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

Abstract

The application discloses a battery heating method and device, a processor and a vehicle. The method comprises the following steps: obtaining driving condition information of the vehicle and state information of the battery in the vehicle during driving of the vehicle, wherein the state information is used to represent the working state of the battery during driving of the vehicle; determining a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model is used to represent the heat generation and / or heat dissipation of the battery; establishing a battery consumption model of the vehicle and corresponding constraint conditions based on the temperature change model, wherein the battery consumption model is used to represent the energy required for driving the vehicle and / or the battery; determining target heating data of the battery based on the battery consumption model and the constraint conditions; and performing a heating operation on the battery based on the target heating data. The application solves the technical problem of poor heating effect of the battery.

Description

Technical Field

[0001] This invention relates to the field of vehicles, and more specifically, to a method, apparatus, processor, and vehicle for heating a battery in a vehicle. Background Technology

[0002] In related technologies, if it is necessary to heat the battery, the current temperature of the battery can be detected. If the battery temperature reaches a certain threshold, the battery heating device needs to be controlled to heat the battery. However, since it is impossible to calculate the heat generation caused by heating and the heat dissipation caused by air cooling in real time according to different operating conditions, and since heating requires the battery's own electrical energy, it may reduce the vehicle's range. Therefore, there is still a technical problem of poor battery heating effect.

[0003] There is currently no effective solution to the technical problem of poor battery heating effect in the aforementioned technologies. Summary of the Invention

[0004] This invention provides a method, apparatus, processor, and vehicle for heating a battery in a vehicle, to at least solve the technical problem of poor battery heating effect.

[0005] According to one aspect of the present invention, a method for heating a battery in a vehicle is provided. The method may include: acquiring driving condition information of the vehicle and state information of the battery during vehicle operation, wherein the state information characterizes the operating state of the battery during vehicle operation; determining a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model characterizes the heat generation and / or heat dissipation of the battery; establishing a battery consumption model and corresponding constraints based on the temperature change model, wherein the battery consumption model characterizes the energy required to drive the vehicle and / or the battery; determining target heating data for the battery based on the battery consumption model and the constraints; and performing a heating operation on the battery based on the target heating data, wherein the target heating data represents the operating state of the battery after the heating operation is performed.

[0006] Optionally, based on driving condition information and state information, a battery temperature change model is determined, including: establishing a battery heat generation model and a battery heat dissipation model based on driving condition information and state information, wherein the heat generation model is used to represent heat generation and the heat dissipation model is used to represent heat dissipation; and determining a battery temperature change model based on the heat generation model and the heat dissipation model.

[0007] Optionally, based on driving condition information and state information, a battery heat generation model and a battery heat dissipation model are established, including: under the driving condition information, a heat generation model is established based on the battery heat generation data, battery volume, battery specific heat capacity, battery mass, and temperature rise data caused by battery heat generation in the state information; under the driving condition, a heat dissipation model is established based on the battery air cooling data, battery temperature, vehicle ambient temperature, specific heat capacity, and mass; and a temperature change model is determined based on the temperature rise data determined in the heat generation model and the temperature drop data determined in the heat dissipation model, wherein the temperature change model is used to predict the temperature change of the battery in the future.

[0008] Optionally, based on the temperature change model, a battery consumption model and corresponding constraints for the vehicle are established, including: determining the regenerative braking capacity and battery consumption data based on the temperature change model; establishing a battery consumption model based on the regenerative braking capacity and battery consumption data; and establishing constraints based on the battery temperature, battery state of charge, and battery temperature rise data.

[0009] Optionally, before determining the regenerative braking capacity and energy consumption data of the battery based on the temperature change model, the method further includes: determining the battery's operating power based on the vehicle's weight, speed, acceleration, and frontal area from the driving condition information, wherein the operating power represents the battery's discharge or charging status; determining the battery's state of charge model based on the state of charge, voltage, capacity, and operating power; determining the battery's regenerative braking power based on the operating power; and determining the battery's regenerative braking capacity and energy consumption data based on the temperature change model, including: determining the regenerative braking capacity based on the temperature change model, state of charge model, and regenerative braking power; and determining the energy consumption data based on the relationship between the battery's temperature rise data and energy consumption in the temperature change model.

[0010] Optionally, before establishing the battery consumption model based on the regenerative braking power and power consumption data, the method further includes: determining the internal resistance power consumption of the battery based on the battery voltage and the battery operating power; determining the vehicle power consumption based on driving condition information, wherein the vehicle power consumption is the power consumed to drive the vehicle; and establishing the battery consumption model based on the regenerative braking power and power consumption data, including: determining the battery consumption model based on the internal resistance power consumption, power consumption data, regenerative braking power, and vehicle power consumption.

[0011] Optionally, during vehicle operation, the vehicle's driving condition information is acquired, including: acquiring navigation information during vehicle operation; and predicting driving condition information based on the vehicle's historical driving data and navigation information.

[0012] 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 probability of the vehicle's speed state at the current moment to the speed state at the next moment; generating the 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 based on the state transition probability matrix; generating a prediction model based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0013] According to another aspect of the present invention, a heating device for a battery in a vehicle is also provided. The device may include: an acquisition unit, configured to acquire driving condition information of the vehicle and state information of the battery in the vehicle during vehicle operation, wherein the state information characterizes the working state of the battery during vehicle operation; a first determination unit, configured to determine a temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model characterizes the heat generation and / or heat dissipation of the battery; an establishment unit, configured to establish a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model characterizes the energy required to drive the vehicle and / or the battery; a second determination unit, configured to determine target heating data for the battery based on the battery consumption model and the constraints; and an execution unit, configured to perform a heating operation on the battery based on the target heating data, wherein the target heating data represents the working state of the battery after the heating operation is performed.

[0014] According to another aspect of the present invention, a computer-readable storage medium is also provided. The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to perform the heating method for a battery in a vehicle according to the embodiments of the present invention.

[0015] According to another aspect of the present invention, a processor is also provided. The processor is used to run a program, wherein the program, when running, executes the method for heating a battery in a vehicle according to the embodiments of the present invention.

[0016] According to another aspect of the present invention, a vehicle is also provided. This vehicle is used to perform the vehicle battery heating method of the present invention embodiments.

