condition feature information; and when the first state information matches the second state information, determining the first battery temperature optimization strategy from multiple battery temperature optimization strategies.

[0033] Exemplarily, taking the case where the first state information includes a first SOC and the second state information includes a second SOC as an example, the matching of the first state information and the second state information includes: the difference between the first SOC and the second SOC is less than or equal to a preset difference (for example, 3%).

[0034] Exemplarily, taking the case where the first state information includes a first battery temperature and the second state information includes a second battery temperature as an example, the matching of the first state information and the second state information includes: the difference between the first temperature and the second temperature is less than or equal to a preset temperature difference (for example, 1°).

[0035] Combined with the first aspect, in some implementation manners of the first aspect, the method further includes: sending the vehicle history record (VHR) to the cloud server; and receiving the updated mapping relationship sent by the cloud server according to the VHR.

[0036] Based on the above technical solution, the vehicle can send the VHR (for example, the actual speed change information or total power change information from location A to location B) to the cloud server, so that the cloud server can determine a more accurate battery temperature optimization strategy by combining the latest working condition data from location A to location B and the state information of the battery. In this way, by sending the updated mapping relationship from the cloud server to the vehicle, it can make the battery temperature optimization strategy determined by the vehicle through the working condition feature information and the state information of the battery closer to the user's usage habits.

[0037] In a second aspect, a battery temperature control method is provided. The method includes: obtaining first working condition feature information and first state information of the battery, where the first working condition feature information includes at least one of vehicle speed information, power information, and navigation information; determining a first battery temperature optimization strategy according to the first working condition feature information and the first state information; and controlling the temperature of the battery according to the first battery temperature optimization strategy.

[0038] Based on the above technical solution, the first battery temperature optimization strategy is determined by multiple factors (operating condition characteristic information and battery state information) that affect the vehicle battery performance at low temperatures, which can ensure that the control method of the battery temperature is more in line with the current operating conditions, help reduce the energy consumption of the vehicle, and thus help improve the cruising range of the vehicle.

[0039] In some possible implementation manners, the battery temperature control method may be executed by a cloud server. The obtaining of the first operating condition characteristic information and the first state information of the battery includes: obtaining the first operating condition characteristic information and the first state information sent by the vehicle; according to the first battery temperature optimization strategy, controlling the temperature of the battery, including: sending the first battery temperature optimization strategy to the vehicle so that the vehicle executes the first battery temperature optimization strategy.

[0040] In some possible implementation manners, the method may be executed by a cloud server. The determining of the first battery temperature optimization strategy according to the first operating condition characteristic information and the first state information includes: determining first operating condition data according to the first operating condition characteristic information; determining the first battery temperature optimization strategy according to the first operating condition data and the first state information.

[0041] Exemplarily, the first operating condition characteristic information includes the vehicle speed change information within a preset duration after the vehicle is powered on, and the first operating condition data includes the vehicle speed change information from a first location to a second location. The first operating condition data includes the vehicle speed change information within a future period of time of the vehicle.

[0042] Exemplarily, when the cloud server determines that the vehicle speed change information within the preset duration after the vehicle is powered on matches the vehicle speed change information from the first location to the second location, it may determine that the vehicle is in the operating condition from the first location to the second location. The cloud server may determine the first battery temperature optimization strategy based on the first operating condition data (for example, the speed change information from the first location to the second location) and the first state information. The cloud server may send the first battery temperature optimization strategy to the vehicle.

[0043] Combined with the second aspect, in some implementation manners of the second aspect, the determining of the first battery temperature optimization strategy according to the first operating condition characteristic information and the first state information includes: determining first operating condition data according to the first operating condition characteristic information; determining the first battery temperature optimization strategy according to the first operating condition data, the operating condition error, and the first state information, where the operating condition error is used to correct the first operating condition data.

[0044] Based on the above technical solution, the first battery temperature optimization strategy can be determined based on the working condition error. Taking the first working condition characteristic information including the vehicle speed change curve as an example, the working condition error can be the correction of the speed change curve. In this way, by introducing the working condition error, it helps to improve the robustness of the vehicle's battery control and also helps to ensure the stability of the endurance benefit.

[0045] Combined with the second aspect, in some implementation manners of the second aspect, determining the first battery temperature optimization strategy according to the first working condition characteristic information and the first state information includes: determining the first battery temperature optimization strategy according to the first working condition characteristic information, the objective function, and the first state information, where the objective function is determined by at least one of the following parameters: the internal resistance of the battery, the OCV of the battery, the loss power of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the loop heat exchange information of the battery, and the heat management PTC loop heat exchange information.

[0046] Based on the above technical solution, the first battery temperature optimization strategy comprehensively considers the relevant factors affecting battery heating, which helps to improve the accuracy of the battery temperature optimization strategy and thus helps to increase the endurance mileage of the vehicle.

[0047] Combined with the second aspect, in some implementation manners of the second aspect, the objective function is determined by the optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

[0048] Based on the above technical solution, the optimization objective can also be considered when formulating the first battery temperature optimization strategy. In this way, the battery temperature optimization strategy can meet the multi-objective requirements of users.

[0049] In a third aspect, a battery temperature control device is provided. The device includes: an acquisition unit configured to acquire first working condition characteristic information and a first state information of the battery, where the first working condition characteristic information includes at least one of vehicle speed information, power information, and navigation information of the vehicle; a determination unit configured to determine a first battery temperature optimization strategy according to the first working condition characteristic information, the first state information, and a mapping relationship, where the mapping relationship includes a mapping relationship between working condition data, the state information of the battery, and multiple battery temperature optimization strategies determined by the working condition data and the state information of the battery, and the multiple battery temperature optimization strategies include the first battery temperature optimization strategy; and a control unit configured to control the temperature of the battery according to the first battery temperature optimization strategy.

[0050] Combined with the third aspect, in some implementation manners of the third aspect, the multiple battery temperature optimization strategies are determined by the working condition data, a working condition error, and the state information of the battery, and the working condition error is used to correct the working condition data.

[0051] In combination with the third aspect, in some implementation manners of the third aspect, the multiple battery temperature optimization strategies are determined by the operating condition data, the state information of the battery, and an objective function, and the objective function is determined by at least one of the following parameters: the internal resistance of the battery, the open circuit voltage OCV of the battery, the loss power of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the loop heat exchange information of the battery, and the heat management positive temperature coefficient PTC loop heat exchange information.

[0052] In combination with the third aspect, in some implementation manners of the third aspect, the objective function is determined by an optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

[0053] In combination with the third aspect, in some implementation manners of the third aspect, the mapping relationship includes the mapping relationship between the operating condition data, the state information of the battery, the state data of the subsystems in the vehicle, and the multiple battery temperature optimization strategies determined by the operating condition data, the state information of the battery, and the state data of the subsystems. The subsystems include at least one of a cockpit system and an electric drive system. The obtaining unit is further configured to obtain first state data of the subsystems; the determining unit is configured to: determine the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state data, the first state information, and the mapping relationship.

[0054] In combination with the third aspect, in some implementation manners of the third aspect, the first operating condition characteristic information includes the first vehicle speed characteristic within a preset power-on duration of the vehicle, and the mapping relationship includes the mapping relationship between the second vehicle speed characteristic from a first location to a second location, the second state information of the battery, and the first battery temperature optimization strategy determined by the second vehicle speed characteristic and the second state information. The determining unit is configured to: determine the first battery temperature optimization strategy when the first vehicle speed characteristic matches the second vehicle speed characteristic and the first state information matches the second state information.

[0055] In combination with the third aspect, in some implementation manners of the third aspect, the device further includes: a sending unit, configured to send a VHR to a cloud server; a receiving unit, configured to receive the updated mapping relationship sent by the cloud server according to the VHR.

