Method and apparatus for cooling battery in vehicle, and processor and vehicle

Abstract

A method and apparatus for cooling a battery in a vehicle, and a processor and a vehicle. The method comprises: during operation of a vehicle, acquiring traveling working condition information of the vehicle and state information of a battery in the vehicle, wherein the state information is used for representing the working state of the battery during operation of the vehicle; on the basis of the traveling working condition information and the state information, establishing an energy consumption state model of the battery and a constraint condition of the energy consumption state model, wherein the energy consumption state model is used for determining the energy consumed by the battery and / or a battery cooling system of the vehicle; determining target cooling policy data of the battery on the basis of the energy consumption state model and the constraint condition, wherein the target cooling policy data is used for representing a policy for executing a cooling operation on the battery; and in response to a control instruction corresponding to the target cooling policy data, controlling the battery cooling system to execute the cooling operation on the battery. The method can improve the battery cooling effect.

Description

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

[0001] Cross-reference

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on September 15, 2023, with application number 202311195655.4 and invention name “Cooling method, device, processor and vehicle for batteries in vehicles”, the entire contents of which are incorporated by reference in this disclosure. Technical Field

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

[0004] In related technologies, if battery cooling is required, the current battery temperature must be detected. When the temperature reaches a certain threshold, the battery cooling device must be controlled to cool the battery. However, since the battery cannot be cooled in real time according to different operating conditions, and since cooling consumes the battery's own power, the vehicle's range may be reduced. Therefore, the technical problem of poor battery cooling effect still exists.

[0005] With respect to the technical problem that the above-mentioned related technologies have poor cooling effects on batteries, no effective solution has been proposed so far.

[0006] Summary of the Invention

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

[0008] According to one aspect of an embodiment of the present disclosure, a method for cooling a battery in a vehicle is provided. The method may include: obtaining driving condition information of the vehicle and status information of the battery in the vehicle during operation, wherein the status information is used to characterize the operating state of the battery during operation; establishing an energy consumption state model of the battery and constraints of the energy consumption state model based on the driving condition information and the status information, wherein the energy consumption state model is used to determine the energy consumed by the battery and / or the vehicle's battery cooling system; determining target cooling strategy data for the battery based on the energy consumption state model and the constraints, wherein the target cooling strategy data is used to represent a strategy for performing cooling operations on the battery; and controlling the battery cooling system to perform cooling operations on the battery in response to control instructions corresponding to the target cooling strategy data.

[0009] Optionally, the battery cooling system includes at least one of the following: a fan device, an air-conditioning device and a water pump device, and the status information includes at least the initial temperature of the battery, the current temperature of the battery and the current power of the battery, wherein, based on the driving condition information and the status information, an energy consumption state model of the battery and constraints of the energy consumption state model are established, including: performing differential processing on the fan power of the fan device, the air-conditioning power of the air-conditioning device and the water pump power of the water pump device to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used to determine the energy consumed by the battery cooling system; based on the fan load of the fan device, determining a second energy consumption state sub-model, wherein the second energy consumption state sub-model is used to determine the fan load of the fan device fan power; based on the air-conditioning compressor load of the air-conditioning equipment, determine the third energy consumption state submodel, wherein the third energy consumption state submodel is used to determine the air-conditioning power; based on the water pump speed of the water pump equipment, determine the fourth energy consumption state submodel, wherein the fourth energy consumption state submodel is used to determine the water pump power; based on the fan load, the air-conditioning compressor load, the water pump speed, the initial temperature and the current power, determine the fifth energy consumption state submodel, wherein the fifth energy consumption state submodel is used to determine the current temperature, the energy consumption state model includes the first energy consumption state submodel, the second energy consumption state submodel, the third energy consumption state submodel, the fourth energy consumption state submodel and the fifth energy consumption state submodel; establish the constraint conditions of the energy consumption state model.

[0010] Optionally, establishing the constraint conditions of the energy consumption state model includes: establishing the constraint conditions based on the fan load, the air-conditioning compressor load, the water pump speed and the current temperature in the energy consumption state model.

[0011] Optionally, based on the energy consumption state model and the constraint conditions, determining the target cooling strategy data of the battery includes: according to the constraint conditions, using a dynamic programming strategy to process the energy consumption state model to determine the target cooling strategy data.

[0012] Optionally, based on the constraints, a dynamic programming strategy is used to process the energy consumption state model to determine the target cooling strategy data, including: initializing the dynamic programming information corresponding to the dynamic programming strategy to obtain an initialization result; discretizing the initialization result to obtain a discrete processing result; and using a dynamic programming strategy to process the discrete processing result and the energy consumption state model to obtain the target cooling strategy data, so that the energy consumption of the battery cooling system reaches the target state.

[0013] Optionally, during the operation of the vehicle, the driving condition information of the vehicle is obtained, including: obtaining navigation information during the operation of the vehicle; and predicting the driving condition information based on the historical driving data and navigation information of the vehicle.

[0014] Optionally, based on the vehicle's historical driving data and navigation information, the driving condition information is predicted, including: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determining the vehicle's state transition probability based on the discrete results, wherein the state transition probability is used to represent the transition probability from the vehicle's current speed state to the vehicle's speed state at the next moment; generating a vehicle's state transition probability matrix based on the state transition probability; determining the vehicle's initial speed state vector and the target speed state vector after the state transition occurs based on the state transition probability matrix; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0015] According to another aspect of the embodiments of the present disclosure, a cooling device for a battery in a vehicle is also provided. The device may include: an acquisition component configured to acquire driving condition information of the vehicle and status information of the battery in the vehicle during operation of the vehicle, wherein the status information is used to characterize the working state of the battery during operation of the vehicle; an establishment component configured to establish an energy consumption state model of the battery and constraints of the energy consumption state model based on the driving condition information and the status information, wherein the energy consumption state model is used to determine the amount of energy consumed by the battery and / or the battery cooling system of the vehicle; a determination component configured to determine target cooling strategy data of the battery based on the energy consumption state model and the constraints, wherein the target cooling strategy data is used to represent a strategy for performing cooling operations on the battery; and a control component configured to control the battery cooling system to perform cooling operations on the battery in response to control instructions corresponding to the target cooling strategy data.

[0016] According to another aspect of an embodiment of the present disclosure, a processor is provided, which is configured to run a program, wherein when the program is run, the method for cooling a battery in a vehicle according to an embodiment of the present disclosure is executed.

[0017] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device containing the computer-readable storage medium is controlled to execute the method for cooling a battery in a vehicle according to the present disclosure.

[0018] According to another aspect of the present disclosure, an electronic device is provided. The electronic device includes one or more processors and a memory, wherein the memory is configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle battery cooling method according to the present disclosure.

[0019] According to another aspect of an embodiment of the present disclosure, a computer program product is also provided, including a non-volatile computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it performs the steps of the cooling method of the battery in the vehicle of the embodiment of the present disclosure.

[0020] According to another aspect of the present disclosure, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is executed by a processor, the device containing the storage medium is controlled to execute the method for cooling a battery in a vehicle according to the present disclosure.

[0021] According to another aspect of the embodiments of the present disclosure, a vehicle is provided, which is used to perform the method for cooling a battery in a vehicle according to the embodiments of the present disclosure.

