Battery heating method, apparatus, processor, and vehicle

Abstract

A battery heating method, an apparatus, a processor, and a vehicle. The method comprises: when a vehicle is being operated, obtaining driving condition information of the vehicle and state information of a battery in the vehicle, the state information being used for representing the operating state of the battery as the vehicle is being operated; determining electrical energy information of the battery on the basis of the driving condition information and the state information, the electrical energy information being used for representing electrical energy consumed by the vehicle during operation; determining target heating data of the battery on the basis of the electrical energy information, the target heating data being used for representing the operating state of the battery after being heated; and executing a heating operation on the battery on the basis of a control policy corresponding to the target heating data.

Description

Battery heating method, device, processor and 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 202311195671.3 and invention name “Battery heating method, device, processor and vehicle”, the entire contents of which are incorporated by reference into this disclosure. Technical Field

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

[0004] In related technologies, if battery heating is required, the current battery temperature is detected. When the battery temperature reaches a certain threshold, the battery heating device is controlled to heat the battery. However, since the benefits and costs of heating cannot be calculated in real time according to different operating conditions, and since heating consumes the battery's own electrical energy, the vehicle's range may be reduced, the technical problem of poor battery heating effectiveness still exists.

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

[0006] Summary of the Invention

[0007] Embodiments of the present disclosure provide a battery heating method, device, processor, and vehicle to at least solve the technical problem of poor battery heating effect.

[0008] According to one aspect of an embodiment of the present disclosure, a battery heating method is provided. The method may include: obtaining driving condition information and battery status information of the vehicle during operation, wherein the status information is used to represent the operating state of the battery during operation; determining battery power information based on the driving condition information and the status information, wherein the power information is used to represent the power consumed by the vehicle during operation; determining target heating data for the battery based on the power information, wherein the target heating data is used to represent the operating state of the battery after heating; and performing a heating operation on the battery based on a control strategy corresponding to the target heating data.

[0009] Optionally, during the operation of the vehicle, the driving condition information of the vehicle is obtained, including: obtaining navigation information of the vehicle during operation; and predicting the driving condition information based on the historical driving data and navigation information of the vehicle.

[0010] Optionally, based on the historical driving data and navigation information of the vehicle, 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 corresponding 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 speed state of the vehicle at the current moment, and the transition probability of transferring to the speed states of the vehicle at multiple next moments; 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 based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0011] Optionally, determining the state transition probability of the vehicle based on the discrete results includes: determining the state transition probability based on the vehicle speed at the current moment in the discrete results and the vehicle speeds at multiple next moments in the discrete results.

[0012] Optionally, based on the discrete results, the state transition probability of the vehicle is determined, including: based on the discrete results, determining the number of transitions from the driving speed at the current moment to the driving speeds at each next moment, and the total number of transitions from the driving speed at the current moment to the driving speeds at multiple next moments; based on the number of transitions and the total number of transitions, determining the state transition probability.

[0013] Optionally, the status information includes at least the discharge power information of the battery, the charging power information of the battery, the first energy consumption information of the battery and the second energy consumption information of the heating device in the battery, the first energy consumption information is used to represent the electric energy consumed by the battery, and the second energy consumption information is used to represent the electric energy consumed by the water pump equipment in the heating device, wherein the electric energy information of the battery is determined based on the driving condition information and the status information, including: performing differential processing on the discharge power information and the charging power information to obtain a differential processing result; obtaining a first calculation model based on the differential processing result, the first energy consumption information and the second energy consumption information; and determining the electric energy information based on the first calculation model.

[0014] Optionally, before determining the target heating data of the battery based on the electric energy information, the method also includes: generating a second calculation model based on the first energy consumption information, the initial temperature of the battery and the target temperature of the battery, wherein the second calculation model is used to determine the first energy consumption information; generating a third calculation model based on the charging power information, the initial temperature, the temperature rise rate of the battery and the state of charge of the battery, wherein the third calculation model is used to determine the charging power information; generating a fourth calculation model based on the temperature rise rate, the initial temperature and the target temperature, wherein the fourth calculation model is used to determine the temperature rise rate; generating a fifth calculation model based on the second energy consumption information and the water pump equipment information, wherein the fifth calculation model is used to determine the second energy consumption information; generating a sixth calculation model based on the water pump equipment information, the initial temperature and the target temperature, wherein the sixth calculation model is used to determine the water pump equipment information; determining the constraints of the target heating data based on the state of charge, discharge power information, charging power information, target temperature and water pump equipment information.