[0056] Fourthly, a battery temperature control device is provided. The device includes: an acquisition unit configured to acquire first operating condition characteristic information and first state information of the battery, where the first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information; a determination unit configured to determine a first battery temperature optimization strategy according to the first operating condition characteristic information and the first state information; and a control unit configured to control the temperature of the battery according to the first battery temperature optimization strategy.

[0057] In combination with the fourth aspect, in some implementation manners of the fourth aspect, the determination unit is configured to: determine the first battery temperature optimization strategy according to first operating condition data determined from the first operating condition characteristic information, an operating condition error, and the first state information, where the operating condition error is used to correct the first operating condition data.

[0058] In combination with the fourth aspect, in some implementation manners of the fourth aspect, the determination unit is configured to: determine a first battery temperature optimization strategy according to the first operating condition characteristic information, an objective function, and the first state information, where the objective function is determined by at least one of the following parameters: internal resistance of the battery, OCV of the battery, loss power of the battery, external heat exchange information of the battery, heat exchange information between the battery and the coolant, loop heat exchange information of the battery, and heat management PTC loop heat exchange information.

[0059] In combination with the fourth aspect, in some implementation manners of the fourth aspect, the objective function is determined by an optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

[0060] Fifthly, a battery temperature control device is provided. The device includes a processing unit and a storage unit, where the storage unit is configured to store instructions, and the processing unit executes the instructions stored in the storage unit so that the device executes any possible vehicle control method in the first aspect or the second aspect.

[0061] Sixthly, the present application provides a battery temperature control system. The control system includes a thermal management system and a computing platform, and the computing platform includes any possible device in the third aspect, or includes the device described in the fourth aspect, or includes the device described in the fifth aspect.

[0062] Seventhly, the present application provides a vehicle. The vehicle includes any possible device in the third aspect, or includes the device described in the fourth aspect, or includes the device described in the fifth aspect, or includes the system described in the sixth aspect.

[0063] Eighthly, the present application provides a server. The vehicle includes the device described in the fourth aspect, or includes the device described in the fifth aspect.

[0064] In a ninth aspect, the present application provides a computer program product, which includes computer program code that, when running on a computer, causes the computer to execute any of the possible methods in the first aspect or the second aspect above.

[0065] It should be noted that the above computer program code can be stored in whole or in part on a first storage medium, where the first storage medium can be packaged together with the processor or separately packaged from the processor. The embodiments of the present application do not make specific limitations on this.

[0066] In a tenth aspect, the present application provides a computer-readable medium storing program code that, when running on a computer, causes the computer to execute any of the possible methods in the first aspect or the second aspect above.

[0067] In an eleventh aspect, the present application provides a chip, which includes a circuit for executing any of the possible methods in the first aspect or the second aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a schematic functional block diagram of a vehicle provided by an embodiment of the present application.

[0069] Figure 2 It is a schematic block diagram of a system architecture provided by an embodiment of the present application.

[0070] Figure 3 It is a schematic diagram of a multi-dimensional graph output by an offline computing module or a cloud server provided by an embodiment of the present application.

[0071] Figure 4 It is a schematic flowchart of a battery temperature control method provided by an embodiment of the present application.

[0072] Figure 5 It is a human-machine interface HMI provided by an embodiment of the present application.

[0073] Figure 6 It is another schematic block diagram of a system architecture provided by an embodiment of the present application.

[0074] Figure 7 It is a schematic diagram of an intelligent optimization module outputting a battery temperature optimization strategy provided by an embodiment of the present application.

[0075] Figure 8 It is another schematic flowchart of a battery temperature control method provided by an embodiment of the present application.

[0076] Figure 9It is another schematic flowchart of the battery temperature control method provided by the embodiments of the present application.

[0077] Figure 10 It is another schematic flowchart of the battery temperature control method provided by the embodiments of the present application.

[0078] Figure 11 It is a schematic block diagram of the battery temperature control device provided by the embodiments of the present application. Detailed implementation manners

[0079] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Among them, in the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B may mean A or B; herein, "and / or" is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B may mean: A exists alone, A and B exist simultaneously, and B exists alone. "At least one item" means one or more than one item. For example, "at least one of A and B" is similar to "A and / or B", describing the association relationship of associated objects, indicating that there can be three relationships. For example, at least one of A and B may mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0080] In the embodiments of the present application, prefix words such as "first" and "second" are only used to distinguish different described objects, and have no restrictive effect on the position, order, priority, quantity, content, etc. of the described objects. The use of ordinal numbers and other prefix words for distinguishing described objects in the embodiments of the present application does not constitute a limitation on the described objects. The statement of the described objects refers to the description in the context of the claims or embodiments, and should not constitute unnecessary limitations due to the use of such prefix words. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "a plurality" is two or more.

[0081] Figure 1 It is a schematic functional block diagram of the vehicle 100 provided by the embodiments of the present application. The vehicle 100 may include a sensing system 110 and a computing platform 120. Among them, the sensing system 110 may include one or more sensors for sensing information about the environment around the vehicle 100. For example, the sensing system 110 may include a positioning system, and the positioning system may be a global positioning system (GPS), or a Beidou system or other positioning systems. Again, for example, the sensing system 110 may include one or more of an inertial measurement unit (IMU), a lidar, a millimeter-wave radar, an ultrasonic radar, and a camera device.

[0082] Some or all functions of the vehicle 100 can be controlled by the computing platform 120. The computing platform 120 may include one or more processors, such as processors 121 to 12n (n is a positive integer). A processor is a circuit with signal processing capabilities. In one implementation, a processor can be a circuit with the ability to read and execute instructions, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a type of microprocessor), or a digital signal processor (DSP), etc.; in another implementation, a processor can achieve certain functions through the logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or can be reconfigured. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field programmable gate array (FPGA). In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the configuration of the hardware circuit can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as a type of ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. In addition, the computing platform 120 may further include a memory for storing instructions, and some or all of the processors 121 to 12n can call the instructions in the memory to implement corresponding functions.

[0083] Figure 2 A schematic block diagram of the system architecture 200 provided by an embodiment of the present application is shown. The system architecture 200 includes an offline computing module 210, a cloud server 220, a vehicle controller 230, a display device 240, a thermal management system 250, a battery management system 260, a cockpit system 270, and an electric drive system 280. Among them, the vehicle controller 230 includes a pre-computation storage module 231, a working condition recognition module 232, and an intelligent optimization module 233; the thermal management system 250 includes a thermal management controller 251 and a thermal management actuator 252.

[0084] The computing storage module 231 can obtain pre-computed information from the offline computing module 210, and the pre-computed information may include a multidimensional map under different working conditions.

[0085] Exemplarily, the multidimensional map may be as shown in Table 1.

[0086] Table 1

[0087]

[0088] The above-mentioned multidimensional map under different working conditions can also be understood as the mapping relationship between the working condition data, SOC, external temperature, cabin temperature required by the user, electric drive temperature, battery temperature, and the output heating power of the battery heating device over time. Among them, the SOC can be the SOC after the vehicle has been powered on for a preset duration (for example, 5 minutes), and the external temperature, cabin temperature required by the user, electric drive temperature, and battery temperature can be the temperatures after the vehicle has been powered on for the preset duration.

[0089] The above-mentioned external temperature (or ambient temperature) can also be included in the working condition data.

[0090] The above-mentioned working condition data may include the vehicle's speed information (or power information), weather information, etc. For example, working condition data 1 may include the curve of the vehicle's speed over time and weather information, etc. during the process from starting point 1 to destination 1, and working condition data 2 may include the curve of the vehicle's speed over time and weather information, etc. during the process from starting point 2 to destination 2.