[0022] In an embodiment of the present disclosure, during vehicle operation, vehicle driving condition information and battery status information are obtained, wherein the status information is used to characterize the operating state of the battery during vehicle operation; based on the driving condition information and the status information, a battery energy consumption state model and constraints of the energy consumption state model are established, wherein the energy consumption state model is used to determine the energy consumed by the battery and / or the vehicle's battery cooling system; based on the energy consumption state model and the constraints, target cooling strategy data for the battery is determined, wherein the target cooling strategy data is used to represent a strategy for performing cooling operations on the battery; and in response to control instructions corresponding to the target cooling strategy data, the battery cooling system is controlled to perform cooling operations on the battery. That is, during vehicle operation, the embodiment of the present disclosure can obtain vehicle driving condition information and battery status information; based on the driving condition information and the status information, a battery energy consumption state model and constraints constraining the energy consumption state model are established to determine the target cooling strategy data for the battery; and when a control instruction corresponding to the target cooling strategy data is obtained, the battery cooling system can be controlled to perform cooling operations on the battery, thereby resolving the technical problem of poor battery cooling performance and achieving the technical effect of improving battery cooling performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the embodiments of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the embodiments of the present disclosure and do not constitute an improper limitation on the embodiments of the present disclosure. In the drawings:

[0024] FIG1 is a flow chart of a method for cooling a battery in a vehicle according to an embodiment of the present disclosure;

[0025] FIG2 is a schematic diagram of a cooling control system for a power battery of a pure electric vehicle in a navigation system according to an embodiment of the present disclosure;

[0026] FIG3 is a schematic diagram of a battery cooling system according to an embodiment of the present disclosure;

[0027] FIG4 is a flow chart of a battery cooling control method according to an embodiment of the present disclosure;

[0028] FIG5 is a schematic diagram of a cooling device for a battery in a vehicle according to an embodiment of the present disclosure;

[0029] FIG6 is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present disclosure;

[0030] FIG7 is a schematic structural diagram of a processor according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present disclosure.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or components is not necessarily limited to those steps or components clearly listed, but may include other steps or components that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] According to an embodiment of the present disclosure, an embodiment of a method for cooling a battery in a vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] FIG1 is a flow chart of a method for cooling a battery in a vehicle according to an embodiment of the present disclosure. As shown in FIG1 , the method may include the following steps:

[0035] Step S101 , during the operation of the vehicle, obtaining the vehicle's driving condition information and the status information of the battery in the vehicle, wherein the status information is used to represent the working status of the battery during the operation of the vehicle.

[0036] In the technical solution provided in the above step S101 of the embodiment of the present disclosure, the vehicle's driving condition information and the status information of the battery in the vehicle can be obtained. Among them, the battery can be a power battery. The vehicle can be a pure electric vehicle. The driving condition information can be the road condition information ahead collected by the navigation system, or it can be the predicted information of the driving condition, such as the distance to the destination, the estimated driving time to the destination, the average speed of the traffic flow, etc. It should be noted that the predicted information in the above driving condition is only for illustration and is not specifically limited here.

[0037] Optionally, the battery status information may be battery information sent by a battery management system (BMS), such as battery temperature, battery state of charge (SOC), and battery allowable charge and discharge power. It should be noted that this is only a preferred implementation for obtaining battery status information and does not specifically limit the method and process for obtaining battery status information, which are not listed here.

[0038] In the disclosed embodiments, the vehicle's starting position, interruption position, average speed, total distance, and other information can be detected in real time during operation, and the aforementioned data can be stored in the cloud. The cloud can also predict the vehicle's driving condition information, where the stored data in the cloud can become historical big data. For example, historical big data can be used to predict the vehicle's driving condition during the current operation. This consideration of the vehicle's driving condition information at different times achieves the technical effect of improving the accuracy of the vehicle's driving condition information during operation.

[0039] Step S102 : establishing a battery energy consumption state model and constraints of the energy consumption state model based on the driving condition information and the state information, wherein the energy consumption state model is used to determine the energy consumed by the battery and / or the battery cooling system of the vehicle.

[0040] In the technical solution provided in step S102 of the embodiment of the present disclosure, after obtaining the vehicle's driving condition information and the status information of the battery in the vehicle, an energy consumption state model of the battery and constraints for constraining the energy consumption state model can be established, thereby achieving the purpose of determining the energy consumed by the battery cooling system using the energy consumption state model. For example, the battery cooling system may include a power battery, an electric water pump, an expansion tank, a radiator (including a cooling fan), an air conditioning system, a three-way valve, and an air conditioning heat exchanger. Here, only the battery cooling system is illustrated as an example, and no specific limitation is given to the devices included in the battery cooling system. The energy consumption state model can be an energy consumption state model determined based on the fan load of the fan device, or an energy consumption state model determined based on the air conditioning compressor load of the air conditioning device, etc. It should be noted that here, only the method of determining the energy consumption state model is illustrated as an example, and the method and process of determining the energy consumption state model are not specifically limited and are not listed here one by one.

[0041] Optionally, a final energy consumption state model is obtained by establishing energy consumption state equations corresponding to other devices, such as energy consumption state equations corresponding to fans, air conditioners, and batteries. The energy consumption state model may include energy consumption state models for fans, air conditioners, and water pumps. This is merely an example.

[0042] In the disclosed embodiment, based on the obtained driving condition information and status information, energy consumption status models of the battery fan equipment, energy consumption status models of the air-conditioning equipment, energy consumption status models of the water pump equipment, etc., as well as constraints on the above energy consumption status models can be established to determine the energy consumed by the battery and / or the battery cooling system of the vehicle. Moreover, since the constraints on hardware equipment such as fans, air conditioners, and water pumps are taken into consideration, the technical effect of accurately establishing the energy consumption status model is achieved.

[0043] It should be noted that the energy consumption state equation is only illustrated here, and the design method of the energy consumption state equation is not specifically limited. As long as the energy consumption state equation is appropriately designed, it is within the scope of protection of this disclosure and is not listed one by one here.

[0044] Step S103 : determining target cooling strategy data of the battery based on the energy consumption state model and the constraint conditions, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery.

[0045] In the technical solution provided in step S103 of the embodiment of the present disclosure, after establishing the energy consumption state model of the battery and the constraints of the energy consumption state model, the target cooling strategy data of the battery can be determined based on the energy consumption state model and the constraints. The target cooling strategy data can be obtained by establishing constraints based on the fan load, the air conditioning compressor load, the battery temperature, and the electric water pump speed. The constraints may include constraints on the fan load, constraints on the air conditioning compressor load, constraints on the battery temperature, and constraints on the electric water pump speed. The target cooling strategy data is only illustrated here, and the method and process for obtaining the target cooling data are not specifically limited.

[0046] Optionally, the fan load of the fan device, the air conditioner compressor load of the air conditioner, the battery temperature, and the electric water pump speed are adjusted so that these values ​​are within a certain range, thereby achieving target battery cooling strategy data. For example, the air conditioner compressor load can be controlled within a certain load range, the electric water pump speed can be controlled within a certain speed range, and the battery temperature can be controlled within a certain temperature range. Furthermore, to prevent large jumps in the fan load, air conditioner compressor load, and electric water pump speed, the rate of change of the fan load, air conditioner compressor load, and electric water pump speed between the current moment and the previous moment must not exceed a certain value, thereby achieving the technical effect of cooling the battery.

[0047] In the embodiment of the present disclosure, based on the obtained energy consumption state model and the constraints established for the fan load, air-conditioning compressor load, and electric water pump speed, the target cooling strategy data of the battery is determined to achieve the purpose of performing cooling operations on the battery.

[0048] Step S104 : In response to the control instruction corresponding to the target cooling strategy data, controlling the battery cooling system to perform a cooling operation on the battery.

[0049] In the technical solution provided in the above step S104 of the embodiment of the present disclosure, after obtaining the target cooling strategy data, the battery cooling system can be controlled to perform a cooling operation on the battery according to the control instruction corresponding to the target cooling strategy data, wherein the control instruction can be a target pre-control instruction.

[0050] Optionally, a dynamic programming strategy is used to obtain target pre-control instructions corresponding to the fan load, air conditioning compressor load and water pump speed, thereby controlling the battery cooling system to perform cooling operations on the battery to achieve the purpose of cooling the battery.

[0051] In the above steps S101 to S104 of the present disclosure, during the operation of the vehicle, the vehicle's driving condition information and the status information of the battery in the vehicle are obtained, wherein the status information is used to characterize the working state of the battery during the operation of the vehicle; based on the driving condition information and the status information, an energy consumption state model of the battery and the constraints of the energy consumption state model are established, wherein the energy consumption state model is used to determine the energy consumed by the battery and / or the battery cooling system of the vehicle; based on the energy consumption state model and the constraints, the target cooling strategy data of the battery is determined, wherein the target cooling strategy data is used to represent the strategy for performing cooling operations on the battery; in response to the control instructions corresponding to the target cooling strategy data, the battery cooling system is controlled to perform cooling operations on the battery. That is to say, the embodiment of the present disclosure can obtain the vehicle's driving condition information and battery status information during the operation of the vehicle, and establish a battery energy consumption state model and constraints on the energy consumption state model based on the above driving condition information and status information, so as to determine the target cooling strategy data of the battery. When the control instructions corresponding to the target cooling strategy data are obtained, the battery cooling system can be controlled to perform cooling operations on the battery to solve the technical problem of poor cooling effect of the battery, thereby achieving the technical effect of improving the cooling effect of the battery.