[0017] In this embodiment of the invention, during vehicle operation, driving condition information and battery status information are acquired. The status information characterizes the battery's operating state during vehicle operation. Based on the driving condition information and status information, a battery temperature change model is determined, characterizing the battery's heat generation and / or heat dissipation. Based on the temperature change model, a battery consumption model and corresponding constraints are established, characterizing the energy required to drive the vehicle and / or the battery. Based on the battery consumption model and constraints, target heating data for the battery is determined. Based on the target heating data, a heating operation is performed on the battery, whereby the target heating data represents the battery's operating state after the heating operation. In other words, this embodiment of the invention can acquire driving condition information and battery status information during vehicle operation, determine a battery temperature change model based on the driving condition information and status information, thereby establishing a battery consumption model and corresponding constraints, and determine target heating data for the battery based on the obtained battery consumption model and constraints, thereby performing a heating operation on the battery. By taking into account the heat generation after heating the battery under different driving conditions and the heat dissipation brought by air cooling, the battery heating operation is performed in a comprehensive manner. This ensures that not only the overall power needs of users are met, but also the driving range in low temperatures is extended. This solves the technical problem of poor battery heating effect and achieves the technical effect of improving battery heating effect. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0019] Figure 1 This is a flowchart of a method for heating a battery in a vehicle according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of a heating control system for a pure electric vehicle power battery according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of a heating system according to an embodiment of the present invention;

[0022] Figure 4 This is a flowchart of a power battery heating method according to an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of a battery heating device in a vehicle according to an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Example 1

[0027] According to an embodiment of the present invention, an embodiment of a method for heating a battery in a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0028] Figure 1 This is a flowchart of a method for heating a battery in a vehicle according to an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0029] Step S101: During the vehicle's operation, obtain the vehicle's driving condition information and the battery's status information. The status information is used to characterize the battery's operating status during the vehicle's operation.

[0030] In the technical solution provided in step S101 of the present invention, vehicle driving condition information and battery status information can be obtained to represent the working state of the battery during vehicle operation. The battery can be a power battery or a battery pack. The vehicle can be a pure electric vehicle. The driving condition information can be road condition information collected by a navigation system or predicted driving condition information, such as the distance to the destination, the estimated travel time to the destination, and the average speed of traffic flow. It should be noted that the predicted driving condition information mentioned above is only illustrative and is not specifically limited here.

[0031] Optionally, the battery status information can be battery information sent by the Battery Management System (BMS), such as battery temperature, battery state of charge (SOC), and allowable charge / discharge power. It should be noted that this is merely a preferred implementation method for obtaining battery status information, and the methods and processes for obtaining battery status information are not specifically limited here; they are not listed individually.

[0032] In this embodiment of the invention, the starting position, interruption position, average speed, total distance, and average acceleration of the vehicle during operation can be detected in real time. This data is then stored in the cloud, and the cloud can also predict the vehicle's driving conditions. The stored data can become historical big data. For example, historical big data can be used to predict the vehicle's driving conditions during the current operation. By considering the vehicle's driving conditions at different times, the accuracy of obtaining driving condition information during operation is improved.

[0033] Step S102: Based on driving condition information and status information, determine the battery temperature change model, wherein the temperature change model is used to characterize the battery's heat generation and / or heat dissipation.

[0034] In the technical solution provided in step S102 of the present invention, based on the vehicle's driving condition information and status information obtained in step S101, a battery temperature change model can be determined to represent the battery's heat generation and / or heat dissipation. The temperature change model can be established using a battery temperature change trajectory, such as a battery temperature rise trajectory. The temperature change model can be used to characterize battery temperature changes and to predict the battery temperature at the next future moment based on the battery temperature at the previous moment. It should be noted that this is only an example of determining the temperature change model, and the process and method for determining the temperature change model are not specifically limited.

[0035] Optionally, based on the obtained vehicle driving condition information and status information, a battery heat generation model and a battery heat dissipation model can be established respectively, and the battery temperature change model can be determined according to the battery heat generation model and heat dissipation model obtained above.

[0036] In this embodiment of the invention, based on the vehicle's starting position, interruption position, average speed, total distance, average acceleration and other driving condition information during the driving process, as well as the battery's state information such as battery temperature, battery state of charge and battery allowable charge and discharge power, a battery temperature change model is determined. This not only achieves the purpose of accurately characterizing the battery's heat generation and dissipation, but also achieves the purpose of accurately obtaining the battery's temperature change, thus realizing the technical effect of improving the accuracy of determining the battery's temperature change.

[0037] Step S103: Based on the temperature change model, establish the vehicle's battery consumption model and corresponding constraints, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery.

[0038] In the technical solution provided in step S103 of the present invention, based on the temperature change model obtained in step S102, a battery consumption model and corresponding constraints for the vehicle can be established to characterize the energy required to drive the vehicle and / or the battery. Specifically, the battery consumption model can be established by considering the energy consumed by the battery as it needs to provide power to various devices in the vehicle throughout the entire operating cycle.

[0039] Optionally, the battery consumption model can be determined by the amount of electricity consumed by the battery's internal resistance, the amount of electricity consumed after the battery is heated, the amount of electricity recovered from the battery, and the amount of electricity consumed to drive the vehicle. Constraints include constraints on battery temperature, constraints on the battery's state of charge, and constraints on the temperature rise caused by battery heating.

[0040] In this embodiment of the invention, a battery consumption model for the vehicle can be determined based on the amount of electricity consumed by the battery's internal resistance, the amount of electricity consumed after the battery is heated, the amount of electricity recovered from the battery, and the amount of electricity consumed to drive the vehicle. Corresponding constraints on battery temperature, battery state of charge, and temperature rise caused by battery heating can also be established to achieve the goal of accurately characterizing the energy required to drive the vehicle and / or the battery.

[0041] Step S104: Based on the battery consumption model and constraints, determine the target heating data for the battery.

[0042] In the technical solution provided by step S104 of the present invention, after establishing the vehicle's battery consumption model and corresponding constraints, the target heating data of the battery can be determined to achieve the purpose of heating the battery. The target heating data can be the optimal target temperature rise.

[0043] In this embodiment of the invention, based on the amount of electricity consumed by the battery's internal resistance, the amount of electricity consumed after battery heating, the amount of electricity recovered from the battery, and the amount of electricity consumed to drive the vehicle, a battery consumption model for the vehicle is determined, along with corresponding constraints on battery temperature, battery state of charge, and temperature rise caused by battery heating. This determines the optimal target temperature rise for the battery. For example, by heating the battery from 0 degrees Celsius (°C) to 40 degrees Celsius in 1°C intervals, target heating data for the battery can be obtained based on the principle of optimal energy consumption. It should be noted that this is merely an example of determining the target heating data for the battery, and the method and process for determining the target heating data are not specifically limited.

[0044] Step S105: Perform a heating operation on the battery based on the target heating data.

[0045] In the technical solution provided by step S105 of the present invention, after determining the target heating data of the battery, a heating operation can be performed on the battery to characterize the working state of the battery. The target heating data represents the working state of the battery after the heating operation is performed.

[0046] Optionally, based on the target heating data, a heating command is sent through the vehicle control unit (VCU) to cause the heating device to operate at maximum load and then heat the battery until the battery temperature rises to the target heating data, and then heating is stopped.