[0091] Exemplarily, starting point 1 can be home, destination 1 can be the company, and working condition data 1 can also be called the working condition data under the commuting working condition. Exemplarily, starting point 2 can be a location in city 1, destination 2 can be a location in city 2, and working condition 2 can also be called the working condition data under the long-distance working condition.

[0092] Exemplarily, both working condition data 1 and working condition data 2 can be the working condition data under the commuting working condition. Among them, working condition data 1 is the data under the working condition of route 1 from home to the company, and working condition 2 is the data under the working condition of route 2 from home to the company.

[0093] Correspondingly, curve 1 and curve 2 can be the curves of the heating device's power over time during the process from starting point 1 to destination 1. Curve 3 and curve 4 can be the curves of the heating device's power over time during the process from starting point 2 to destination 2.

[0094] Exemplarily, the heating device can be a heating device such as a PTC thermistor, a heat pump, etc.

[0095] The power variation curve of the above heating device over time can also be the temperature variation curve of the battery over time.

[0096] The outside temperature and the cabin temperature required by the user in Table 1 above can also be understood as the power required by the cabin. The power required by the cabin can include one or more of the power required for air-conditioning heating, seat heating, steering wheel heating, and heating of heat radiation devices.

[0097] Table 1 above is only illustrative, and the embodiments of the present application are not limited thereto. For example, the multi-dimensional graph may include more or fewer dimensions, such as the mapping relationship between operating condition data, SOC, outside temperature, cabin temperature required by the user, battery temperature, and the heating power variation curve of the battery heating device over time.

[0098] The multi-dimensional graph shown in Table 1 above may be stored in the vehicle when it leaves the factory, or the vehicle may obtain it from the cloud server 220 through over-the-air (OTA) technology.

[0099] In one embodiment, the multi-dimensional graph may also include dimensions of economy, power performance, and dimensions that take into account both economy and power performance. Exemplarily, Table 2 shows another multi-dimensional graph.

[0100] Table 2

[0101]

[0102] The operating condition recognition module 232 can obtain the data collected by the vehicle speed sensor and determine the vehicle speed variation curve over time through this data. For example, after the vehicle is powered on, the operating condition recognition module 232 can obtain the vehicle speed characteristics within 5 minutes after the vehicle is powered on through the data collected by the vehicle speed sensor.

[0103] Exemplarily, the operating condition recognition module 232 may store the vehicle speed information under different operating conditions. Table 3 shows the mapping relationship between operating conditions and speed information.

[0104] Table 3

[0105] Operating condition data Speed information Operating condition data 1 Speed information 1 Operating condition data 2 Speed information 2 Operating condition data 3 Speed information 3 … …

[0106] Taking the operating condition data 1 as an example of the operating condition data under operating condition 1 (the commuting operating condition from home to company), the speed information 1 can be the vehicle speed variation curve over time during the process from home to company. The operating condition recognition module 232 can determine the operating condition of the vehicle according to the vehicle speed characteristics within 5 minutes after the vehicle is powered on. For example, when the vehicle speed characteristics within 5 minutes after the vehicle is powered on match the operating condition data 1, it can be determined that the vehicle is in operating condition 1 (the commuting operating condition from home to company).

[0107] Exemplarily, the working condition recognition module 232 can determine the working condition of the vehicle based on the speed characteristics of the vehicle within 5 minutes after the vehicle is powered on and the time information (for example, information about weekdays or weekends and information about time periods). For example, when the working condition recognition module 232 determines that the speed characteristics within 5 minutes after the vehicle is powered on match the working condition data 1, and the detection time of the working condition is between 7:00 - 9:00 am on a weekday, it can determine that the working condition of the vehicle is working condition 1.

[0108] Exemplarily, the working condition recognition module 232 can determine the working condition of the vehicle based on the speed characteristics of the vehicle within 5 minutes after the vehicle is powered on and the data collected by the imaging device. For example, on the route from home to the company, the vehicle will pass through traffic light intersection 1 after about 3 minutes of driving. Then, when the working condition recognition module 232 determines that the speed characteristics within 5 minutes after the vehicle is powered on match the working condition data 1, and determines according to the data collected by the imaging device that the vehicle passes through traffic light intersection 1 at 2 minutes and 40 seconds after being powered on, it can determine that the working condition of the vehicle is working condition 1.

[0109] In one embodiment, the working condition recognition module 232 can determine the working condition of the vehicle based on the navigation information. Exemplarily, if the navigation information indicates that the starting point is home and the destination is the company, the working condition recognition module 232 can determine that the working condition of the vehicle is working condition 1.

[0110] After determining the working condition of the vehicle, the working condition recognition module 232 can send a working condition flag bit to the pre - calculation storage module 231. For example, the flag bit for working condition 1 (for example, commuting working condition) is 00, and the flag bit for working condition 2 (for example, long - distance working condition) is 01. After the working condition recognition module 232 determines that the vehicle is in working condition 1, it can send the working condition flag bit 00 to the pre - calculation storage module 231, so that the pre - calculation storage module 231 can determine that the vehicle is in working condition 1. The calculation storage module 231 can select multiple battery temperature optimization strategies corresponding to the working condition data 1 (for example, multiple curves of battery temperature and heating power of the battery heating device changing with time) shown in Table 1 and send them to the intelligent optimization module 233.

[0111] The intelligent optimization module 233 can obtain the data sent by the battery management system 260, the cockpit system 270, and the electric drive system 280. For example, the battery management system 260 can obtain battery status data (such as the SOC and battery temperature 5 minutes after the vehicle is powered on) and send it to the intelligent optimization module 233. The cockpit system 270 can obtain cockpit status data (such as the external temperature and the required cockpit temperature of the user 5 minutes after the vehicle is powered on) and send it to the intelligent optimization module 233. The electric drive system 280 can obtain electric drive status data (such as the electric drive temperature 5 minutes after the vehicle is powered on) and send it to the intelligent optimization module 233. After obtaining the battery status data, cockpit status data, and electric drive status data, the intelligent optimization module 233 can select a certain battery temperature optimization strategy from multiple battery temperature optimization strategies according to the obtained data and send it to the thermal management system 250.

[0112] In one embodiment, the intelligent optimization module 233 can also obtain the information sent by the display device 240, and this information is used to indicate the selection result of the user from economy, power performance, and a balance of economy and power performance. The intelligent optimization module 233 can select a certain battery temperature optimization strategy from multiple battery temperature optimization strategies according to the multiple battery temperature optimization strategies sent by the pre-computation storage module 231, the data sent by each subsystem (such as the battery management system 260, the cockpit system 270, and the electric drive system 280), and the information sent by the display device 240, and send it to the thermal management system 250.

[0113] After receiving the battery temperature optimization strategy sent by the intelligent optimization module 233, the thermal management controller 251 can control the thermal management actuator 252 to execute this battery temperature optimization strategy.

[0114] In one embodiment, if the intelligent optimization module 233 cannot match and obtain a certain battery temperature optimization strategy according to the multiple battery temperature optimization strategies sent by the pre-computation storage module 231 and the data sent by each subsystem (such as the battery management system 260, the cockpit system 270, and the electric drive system 280), then the thermal management controller 251 can execute the conventional battery temperature optimization strategy according to the battery conventional heating demand sent by the battery management system 260, the cockpit heating demand sent by the cockpit system 270, and the electric drive status data sent by the electric drive system 280.

[0115] In one embodiment, the vehicle can also send the working condition data to the cloud server 220 so that the cloud server 220 can update the multi-dimensional map according to the working condition data sent by the vehicle. After updating the multi-dimensional map, the cloud server can send the updated multi-dimensional map to the vehicle through OTA technology.