[0052] The above method of this embodiment is further introduced below.

[0053] As an optional embodiment, step S102, the battery cooling system includes at least one of the following: a fan device, an air conditioning device, and a water pump device, and the status information includes at least the initial temperature of the battery, the current temperature of the battery, and the current power of the battery, wherein, based on the driving condition information and the status information, an energy consumption state model of the battery and the constraints of the energy consumption state model are established, including: performing differential processing on the fan power of the fan device, the air conditioning power of the air conditioning device, and the water pump power of the water pump device to obtain a first energy consumption state submodel, wherein the first energy consumption state submodel is used to determine the energy consumed by the battery cooling system; based on the fan load of the fan device, determining a second energy consumption state submodel, wherein the second energy consumption state The submodel is used to determine the fan power; based on the air-conditioning compressor load of the air-conditioning equipment, the third energy consumption state submodel is determined, wherein the third energy consumption state submodel is used to determine the air-conditioning power; based on the water pump speed of the water pump equipment, the fourth energy consumption state submodel is determined, wherein the fourth energy consumption state submodel is used to determine the water pump power; based on the fan load, the air-conditioning compressor load, the water pump speed, the initial temperature and the current power, the fifth energy consumption state submodel is determined, wherein the fifth energy consumption state submodel is used to determine the current temperature, the energy consumption state model includes the first energy consumption state submodel, the second energy consumption state submodel, the third energy consumption state submodel, the fourth energy consumption state submodel and the fifth energy consumption state submodel; and the constraint conditions of the energy consumption state model are established.

[0054] In this embodiment, a control system for a battery cooling system can be pre-deployed. The battery cooling system can include: a fan device, an air conditioning device, and a water pump device. The fan device can be a cooling fan device or a cooling fan device. The air conditioning device can be an air conditioning controller, an air conditioning compressor, or an air conditioning heat exchanger. The water pump device can be an electric water pump. The fan device, air conditioning device, and water pump device are only examples provided here, and the devices referred to by the fan device, air conditioning device, and water pump device are not specifically limited. The battery status information can include the battery's initial temperature, the battery's current temperature, and the battery's current power.

[0055] In this embodiment, the fan power of the fan device, the air conditioning power of the air conditioning device, and the water pump power of the water pump device are differentiated to obtain a first energy consumption state submodel. This first energy consumption state submodel can be used to determine the energy consumed by the battery cooling system. Based on the fan load of the fan device, a second energy consumption state submodel can be determined to further determine the fan power. Based on the air conditioning compressor load of the air conditioning device, a third energy consumption state submodel can be determined to determine the air conditioning power. Based on the water pump speed of the water pump device, a fourth energy consumption state submodel can be determined to determine the water pump power. Based on the fan load, air conditioning compressor load, water pump speed, initial temperature, and current power, a fifth energy consumption state submodel can be determined to determine the battery temperature. Based on the obtained first, second, third, fourth, and fifth energy consumption state submodels, the constraints of the energy consumption state model can be established.

[0056] Optionally, the energy consumption state model may include a first energy consumption state sub-model, a second energy consumption state sub-model, a third energy consumption state sub-model, a fourth energy consumption state sub-model and a fifth energy consumption state sub-model, and the energy consumption state model may be constrained by the above-mentioned first energy consumption state equation, second energy consumption state equation, third energy consumption state equation, fourth energy consumption state equation and fifth energy consumption state equation, and the energy consumption state equation is shown as follows: E auxi =∫(P fan +P AC +P pump )dt P fan =f1(Load fan ) P AC =f2(Load AC ) P pump =f3(n pump )

[0057] Among them, P fan Indicates the fan power, P ACIndicates the air conditioning power, P pump Indicates the power of the electric water pump, E auxi It represents the power consumed by the accessories during the entire cycle, where Load fan Indicates the fan load. AC It represents the load of the air conditioner compressor, n pump It indicates the speed of the electric water pump, Temp batt It indicates the real-time temperature of the battery. Indicates the initial temperature of the battery, P batt It indicates the real-time power of the battery. The initial temperature of the battery can be The current temperature of the battery can be the real-time temperature of the battery and can be expressed by Temp batt The current power of the battery can be the real-time power of the battery, which can be expressed by P batt To express.

[0058] Optionally, the first energy consumption state submodel can be constrained by the first energy consumption state equation, and the first energy consumption state equation can be E auxi =∫(P fan +P AC +P pump )dt.

[0059] Optionally, the second energy consumption state submodel can be constrained by a second energy consumption state equation, and the second energy consumption state equation can be P fan =f1(Load fan ).

[0060] Optionally, the third energy consumption state submodel can be constrained by a third energy consumption state equation, and the third energy consumption state equation can be P AC =f2(Load AC ).

[0061] Optionally, the fourth energy consumption state submodel can be constrained by a fourth energy consumption state equation, and the fourth energy consumption state equation can be P pump =f3(n pump ).

[0062] Optionally, the fifth energy consumption state submodel can be constrained by the fifth energy consumption state equation, and the fifth energy consumption state equation can be

[0063] As an optional embodiment, step S102, establishing constraints of the energy consumption state model, includes: establishing constraints based on fan load, air conditioning compressor load, water pump speed and current temperature in the energy consumption state model.

[0064] In this embodiment, the constraints of the energy consumption state model can be established based on the fan load, air conditioner compressor load, water pump speed, and current battery temperature in the energy consumption state model. The air conditioner compressor load can be simply referred to as the compressor load, the water pump speed can be the electric water pump speed, and the current battery temperature can be the real-time battery temperature.

[0065] Optionally, the constraints for establishing the energy consumption state model can include the fan load being controlled between maximum and minimum loads, the compressor load being controlled between maximum and minimum loads, the electric water pump speed being controlled between maximum and minimum speeds, and the battery temperature being controlled within a certain range. To prevent excessive jumps in the fan load, air conditioner compressor load, and electric water pump speed, it is necessary to ensure that the rate of change of the fan load, air conditioner compressor load, and electric water pump speed between the current moment and the previous moment does not exceed a certain value. The certain value can be a rate of change threshold corresponding to the rate of change of the fan load, air conditioner compressor load, and electric water pump speed, or the rate of change threshold can also be a preset threshold.

[0066] From the above, we can see that the constraints for establishing the energy consumption state model can be expressed by the following formula:

[0067] in, Expressed as the maximum value of the fan load, Expressed as the minimum value of fan load, It represents the fan load at the previous moment relative to the current moment. Expressed as the fan load change rate threshold, Expressed as the minimum value of the air conditioning compressor load, Expressed as the maximum value of the air conditioning compressor load, It represents the air conditioning compressor load at the previous moment relative to the current moment. It represents the change rate threshold of the air-conditioning compressor load. It indicates the minimum speed of the electric water pump. It indicates the maximum speed of the electric water pump. It represents the speed of the electric water pump at the previous moment relative to the current moment. It represents the threshold value of the rate of change of the electric water pump speed. It indicates the minimum value of the real-time battery temperature. Indicates the maximum real-time temperature of the battery.

[0068] As an optional embodiment, step S103 determines the target cooling strategy data of the battery based on the energy consumption state model and the constraints, including: processing the energy consumption state model using a dynamic programming strategy according to the constraints to determine the target cooling strategy data.

[0069] In this embodiment, based on the established energy consumption state model and constraints, a dynamic programming strategy is used to process the energy consumption state model, thereby determining target cooling strategy data. The dynamic programming strategy may be a dynamic programming algorithm that can be used to solve problems with certain optimal properties.

[0070] Optionally, based on the principle of minimum energy consumption, a dynamic programming algorithm is used to solve the problem and obtain target pre-control instructions for fan load, air-conditioning compressor load and water pump speed, so that target cooling strategy data can be determined to cool the battery.

[0071] Optionally, the dynamic programming algorithm can first initialize the dynamic programming information, and then, after completing the initialization of the dynamic programming information, discretize the variables to obtain the optimal control quantity at each moment in order to determine the target cooling strategy data. It should be noted that this is only a preferred embodiment for determining the target cooling strategy data, and does not specifically limit the process and method for determining the target cooling strategy data. As long as the method and process for obtaining the target cooling strategy data are within the scope of protection of this disclosure, they are not listed here one by one.