[0047] In steps S101 to S105 of this application, during vehicle operation, the vehicle's driving condition information and the battery's state information are acquired. The state information characterizes the battery's operating state during vehicle operation. Based on the driving condition information and state information, a battery temperature change model is determined, characterizing the battery's heat generation and / or heat dissipation. Based on the temperature change model, a battery consumption model and corresponding constraints are established, characterizing the energy required to drive the vehicle and / or the battery. Based on the battery consumption model and constraints, target heating data for the battery is determined. Based on the target heating data, a heating operation is performed on the battery, whereby the target heating data represents the battery's operating state after the heating operation. In other words, this embodiment of the invention can acquire vehicle driving condition information and battery state information during vehicle operation, determine a battery temperature change model based on the driving condition information and state information, thereby establishing a vehicle battery consumption model and corresponding constraints, and determine target heating data for the battery based on the obtained battery consumption model and constraints, thereby performing a heating operation on the battery. By taking into account the heat generation after heating the battery under different driving conditions and the heat dissipation brought by air cooling, the battery heating operation is performed in a comprehensive manner. This ensures that not only the overall power needs of users are met, but also the driving range in low temperatures is extended. This solves the technical problem of poor battery heating effect and achieves the technical effect of improving battery heating effect.

[0048] The method described in this embodiment will be further described below.

[0049] As an optional embodiment, step S102, based on driving condition information and state information, determines the battery temperature change model, including: establishing a battery heat generation model and a battery heat dissipation model based on driving condition information and state information, wherein the heat generation model is used to represent heat generation and the heat dissipation model is used to represent heat dissipation; and determining the battery temperature change model based on the heat generation model and the heat dissipation model.

[0050] In this embodiment, based on driving condition information and state information, a battery heat generation model and a battery heat dissipation model can be established, thereby determining the battery temperature change model to accurately characterize the battery's heat generation and heat dissipation.

[0051] Alternatively, the heat generation model of the battery can be expressed by the following formula:

[0052] (1)

[0053] in, It can be used to represent the temperature rise of a battery per unit time. It can be used to indicate the heat generation rate of a battery. It can be used to indicate battery volume. It can be used to represent the specific heat capacity of a battery. It can be used to indicate battery quality. It can be used to represent the temperature rise caused by battery heating per unit time.

[0054] Alternatively, the heat dissipation model of the battery can be represented by the following formula:

[0055] (2)

[0056] in, Used to indicate the temperature drop of a battery per unit time. Used to represent the air-cooled convective heat transfer coefficient Used to represent the convective heat transfer area of ​​air-cooled heat dissipation. Used to represent the battery temperature at time i. Used to indicate ambient temperature Used to indicate the specific heat capacity of a battery Used to indicate battery quality.

[0057] As an optional embodiment, step S102 involves establishing a battery heat generation model and a battery heat dissipation model based on driving condition information and state information. This includes: establishing a heat generation model based on the battery heat generation data, battery volume, battery specific heat capacity, battery mass, and temperature rise data caused by battery heat generation in the state information under the corresponding driving condition; establishing a heat dissipation model based on the battery air cooling data, battery temperature, vehicle ambient temperature, specific heat capacity, and mass under the driving condition; and determining a temperature change model based on the temperature rise data determined in the heat generation model and the temperature drop data determined in the heat dissipation model. The temperature change model is used to predict the temperature change of the battery in the future.

[0058] In this embodiment, under the driving conditions corresponding to the driving condition information, the heat generation model can be established based on the battery's heat generation data, battery volume, specific heat capacity, battery mass, and temperature rise data caused by battery heat generation from the state information. The heat dissipation model can be established based on the battery's air cooling data, battery temperature, vehicle ambient temperature, battery specific heat capacity, and battery mass. Based on the temperature rise data determined in the heat generation model and the heat dissipation model, a temperature change model is determined to predict the battery's temperature changes in the future, thereby achieving the technical effect of accurately obtaining the battery's temperature changes in the future.

[0059] Alternatively, the temperature change model can be expressed by the following formula:

[0060] (3)

[0061] Among them, the battery's heat generation data can be represented as the battery's heat generation rate, which can be obtained through... This can be represented. The volume of the battery can be expressed through... This can be represented. The specific heat capacity of a battery can be expressed as follows: To express, Used to indicate battery quality. The temperature rise data caused by battery heat generation can be the temperature rise of the battery per unit time, or the temperature rise caused by battery heating per unit time. This can be achieved through... This is represented. The battery's air-cooling data can be determined by multiplying the air-cooling convection heat transfer coefficient by the air-cooling convection heat transfer area. The air-cooling convection heat transfer coefficient can be obtained by... To express, Used to represent the convective heat transfer area of ​​air-cooled heat dissipation. The ambient temperature of the vehicle can be measured via... To express. Used to represent the battery temperature at time i. Used to represent the battery temperature at time i+1.

[0062] As an optional embodiment, step S103 involves establishing a vehicle battery consumption model and corresponding constraints based on a temperature change model, including: determining the battery's regenerative braking charge and battery power consumption data based on the temperature change model; establishing a battery consumption model based on the regenerative braking charge and power consumption data; and establishing constraints based on the battery's temperature, state of charge, and temperature rise data.

[0063] In this embodiment of the invention, after determining the temperature change model, the regenerative braking capacity and the battery's energy consumption data are determined, thereby establishing a battery consumption model. Constraints can be established based on the battery's temperature, state of charge, and temperature rise data.

[0064] Alternatively, the regenerative braking energy can be expressed by the following formula:

[0065] (8)

[0066] (9)

[0067] (10)

[0068] From the above formula, we can obtain that, using Divide by the corresponding positive power to obtain the power recovered. The allowable charging power of the battery is determined based on the battery's state of charge (SOC) and temperature. The power recovered from the above batteries With battery allowable charging power Take the smaller value, then accumulate and integrate to obtain the recovered electricity. .

[0069] Optionally, the constraints established based on the battery's temperature, state of charge, and temperature rise data can be expressed by the following formula:

[0070]

[0071] Where T represents the battery temperature. This indicates the battery's maximum temperature. This indicates the minimum temperature of the battery. This indicates the battery's state of charge. This represents the maximum value of the battery's state of charge. This represents the minimum state of charge of the battery. This indicates the maximum temperature rise caused by battery heating. This represents the minimum temperature rise caused by battery heating.

[0072] As an optional embodiment, in step S103, before determining the regenerative braking capacity and the battery's energy consumption data based on the temperature change model, the method further includes: determining the battery's operating power based on the vehicle's weight, speed, acceleration, and frontal area from the driving condition information, wherein the operating power represents the battery's discharge or charging status; determining the battery's state of charge model based on the state of charge, voltage, capacity, and operating power; determining the battery's regenerative braking power based on the operating power; and determining the battery's regenerative braking capacity and energy consumption data based on the temperature change model, including: determining the regenerative braking capacity based on the temperature change model, state of charge model, and regenerative braking power; and determining the energy consumption data based on the relationship between the battery's temperature rise data and energy consumption in the temperature change model.