[0116] Figure 3The figure shows a schematic diagram of the multi-dimensional graph output by the offline calculation module 210 or the cloud server 220 provided by the embodiments of the present application. The offline calculation module 210 may be located in a personal computer (PC) of a vehicle factory. The process of outputting the multi-dimensional graph is as follows:

[0117] (1) The offline calculation module 210 or the cloud server 220 selects the working condition data under a certain working condition from the working condition scenario library (obtained with reference to big data).

[0118] (2) The offline calculation module 210 or the cloud server 220 determines other input information.

[0119] For example, the other input information includes but is not limited to the temperature and SOC of the battery, the temperature of the electric drive, and the working condition error, etc. after the vehicle has been powered on for a preset duration.

[0120] The above working condition error can be understood as a correction parameter for the working condition data. Taking the working condition data including the speed change curve from home to the company as an example, by adding a random error to the speed change curve, the robustness of the battery temperature optimization strategy can be improved.

[0121] (3) The offline calculation module 210 or the cloud server 220 determines the economic target, the power target, the target that takes into account both economy and power, and the safety boundary, etc. in the objective function.

[0122] For example, the safety boundary includes but is not limited to that the temperature of the battery cannot exceed 55°C.

[0123] By inputting the economic target and the safety boundary into the vehicle energy flow model, the objective function corresponding to the economic target (mainly considering the power performance of the vehicle) can be obtained. By inputting the power target (mainly considering the endurance and energy consumption of the vehicle, with energy conservation being a priority) and the safety boundary into the vehicle energy flow model, the objective function corresponding to the power target can be obtained. By inputting the target that takes into account both economy and power and the safety boundary into the vehicle energy flow model, the objective function corresponding to the target that takes into account both economy and power can be obtained.

[0124] In the embodiments of the present application, the vehicle energy flow model includes factors that affect power performance and economy at low temperatures. Among them, the influencing factors include but are not limited to one or more of factors such as SOC, SOH, battery capacity, SOP, driving mileage, battery temperature, battery internal resistance, OCV of the battery, battery loss power, heat exchange of the battery to the outside, heat exchange between the battery and the coolant, heat exchange in the battery circuit, heat exchange in the thermal management PTC circuit, and cockpit power demand. At the same time, the energy flow model can be updated regularly based on the changing state of the vehicle, so as to reduce errors.

[0125] (4) Combine the input information and the objective function, and run the optimization algorithm to obtain the battery temperature optimization strategy under this working condition.

[0126] The above optimization algorithms may include ant colony algorithm, genetic algorithm, simulated annealing algorithm, particle swarm algorithm, or neural network (NN), etc.

[0127] (5) By traversing the input conditions, the battery temperature optimization strategies under different working conditions can be obtained, that is, the above multi-dimensional graph.

[0128] Figure 4 FIG. shows a schematic flowchart of the battery temperature control method 400 provided by an embodiment of the present application. The method 400 includes:

[0129] S410, the offline calculation module 210 or the cloud server 220 collects the working condition data based on the big data platform to obtain a scenario library.

[0130] Exemplarily, the working condition data collected by the big data platform includes but is not limited to the working condition data during the process of a vehicle from a certain starting point to a certain destination. For example, the curve of the vehicle speed changing with time, environmental information (such as weather), and the curve of the power of high-voltage devices changing with time. The offline calculation module 210 or the cloud server can preliminarily construct a generalized scenario library based on these data.

[0131] S420, the offline calculation module 210 or the cloud server 220 uses the optimization algorithm to calculate the battery temperature optimization strategies under different working conditions respectively with the economic objective, the dynamic performance objective, and the objective of taking into account both economy and dynamic performance based on the scenario library, the state information of the battery, and the vehicle energy flow model.

[0132] Exemplarily, the state information of the battery may include the change curve of SOC, the change curve of battery temperature, and the change curve of electric drive temperature during the driving process of the vehicle from the starting point to the destination.

[0133] Exemplarily, the state information of the battery may include the SOC and temperature of the battery, the electric drive temperature, etc. after the vehicle is powered on for a preset duration.

[0134] Exemplarily, the battery temperature optimization strategies under different working conditions may be the above multi-dimensional graph. Calculating the multi-dimensional graph may refer to the process shown above, which will not be elaborated here. Figure 3 as shown.

[0135] The above economic objective can also be understood as aiming at minimizing the energy consumption of the vehicle, and the dynamic performance objective can also be understood as ensuring that the vehicle's 0-100 km / h acceleration time is not higher than a preset duration (for example, 5 seconds).

[0136] S430. The offline calculation module 210 or the cloud server 220 sends the battery temperature optimization strategies under different working conditions to the vehicle.

[0137] Exemplarily, as Figure 2 shown, the offline calculation module 210 can send the battery temperature optimization strategies under different working conditions to the pre-calculation storage module 231. The battery temperature optimization strategies under different working conditions can be initialized in the vehicle controller 230 before the vehicle leaves the factory.

[0138] S440. After the vehicle 100 is powered on for a preset duration, it identifies the working condition 1 of the vehicle through the working condition characteristic information.

[0139] Exemplarily, the working condition characteristic information may include the vehicle speed characteristic of the vehicle. The vehicle can match the vehicle speed characteristic within 5 minutes after power on with the working condition data in the multi-dimensional map stored in the vehicle in S430 above, so as to obtain that the working condition of the vehicle is working condition 1.

[0140] S450. The vehicle 100 determines the battery temperature optimization strategy 1 corresponding to the working condition 1 according to the working condition 1 it is in, the real-time state information of each subsystem, and the battery temperature optimization strategies under different working conditions above.

[0141] Exemplarily, the real-time state information of each subsystem may include the real-time temperature and SOC of the battery after the vehicle is powered on for a preset duration, the real-time temperature of the electric drive, etc.

[0142] Optionally, after determining that the working condition of the vehicle is working condition 1, the user can also be prompted to select an optimization target. The vehicle can determine the battery temperature optimization strategy 1 according to the working condition 1, the optimization target input by the user, and the battery temperature optimization strategies under different working conditions.

[0143] Exemplarily, Figure 5 shows the human machine interface (HMI) provided by the embodiment of the present application. The vehicle can display the prompt information "It has been detected that the vehicle is on the route from home to company. Please select your preferred optimization target", the priority economy control 501, the priority power control 502, and the control 503 that takes both economy and power into account on the central control screen. When detecting the input of the user for the priority economy control 501, the vehicle can determine the battery temperature optimization strategy according to the working condition 1, the economy target, and the battery temperature optimization strategies under different working conditions.

[0144] S460. The vehicle 100 executes the battery temperature optimization strategy 1.

[0145] Exemplarily, vehicle 100 executes the battery temperature optimization strategy 1, including: the thermal management controller 251 controls the thermal management actuator 252 to execute the battery temperature optimization strategy 1.

[0146] Optionally, the method 400 further includes: vehicle 100 may send VHR (including the driving condition data of the vehicle from home to company during this driving process) to the cloud server 220, so as to update the scenario library in the cloud server. The cloud server 220 may send the updated battery temperature optimization strategy to vehicle 100 through OTA technology according to the updated scenario library.

[0147] Exemplarily, the vehicle may store the battery temperature optimization strategies corresponding to driving condition 1 and driving condition 2. When the user drives the vehicle on a new route, for example, from home to scenic spot A, the vehicle may send speed information or power information, etc. during the process from home to scenic spot A to the cloud server, so that the cloud server may be based on the above Figure 3 shown calculation process to calculate the battery temperature optimization strategy corresponding to the new driving condition.

[0148] Figure 6 Fig. shows a schematic block diagram of the system architecture 600 provided by an embodiment of the present application. The system architecture 600 includes a cloud server 610, a display device 620, a thermal management system 630, a battery management system 640, a cockpit system 650, an electric drive system 660, and an anomaly detection module 670. Among them, the cloud server 610 includes a driving condition identification module 611 and an intelligent optimization module 612; the thermal management system 630 includes a thermal management controller 631 and a thermal management actuator 632.