[0072] As an optional implementation method, step S103 uses a dynamic programming strategy to process the energy consumption state model according to the constraint conditions to determine the target cooling strategy data, including: initializing the dynamic programming information corresponding to the dynamic programming strategy to obtain an initialization result; discretizing the initialization result to obtain a discrete processing result; using the dynamic programming strategy to process the discrete processing result and the energy consumption state model to obtain the target cooling strategy data, so that the energy consumption of the battery cooling system reaches the target state.

[0073] In this embodiment, when a dynamic programming strategy is used to process the energy consumption state model, the dynamic programming information corresponding to the dynamic programming strategy can be initialized to obtain an initialization result, and then the initialization result can be discretized to obtain a discrete processing result. Finally, the dynamic programming strategy can be used to process the discrete processing result and the energy consumption state model to obtain target cooling strategy data, so that the energy consumption of the battery cooling system can reach the target state.

[0074] Optionally, based on the principle of minimum energy consumption, a dynamic programming algorithm is used to solve the target pre-control instructions for the fan load, air conditioning compressor load, and water pump speed. The dynamic programming algorithm can be implemented by first initializing the dynamic programming information, including the battery temperature (i.e., the initial temperature reported by the BMS), the fan load (initially 0), the air conditioning compressor load (initially 0), and the electric water pump speed (initially 0).

[0075] Optionally, after completing the initialization of the dynamic programming information, the variables need to be discretized, including known working conditions (for example, predicted working conditions) and control variables (for example, fan load, compressor load, electric water pump speed). The goal of the optimal decision is to find the optimal control quantity at each moment to minimize the performance index. Then, after obtaining the optimal control sequence of the entire process, the optimal state trajectory of each component can be obtained by substituting it into the state equation. After traversing all state points at the kth moment, push it to the previous moment, and for the i-th discrete state point in the kth step, calculate its transfer cost to the next state under the action of each possible discrete control quantity, so as to obtain the corresponding optimal cost vector from the current moment to the end moment, and save it: repeat this process until the second moment, and the backward calculation of the dynamic programming can be completed. Finally, starting from the initial state, the above steps are repeated to calculate the optimal control quantity in the first step, and then the state equation is forward calculated to obtain the state point at the next moment in turn, and the optimal control sequence is solved by interpolation. Among them, the discrete state points can be discrete time points for obtaining the variable values ​​of various driving processes of the vehicle. For example, the variable values ​​of fan load, electric water pump speed, etc. can be obtained at a certain time point. Here, only examples of discrete state points are given, and no specific restrictions are imposed on the type of reference values ​​obtained in the discrete state points and the number of discrete state points.

[0076] As an optional embodiment, step S101 obtains the vehicle's driving condition information during vehicle operation, including: obtaining navigation information during vehicle operation; and predicting the driving condition information based on the vehicle's historical driving data and navigation information.

[0077] In this embodiment, navigation information from the vehicle during operation can be obtained, and based on the vehicle's historical driving data and navigation data, the vehicle's driving condition information can be predicted. The navigation information can be road traffic information transmitted by the navigation system, such as the average speed of traffic, the distance from the vehicle's starting point to the destination, or the estimated time of arrival of the vehicle at the destination. Driving condition information can be simply referred to as condition information. The vehicle's historical driving data can be stored on the vehicle's cloud server or can be the vehicle's historical usage data, which can be simply referred to as historical big data.

[0078] Optionally, based on navigation information and historical big data stored in a cloud server, the vehicle's vehicle control unit (VCU) is used to predict the vehicle's driving condition information.

[0079] Optionally, the vehicle's navigation controller can record and store the vehicle's starting and ending positions for each driving cycle. The VCU records and stores the vehicle's average speed, total distance traveled, average acceleration, and other information for each driving cycle. The air conditioning controller stores the average ambient temperature for each driving cycle. After each driving cycle, the navigation and VCU upload this information to a cloud server for storage.

[0080] As an optional embodiment, step S101 predicts driving condition information based on the vehicle's historical driving data and navigation information, including: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determining the vehicle's state transition probability based on the discrete results, wherein the state transition probability is used to represent the transition probability from the vehicle's current speed state to the vehicle's next speed state; generating a vehicle's state transition probability matrix based on the state transition probability; determining the vehicle's initial speed state vector and the target speed state vector after the state transition occurs based on the state transition probability matrix; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0081] In this embodiment, when predicting driving condition information based on the vehicle's historical driving data and navigation information, the speed data in the historical driving data and the speed data in the navigation information can first be discretized to obtain a discrete result, thereby determining the vehicle's state transition probability. Based on the obtained vehicle state transition probability, a vehicle state transition probability matrix is ​​generated, and then the vehicle's initial speed state vector and the target speed state vector after the state transition are determined. Based on the obtained initial speed state vector and target speed state vector, a prediction model corresponding to the driving condition information can be generated to achieve the purpose of predicting the vehicle's driving condition information. The speed data in the historical driving data and the speed data in the navigation information can be collectively referred to as the driving speed.

[0082] Optionally, the speed data in the historical driving data and the speed data in the navigation information are discretized using the nearest neighbor method, so that the vehicle speed can be discretized into finite values. The discretization of the vehicle speed into finite values ​​can be expressed as v s ∈{v1,v2,…,v N}, where v1, v2, v N and vs It can be expressed as the speed of the vehicle at different times. Here, only an example is given to illustrate the result of the discretization process, and no specific limitation is imposed on the result of the discretization process.

[0083] Alternatively, based on the discrete results, the vehicle speed during driving is first divided into different states. Based on the different states, the state transition probability of the vehicle can be determined. For example, the vehicle speed during driving is divided into 100 possible states, the speed discrete interval is 5 kilometers per hour (km / h), the driving speed state number is U = 1, 2, ..., 25, and the vehicle speed is determined by the current speed state U i The vehicle speed state U at the next moment j The probability of state transition probability P i,j At the current moment, the vehicle speed is v i At the next moment, the vehicle speed is v j The probability is: P i,j =P(v(k+1)=v j |v(k)=v i )

[0084] Among them, P i,j It represents the element in row i and column j of the state transition probability matrix.

[0085] Optionally, P i,j The value of can be obtained by the maximum likelihood estimation method, that is, by the following formula: Among them, F i,j It refers to the vehicle's speed from v i Transfer to v j The number of times, F i It refers to the vehicle's speed from v i The total number of transfers, where i, j = 0, 1, …, N.

[0086] Optionally, the state transition probability matrix may be a Markov transition probability matrix, which may be represented by P. The Markov transition probability matrix P may be generated by calculating the transition probability and number of times from the current driving speed to the next driving speed, and combining each state probability value. The matrix P is shown below:

[0087] Optionally, the initial speed state vector may be an initial vehicle speed state vector. For example, assuming that the system has n independent vehicle speed states, the initial vehicle speed state vector of the system is: S(0) = [S1(0), S2(0), ..., S m (0),…,S n (0)], where S m (0) refers to the initial probability when the vehicle speed state is m.

[0088] Alternatively, the target speed state vector can be a state vector after state transition. For example, after k steps of state transition, the probability of the system being in speed state m is S m (k), the state vector after state transfer is: S(k)=[S1(k),S2(k),…,S m (k),…,S n (k)], where S m (k) refers to the probability that the system is in state m at time k.

[0089] Optionally, the prediction model can be a Markov prediction model, and the Markov prediction model can be expressed as Based on the prediction model of the Markov chain and the solution of the state transfer matrix, the predicted speed value is: v(k)=[(U k -1)+r k ]d

[0090] Among them, v(k) represents the vehicle speed at time k, U k It represents the speed state of the vehicle at time k, d represents the division length of the speed state, and its value can be 5, and r represents a random number uniformly distributed at time k.

[0091] In the disclosed embodiment, the vehicle's driving condition information and battery status information can be obtained during operation. Based on the above driving condition information and status information, a battery energy consumption state model and constraints on the energy consumption state model are established to determine the target cooling strategy data of the battery. When the control instructions corresponding to the target cooling strategy data are obtained, the battery cooling system can be controlled to perform cooling operations on the battery to solve the technical problem of poor cooling effect on the battery, thereby achieving the technical effect of improving the cooling effect of the battery.

[0092] The technical solutions of the embodiments of the present disclosure are illustrated below with reference to preferred implementations.