[0073] In this embodiment, based on the vehicle's weight, speed, acceleration, and frontal area from the driving condition information, the battery's operating power can be determined, thereby representing the battery's discharge or charging status. Based on the state of charge (SOC), battery voltage, capacity, and operating power, a SOC model can be determined. Based on the obtained operating power, the battery's regenerative braking power can be determined. After determining the temperature change model, the battery's regenerative braking capacity and energy consumption data can be determined.

[0074] Optionally, based on the obtained temperature change model, state of charge model, and regenerative braking power, the regenerative braking power can be determined. Based on the relationship between the battery temperature rise data and the battery power consumption in the temperature change model, the power consumption data can be determined.

[0075] Optionally, the battery's operating power can be the battery's discharge (charge) power. The battery's state of charge (SOC) model can be established using the SOC trajectory, or it can be expressed by the following formula:

[0076] (5)

[0077] in, This represents the SOC at time i+1. This represents the SOC at time i. This represents the battery discharge (charge) power at time i. This represents the battery voltage at time i. This indicates the total capacity of the battery. This represents the time interval between time i and time i+1.

[0078] Alternatively, the regenerative braking power can be expressed by the following formula:

[0079] (8)

[0080] From the above formula, we can obtain that, using Divide by the corresponding positive power to obtain the power recovered. Battery power consumption data can be expressed using the following formula:

[0081] (11)

[0082] As can be seen from the above formula, the power consumption of the battery can be determined by the battery temperature over a time interval, and the above formula is used to characterize the corresponding functional relationship between power consumption and battery temperature rise.

[0083] As an optional embodiment, in step S103, before establishing the battery consumption model based on the regenerative braking power and power consumption data, the method further includes: determining the internal resistance power consumption of the battery based on the battery voltage and the battery operating power; determining the vehicle power consumption based on driving condition information, wherein the vehicle power consumption is the power consumed to drive the vehicle; and establishing the battery consumption model based on the regenerative braking power and power consumption data, including: determining the battery consumption model based on the internal resistance power consumption, power consumption data, regenerative braking power, and vehicle power consumption.

[0084] In this embodiment, the battery's internal resistance power consumption can be determined based on the battery's voltage and operating power. Based on the vehicle's driving condition information, the vehicle's power consumption can be determined to accurately represent the power consumed to drive the vehicle. Based on the obtained internal resistance power consumption, power consumption data, regenerative braking power consumption, and vehicle power consumption, a battery consumption model can be determined.

[0085] Alternatively, the power consumption due to the battery's internal resistance can be calculated using the following formula:

[0086] (7)

[0087] The battery's internal resistance can be calculated using the following formula:

[0088] (4)

[0089] As can be seen from the above formula, the internal resistance of a battery can be determined by its state of charge and temperature. The predicted operating conditions can be calculated and determined using vehicle dynamics formulas, as shown below:

[0090] (6)

[0091] In the above formula, Used to indicate vehicle weight Used to represent gravitational acceleration Used to represent the rolling resistance coefficient Used to represent the drag coefficient. Used to indicate the windward area. Used to indicate vehicle speed Used to represent the vehicle rotation conversion factor. Used to represent the acceleration of the vehicle at time i.

[0092] Optionally, This represents the amount of electricity consumed to power the car. Battery consumption can be calculated using the following formula:

[0093] (12)

[0094] According to the above formula, the energy consumption of the vehicle's battery throughout the entire operating cycle is related to the operating conditions and the power of the vehicle's accessories, and can be considered to have little correlation with battery temperature. Therefore, minimizing the energy consumption throughout the entire cycle is equivalent to finding the minimum energy consumption. Minimum.

[0095] Optionally, the energy consumed in the entire cycle can be determined using formulas (1) to (12) and constraints mentioned in the embodiments of the present invention. Corresponding one-to-one with the battery heating temperature rise, the battery heating temperature rise is from (Preferred temperature 0℃) from start to (Preferred temperature: 40℃) Substitute each temperature into the above formula at 1℃ intervals to easily solve for n (e.g., 41) heating schemes, corresponding to n total power consumption schemes. Find the smallest one from them. corresponding That is, the optimal target temperature rise.

[0096] Optionally, a heating command can be sent to the heating device via the VCU to control the heating device to operate at maximum load and heat the battery until the battery temperature rises to a certain level. Then stop heating.

[0097] As an optional embodiment, step S101 involves acquiring vehicle driving condition information during vehicle operation, including: acquiring navigation information during vehicle operation; and predicting driving condition information based on historical vehicle driving data and navigation information.

[0098] In this embodiment, navigation information during vehicle operation can be acquired. 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 sent through the navigation system, such as the average speed of traffic flow, the distance from the starting point to the destination, or the estimated arrival time. Driving condition information can be simply referred to as operating condition information. The vehicle's historical driving data can be stored on the vehicle's cloud server or it can be historical usage data, which can be simply referred to as historical big data.

[0099] Optionally, the vehicle's driving condition information can be predicted using the vehicle's whole-vehicle controller based on navigation information and historical big data stored on a cloud server.

[0100] Optionally, the vehicle's navigation controller can record and store the vehicle's start and end positions for each driving cycle, the VCU can record and store the vehicle's average speed, total distance traveled, and average acceleration for each driving cycle, and the air conditioning controller can store the average ambient temperature data for each driving cycle. After each driving cycle, the navigation and VCU upload this information to a cloud server for storage.

[0101] As an optional embodiment, step S101 involves predicting 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 represents the probability of the vehicle's speed state transitioning from its current state to its 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 based on the state transition probability matrix; generating a prediction model based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0102] 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 be discretized to obtain discrete results. This allows the determination of the vehicle's state transition probability. Based on the obtained state transition probability, a vehicle state transition probability matrix is ​​generated, thereby determining the vehicle's initial speed state vector and the target speed state vector after the state transition. Based on the obtained initial speed state vector and target speed state vector, a prediction model can be generated to achieve the purpose of predicting the vehicle's driving condition information. The speed data in the historical driving data and the speed data in the navigation information can be collectively referred to as vehicle speed.

[0103] Optionally, the nearest neighbor method can be used to discretize the speed data in historical driving data and the speed data in navigation information, thereby discretizing the vehicle speed into finite values. Discretizing the vehicle speed into finite values ​​can be expressed as: ,in, , , and This can be represented as the vehicle's speed at different times. This example only illustrates the result of discretization and does not impose any specific limitations on the discretization process.

[0104] Optionally, based on the discrete results, the vehicle speed during the driving process can be divided into different states. Based on these different states, the state transition probabilities of the vehicle can be determined. For example, the vehicle speed during the driving process can be divided into 100 possible states, with a speed discrete interval of 5 kilometers per hour (km / h), and the driving speed state numbers U=1,2,... 25. The car's speed changes from its current speed state U. i Vehicle speed state U at the next moment j The probability is the state transition probability. The vehicle's current speed is... At that moment, the vehicle speed at the next moment is The probability is:

[0105]

[0106] in, This represents the element in the i-th row and j-th column of the state transition probability matrix.