[0015] Optionally, based on the electric energy information, the target heating data of the battery is determined, including: based on the first calculation model, the second calculation model, the third calculation model, the fourth calculation model, the fifth calculation model, the sixth calculation model and the constraints, the electric energy information is processed to minimize energy consumption to determine the target heating data.

[0016] Optionally, based on the control strategy corresponding to the target heating data, a heating operation is performed on the battery, including: in response to the actual temperature of the battery being lower than the target heating data, controlling the heating device of the battery to operate at a target load and a target speed so that the heating device performs a heating operation; in response to the actual temperature of the battery being equal to the target heating data, controlling the heating device to operate at half the target speed for a target time so that the heating device performs a heating operation.

[0017] According to another aspect of the embodiments of the present disclosure, a battery heating device 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, wherein the status information is used to represent the operating state of the battery during operation; a first determination component configured to determine the battery's electrical energy information based on the driving condition information and the status information, wherein the electrical energy information is used to represent the amount of electrical energy consumed by the vehicle during operation; a second determination component configured to determine target heating data for the battery based on the electrical energy information, wherein the target heating data is used to represent the operating state of the battery after heating; and an execution component configured to perform a heating operation on the battery based on a control strategy corresponding to the target heating data.

[0018] According to another aspect of an embodiment of the present disclosure, a processor is further provided, wherein the processor is configured to run a program, wherein when the program is run, the battery heating method according to an embodiment of the present disclosure is executed.

[0019] 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 where the storage medium is located is controlled to execute the battery heating method according to the embodiment of the present disclosure.

[0020] 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 battery heating method of the present disclosure.

[0021] According to another aspect of an embodiment of the present disclosure, a computer program product is provided, including a non-volatile computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps of the battery heating method of the embodiment of the present disclosure are performed.

[0022] According to another aspect of the embodiments of the present disclosure, a vehicle is provided, which is used to perform the battery heating method according to the embodiments of the present disclosure.

[0023] In an embodiment of the present disclosure, during the operation of a 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 in the vehicle during operation; based on the driving condition information and the status information, the battery's electric energy information is determined, wherein the electric energy information is used to characterize the electric energy consumed by the vehicle during operation; based on the electric energy information, the battery's target heating data is determined, wherein the target heating data is used to characterize the working state of the battery after heating; and based on a control strategy corresponding to the target heating data, a heating operation is performed on the battery. In other words, during the operation of a vehicle, an embodiment of the present disclosure can obtain the vehicle's driving condition information and the status information of the battery in the vehicle, determine the battery's electric energy information based on the above-mentioned driving condition information and status information, thereby determining the battery's target heating data, and perform a heating operation on the battery based on the control strategy corresponding to the above-mentioned target heating data. By taking into account the actual driving conditions of the vehicle, as well as information such as the electrical energy consumed by various devices in the entire vehicle during operation, the control strategy for battery heating is comprehensively considered to ensure that the obtained control strategy not only meets the user's overall power needs, but also extends the vehicle's low-temperature cruising range, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the embodiments of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the 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:

[0025] FIG1 is a flow chart of a battery heating method according to an embodiment of the present disclosure;

[0026] FIG2 is a schematic diagram of a heating control system for a power battery of a pure electric vehicle in a navigation system according to an embodiment of the present disclosure;

[0027] FIG3 is a schematic diagram of a heating system principle diagram according to an embodiment of the present disclosure;

[0028] FIG4 is a flow chart of a power battery heating method according to an embodiment of the present disclosure;

[0029] FIG5 is a schematic diagram of a battery heating device according to an embodiment of the present disclosure;

[0030] FIG6 is a schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present disclosure;

[0031] FIG7 is a schematic structural diagram of a processor according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] 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.

[0033] 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.

[0034] According to an embodiment of the present disclosure, an embodiment of a battery heating method 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.

[0035] FIG1 is a flow chart of a method for heating a battery according to an embodiment of the present disclosure. As shown in FIG1 , the method may include the following steps:

[0036] 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.

[0037] In the technical solution provided in the above step S101 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 or a battery pack. The vehicle can be a pure electric vehicle. The driving condition information can be the road condition information ahead collected by the navigation system, or it can be the predicted information of the driving condition, such as the distance to the end point, the estimated driving time to the end point, the average speed of the traffic flow, etc. It should be noted that the predicted information in the above driving condition is only for illustration and is not specifically limited here.