[0093] Currently, when pure electric vehicles operate in high-temperature environments, when the power battery temperature reaches a certain upper limit, it affects both the battery's charge and discharge power output and its lifespan. Current existing technologies primarily utilize different cooling methods based on the actual battery temperature. When the battery temperature is not too high, radiators are used for cooling; when the temperature exceeds a certain value, air conditioning systems are used to cool the battery. However, current technologies can easily cause battery cooling lags, thus affecting battery lifespan. Furthermore, the cooling process is not intelligent enough, fails to consider road conditions ahead, and fails to comprehensively consider cooling energy consumption. Consequently, these methods suffer from poor battery cooling effectiveness.

[0094] In one possible implementation, a cooling system for a power battery is proposed. The system may include an internal coolant channel, disposed inside the power battery, having an inlet and an outlet for circulating coolant; an external cooling assembly, disposed outside the power battery, including an external coolant channel connected to the inlet and outlet of the internal coolant channel; and a reversing assembly for periodically changing the flow direction of the coolant so that the coolant in the external coolant channel can flow from the inlet and out of the outlet through the internal coolant channel, or flow from the outlet and out of the inlet through the internal coolant channel. The cooling system can effectively balance the temperature of each battery cell in the power battery, thereby improving the charge and discharge efficiency of the power battery and extending its service life. However, the above method still has the technical problem of poor battery cooling effect.

[0095] In another possible implementation, a cooling method for a vehicle and its power battery is proposed. The method may include the following steps: obtaining the vehicle's current driving condition, the currently adopted driving mode, and the target state of the current environment; determining the estimated temperature coefficient of the power battery in the vehicle based on the driving condition, driving mode, and target state; obtaining the current temperature of the power battery, and obtaining the estimated temperature of the power battery based on the current temperature and the estimated temperature coefficient; and controlling the cooling of the power battery based on the estimated temperature. The cooling method disclosed herein can estimate the power battery based on the vehicle's driving condition, driving mode, current temperature, and the temperature of the power battery, avoiding controlling the battery cooling based solely on the collected temperature, thereby reducing battery life degradation and energy consumption, maintaining optimal battery discharge capacity, and preventing untimely cooling caused by delayed temperature collection and thermal runaway caused by battery heating. However, the above method still has the technical problem of poor battery cooling effect.

[0096] To solve the above problems, an embodiment of the present disclosure proposes a cooling method for a battery in a vehicle, which can obtain the vehicle's driving condition information and battery status information during operation, and establish a battery energy consumption state model and constraints on the energy consumption state model based on the above driving condition information and status information, so as to determine the target cooling strategy data of the battery. When the control instructions corresponding to the target cooling strategy data are obtained, the battery cooling system can be controlled to perform a cooling operation on the battery to solve the technical problem of poor cooling effect of the battery, thereby achieving the technical effect of improving the cooling effect of the battery.

[0097] The following is a further introduction to the embodiments of the present disclosure.

[0098] Figure 2 is a schematic diagram of a cooling control system for a pure electric vehicle power battery of a navigation system according to an embodiment of the present disclosure. As shown in Figure 2, the control system may include: an on-board navigation system 201, a vehicle controller (VCU) 202, a cooling fan 203, an air conditioning controller 204, a battery management system (BMS) 205, an electric water pump 206 and a cloud server 207.

[0099] The vehicle navigation system 201 sends the road condition information ahead to the VCU in real time, such as the distance from the starting point to the end point of the vehicle, the estimated travel time from the starting point to the end point, the average speed of the traffic flow, etc.

[0100] The cooling fan 203 executes the fan load instruction sent by the VCU and feeds back the actual load status to the VCU.

[0101] The air conditioning controller 204 executes the air conditioning compressor load control instruction sent by the VCU and feeds back the air conditioning compressor load status to the VCU.

[0102] The battery management system 205 can feed back the battery pack temperature, SOC and available charge and discharge power of the battery pack to the VCU.

[0103] The cloud server 207 sends the vehicle's historical big data to the VCU. The electric water pump 206 executes the electric water pump speed command sent by the VCU and feeds back the actual water pump speed to the VCU. After the VCU makes a comprehensive judgment on the received information, it can obtain the fan control command, air-conditioning compressor control command and electric water pump control command, etc., and then send them to the corresponding components.

[0104] Figure 3 is a schematic diagram of a battery cooling system according to an embodiment of the present disclosure. As shown in Figure 3, the battery cooling system may include: a power battery 301, an electric water pump 302, an expansion water tank 303, a radiator (including a cooling fan) 304, an air-conditioning system 305, a three-way valve 306, an air-conditioning heat exchanger 307, a three-way valve port 308, a three-way valve port 309 and a three-way valve port 310, wherein the three-way valve 306 includes a three-way valve port 308, a three-way valve port 309 and a three-way valve port 310.

[0105] In this embodiment, in the battery cooling system, when the three-way valve port 308 and the three-way valve port 309 are connected and the three-way valve port 310 is closed, the radiator 304 cools the power battery; when the three-way valve port 309 is closed and the three-way valve port 308 and the three-way valve port 310 are connected, the air conditioning system cools the power battery.

[0106] FIG4 is a flow chart of a battery cooling control method according to an embodiment of the present disclosure. As shown in FIG4 , the battery cooling control method may include the following steps:

[0107] In step S401, the navigation system collects road condition information ahead and sends it to the vehicle controller.

[0108] In the technical solution provided in the above step S401 of the present disclosure, the navigation system collects road condition information ahead and sends it to the VCU. The specific road condition information includes the distance to the destination, the estimated travel time to the destination, the average speed of the traffic flow, etc.

[0109] In step S402, the vehicle controller predicts the operating condition based on the navigation information and historical big data.

[0110] In the technical solution provided in the above step S402 of the present disclosure, the VCU performs operating condition prediction based on the navigation information and the historical big data stored in the cloud server.

[0111] Optionally, the navigation controller records and stores the vehicle's starting and ending positions for each drive cycle. The VCU records and stores the vehicle's average speed, total distance traveled, and average acceleration for each drive cycle. The air conditioning controller stores the average ambient temperature for each drive cycle. Table 1 shows the vehicle's historical usage data. The stored historical vehicle usage data obtained above is shown in Table 1. After each drive cycle, the navigation and VCU upload this information to a cloud server for storage.

[0112] Optionally, the working condition prediction is performed according to the Markov theory, and the specific implementation and operation method are as follows: first calculate the state transition probability matrix, and use the nearest neighbor method to discretize the driving speed into a finite value: v s ∈{v1,v2,…,v N}, where v1, v2, v N and v s It can be expressed as the vehicle speed at different times.

[0113] Optionally, the vehicle speed during the driving process is divided into 100 possible states, with a discrete speed interval of 5 km / h. The driving speed state number is U = 1, 2, ..., 25. The vehicle speed is determined by the current speed state U i The vehicle speed state U at the next moment j The probability of state transition probability P i,j At the current moment, the vehicle speed is v i At the next moment, the vehicle speed is v j The probability is: P i,j =P(v(k+1)=v j |v(k)=v i )

[0114] Among them, P i,j It represents the element of row i and column j of the state transition probability matrix. i,jThe value of can be obtained by the maximum likelihood estimation method, that is, by the following formula: Among them, F i,j It refers to the vehicle's speed from v i Transfer to v j The number of times, F i It refers to the vehicle's speed from v i The total number of transfers, where i, j = 0, 1, …, N.

[0115] Table 1 Historical usage data of vehicles

[0116] Optionally, by calculating the transition probability and number of times from the current driving speed to the next driving speed, each state probability value is combined to generate a Markov transition probability matrix P, and the matrix P is as follows: Assuming that the system has n independent driving speed states, the initial speed state vector of the system is: S(0) = [S1(0), S2(0), ..., S m (0),…,S n (0)], where S m (0) refers to the initial probability when the vehicle speed state is m. After k steps of state transition, the probability of the system being in the vehicle speed state m is S m (k), the state vector after state transfer is: S(k)=[S1(k),S2(k),…,S m (k),…,S n (k)], where S m (k) refers to the probability that the system is in state m at time k. The Markov prediction model can be expressed as Based on the prediction model of the Markov chain and the solution of the state transfer matrix, the predicted speed value is: v(k)=[(U k -1)+r k ]d

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

[0118] Step S403: establishing an energy consumption state equation according to the predicted working conditions.