[0107] Optionally, The value of can be obtained through the maximum likelihood estimation method, that is, through the following formula: ,in, This refers to the vehicle's speed from Transferred to Number of times, This refers to the vehicle's speed from The total number of transitions, where i, j = 0, 1, ,N.

[0108] Optionally, the state transition probability matrix can be a Markov transition probability matrix, which can be represented by P. Specifically, the Markov transition probability matrix P is generated by calculating the transition probability and number of times from the current vehicle speed to the next vehicle speed, and combining each state probability value, as shown below:

[0109] .

[0110] Optionally, the initial velocity state vector can be the initial vehicle speed state vector. For example, assuming the system has n independent vehicle speed states, the initial vehicle speed state vector of the system is: ,in, It refers to the initial probability when the vehicle speed is m.

[0111] Optionally, the target velocity state vector can be the state vector after state transitions. For example, after k state transitions, the probability that the system is in the vehicle speed state m is... The state vector after the state transition is: ,in, It refers to the probability that the system is in state m at time k.

[0112] Alternatively, the prediction model can be a Markov prediction model, which can then be expressed as: Furthermore, based on the Markov chain prediction model and the solution of the state transition matrix, the predicted velocity value is obtained as follows:

[0113] in, This represents the vehicle's speed at time k. This represents the vehicle speed at time k. This represents the division length of the velocity state, and its value can be 5. This represents a random number that is uniformly distributed at time k.

[0114] In this embodiment of the invention, during vehicle operation, driving condition information and battery status information can be acquired. Based on this information, a battery temperature change model is determined, thereby establishing a battery consumption model and corresponding constraints. Based on the obtained battery consumption model and constraints, target heating data for the battery is determined, and a heating operation is performed on the battery. By comprehensively considering the heat generation after heating the battery under different driving conditions and the heat dissipation from air cooling, the heating operation is performed to ensure that not only are the user's overall power requirements met, but the vehicle's low-temperature driving range is also extended. This solves the technical problem of poor battery heating effect and achieves a significant improvement in battery heating efficiency.

[0115] Example 2

[0116] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0117] Currently, at low temperatures, the internal resistance of power batteries is relatively high, and both their discharge and recharge performance are significantly reduced compared to normal temperatures. The decrease in battery discharge power leads to poorer vehicle acceleration, and the decrease in battery recharge power results in reduced regenerative braking energy and increased energy consumption, directly impacting vehicle power and driving range. To address the poor performance of power batteries at low temperatures, various heating methods have been proposed. Some utilize the battery's own charge and external heating equipment, others use motor pulses to create a self-discharge and self-charge circuit, and still others use a stalled motor. However, because these methods do not comprehensively consider the different driving conditions of the vehicle and the energy consumed by the battery throughout the entire cycle, the technical problem of poor battery heating effectiveness remains.

[0118] In a related technology, a heating system for a power battery is proposed. The system may include: a heat source device, a circulation pipeline, and a controller. The heat source device is used to provide heat energy. A circulation pump and a heating chamber are provided on the circulation pipeline, and the power battery pack of a pure electric vehicle is connected to the circulation pipeline. The circulation pump is used to drive the refrigerant to circulate in the circulation pipeline. When the refrigerant flows through the heating chamber, it absorbs the heat energy provided by the heat source device and is heated. After being heated, the refrigerant circulates to the power battery pack to heat the power battery in the power battery pack. The controller controls the heat source device and the circulation pump according to the heating start signal.

[0119] In another related technology, a method, system, and vehicle for controlling the self-heating of a power battery are proposed. The method may include: switching to the self-heating mode of the power battery when the real-time temperature of the power battery is lower than a preset low-temperature threshold; determining the switching frequency and duty cycle required for the heating circuit to heat the power battery after obtaining the current open-circuit voltage and AC internal resistance of the power battery based on the current SOC; controlling the on / off state of the transistors in the three-phase bridge structure module according to the determined switching frequency and duty cycle, so that the self-heating circuit between the three-phase bridge structure module and the power battery is connected; performing self-heating operation on the power battery through the connected self-heating circuit until the real-time temperature of the power battery is higher than the preset target temperature threshold, then disconnecting the self-heating circuit and exiting the self-heating mode of the power battery.

[0120] However, the above method still has the technical problem of poor heating effect on the battery.

[0121] To address the aforementioned problems, this invention proposes a method for heating a battery in a vehicle. This method includes: acquiring vehicle driving condition information and battery status information during vehicle operation; determining a battery temperature change model based on the driving condition information and battery status information; establishing a battery consumption model and corresponding constraints; determining target heating data for the battery based on the obtained battery consumption model and constraints; and performing a heating operation on the battery. By comprehensively considering different vehicle driving conditions and the energy consumed by the battery throughout the entire cycle, the method ensures that the overall power requirements of the user are met, while also extending the vehicle's low-temperature driving range. This solves the technical problem of poor battery heating effectiveness and achieves a significant improvement in battery heating efficiency.

[0122] The embodiments of the present invention will be further described below.

[0123] Figure 2 This is a schematic diagram of a heating control system for a pure electric vehicle's power battery according to an embodiment of the present invention. Figure 2As shown, the heating control system may include: an in-vehicle navigation system 201, a vehicle control unit (VCU) 202, a heating device 203, a battery management system (BMS) 204, and a cloud server 205.

[0124] In this embodiment, the VCU, as the core of the entire control system, can receive road traffic information sent by the navigation system, including average vehicle speed, distance from origin to destination, and estimated arrival time. The VCU can also receive battery information from the BMS, primarily battery temperature, battery SOC (State of Charge), and allowable charge / discharge power. Furthermore, the VCU can receive historical vehicle data stored on a cloud server. The VCU controls the heating device to heat the battery, and the heating device reports its own operating status (load).

[0125] Figure 3 This is a schematic diagram of a heating system according to an embodiment of the present invention, such as... Figure 3 As shown, the heating system may include: a power battery 301, an electric water pump 302, an expansion tank 303, and a heating device 304.

[0126] In this embodiment, a heating circuit can be formed by several components such as a power battery 301, an electric water pump 302, an expansion tank 303, and a heating device 304. After the heating device generates heat, it can transfer the heat to the power battery through the operation of the electric water pump to heat up the power battery.

[0127] Figure 4 This is a flowchart of a power battery heating method according to an embodiment of the present invention, such as... Figure 4 As shown, the power battery heating method may include the following steps:

[0128] Step S401: The navigation system collects road condition information ahead and sends it to the VCU.

[0129] In the technical solution provided in step S401 of the present invention, the navigation system collects road condition information ahead and sends it to the VCU. Specific road condition information includes the distance to the destination, the estimated travel time to the destination, and the average speed of traffic flow. This is merely an example and not a specific limitation.