[0149] The vehicle may send driving condition characteristic information (for example, vehicle speed information, power information, or navigation information within 5 minutes after the vehicle is powered on) to the driving condition identification module 611. Taking the driving condition characteristic information as the vehicle speed characteristic, the driving condition identification module 611 determines the current driving condition of the vehicle according to the vehicle speed characteristic. The process of the driving condition identification module 611 identifying the driving condition of the vehicle according to the vehicle speed information may refer to the description in the above embodiment, and will not be elaborated here.

[0150] After the driving condition identification module 611 identifies the driving condition of the vehicle, it may send the driving condition data corresponding to the driving condition to the intelligent optimization module 612. The intelligent optimization module 612 may also receive data sent by each subsystem in the vehicle (for example, the battery management system 640, the cockpit system 650, and the electric drive system 660). The intelligent optimization module 612 determines the battery temperature optimization strategy in real time according to the driving condition data and the data sent by each subsystem.

[0151] Figure 7The figure shows a schematic diagram of the intelligent optimization module 612 provided in the embodiment of the present application outputting a battery temperature optimization strategy. As Figure 7 shown, the intelligent optimization module 612 combines the input operating condition data, the data sent by each subsystem, and the objective function, runs the optimization algorithm, and obtains the battery temperature optimization strategy under this operating condition.

[0152] The process of the above intelligent optimization module 612 obtaining the battery temperature optimization strategy can refer to the process in the above Figure 3 where the offline calculation module 210 or the cloud server 220 determines the battery temperature optimization strategy under the corresponding operating condition based on the operating condition data, the data of each subsystem, and the objective function collected by the big data platform, which will not be elaborated here.

[0153] Different from the above Figure 3 In the calculation process shown, the intelligent optimization module 612 can determine the battery temperature optimization strategy under the current vehicle operating condition in real time through the optimization algorithm. While Figure 7 in the calculation process shown, the offline calculation module 210 or the cloud server 220 can determine the battery temperature optimization strategies under different operating conditions in advance and send them to the pre-calculation storage module 231. Figure 3 After the intelligent optimization module 612 obtains the battery temperature optimization strategy under the current vehicle operating condition, it can send a data packet to the anomaly detection module 670, and this data packet includes this battery temperature optimization strategy. The anomaly detection module 670 can detect whether the data packet is lost. If there is no loss of the data packet sent by the intelligent optimization module 612, the anomaly detection module 670 can send this battery temperature optimization strategy to the thermal management controller 631, so that the thermal management controller 631 can control the thermal management actuator 632 to execute this battery temperature optimization strategy.

[0154] The figure shows a schematic flowchart of the battery temperature control method 800 provided in the embodiment of the present application. This method 800 includes:

[0155] Figure 8 S810, the cloud server 610 collects operating condition data based on the big data platform to obtain a scenario library.

[0156] Exemplarily, the operating condition data includes but is not limited to the operating condition data during the process of the vehicle from a certain starting point to a certain destination. For example, the curve of the vehicle speed changing with time, environmental information (such as weather), and the curve of the power of high-voltage devices changing with time. The cloud server 610 can initially construct a generalized scenario library based on these data.

[0157]

[0158] S820, the vehicle 100 sends the current operating condition characteristic information of the vehicle and the data collected by each subsystem to the cloud server 620.​

[0159] Exemplarily, each of the subsystems includes the above-mentioned battery management system 640, cockpit system 650, and electric drive system 660.

[0160] Exemplarily, the current operating condition characteristic information of the vehicle includes at least one of speed information, power information, or navigation information after the vehicle has been powered on for a preset duration.

[0161] S830, the cloud server 620 determines that the current operating condition of the vehicle is operating condition 1 based on the current operating condition characteristic information of the vehicle and the scenario library.

[0162] The above process in which the cloud server 620 determines the current operating condition of the vehicle based on the current operating condition characteristic information of the vehicle may refer to the process in which the operating condition recognition module 231 in the above Figure 2 recognizes the operating condition of the vehicle, and will not be elaborated here.

[0163] S840, the cloud server 620 determines the battery temperature optimization strategy 1 based on the operating condition data corresponding to the current operating condition 1 of the vehicle and the data collected by each subsystem, and sends it to the vehicle.

[0164] Optionally, the cloud server 620 determines the battery temperature optimization strategy 1 according to the operating condition data corresponding to the current operating condition 1 of the vehicle and the data collected by each subsystem, including: the cloud server 620 determines the battery temperature optimization strategy 1 according to the current operating condition 1 of the vehicle, the data collected by each subsystem, and the optimization target.

[0165] Taking operating condition 1 as a commuting operating condition as an example, when the cloud server 620 determines that the current operating condition of the vehicle is a commuting operating condition, it can send an indication message to the vehicle 100, and this indication message is used to indicate that the vehicle is in a commuting operating condition. After receiving this indication message, the vehicle 100 can control the prompting device (for example, the central control screen) to prompt the user to select an optimization target. The above process of prompting the user to select an optimization target may refer to the above Figure 5 shown HMI. The vehicle can send the optimization target input by the user to the cloud server 620. The cloud server can determine the battery temperature optimization strategy corresponding to the vehicle operating condition 1 based on the current operating condition data of the vehicle, the data collected by each subsystem, and the optimization target.

[0166] S850, the vehicle 100 executes the battery temperature optimization strategy 1.

[0167] Figure 9 The schematic flowchart of the battery temperature control method 900 provided by the embodiment of the present application is shown. The method 900 includes:

[0168] S910, obtain the first operating condition characteristic information and the first state information of the battery.

[0169] Optionally, the first state information may be data collected by the BMS, and the first state information includes at least one of the SOC, temperature, SOH, battery capacity, and SOP of the battery.

[0170] Exemplarily, the first state information includes at least one of the SOC, temperature, SOH, battery capacity, and SOP of the battery after the vehicle has been powered on for a preset duration (e.g., 5 min).

[0171] Optionally, the first operating condition characteristic information includes at least one of the vehicle speed information, power information, and navigation information.

[0172] Exemplarily, the first operating condition characteristic information may be the vehicle speed change information within a preset duration after the vehicle is powered on.

[0173] Exemplarily, the first operating condition characteristic information may be the total power change information within a preset duration after the vehicle is powered on.

[0174] Exemplarily, the vehicle speed information may be determined from data collected by a vehicle speed sensor of the vehicle. For example, the speed information may be a speed characteristic, or a speed versus time curve, etc.

[0175] S920. Determine a first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and the mapping relationship. The mapping relationship includes a mapping relationship between operating condition data, the state information of the battery, and multiple battery temperature optimization strategies determined by the operating condition data and the state information of the battery. The multiple battery temperature optimization strategies include the first battery temperature optimization strategy.

[0176] Exemplarily, Table 4 shows a mapping relationship provided by an embodiment of the present application.

[0177] Table 4

[0178] Operating condition data Battery status information Battery temperature optimization strategy Operating condition data 1 Status information 1 Battery temperature optimization strategy 1 Operating condition data 2 Status information 2 Battery temperature optimization strategy 2 … … …

[0179] Exemplarily, the operating condition data 1 may be commuting operating condition data of a large number of users collected from location A (e.g., xx community) to location B (e.g., xx building) based on a big data platform. For example, the operating condition data 1 may include speed change information or power change information during the vehicle's travel from location A to location B. The state information 1 may include the battery temperature and SOC after the vehicle has been powered on for a preset duration. The cloud server may obtain the battery temperature optimization strategy based on the operating condition data 1, the state information 1, and an optimization algorithm.