[0038] 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.

[0039] In the disclosed embodiments, the vehicle's starting position, interruption position, average speed, total distance, average acceleration, and other data can be detected in real time during operation, and the aforementioned data can be stored in the cloud. The cloud can also predict the vehicle's driving condition information, wherein the data stored in the cloud can become historical big data. For example, historical big data can be used to predict the vehicle's driving condition during the current driving process. In this case, by taking into account the vehicle's driving condition information at different times, the technical effect of improving the accuracy of the vehicle's driving condition information during operation is achieved.

[0040] Step S102 : determining the battery power information based on the driving condition information and the state information, wherein the power information is used to represent the power consumed by the vehicle during operation.

[0041] In the technical solution provided by the above step S102 of the present disclosure, the electric energy information of the battery in the vehicle can be determined based on the driving condition information and status information obtained in step S101, so as to obtain the electric energy consumed by the vehicle during operation. The electric energy information can be the total energy expected to be consumed by the battery end in the entire road section, which can be obtained through the total energy model of the battery end or through E batt To express.

[0042] Optionally, during the operation of the vehicle, the battery in the vehicle needs to power different devices according to the power supply requirements of different devices in the vehicle. Since these devices will consume the battery's power, before determining the battery's energy information, it is necessary to consider the electric energy consumed by each device to determine the battery's energy information. For example, the electric energy consumed by the electric water pump during the entire heating process, the instantaneous discharge power, instantaneous charging power, and electric energy consumed by the heating battery at each moment, etc., are used to establish a total energy model for determining the electric energy information. Among them, the electric energy consumed by the heating battery of the vehicle throughout the entire road section is related to the initial temperature and target temperature of the battery pack heating. The instantaneous charging power at the battery end is related to the state of charge of the battery pack and the battery temperature.

[0043] It should be noted that this is only a preferred implementation method for determining the battery power information, and does not specifically limit the method and process for determining the battery power information, which are not listed here one by one.

[0044] In the disclosed embodiments, the battery energy information of the vehicle can be determined based on driving condition information such as the vehicle's starting position, interruption position, average speed, total distance, and average acceleration, as well as battery status information such as battery temperature, battery state of charge, and battery allowable charge and discharge power. By determining the battery energy information based on the vehicle's driving condition information and battery status information under different operating conditions, the technical effect of improving the accuracy of the battery energy information is achieved.

[0045] Step S103 : determining target heating data of the battery based on the electric energy information, wherein the target heating data is used to represent the working state of the battery after heating.

[0046] In the technical solution provided in the above step S103 of the present disclosure, after obtaining the electric energy information of the battery, the target heating data of the battery can be determined, so that the working state of the battery after heating the battery can be accurately obtained. Among them, the target heating data can be the battery heating target temperature, and the battery heating target temperature can be the target temperature to be reached by the battery after heating the battery. The battery can be called a battery pack, and the battery heating target temperature can be obtained by calculating the total battery energy consumed by the vehicle in the entire road section through the vehicle control unit (VCU) based on the principle of minimizing electric energy consumption. It should be noted that this is only a preferred embodiment for obtaining the battery heating target temperature, and does not specifically limit the process and method for obtaining the battery heating target temperature.

[0047] Optionally, based on the principle of minimizing the electric energy consumed by the vehicle to heat the battery throughout the entire road section, a dynamic programming algorithm is used to solve the total energy model consumed by the vehicle at the battery end during the entire road section, thereby obtaining the optimal battery pack heating target temperature, so as to characterize the working state of the battery after the battery is heated.

[0048] In the embodiment of the present disclosure, after determining the battery's power information based on the vehicle's instantaneous discharge power, instantaneous charging power, electric energy consumed by heating the battery at each moment in the entire road section, and electric energy consumed by the electric water pump during the entire heating process, the vehicle controller calculates the total energy model of the battery consumed by the vehicle in the entire road section to obtain the target heating data of the battery, so as to achieve the purpose of representing the state of the heated battery.

[0049] Step S104 : performing a heating operation on the battery based on a control strategy corresponding to the target heating data.

[0050] In the technical solution provided in step S104 of the present disclosure, after determining the target heating data for the battery, a heating operation can be performed on the battery according to a control strategy corresponding to the target heating data to achieve the purpose of heating the battery. The control strategy can be a strategy for performing the heating operation on the battery only when a preset condition is met.