[0130] In step S402, the VCU performs operational condition prediction based on navigation information and historical big data.

[0131] In the technical solution provided by step S402 of the present invention, the VCU performs operating condition prediction based on navigation information and historical big data stored on the cloud server.

[0132] Optionally, the navigation controller records and stores the vehicle's start and end positions for each driving cycle, the VCU records and stores the vehicle's average speed, total distance traveled, and average acceleration for each driving cycle, and the air conditioning controller stores the average ambient temperature data for each driving cycle. Table 1 shows the vehicle's historical usage data, as obtained above. Then, after each driving cycle, the navigation and VCU upload this information to a cloud server for storage.

[0133] Table 1 Historical Usage Data of Vehicles

[0134]

[0135] Optionally, operating condition prediction can be performed based on Markov theory, and the specific implementation and operation method are as follows: First, calculate the state transition probability matrix, and then use the nearest neighbor method to discretize the vehicle speed into finite values: ,in, , , and It can be expressed as the vehicle's speed at different times.

[0136] Optionally, the vehicle speed during the driving process is divided into 100 possible states, with a speed discrete interval of 5 km / h, and the driving speed state number U=1,2, 25. The car's speed changes from its current speed state U. i Vehicle speed state U at the next moment j The probability is the state transition probability. The vehicle's current speed is... At that moment, the vehicle speed at the next moment is The probability is:

[0137]

[0138] in, This represents the element in the i-th row and j-th column of the state transition probability matrix. The value of can be obtained through the maximum likelihood estimation method, that is, through the following formula: ,in, This refers to the vehicle's speed from Transferred to Number of times, This refers to the vehicle's speed from The total number of transitions, where i, j = 0, 1, ,N.

[0139] Optionally, the transition probability and number of times from the current vehicle speed to the next vehicle speed can be calculated, and the probability values ​​of each state can be combined to generate a Markov transition probability matrix P, as shown below: Assuming the system has n independent vehicle speed states, the initial vehicle speed state vector of the system is: ,in, This refers to the initial probability when the vehicle speed is m. After k state transitions, the probability of the system being in vehicle speed state m is... The state vector after the state transition is: ,in, This refers to the probability that the system is in state m at time k. A Markov prediction model can be expressed as... Furthermore, based on the Markov chain prediction model and the solution of the state transition matrix, the predicted velocity value is obtained as follows:

[0140] in, This represents the vehicle's speed at time k. This represents the vehicle speed at time k. This represents the division length of the velocity state, and its value can be 5. This represents a random number that is uniformly distributed at time k.

[0141] Step S403: Establish a battery heat generation and dissipation model and predict the battery temperature change trajectory.

[0142] In the technical solution provided by step S403 of the present invention, a battery heat generation and dissipation model can be established, and the battery temperature change trajectory can be predicted according to the operating conditions. The battery temperature change trajectory can be referred to as the battery temperature rise trajectory.

[0143] Alternatively, the battery heat generation model can be:

[0144] (1)

[0145] in, It can be used to represent the temperature rise of a battery per unit time. It can be used to indicate the heat generation rate of a battery. It can be used to indicate battery volume. It can be used to represent the specific heat capacity of a battery. It can be used to indicate battery quality. It can be used to represent the temperature rise caused by battery heating per unit time.

[0146] Alternatively, the heat dissipation model of the battery can be:

[0147] (2)

[0148] in, Used to indicate the temperature drop of a battery per unit time. Used to represent the air-cooled convective heat transfer coefficient Used to represent the convective heat transfer area of ​​air-cooled heat dissipation. Used to represent the battery temperature at time i. Used to indicate ambient temperature Used to indicate the specific heat capacity of a battery Used to indicate battery quality.

[0149] Optionally, the battery temperature change trajectory can be predicted based on the battery heat generation and heat dissipation models established above, wherein the battery temperature rise trajectory can be:

[0150] (3)

[0151] in, The formula above is used to represent the battery temperature at time i+1. Based on the battery's heat generation and dissipation, and the battery temperature at time i, the battery temperature at the next time step can be predicted.

[0152] Step S404: Calculate the amount of electricity consumed by internal resistance based on the temperature change trajectory, and calculate the amount of electricity recovered by braking based on the predicted operating conditions.

[0153] In the technical solution provided by step S404 of the present invention, the trajectory of internal resistance change is first determined. The internal resistance is related to the battery SOC and the battery temperature, i.e.

[0154] (4)

[0155] As can be seen from the above formula, this functional relationship, once determined in the battery pack, can also be obtained through bench testing.

[0156] Alternatively, the trajectory of the SOC can be determined according to the following formula:

[0157] (5)

[0158] in, Used to represent the SOC at time i+1. Used to represent the SOC at time i. Used to represent the battery discharge (charge) power at time i. Used to represent the battery voltage at time i. Used to indicate the total capacity of the battery Used to represent the time interval between time i and time i+1.

[0159] Optionally, The predicted operating conditions can be calculated and determined using vehicle dynamics formulas, as shown below:

[0160] (6)

[0161] in, Used to indicate vehicle weight Used to represent gravitational acceleration Used to represent the rolling resistance coefficient Used to represent the drag coefficient. Used to indicate the windward area. Used to indicate vehicle speed Used to represent the vehicle rotation conversion factor. Used to represent the acceleration of the vehicle at time i.

[0162] Alternatively, the power consumption due to internal resistance can be calculated using the following formula:

[0163] (7)

[0164] The meaning of the parameters in the above formula has already been explained in the text, and will not be repeated here.

[0165] Alternatively, the regenerative braking energy can be calculated using the following formula:

[0166] (8)

[0167] (9)

[0168] (10)

[0169] From the above formula, we can obtain that, using Divide by the corresponding positive power to obtain the power recovered. The allowable charging power of the battery is determined based on the battery's state of charge (SOC) and temperature. The power recovered from the above batteries With battery allowable charging power Take the smaller value, then accumulate and integrate to obtain the recovered electricity. .

[0170] Step S405: Determine the amount of electricity consumed by the heated battery based on the experiment, and obtain the electricity consumption corresponding to different temperature rises.

[0171] In the technical solution provided by step S405 of the present invention, a heating test can be conducted on the power battery in advance to statistically analyze the relationship between battery temperature rise and power consumption, as shown in Table 2. Table 2 is a table showing the relationship between battery temperature rise and power consumption. Typically, battery temperature rise and power consumption are approximately linearly proportional. Based on Table 2, the functional relationship between power consumption and battery temperature rise can be:

[0172] (11)

[0173] in, Used to indicate the amount of electricity consumed by the battery.

[0174] Table 2. Correspondence between battery heating temperature rise and power consumption

[0175]

[0176] Step S406: Based on the obtained state equation of power consumption under the entire operating condition, formulate the constraints.