[0119] In this embodiment, an energy consumption state equation can be established based on the predicted working conditions. Since the working conditions have been predicted, the energy consumed by the drive motor is closely related to the working conditions, and this time we only focus on the situation under high temperature and whether the battery discharge and recovery power capabilities can meet the needs. Therefore, after the working conditions are determined, the energy consumed by the drive is also determined accordingly. The optimization is mainly for the electric energy consumed by the battery cooling accessories. The energy consumption of the accessories mainly includes the energy consumed by the fan, air conditioner and electric water pump. The energy consumption state equation is established as follows: E auxi =∫(P fan +P AC +P pump )dt P fan =f1(Load fan ) P AC =f2(Load AC ) P pump =f3(n pump )

[0120] Among them, P fan Indicates the fan power, P AC Indicates the air conditioning power, P pump Indicates the power of the electric water pump, E auxi It represents the power consumed by the accessories during the entire cycle, where Load fan Indicates the fan load. AC It represents the load of the air conditioner compressor, n pump It indicates the speed of the electric water pump, Temp batt It indicates the real-time temperature of the battery. Indicates the initial temperature of the battery, P batt It indicates the real-time power of the battery. The initial temperature of the battery can be The current temperature of the battery can be the real-time temperature of the battery and can be expressed by Temp batt The current power of the battery can be the real-time power of the battery, which can be expressed by P batt To express.

[0121] Step S404: determining constraint conditions according to the energy consumption state equation.

[0122] In this embodiment, the constraint condition can be determined based on the energy consumption state equation. The constraint condition can be expressed as follows:

[0123] in, Expressed as the maximum value of the fan load, Expressed as the minimum value of fan load, It represents the fan load at the previous moment relative to the current moment. Expressed as the fan load change rate threshold, Expressed as the minimum value of the air conditioning compressor load, Expressed as the maximum value of the air conditioning compressor load, It represents the air conditioning compressor load at the previous moment relative to the current moment. It represents the change rate threshold of the air-conditioning compressor load. It indicates the minimum speed of the electric water pump. It indicates the maximum speed of the electric water pump. It represents the speed of the electric water pump at the previous moment relative to the current moment. It represents the threshold value of the rate of change of the electric water pump speed. It indicates the minimum value of the real-time battery temperature. Indicates the maximum real-time temperature of the battery.

[0124] Optionally, the fan load needs to be controlled between the maximum and minimum loads, the compressor load needs to be controlled between the maximum and minimum loads, the electric water pump speed needs to be controlled between the highest and lowest speeds, and the battery temperature is controlled within a certain range. Then, in order to prevent the fan load, air conditioning compressor load and electric water pump speed from jumping too much, it is necessary to ensure that the change rate of the fan load, air conditioning compressor load and electric water pump speed does not exceed a certain value compared with the current moment and the previous moment.

[0125] Step S405 , based on the principle of minimum energy consumption, a dynamic programming strategy is used to solve the problem and obtain target pre-control instructions for the fan load, the air-conditioning compressor load and the water pump speed.

[0126] In this embodiment, based on step S403 and step S404, a dynamic programming strategy is used to solve and obtain real-time pre-control instructions for fan load, air-conditioning compressor load and electric water pump speed, which are sent to corresponding accessories for execution.

[0127] Optionally, the dynamic programming information is initialized, including the battery temperature (i.e., the initial temperature reported by the BMS), the fan load (initially 0), the air conditioning compressor load (initially 0), and the electric water pump speed (initially 0). Then, after completing the initialization of the dynamic programming information, the variables need to be discretized, including the known working conditions (i.e., the predicted working conditions) and the control variables (e.g., fan load, compressor load, electric water pump speed). The goal of the optimal decision is to find the optimal control quantity at each moment to minimize the performance index. After obtaining the optimal control sequence for the entire process, the optimal state trajectory of each component can be obtained by substituting it into the state equation. After traversing all state points at time k, push it to the previous moment, and for the i-th discrete state point in the k-th step, calculate its transfer cost to the next state under the action of each possible discrete control quantity, and obtain the corresponding optimal cost vector from the current moment to the terminal and save it: repeat this process until the second moment, and complete the backward calculation of the dynamic programming. Finally, starting from the initial state, repeat the above steps to calculate the optimal control quantity of the first step, and then calculate forward according to the state equation to obtain the state point at the next moment in turn, and at the same time interpolate to solve the optimal control sequence.

[0128] According to an embodiment of the present disclosure, a cooling device for a battery in a vehicle is also provided. It should be noted that the cooling device for a battery in a vehicle can be used to implement the cooling method for a battery in a vehicle in Example 1.

[0129] Figure 5 is a schematic diagram of a cooling device for a battery in a vehicle according to an embodiment of the present disclosure. As shown in Figure 5 , the cooling device 500 for a battery in a vehicle may include: an acquisition component 501, an establishment component 502, a determination component 503 and a control component 504.

[0130] The acquisition component 501 is configured to acquire the vehicle's driving condition information and the status information of the battery in the vehicle during the vehicle's operation, wherein the status information is used to represent the working status of the battery during the vehicle's operation.

[0131] Establishing component 502 is configured to establish a battery energy consumption state model and constraints of the energy consumption state model based on driving condition information and state information, wherein the energy consumption state model is used to determine the amount of energy consumed by the battery and / or the battery cooling system of the vehicle.

[0132] The determination component 503 is configured to determine target cooling strategy data of the battery based on the energy consumption state model and the constraint conditions, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery.

[0133] The control component 504 is configured to control the battery cooling system to perform a cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data.

[0134] Optionally, the establishment component 502 may include: a first acquisition component, configured to perform differential processing on the fan power of the fan device, the air-conditioning power of the air-conditioning device, and the water pump power of the water pump device to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used to determine the energy consumed by the battery cooling system; a first determination component, configured to determine a second energy consumption state sub-model based on the fan load of the fan device, wherein the second energy consumption state sub-model is used to determine the fan power; a second determination component, configured to determine a third energy consumption state sub-model based on the air-conditioning compressor load of the air-conditioning device, wherein the third energy consumption state sub-model is used to determine Air conditioning power; a third determination component, configured to determine the fourth energy consumption state sub-model based on the water pump speed of the water pump equipment, wherein the fourth energy consumption state sub-model is used to determine the water pump power; a fourth determination component, configured to determine the fifth energy consumption state sub-model based on the fan load, the air conditioning compressor load, the water pump speed, the initial temperature and the current power, wherein the fifth energy consumption state sub-model is used to determine the current temperature, the energy consumption state model includes the first energy consumption state sub-model, the second energy consumption state sub-model, the third energy consumption state sub-model, the fourth energy consumption state sub-model and the fifth energy consumption state sub-model; an establishment component, used to establish the constraint conditions of the energy consumption state model.

[0135] Optionally, establishing the component may include: establishing a subcomponent, which is set to establish constraint conditions based on the fan load, air conditioner compressor load, water pump speed and current temperature in the energy consumption state model.

[0136] Optionally, the determination component 503 may include: a fifth determination component configured to process the energy consumption state model using a dynamic programming strategy according to the constraint conditions to determine the target cooling strategy data.

[0137] Optionally, the fifth determination component may include: a first acquisition sub-component, configured to initialize the dynamic programming information corresponding to the dynamic programming strategy to obtain an initialization result; a second acquisition sub-component, configured to discretize the initialization result to obtain a discrete processing result; a third acquisition sub-component, configured to use the dynamic programming strategy to process the discrete processing result and the energy consumption state model to obtain target cooling strategy data, so that the energy consumption of the battery cooling system reaches the target state.

[0138] Optionally, the acquisition component 501 may include: a second acquisition component configured to acquire navigation information during vehicle operation; and a prediction component configured to predict driving condition information based on historical driving data and navigation information of the vehicle.

[0139] Optionally, the prediction component may include: a fourth acquisition subcomponent, configured to discretize the speed data in the historical driving data and the speed data in the navigation information to obtain a discrete result; a first determination subcomponent, configured to determine the state transition probability of the vehicle based on the discrete result, wherein the state transition probability is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; a first generation subcomponent, configured to generate a state transition probability matrix of the vehicle based on the state transition probability; a second determination subcomponent, configured to determine the initial speed state vector of the vehicle and the target speed state vector after the state transition occurs based on the state transition probability matrix; a second generation subcomponent, configured to generate a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; a prediction subcomponent, configured to predict the driving condition information based on the prediction model.