[0180] Exemplarily, the information on different working conditions may include speed change information under different working conditions (e.g., vehicle speed change characteristics or vehicle speed change curves). For example, the commuting working condition may include the vehicle speed characteristics or vehicle speed change curves during the driving process from home to the company, and the long-distance working condition may include the vehicle speed characteristics or vehicle speed change curves during the driving process from home to scenic spot A.

[0181] Exemplarily, the information on different working conditions may include total power change information of the vehicle under different working conditions (e.g., total power change characteristics or total power change curves of the vehicle). For example, the commuting working condition may include the total power change curve of the vehicle during the driving process from home to the company, and the long-distance working condition may include the total power change curve of the vehicle during the driving process from home to scenic spot A.

[0182] Optionally, the working condition data may further include the ambient temperature.

[0183] Optionally, the battery temperature optimization strategy in the mapping relationship may be determined by the working condition data, the state information of the battery, and the objective function. For example, the objective function may include a vehicle energy flow model.

[0184] Optionally, the method 900 further includes: obtaining the mapping relationship from a cloud server. Exemplarily, the cloud server may determine multiple battery temperature optimization strategies based on the working condition data and the state information of the battery, and establish a mapping relationship between the working condition data, the state information of the battery, and the battery temperature optimization strategy. The cloud server may send the mapping relationship to the vehicle.

[0185] Exemplarily, the cloud server may obtain battery temperature optimization strategies under different working conditions based on the working condition data, the state information of the battery, and an optimization algorithm.

[0186] Exemplarily, the optimization algorithm may include an ant colony algorithm, a genetic algorithm, a simulated annealing algorithm, a particle swarm algorithm, or an NN, etc.

[0187] Optionally, determining the first battery temperature optimization strategy according to the first working condition characteristic information, the first state information, and the mapping relationship includes: determining the first working condition (e.g., the commuting working condition from home to the company) where the vehicle is located according to the first working condition characteristic information (e.g., navigation information); and determining the first battery temperature optimization strategy according to the first working condition, the first state information, and the mapping relationship.

[0188] Optionally, the first battery temperature optimization strategy includes the change curve of the heating power output by the heating device of the battery over time, or the change curve of the battery temperature over time.

[0189] Optionally, the mapping relationship includes the mapping relationship between the working condition data, the state information of the battery, the state data of the subsystems in the vehicle, and the multiple battery temperature optimization strategies determined by the working condition data, the state information of the battery, and the state data of the subsystems. The subsystems include at least one of the cockpit system and the electric drive system. The method further includes: obtaining first state data of the subsystem; wherein, determining a first battery temperature optimization strategy according to the first working condition characteristic information, the first state information, and the mapping relationship includes: determining the first battery temperature optimization strategy according to the first working condition characteristic information, the first state data, the first state information, and the mapping relationship.

[0190] Exemplarily, Table 5 shows a mapping relationship provided by an embodiment of the present application.

[0191] Table 5

[0192] Operating condition data Battery status information Required power of the cockpit system Electric drive temperature Battery temperature optimization strategy Operating condition data 1 Status information 1 Required power 1 Electric drive temperature 1 Battery temperature optimization strategy 1 Operating condition data 2 Status information 2 Required power 2 Electric drive temperature 2 Battery temperature optimization strategy 2 … … … … …

[0193] Optionally, the mapping relationship may be stored in the vehicle when the vehicle leaves the factory. After receiving the VHR sent by the vehicle, the cloud server may update the mapping relationship and send the updated mapping relationship to the vehicle.

[0194] Optionally, the multiple battery temperature optimization strategies are determined by the working condition data, the working condition error, and the state information of the battery. The working condition error is used to correct the working condition data.

[0195] Optionally, different working condition errors may be selected for different working conditions.

[0196] For example, when the working condition data is the speed change information of the vehicle, if the speed change information indicates that the average speed of the vehicle is above 100 kph, working condition error 1 may be selected; if the speed change information indicates that the average speed of the vehicle is in (60 kph, 80 kph), working condition error 2 may be selected. Different working condition errors may be selected for different working condition data, which can further improve the robustness of battery temperature control.

[0197] Optionally, the multiple battery temperature optimization strategies are determined by the working condition data, the state information of the battery, and the objective function. The objective function is determined by at least one of the following parameters: the internal resistance of the battery, the OCV of the battery, the loss power of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the loop heat exchange information of the battery, and the loop heat exchange information of the thermal management PTC.

[0198] Optionally, the objective function is determined by the optimization objective and the at least one parameter. The optimization objective includes at least one of an economic objective, a power performance objective, and an objective that takes into account both economic and power performance objectives.

[0199] Optionally, based on the first operating condition characteristic information, the first state information, and the mapping relationship, determine a first battery temperature optimization strategy, including: based on the first operating condition characteristic information, the first state information, the mapping relationship, and the user input, determine a first battery temperature optimization strategy, where the user input is used to indicate a first optimization target, and the first optimization target includes an economic target, a power performance target, and a target that takes into account both economic and power performance.

[0200] Exemplarily, as Figure 5 shown, the data collected by the vehicle speed sensor 5 minutes after the vehicle is powered on can be used to obtain the speed characteristic of the vehicle and determine that the vehicle is on the route from home to company based on the speed characteristic. The vehicle can control the display screen to display a prompt message, and the prompt message can be used to prompt the user to select an optimization target. The vehicle can select a suitable battery temperature optimization strategy from the mapping relationship based on the optimization target selected by the user, so that the selected battery temperature optimization strategy better meets the user's needs. For example, when the user selects the power performance target, the battery temperature optimization strategy selected from the mapping relationship can ensure that the vehicle's 100-meter acceleration time is less than 5 seconds.

[0201] Optionally, the first operating condition characteristic information includes the first vehicle speed characteristic within a preset duration after the vehicle is powered on, the mapping relationship includes the mapping relationship between the second vehicle speed characteristic from the first location to the second location, the second state information of the battery, and the first battery temperature optimization strategy determined by the second vehicle speed characteristic and the second state information. Based on the first operating condition characteristic information, the first state information, and the mapping relationship, determine a first battery temperature optimization strategy, including: when the first vehicle speed characteristic matches the second vehicle speed characteristic and the first state information matches the second state information, determine the first battery temperature optimization strategy.

[0202] S930, control the temperature of the battery according to the first battery temperature optimization strategy.

[0203] Exemplarily, the vehicle can control the operation of the thermal management system according to the first battery temperature optimization strategy.

[0204] Optionally, the method 900 further includes: sending the vehicle historical record VHR to the cloud server; receiving the updated mapping relationship sent by the cloud server according to the VHR.

[0205] Exemplarily, the vehicle may send the speed information of the vehicle traveling from home to the company within the last two weeks to the cloud server. The cloud server may determine the operating condition data 3 from home to the company based on the data sent by the vehicle. The cloud server may determine the battery temperature heating strategy 3 based on the operating condition data 3 and the state information 1 of the battery, and establish a mapping relationship among the operating condition data 3, the state information 1 of the battery, and the battery temperature optimization strategy 3. The vehicle may obtain the new mapping relationship from the cloud server through OTA upgrade and update the locally saved mapping relationship.

[0206] Exemplarily, Table 6 shows the updated mapping relationship provided by the embodiments of the present application.

[0207] Table 6

[0208] Operating condition data Battery status information Battery temperature optimization strategy Operating condition data 3 Status information 1 Battery temperature optimization strategy 3 Operating condition data 2 Status information 2 Battery temperature optimization strategy 2 … … …

[0209] Figure 10 Fig. 13 shows a schematic flowchart of the battery temperature control method 1000 according to the embodiments of the present application. The method 1000 includes:

[0210] S1010, obtaining first operating condition characteristic information and first state information of the battery, where the first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information.