[0177] In the technical solution provided by step S406 of the present invention, the energy consumed by the battery during the entire cycle can be expressed by the following formula:

[0178] (12)

[0179] in, This represents the amount of electricity consumed to drive the car, and it is related to the operating conditions and the power of the car's accessories. It can also be considered to have little correlation with battery temperature. Therefore, finding the minimum energy consumption for the entire cycle is equivalent to finding the minimum energy consumption for the entire cycle. To reach the minimum.

[0180] Alternatively, the minimum energy consumed throughout the cycle can be determined by the following constraint:

[0181]

[0182] Where T represents the battery temperature. This indicates the battery's maximum temperature. This indicates the minimum temperature of the battery. This indicates the battery's state of charge. This represents the maximum value of the battery's state of charge. This represents the minimum state of charge of the battery. This indicates the maximum temperature rise caused by battery heating. This represents the minimum temperature rise caused by battery heating.

[0183] Step S407: Based on the optimal energy consumption solution, the optimal target temperature rise for battery heating is obtained.

[0184] In the technical solution provided by step S407 of the present invention, the energy consumed in the entire cycle can be determined according to the above formulas (1) to (12) and the above constraints. Corresponding one-to-one with the battery heating temperature rise, the battery heating temperature rise is from (Preferred temperature 0℃) from start to (Preferred temperature: 40℃) Substitute each temperature at 1℃ intervals into the above formula to easily solve for n heating schemes (e.g., 41), corresponding to the total power consumption of n schemes. Find the smallest one from them. corresponding That is, the optimal target temperature rise.

[0185] In step S408, the VCU controls the heating device to heat the battery to the target temperature.

[0186] In the technical solution provided in step S408 of the present invention, the VCU sends a heating command, and the heating device operates at maximum load to heat the battery until the battery temperature rises to a certain level. Then stop heating.

[0187] In this embodiment of the invention, a navigation system can collect road condition information ahead and send it to the VCU. The VCU performs operating condition prediction based on the navigation information and historical big data stored on the cloud server, and then establishes a battery heat generation and dissipation model. It can also predict the battery temperature change trajectory based on the operating conditions, calculate the internal resistance power consumption based on the obtained temperature change trajectory, calculate the braking recovery power based on the predicted operating conditions, and thus determine the power consumption for heating the battery based on experiments. This yields the power consumption corresponding to different temperature rises, so as to obtain the power consumption state equation under the entire operating condition, formulate corresponding constraints, and obtain the optimal target temperature rise for battery heating based on the energy consumption optimization solution. The VCU can then control the heating device to heat the battery until it reaches the target temperature, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect.

[0188] Example 3

[0189] According to an embodiment of the present invention, a heating device for a battery in a vehicle is also provided. It should be noted that this heating device for a battery in a vehicle can be used to perform the heating method for a battery in a vehicle as described in Embodiment 1.

[0190] Figure 5 This is a schematic diagram of a battery heating device in a vehicle according to an embodiment of the present invention, as shown below. Figure 5As shown, the battery heating device 500 in the vehicle may include: an acquisition unit 501, a first determination unit 502, an establishment unit 503, a second determination unit 504, and an execution unit 505.

[0191] The acquisition unit 501 is used to acquire the vehicle's driving condition information and the battery's status information during the vehicle's operation. The status information is used to characterize the battery's operating state during the vehicle's operation.

[0192] The first determining unit 502 is used to determine the temperature change model of the battery based on driving condition information and state information, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation of the battery.

[0193] Establishment unit 503 is used to establish a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery.

[0194] The second determining unit 504 is used to determine the target heating data of the battery based on the battery consumption model and constraints.

[0195] The execution unit 505 is used to perform a heating operation on the battery based on the target heating data, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed.

[0196] Optionally, the first determining unit 502 may include: a first establishing module, used to establish a heat generation model and a heat dissipation model of the battery based on driving condition information and state information, wherein the heat generation model is used to represent the heat generation situation and the heat dissipation model is used to represent the heat dissipation situation; and a first determining module, used to determine a temperature change model of the battery based on the heat generation model and the heat dissipation model.

[0197] Optionally, the first establishment module may include: a first establishment submodule, used to establish a heat generation model based on the battery's heat generation data, battery volume, battery specific heat capacity, battery mass, and temperature rise data caused by battery heat generation in the state information under the corresponding driving conditions; a second establishment submodule, used to establish a heat dissipation model based on the battery's air cooling data, battery temperature, vehicle ambient temperature, specific heat capacity, and mass under the driving conditions; and a first determination submodule, used to determine a temperature change model based on the temperature rise data determined in the heat generation model and the temperature drop data determined in the heat dissipation model, wherein the temperature change model is used to predict the temperature change of the battery at future times.

[0198] Optionally, the establishment unit 503 may include: a second determining module, used to determine the regenerative braking capacity and the energy consumption data of the battery based on a temperature change model; a second establishing module, used to establish a battery consumption model based on the regenerative braking capacity and energy consumption data; and a third establishing module, used to establish constraints based on the battery temperature, the battery state of charge, and the battery temperature rise data.

[0199] Optionally, the second determining module may include: a second determining submodule, used to determine the battery's operating power based on the vehicle's weight, speed, acceleration, and frontal area from the driving condition information, wherein the operating power represents the battery's discharge or charging status; a third determining submodule, used to determine the battery's state of charge model based on the state of charge, battery voltage, battery capacity, and operating power; a fourth determining submodule, used to determine the battery's regenerative braking power based on the operating power; and a fifth determining submodule, used to determine the battery's regenerative braking capacity and energy consumption data based on a temperature change model. The fifth determining submodule may include: determining the regenerative braking capacity based on the temperature change model, the state of charge model, and the regenerative braking power; and determining the energy consumption data based on the relationship between the battery's temperature rise data and its energy consumption in the temperature change model.

[0200] Optionally, the second establishment module may include: a sixth determining submodule, used to determine the battery's internal resistance power consumption based on the battery's voltage and operating power; a seventh determining submodule, used to determine the vehicle's power consumption based on driving condition information, wherein the vehicle's power consumption is the power consumed to drive the vehicle; and a third establishment submodule, used to establish a battery consumption model based on regenerative braking power and power consumption data. The third establishment submodule includes: determining the battery consumption model based on internal resistance power consumption, power consumption data, regenerative braking power, and vehicle power consumption.

[0201] Optionally, the acquisition unit 501 may include: an acquisition module for acquiring navigation information during vehicle operation; and a prediction module for predicting driving condition information based on the vehicle's historical driving data and navigation information.