[0051] Alternatively, the control strategy may compare the actual battery temperature with target heating data. If the actual battery temperature is lower than the target heating data, the battery's heating device is controlled to perform a heating operation. If the actual battery temperature is equal to the target heating data, the heating device is operated at half the target speed for a period of time to achieve the purpose of heating the battery. It should be noted that this is only a preferred embodiment of the control strategy execution process and does not specifically limit the implementation method and process of the control strategy.

[0052] In the above steps S101 to S104 of the present disclosure, 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 in the vehicle during operation; based on the driving condition information and the status information, the electric energy information of the battery is determined, wherein the electric energy information is used to characterize the electric energy consumed by the vehicle during operation; based on the electric energy information, the target heating data of the battery is determined, wherein the target heating data is used to characterize the working state of the battery after heating; based on the control strategy corresponding to the target heating data, a heating operation is performed on the battery. That is to say, during the operation of the vehicle, the embodiment of the present disclosure can obtain the driving condition information of the vehicle and the status information of the battery in the vehicle, determine the electric energy information of the battery based on the above driving condition information and status information, thereby determining the target heating data of the battery, and perform a heating operation on the battery based on the control strategy corresponding to the above target heating data. By taking into account the actual driving conditions of the vehicle, as well as information such as the electrical energy consumed by various devices in the entire vehicle during operation, the control strategy for battery heating is comprehensively considered to ensure that the obtained control strategy not only meets the user's overall power needs, but also extends the vehicle's low-temperature cruising range, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect.

[0053] The above method of this embodiment is further introduced below.

[0054] 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.

[0055] 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.

[0056] Optionally, based on navigation information and historical big data stored in a cloud server, the vehicle's vehicle controller is used to predict the vehicle's driving condition information.

[0057] 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.

[0058] 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 corresponding discrete results; determining the vehicle's state transition probability based on the discrete results, wherein the state transition probability is used to represent the speed state of the vehicle at a current moment, and the transition probability of transferring to the vehicle's speed states at multiple next moments; 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 based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0059] In this embodiment, when predicting driving condition information based on the vehicle's historical driving data and navigation information, the speed data in the historical driving data and the speed data in the navigation information can first be discretized to obtain a discrete result, thereby determining the vehicle's state transition probability. Based on the obtained vehicle state transition probability, a vehicle state transition probability matrix is ​​generated, and then the vehicle's initial speed state vector and the target speed state vector after the state transition are determined. Based on the obtained initial speed state vector and target speed state vector, a prediction model can be generated to achieve the purpose of predicting the vehicle's driving condition information. The speed data in the historical driving data and the speed data in the navigation information can be collectively referred to as the driving speed.

[0060] 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 v s It can be expressed as the speed of the vehicle at different times. Here, only an example is given to illustrate the result of the discretization process, and no specific limitation is imposed on the result of the discretization process.

[0061] 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:

[0062] P i,j =P(v(k+1)=v j |v(k)=v i )

[0063] Among them, P i,j It represents the element in row i and column j of the state transition probability matrix.

[0064] Optionally, P i,j The value of can be obtained by the maximum likelihood estimation method, that is, by the following formula:

[0065] 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.

[0066] 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:

[0067] Alternatively, the initial speed state vector may be an initial vehicle speed state vector. For example, assuming that the system has n independent driving speed states, the initial vehicle speed state vector of the system is:

[0068] 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.

[0069] 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.

[0070] 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:

[0071] v(k)=[(U k -1)+r k ] d

[0072] 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.

[0073] As an optional embodiment, in step S102, the status information includes at least the discharge power information of the battery, the charging power information of the battery, the first energy consumption information of the battery and the second energy consumption information of the heating device in the battery, the first energy consumption information is used to represent the electric energy consumed by the battery, and the second energy consumption information is used to represent the electric energy consumed by the water pump equipment in the heating device, wherein the electric energy information of the battery is determined based on the driving condition information and the status information, including: performing differential processing on the discharge power information and the charging power information to obtain a differential processing result; obtaining a first calculation model based on the differential processing result, the first energy consumption information and the second energy consumption information; and determining the electric energy information based on the first calculation model.

[0074] In this embodiment, the battery discharge power information and the battery charging power information can be differentially processed to obtain a differential processing result. Based on the differential processing result, the first energy consumption information and the second energy consumption information obtained above, a first calculation model can be obtained to determine the battery's electrical energy information.