[0211] Optionally, the battery temperature control method 1000 may be executed by the cloud server. Obtaining the first operating condition characteristic information and the first state information of the battery includes: obtaining the first operating condition characteristic information and the first state information sent by the vehicle; according to the first battery temperature optimization strategy, controlling the temperature of the battery includes: sending the first battery temperature optimization strategy to the vehicle so that the vehicle executes the first battery temperature optimization strategy.

[0212] The above S1010 may refer to the description of S910 above and will not be elaborated here.

[0213] S1020, determining a first battery temperature optimization strategy according to the first operating condition characteristic information and the first state information.

[0214] Optionally, determining the first battery temperature optimization strategy according to the first operating condition characteristic information and the first state information includes: determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and an optimization algorithm.

[0215] Exemplarily, the optimization algorithm may include an ant colony algorithm, a genetic algorithm, a simulated annealing algorithm, a particle swarm algorithm, or NN, etc.

[0216] Optionally, the method 1000 can be executed by a cloud server to determine a first battery temperature optimization strategy based on the first operating condition characteristic information and the first state information, including: determining first operating condition data based on the first operating condition characteristic information; determining a first battery temperature optimization strategy based on the first operating condition data and the first state information.

[0217] Exemplarily, the first operating condition characteristic information includes vehicle speed information within a preset duration of vehicle power-on, and the first operating condition data includes vehicle speed information from a first location to a second location. The first operating condition data includes vehicle speed information for a period of time in the future.

[0218] For example, when the cloud server determines that the vehicle speed information within the preset power-on time matches the vehicle speed information from the first location to the second location, it can be determined that the vehicle is in the operating condition from the first location to the second location. The cloud server can determine the first battery temperature optimization strategy based on the first operating condition data (e.g., speed information from the first location to the second location) and the first state information.

[0219] The above method of obtaining the battery temperature optimization strategy in combination with the operating condition error can be referred to the description in the above embodiment, which will not be repeated here.

[0220] Optionally, a first battery temperature optimization strategy is determined based on the first operating condition characteristic information and the first state information, including: determining the first battery temperature optimization strategy based on the first operating condition characteristic information, an objective function and the first state information, the objective function being determined by at least one of the following parameters: the internal resistance of the battery, the OCV of the battery, the power loss of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the circuit heat exchange information of the battery, and the thermal management PTC circuit heat exchange information.

[0221] Optionally, the objective function is determined by an optimization target and the at least one parameter, and the optimization target includes an economic target, a dynamic target, and at least one of an economic target and a dynamic target.

[0222] S1030: Control the temperature of the battery according to the first battery temperature optimization strategy.

[0223] Different from Method 900, in Method 1000, the vehicle can send the vehicle's operating condition characteristic information and the battery's state information to the cloud server, so that the cloud server can obtain the corresponding operating condition data based on the vehicle's operating condition characteristic information. The cloud server can determine the appropriate battery temperature optimization strategy for the vehicle under the current operating condition in real time based on the operating condition data and the battery's state information and send it to the vehicle. In Method 900, the cloud server can determine multiple battery temperature optimization strategies in advance based on the scenario library and the battery's state information, and the vehicle can select the appropriate battery temperature optimization strategy from the multiple battery temperature optimization strategies based on the collected operating condition characteristic information.

[0224] The above Method 900 or Method 1000 can be executed by the vehicle 100 shown above Figure 1 or, Method 900 or Method 1000 can be executed by the above-mentioned computing platform 120, or, Method 900 or Method 1000 can be executed by a system composed of the computing platform 120 and the thermal management system, or, Method 900 or Method 1000 can be executed by the system-on-chip (SoC) in the above-mentioned computing platform 120, or, Method 900 or Method 1000 can be executed by the processor, circuit or chip in the computing platform 120, or, Method 900 or Method 1000 can be executed by the vehicle controller 230.

[0225] Alternatively, the above Method 1000 can also be executed by the cloud server (for example, the intelligent optimization module 612 in the cloud server 610).

[0226] Figure 11 The schematic block diagram of a battery temperature control device 1100 provided by an embodiment of the present application is shown. The device 1100 includes: an acquisition unit 1110, configured to acquire first operating condition characteristic information and the first state information of the battery, where the first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information of the vehicle; a determination unit 1120, configured to determine a first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and the mapping relationship, where the mapping relationship includes a mapping relationship between operating condition data, the battery's state information, and multiple battery temperature optimization strategies determined by the operating condition data and the battery's state information, and the multiple battery temperature optimization strategies include the first battery temperature optimization strategy; a control unit 1130, configured to control the temperature of the battery according to the first battery temperature optimization strategy.

[0227] Optionally, the multiple battery temperature optimization strategies are determined by the operating condition data, the operating condition error, and the battery's state information, and the operating condition error is used to correct the operating condition data.

[0228] Optionally, the multiple battery temperature optimization strategies are determined by the operating condition data, the state information of the battery, and an objective function, and the objective function is determined by at least one of the following parameters: the internal resistance of the battery, the OCV of the battery, the power loss of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the loop heat exchange information of the battery, and the heat management PTC loop heat exchange information.

[0229] Optionally, the objective function is determined by an optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance.

[0230] Optionally, the mapping relationship includes the mapping relationship between the operating condition data, the state information of the battery, the state data of the subsystems in the vehicle, and the multiple battery temperature optimization strategies determined by the operating condition data, the state information of the battery, and the state data of the subsystems. The subsystems include at least one of a cockpit system and an electric drive system. The obtaining unit 1110 is further configured to obtain first state data of the subsystem; the determining unit 1120 is configured to: determine the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state data, the first state information, and the mapping relationship.

[0231] Optionally, the first operating condition characteristic information includes a first vehicle speed characteristic within a preset power-on duration of the vehicle, and the mapping relationship includes the mapping relationship between a second vehicle speed characteristic from a first location to a second location, the second state information of the battery, and the first battery temperature optimization strategy determined by the second vehicle speed characteristic and the second state information. The determining unit 1120 is configured to: determine the first battery temperature optimization strategy when the first vehicle speed characteristic matches the second vehicle speed characteristic and the first state information matches the second state information.

[0232] Optionally, the apparatus 1000 further includes: a sending unit, configured to send a VHR to a cloud server; a receiving unit, configured to receive the updated mapping relationship sent by the cloud server according to the VHR.

[0233] In one embodiment, the obtaining unit 1110 is configured to obtain first operating condition characteristic information and first state information of the battery. The first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information of the vehicle. The determining unit 1120 is configured to determine a first battery temperature optimization strategy according to the first operating condition characteristic information and the first state information. The control unit 1130 is configured to control the temperature of the battery according to the first battery temperature optimization strategy.

[0234] Optionally, the determining unit 1120 is configured to: determine the first battery temperature optimization strategy according to the first operating condition data determined from the first operating condition feature information, the operating condition error, and the first state information, where the operating condition error is used to correct the first operating condition data.

[0235] Optionally, the objective function is determined by an optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

[0236] It should be understood that the division of each unit in the above device is only a division of logical functions. In actual implementation, all or part of them can be integrated into a physical entity, or physically separated. In addition, the units in the device can be implemented in the form of a processor calling software; for example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or the functions of each unit of the device. The processor is, for example, a general-purpose processor, such as a CPU or a microprocessor, and the memory is a memory inside or outside the device. Alternatively, the units in the device can be implemented in the form of a hardware circuit, and the functions of some or all of the units can be implemented by designing the hardware circuit. The hardware circuit can be understood as one or more processors; for example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units are implemented by designing the logical relationship between the components in the circuit; again, in another implementation, the hardware circuit can be implemented by a PLD. Taking an FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through a configuration file to implement the functions of some or all of the above units. All units of the above device can be implemented entirely in the form of a processor calling software, or entirely in the form of a hardware circuit, or part in the form of a processor calling software, and the remaining part in the form of a hardware circuit.