[0140] In an embodiment of the present disclosure, the vehicle's driving condition information and status information of the battery in the vehicle are obtained by acquiring the component during the operation of the vehicle, wherein the status information is used to characterize the working state of the battery during the operation of the vehicle; an energy consumption state model of the battery and constraints of the energy consumption state model are established based on the driving condition information and the status information by establishing the component, wherein the energy consumption state model is used to determine the amount of energy consumed by the battery and / or the battery cooling system of the vehicle; target cooling strategy data of the battery is determined based on the energy consumption state model and the constraints by determining the component, wherein the target cooling strategy data is used to represent a strategy for performing cooling operations on the battery; the battery cooling system is controlled to perform cooling operations on the battery by controlling the component in response to control instructions corresponding to the target cooling strategy data, thereby solving the technical problem of poor cooling effect on the battery and achieving the technical effect of improving cooling effect on the battery.

[0141] The various functional components provided in the embodiments of the present disclosure may be operated in a battery cooling method in a vehicle or a similar computing device, and may also be stored as part of a computer-readable storage medium.

[0142] FIG6 is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present disclosure. As shown in FIG6 , a program product 60 in a real-time manner according to the present disclosure is provided, on which a computer program is stored. When the computer program is executed by a processor, program code for implementing the following steps is implemented:

[0143] During the operation of the vehicle, the vehicle's driving condition information and the status information of the battery in the vehicle are obtained, wherein the status information is used to represent the working status of the battery during the operation of the vehicle;

[0144] Establishing an energy consumption state model of the battery and constraints of the energy consumption state model based on the driving condition information and the state information, wherein the energy consumption state model is used to determine energy consumed by the battery and / or a battery cooling system of the vehicle;

[0145] Determining target cooling strategy data for the battery based on the energy consumption state model and the constraint conditions, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery;

[0146] In response to the control instruction corresponding to the target cooling strategy data, the battery cooling system is controlled to perform a cooling operation on the battery.

[0147] Optionally, the computer program is further configured to implement program code for the following steps when executed by the processor: the battery cooling system includes at least one of the following: a fan device, an air conditioning device, and a water pump device, the status information includes at least the initial temperature of the battery, the current temperature of the battery, and the current power of the battery, the fan power of the fan device, the air conditioning power of the air conditioning device, and the water pump power of the water pump device are differentiated to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used to determine the energy consumed by the battery cooling system; based on the fan load of the fan device, a second energy consumption state sub-model is determined, wherein the second energy consumption state sub-model is used to determine the fan power; based on the fan load of the air conditioning device, a second energy consumption state sub-model is determined, wherein the second energy consumption state sub-model is used to determine the fan power; based on the fan load of the air conditioning device, a second energy consumption state sub-model is determined, wherein the second energy consumption state sub-model is used to determine the fan power. The third energy consumption state submodel is determined based on the air-conditioning compressor load, wherein the third energy consumption state submodel is used to determine the air-conditioning power; the fourth energy consumption state submodel is determined based on the water pump speed of the water pump equipment, wherein the fourth energy consumption state submodel is used to determine the water pump power; the fifth energy consumption state submodel is determined based on the fan load, the air-conditioning compressor load, the water pump speed, the initial temperature and the current power, wherein the fifth energy consumption state submodel is used to determine the current temperature, and the energy consumption state model includes the first energy consumption state submodel, the second energy consumption state submodel, the third energy consumption state submodel, the fourth energy consumption state submodel and the fifth energy consumption state submodel; and the constraint conditions of the energy consumption state model are established.

[0148] Optionally, the computer program further implements program codes of the following steps when executed by the processor: establishing constraint conditions based on the fan load, air conditioner compressor load, water pump speed and current temperature in the energy consumption state model.

[0149] Optionally, the computer program further implements program codes of the following steps when executed by the processor: processing the energy consumption state model using a dynamic programming strategy according to the constraint conditions to determine target cooling strategy data.

[0150] Optionally, the computer program also implements the degree code of the following steps when executed by the processor: initializing the dynamic programming information corresponding to the dynamic programming strategy to obtain an initialization result; discretizing the initialization result to obtain a discrete processing result; using the dynamic programming strategy to process the discrete processing result and the energy consumption state model to obtain target cooling strategy data, so that the energy consumption of the battery cooling system reaches the target state.

[0151] Optionally, the computer program also implements program codes of the following steps when executed by the processor: obtaining navigation information during the operation of the vehicle; and predicting driving condition information based on historical driving data and navigation information of the vehicle.

[0152] Optionally, the computer program is also a program code that implements the following steps when executed by the processor: discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determining the state transition probability of the vehicle based on the discrete results, wherein the state transition probability is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; generating a state transition probability matrix of the vehicle based on the state transition probability; determining the initial speed state vector of the vehicle and the target speed state vector after the state transition occurs based on the state transition probability matrix; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0153] Optionally, in the embodiment of the present disclosure, the computer-readable storage medium may also be configured as program codes for various preferred or optional method steps provided by the method for cooling a battery in a vehicle.

[0154] Optionally, the specific examples in the embodiments of the present disclosure may refer to the examples described in the above embodiments, and this embodiment will not be repeated here.

[0155] Computer-readable storage media may include a data signal transmitted in baseband or as part of a carrier wave, carrying readable program code. Such a transmitted data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Non-volatile storage media may transmit, propagate, or transfer programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0156] The program code contained in the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the foregoing.

[0157] According to an embodiment of the present disclosure, a processor is also provided. FIG7 is a schematic diagram of the structure of a processor according to an embodiment of the present disclosure. As shown in FIG7 , the processor 70 is configured to run a program, wherein the program, when running, executes the vehicle battery heating method according to the embodiment of the present disclosure.

[0158] In the embodiment of the present disclosure, the processor 70 may execute an operating program of a vehicle fault information processing method.

[0159] Optionally, in the embodiment of the present disclosure, the processor 70 may be configured to perform the following steps:

[0160] During the operation of the vehicle, the vehicle's driving condition information and the status information of the battery in the vehicle are obtained, wherein the status information is used to represent the working status of the battery during the operation of the vehicle;

[0161] Establishing an energy consumption state model of the battery and constraints of the energy consumption state model based on the driving condition information and the state information, wherein the energy consumption state model is used to determine energy consumed by the battery and / or a battery cooling system of the vehicle;

[0162] Determining target cooling strategy data for the battery based on the energy consumption state model and the constraint conditions, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery;

[0163] In response to the control instruction corresponding to the target cooling strategy data, the battery cooling system is controlled to perform a cooling operation on the battery.

[0164] Optionally, the processor 70 may be further configured to perform the following steps: the battery cooling system includes at least one of the following: a fan device, an air conditioning device, and a water pump device, the state information includes at least the initial temperature of the battery, the current temperature of the battery, and the current power of the battery, the fan power of the fan device, the air conditioning power of the air conditioning device, and the water pump power of the water pump device are differentiated to obtain a first energy consumption state sub-model, wherein the first energy consumption state sub-model is used to determine the energy consumed by the battery cooling system; based on the fan load of the fan device, a second energy consumption state sub-model is determined, wherein the second energy consumption state sub-model is used to determine the fan power; based on the air conditioning load of the air conditioning device, a second energy consumption state sub-model is determined, wherein the second energy consumption state sub-model is used to determine the fan power; based on the air conditioning load of the air conditioning device, a second energy consumption state sub-model is determined, wherein the second energy consumption state sub-model is used to determine the fan power. The compressor load is used to determine the third energy consumption state submodel, wherein the third energy consumption state submodel is used to determine the air conditioning power; based on the water pump speed of the water pump equipment, the fourth energy consumption state submodel is determined, wherein the fourth energy consumption state submodel is used to determine the water pump power; based on the fan load, the air conditioning compressor load, the water pump speed, the initial temperature and the current power, the fifth energy consumption state submodel is determined, wherein the fifth energy consumption state submodel is used to determine the current temperature. The energy consumption state model includes the first energy consumption state submodel, the second energy consumption state submodel, the third energy consumption state submodel, the fourth energy consumption state submodel and the fifth energy consumption state submodel; and the constraints of the energy consumption state model are established.

[0165] Optionally, the processor 70 may be further configured to perform the following steps: establishing constraint conditions based on the fan load, the air-conditioning compressor load, the water pump speed, and the current temperature in the energy consumption state model.

[0166] Optionally, the processor 70 may be further configured to perform the following steps: according to the constraint conditions, a dynamic programming strategy is used to process the energy consumption state model to determine the target cooling strategy data.