[0202] Optionally, the prediction module may include: an acquisition submodule, used to discretize the speed data in historical driving data and the speed data in navigation information to obtain discrete results; an eighth determination submodule, used to determine the vehicle's state transition probability based on the discrete results, wherein the state transition probability represents the probability of the vehicle's speed state at the current moment to the vehicle's speed state at the next moment; a first generation submodule, used to generate the vehicle's state transition probability matrix based on the state transition probability; a ninth determination submodule, used to determine the vehicle's initial speed state vector and the target speed state vector after the state transition based on the state transition probability matrix; a second generation submodule, used to generate a prediction model based on the initial speed state vector and the target speed state vector; and a prediction submodule, used to predict driving condition information based on the prediction model.

[0203] In this embodiment of the invention, an acquisition unit acquires vehicle driving condition information and battery status information during vehicle operation, wherein the status information characterizes the battery's operating state during vehicle operation; a first determining unit determines a battery temperature change model based on the driving condition information and status information, wherein the temperature change model characterizes the battery's heat generation and / or heat dissipation; an establishing unit establishes a vehicle battery consumption model and corresponding constraints based on the temperature change model, wherein the battery consumption model characterizes the energy required to drive the vehicle and / or the battery; a second determining unit determines target heating data for the battery based on the battery consumption model and constraints; and an execution unit performs a heating operation on the battery based on the target heating data, wherein the target heating data represents the battery's operating state after the heating operation, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving battery heating effect.

[0204] Example 4

[0205] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program executes the method for heating a battery in a vehicle as described in Embodiment 1.

[0206] Example 5

[0207] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the method for heating a battery in a vehicle as described in Embodiment 1.

[0208] Example 6

[0209] According to an embodiment of the present invention, a vehicle is also provided for performing the vehicle battery heating method of embodiment 1 of the present invention.

[0210] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0211] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0212] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0213] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0214] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0215] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0216] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for heating a battery in a vehicle, characterized in that, include: During vehicle operation, the vehicle's operating condition information and the battery's status information are acquired, wherein the status information is used to characterize the battery's operating status during vehicle operation. Based on the driving condition information and the state information, a temperature change model for the battery is determined, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation of the battery. Based on the temperature change model, a battery consumption model for the vehicle and corresponding constraints are established, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; Based on the battery consumption model and the constraints, the target heating data for the battery is determined; Based on the target heating data, a heating operation is performed on the battery, wherein the target heating data is used to represent the working state of the battery after the heating operation is performed; Based on the temperature change model, a battery consumption model for the vehicle and corresponding constraints are established, including: determining the regenerative braking charge and the battery's power consumption data based on the temperature change model; establishing the battery consumption model based on the regenerative braking charge and the power consumption data; and establishing the constraints based on the battery's temperature, state of charge, and temperature rise data.

2. The method according to claim 1, characterized in that, Based on the driving condition information and the state information, a temperature change model for the battery is determined, including: Based on the driving condition information and the state information, a heat generation model and a heat dissipation model of the battery are established, wherein the heat generation model is used to represent the heat generation situation and the heat dissipation model is used to represent the heat dissipation situation. Based on the heat generation model and the heat dissipation model, the temperature change model of the battery is determined.

3. The method according to claim 2, characterized in that, Based on the driving condition information and the state information, a heat generation model and a heat dissipation model for the battery are established, including: Under the driving conditions corresponding to the driving conditions mentioned in the driving condition information, the heat generation model is established based on the heat generation data of the battery, the volume of the battery, the specific heat capacity of the battery, the mass of the battery, and the temperature rise data caused by the heat generation of the battery in the state information. Under the aforementioned driving conditions, a heat dissipation model is established based on the battery's air-cooling data, the battery's temperature, the vehicle's ambient temperature, the specific heat capacity, and the mass. Based on the temperature rise data determined in the heat generation model and the temperature drop data determined in the heat dissipation model, the temperature change model is determined, wherein the temperature change model is used to predict the temperature change of the battery at future times.

4. The method according to claim 1, characterized in that, Before determining the regenerative braking capacity and the battery's power consumption data based on the temperature change model, the method further includes: Based on the vehicle's weight, speed, acceleration, and frontal area in the driving condition information, the operating power of the battery is determined, wherein the operating power is used to indicate the battery's discharge or charging status. Based on the state of charge, the battery voltage, the battery capacity, and the operating power, determine the state of charge model of the battery; Based on the operating power, the regenerative braking power of the battery is determined; Based on the temperature change model, the regenerative braking capacity of the battery and the battery's power consumption data are determined, including: The amount of energy recovered during braking is determined based on the temperature change model, the state of charge model, and the regenerative braking power. Based on the relationship between the battery's temperature rise data and the battery's power consumption in the temperature change model, the power consumption data is determined.

5. The method according to claim 1, characterized in that, Before establishing the battery consumption model based on the regenerated braking energy and the consumed energy data, the method further includes: Based on the battery voltage and the battery operating power, determine the battery's internal resistance and power consumption. Based on the driving condition information, the vehicle's power consumption is determined, wherein the vehicle's power consumption is the power consumed to drive the vehicle; Based on the regenerative braking power and the power consumption data, the battery consumption model is established, including: The battery consumption model is determined based on the internal resistance power consumption, the power consumption data, the regenerative braking power consumption, and the vehicle power consumption.

6. The method according to claim 1, characterized in that, During vehicle operation, the vehicle's operating condition information is acquired, including: The navigation information of the vehicle during its driving process is obtained; Based on the vehicle's historical driving data and the navigation information, the driving condition information is predicted.

7. The method according to claim 6, characterized in that, Based on the vehicle's historical driving data and the navigation information, the driving condition information is predicted, including: Discretize 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, the state transition probability of the vehicle is determined, wherein the state transition probability is used to represent the transition probability from the current speed state of the vehicle to the speed state of the vehicle at the next moment. Generate the state transition probability matrix of the vehicle based on the state transition probabilities; Based on the state transition probability matrix, the initial velocity state vector of the vehicle and the target velocity state vector after the state transition are determined. A prediction model is generated based on the initial velocity state vector and the target velocity state vector; Based on the prediction model, the driving condition information is predicted.

8. A heating device for a battery in a vehicle, characterized in that, The apparatus for performing the method according to any one of claims 1 to 7, the apparatus comprising: The acquisition unit is used to acquire the driving condition information of the vehicle and the status information of the battery in the vehicle during the driving process, wherein the status information is used to characterize the working state of the battery during the driving process. The first determining unit is used to determine the temperature change model of the battery based on the driving condition information and the state information, wherein the temperature change model is used to characterize the heat generation and / or heat dissipation of the battery. A modeling unit is used to establish a battery consumption model of the vehicle and corresponding constraints based on the temperature change model, wherein the battery consumption model is used to characterize the energy required to drive the vehicle and / or the battery; The second determining unit is used to determine the target heating data of the battery based on the battery consumption model and the constraints. An execution unit is configured to perform a heating operation on the battery based on the target heating data, wherein the target heating data is used to indicate the working state of the battery after the heating operation is performed.

9. A vehicle, characterized in that, Used to perform the method according to any one of claims 1 to 7.

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