[0237] Each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, for example: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0238] In addition, each unit in the above device can be integrated in whole or in part, or can be independently implemented. In one implementation, these units are integrated together and implemented in the form of a SoC. The SoC can include at least one processor for implementing any of the above methods or the functions of each unit of the device. The types of the at least one processor can be different, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0239] The embodiments of the present application further provide a device, which includes a processing unit and a storage unit. The storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit so that the device executes the method or steps executed in the above embodiments.

[0240] Optionally, if the device is located in a vehicle, the above processing unit may be Figure 1 the processors 121-12n shown.

[0241] The embodiments of the present application further provide a battery temperature control system, which includes a thermal management system and a computing platform, and the computing platform includes the above device 1100.

[0242] The embodiments of the present application further provide a vehicle, which may include the above device 1100 or the above system.

[0243] The embodiments of the present application further provide a computer program product, which includes: computer program code. When the computer program code runs on a computer, the computer is enabled to execute the above method.

[0244] The embodiments of the present application further provide a computer-readable medium, which stores program code. When the computer program code runs on a computer, the computer is enabled to execute the above method.

[0245] In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by the combination of the hardware and software modules in the processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0246] It should be understood that in the embodiments of the present application, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor.

[0247] It should also be understood that in various embodiments of the present application, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The order of execution of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0248] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0249] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0250] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0251] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0252] In addition, the functional units in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0253] When the above-mentioned functions are implemented in the form of software functional units 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 this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0254] As described above, the above are only specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A battery temperature control method, It is characterized in that include: Acquire first operating condition characteristic information and first state information of the battery, wherein the first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information of the vehicle; determining a first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and a mapping relationship, the mapping relationship including operating condition data, battery state information, and a mapping relationship between a plurality of battery temperature optimization strategies determined by the operating condition data and the battery state information, the plurality of battery temperature optimization strategies including the first battery temperature optimization strategy; The temperature of the battery is controlled according to the first battery temperature optimization strategy.

2. The method according to claim 1, It is characterized in that The multiple battery temperature optimization strategies are determined by the operating condition data, the operating condition error and the state information of the battery, and the operating condition error is used to correct the operating condition data.

3. The method according to claim 1 or 2, It is characterized in that The plurality of battery temperature optimization strategies are determined by the operating condition data, the battery state information and an objective function, wherein the objective function is determined by at least one of the following parameters: The internal resistance of the battery, the open circuit voltage OCV of the battery, the power loss of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the circuit heat exchange information of the battery, and the thermal management positive temperature coefficient PTC circuit heat exchange information.

4. The method according to claim 3, It is characterized in that The objective function is determined by an optimization target and the at least one parameter, wherein the optimization target includes at least one of an economic target, a dynamic target, and a target that takes both economic and dynamic targets into consideration.

5. The method according to any one of claims 1 to 4, It is characterized in that The mapping relationship includes the operating condition data, the battery status information, the status data of the subsystem in the vehicle, and the mapping relationship between the multiple battery temperature optimization strategies determined by the operating condition data, the battery status information, and the subsystem status data, the subsystem includes at least one of a cockpit system and an electric drive system, and the method further includes: Acquire first status data of the subsystem; Wherein, determining a first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information and the mapping relationship includes: The first battery temperature optimization strategy is determined according to the first operating condition characteristic information, the first state data, the first state information, and the mapping relationship.

6. The method according to any one of claims 1 to 5, It is characterized in that The first operating condition characteristic information includes the first vehicle speed characteristic within a preset duration after the vehicle is powered on. The mapping relationship includes the mapping relationship between the second vehicle speed characteristic from the first location to the second location, the second state information of the battery, and the first battery temperature optimization strategy determined by the second vehicle speed characteristic and the second state information. Determining the first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and the mapping relationship includes: When the first vehicle speed characteristic matches the second vehicle speed characteristic and the first state information matches the second state information, determining the first battery temperature optimization strategy.

7. The method according to any one of claims 1 to 6, wherein, the method further includes: sending a vehicle historical record VHR to a cloud server; receiving the updated mapping relationship sent by the cloud server according to the VHR.

8. A battery temperature control device, wherein, it includes: an acquisition unit configured to acquire first operating condition characteristic information and first state information of the battery, where the first operating condition characteristic information includes at least one of vehicle speed information, power information, and navigation information of the vehicle; a determination unit configured to determine a first battery temperature optimization strategy according to the first operating condition characteristic information, the first state information, and a mapping relationship, where the mapping relationship includes a mapping relationship between operating condition data, state information of the battery, and multiple battery temperature optimization strategies determined by the operating condition data and the state information of the battery, and the multiple battery temperature optimization strategies include the first battery temperature optimization strategy; a control unit configured to control the temperature of the battery according to the first battery temperature optimization strategy.

9. The device according to claim 8, wherein, the multiple battery temperature optimization strategies are determined by the operating condition data, an operating condition error, and the state information of the battery, and the operating condition error is used to correct the operating condition data.

10. The device according to claim 8 or 9, wherein, the multiple battery temperature optimization strategies are determined by the operating condition data, the state information of the battery, and an objective function, and the objective function is determined by at least one of the following parameters: the internal resistance of the battery, the open circuit voltage OCV of the battery, the loss power of the battery, the external heat exchange information of the battery, the heat exchange information between the battery and the coolant, the loop heat exchange information of the battery, and the loop heat exchange information of the thermal management positive temperature coefficient PTC.

11. The device according to claim 10, wherein, the objective function is determined by an optimization objective and the at least one parameter, and the optimization objective includes at least one of an economic objective, a dynamic performance objective, and an objective that takes into account both economic and dynamic performance objectives.

12. The device according to any one of claims 8 to 11, wherein, The mapping relationship includes the mapping relationship between the working condition data, the state information of the battery, the state data of the subsystems in the vehicle, and the multiple battery temperature optimization strategies determined by the working condition data, the state information of the battery, and the state data of the subsystems. The subsystems include at least one of a cockpit system and an electric drive system. The obtaining unit is further configured to obtain first state data of the subsystem. The determining unit is configured to: determine the first battery temperature optimization strategy according to the first working condition characteristic information, the first state data, the first state information, and the mapping relationship.

13. The device according to any one of claims 8 to 12, wherein, The first working condition characteristic information includes a first vehicle speed characteristic within a preset power-on duration of the vehicle. The mapping relationship includes the mapping relationship between a second vehicle speed characteristic from a first location to a second location, the second state information of the battery, and the first battery temperature optimization strategy determined by the second vehicle speed characteristic and the second state information. The determining unit is configured to: determine the first battery temperature optimization strategy when the first vehicle speed characteristic matches the second vehicle speed characteristic and the first state information matches the second state information.

14. The device according to any one of claims 8 to 13, wherein, The device further includes: a sending unit, configured to send a vehicle history record VHR to a cloud server; a receiving unit, configured to receive the updated mapping relationship sent by the cloud server according to the VHR.

15. A battery temperature control device, wherein, comprising: a memory, configured to store a computer program; a processor, configured to execute the computer program stored in the memory, so that the device executes the method according to any one of claims 1 to 7.

16. A battery temperature control system, wherein, comprising a thermal management system and a computing platform, and the computing platform includes the device according to any one of claims 8 to 15.

17. A vehicle, wherein, comprising the device according to any one of claims 8 to 15, or comprising the system according to claim 16.

18. A computer-readable storage medium, wherein, a computer program is stored thereon, and when the computer program is executed by a computer, the method according to any one of claims 1 to 7 is implemented.

19. A chip, wherein, comprising: a circuit, and the circuit is configured to execute the method according to any one of claims 1 to 7.

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