[0167] Optionally, the processor 70 can also be configured to perform the following steps: initialize the dynamic programming information corresponding to the dynamic programming strategy to obtain an initialization result; discretize the initialization result to obtain a discrete processing result; use the dynamic programming strategy to process the discrete processing result and the energy consumption state model to obtain target cooling strategy data, so that the energy consumption of the battery cooling system reaches the target state.

[0168] Optionally, the processor 70 may be further configured to perform the following steps: obtaining navigation information during vehicle operation; and predicting driving condition information based on historical driving data and navigation information of the vehicle.

[0169] Optionally, the processor 70 can also be configured to perform the following steps: discretize the speed data in the historical driving data and the speed data in the navigation information to obtain discrete results; determine the state transition probability of the vehicle based on the discrete results, wherein the state transition probability is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; generate a state transition probability matrix of the vehicle based on the state transition probability; determine the initial speed state vector of the vehicle and the target speed state vector after the state transition occurs based on the state transition probability matrix; generate a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; and predict the driving condition information based on the prediction model.

[0170] The processor 70 can execute various functional applications and data processing by running software programs and components stored in the memory, that is, to implement the above-mentioned method for cooling the battery in the vehicle.

[0171] According to another aspect of the present disclosure, an electronic device is provided. The electronic device includes one or more processors and a memory, wherein the memory is configured to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle battery cooling method according to the present disclosure.

[0172] According to another aspect of an embodiment of the present disclosure, a computer program product is also provided, including a non-volatile computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it performs the steps of the cooling method of the battery in the vehicle of the embodiment of the present disclosure.

[0173] According to an embodiment of the present disclosure, a vehicle is further provided, which is used to execute the method for cooling a battery in a vehicle in embodiment 1 of the present disclosure.

[0174] The serial numbers of the above-mentioned embodiments of the present disclosure are for description only and do not represent the advantages or disadvantages of the embodiments.

[0175] In the above embodiments of the present disclosure, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0176] In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the components can be a logical function division. In actual implementation, there may be other division methods, such as multiple components or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of components or components, which can be electrical or other forms.

[0177] The components described as separate parts may or may not be physically separate, and the components shown as components may or may not be physical components, that is, they may be located in one place or distributed across multiple components. Some or all of the components may be selected according to actual needs to achieve the purpose of the present embodiment.

[0178] In addition, the functional components in the various embodiments of the present disclosure may be integrated into a single processing component, each component may exist physically separately, or two or more components may be integrated into a single component. The aforementioned integrated components may be implemented in the form of hardware or software functional components.

[0179] If the integrated components are implemented in the form of software functional components and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0180] The above is only a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications should also be regarded as within the scope of protection of the present disclosure. Industrial Applicability

[0181] The solution provided in the embodiment of the present application can be applied to whether the battery needs cooling and to cool the battery when necessary. During the operation of the vehicle, the vehicle's driving condition information and battery status information can be obtained. Based on the above driving condition information and status information, an energy consumption state model of the battery and constraints on the energy consumption state model are established to determine the target cooling strategy data of the battery. When the control instructions corresponding to the target cooling strategy data are obtained, the battery cooling system can be controlled to perform cooling operations on the battery to solve the technical problem of poor cooling effect of the battery, thereby achieving the technical effect of improving the cooling effect of the battery.

Claims

1. A method for cooling a battery in a vehicle, comprising: During the operation of the vehicle, the driving condition information of the vehicle and the status information of the battery in the vehicle are obtained, wherein the status information is used to characterize the working state of the battery during the operation of the vehicle; Based on the driving condition information and the state information, establishing an energy consumption state model of the battery and constraints of the energy consumption state model, wherein the energy consumption state model is used to determine energy consumed by the battery and / or a battery cooling system of the vehicle; Determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery; In response to a control instruction corresponding to the target cooling strategy data, the battery cooling system is controlled to perform the cooling operation on the battery.

2. The method according to claim 1, wherein: The battery cooling system includes at least one of the following: a fan device, an air conditioning device, and a water pump device, and the state information includes at least an initial temperature of the battery, a current temperature of the battery, and a current power of the battery, wherein an energy consumption state model of the battery and constraints of the energy consumption state model are established based on the driving condition information and the state information, including: Performing differentiation processing on the fan power of the fan device, the air conditioning power of the air conditioning device, and the water pump power of the water pump device to obtain a first energy consumption state submodel, wherein the first energy consumption state submodel is used to determine the energy consumed by the battery cooling system; Determine a second energy consumption state sub-model based on the fan load of the fan device, wherein the second energy consumption state sub-model is used to determine the fan power; Determine a third energy consumption state sub-model based on the air-conditioning compressor load of the air-conditioning equipment, wherein the third energy consumption state sub-model is used to determine the air-conditioning power; Determine a fourth energy consumption state sub-model based on the water pump speed of the water pump device, wherein the fourth energy consumption state sub-model is used to determine the water pump power; Based on the fan load, the air conditioner compressor load, the water pump speed, the initial temperature and the current power, a fifth energy consumption state submodel is determined, wherein the fifth energy consumption state submodel is used Determine the current temperature, the energy consumption state model includes the first energy consumption state sub-model, the second energy consumption state sub-model, the third energy consumption state sub-model, the fourth energy consumption state sub-model and the fifth energy consumption state sub-model; The constraint conditions of the energy consumption state model are established.

3. The method according to claim 2, wherein: The constraint conditions for establishing the energy consumption state model include: The constraint condition is established based on the fan load, the air-conditioning compressor load, the water pump speed and the current temperature in the energy consumption state model.

4. The method according to claim 1, wherein: Determining target cooling strategy data of the battery based on the energy consumption state model and the constraint condition includes: According to the constraint conditions, the energy consumption state model is processed using a dynamic programming strategy to determine the target cooling strategy data.

5. The method according to claim 4, wherein: According to the constraint conditions, the energy consumption state model is processed using a dynamic programming strategy to determine the target cooling strategy data, including: Initializing the dynamic programming information corresponding to the dynamic programming strategy to obtain an initialization result; Performing discretization processing on the initialization result to obtain a discrete processing result; The dynamic programming strategy is used to process the discrete processing results and the energy consumption state model to obtain the target cooling strategy data, so that the energy consumption of the battery cooling system reaches the target state.

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

7. The method according to claim 6, wherein: Predicting the driving condition information based on the historical driving data of the vehicle and the navigation information includes: Discretizing the speed data in the historical driving data and the speed data in the navigation information to obtain a discrete result; Based on the discrete results, a state transition probability of the vehicle is determined, wherein the state transition probability The rate is used to represent the transition probability from the speed state of the vehicle at the current moment to the speed state of the vehicle at the next moment; Generating a state transition probability matrix of the vehicle according to the state transition probability; Based on the state transition probability matrix, determining an initial speed state vector of the vehicle and a target speed state vector after state transition occurs; generating a prediction model corresponding to the driving condition information based on the initial speed state vector and the target speed state vector; Based on the prediction model, the driving condition information is predicted.

8. A cooling device for a battery in a vehicle, comprising: an acquisition component, configured to acquire driving condition information of the vehicle and status information of a battery in the vehicle during operation of the vehicle, wherein the status information is used to characterize the working status of the battery during operation of the vehicle; Establishing a component, configured to establish an energy consumption state model of the battery and constraints of the energy consumption state model based on the driving condition information and the state information, wherein the energy consumption state model is used to determine the amount of energy consumed by the battery and / or the battery cooling system of the vehicle; a determination component, configured to determine target cooling strategy data of the battery based on the energy consumption state model and the constraint condition, wherein the target cooling strategy data is used to represent a strategy for performing a cooling operation on the battery; The control component is configured to control the battery cooling system to perform the cooling operation on the battery in response to a control instruction corresponding to the target cooling strategy data.

9. A processor, characterized in that: The processor is used to run a program, wherein the program, when run by the processor, executes the method for cooling a battery in a vehicle according to any one of claims 1 to 7.

10. A vehicle, characterized in that: A method for cooling a battery in a vehicle according to any one of claims 1 to 7.

11. A computer-readable storage medium, wherein: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for cooling a battery in a vehicle according to any one of claims 1 to 7.

12. An electronic device, wherein: The invention comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the method for cooling a battery in a vehicle according to any one of claims 1 to 7.

13. A computer program product, wherein: The invention comprises a computer program, which, when executed by a processor, implements the cooling method for a battery in a vehicle according to any one of claims 1 to 7.

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