[0075] Optionally, the water pump device may be an electric water pump. The battery discharge power information may be the instantaneous discharge power of the battery at each moment as the vehicle traverses the entire road section. The battery charging power information may be the instantaneous charging power of the battery at each moment as the vehicle traverses the entire road section. The first battery energy consumption information may be the electrical energy consumed by the battery to heat the vehicle while traversing the entire road section. The first battery energy consumption information may be the electrical energy consumed by the electric water pump during the entire heating process.

[0076] Optionally, the first calculation model may be the total energy consumed by the battery of the vehicle during the entire road section, which may be expressed by the following formula:

[0077] E batt =∫P dischar dt-∫P char dt+ΔE heat +ΔE pump

[0078] Among them, P dischar It represents the instantaneous discharge power of the battery at each moment when the vehicle is on the entire road section. char It represents the instantaneous charging power of the battery at every moment when the vehicle is on the entire road section. heat It represents the electric energy consumed by the vehicle to heat the battery during the entire road section. pump It represents the electric energy consumed by the electric water pump during the entire heating process.

[0079] As an optional embodiment, step S102, before determining the target heating data of the battery based on the electric energy information, the method also includes: generating a second calculation model based on the first energy consumption information, the initial temperature of the battery and the target temperature of the battery, wherein the second calculation model is used to determine the first energy consumption information; generating a third calculation model based on the charging power information, the initial temperature, the temperature rise rate of the battery and the state of charge of the battery, wherein the third calculation model is used to determine the charging power information; generating a fourth calculation model based on the temperature rise rate, the initial temperature and the target temperature, wherein the fourth calculation model is used to determine the temperature rise rate; generating a fifth calculation model based on the second energy consumption information and the water pump equipment information, wherein the fifth calculation model is used to determine the second energy consumption information; generating a sixth calculation model based on the water pump equipment information, the initial temperature and the target temperature, wherein the sixth calculation model is used to determine the water pump equipment information; determining the constraints of the target heating data based on the state of charge, discharge power information, charging power information, target temperature and water pump equipment information.

[0080] In this embodiment, a second calculation model is generated based on the first energy consumption information, the initial battery temperature, and the target battery temperature to determine the first energy consumption information. A third calculation model is generated based on the battery charging power information, the initial battery temperature, the battery temperature rise rate, and the battery state of charge to determine the battery charging power. A fourth calculation model is generated based on the battery temperature rise rate, the initial temperature, and the target temperature to determine the battery temperature rise rate. A sixth calculation model is generated based on the water pump equipment information, the initial temperature, and the target temperature to determine the water pump equipment information. The water pump equipment information may be the water pump speed.

[0081] Optionally, the second calculation model may be a model for determining the electric energy consumed by the heating battery for the entire road section, which may be expressed by the following formula: ΔE heat =f1(T0_batt,T target _batt), where T0_batt represents the initial temperature of the battery pack heating, T target _batt represents the target temperature for battery pack heating.

[0082] Optionally, the third calculation model may be a model for determining the instantaneous charging power of the battery at each moment when the vehicle is on the entire road section, which may be expressed by the following formula:

[0083] P char =f2(SOC,T_batt)=f2(SOC,T0_batt+K heat *t)

[0084] Among them, T0_batt represents the initial temperature of the battery pack heating, T_batt represents the temperature data of the battery pack obtained by calculation, K heat It represents the temperature rise rate of the battery pack, t represents the time, and SOC represents the current state of charge of the battery pack.

[0085] Optionally, the fourth calculation model may be a model for determining the temperature rise rate of the battery pack, which may be expressed by the following formula: K heat =f3(T target _batt).

[0086] Optionally, the fifth calculation model may be a model for determining the electric energy consumed by the electric water pump during the entire heating process, which may be expressed by the following formula: ΔE pump =f4(n_pump), where n_pump represents the speed of the electric water pump.

[0087] Optionally, the sixth calculation model may be a model for determining the rotational speed of the electric water pump, which may be expressed by the following formula: n_pump=f5(T0_batt, Ttarget _batt), wherein the electric water pump speed can be determined by the initial temperature of the battery pack heating and the target temperature of the battery pack heating.

[0088] As an optional embodiment, step S102 determines the target heating data of the battery based on the electric energy information, including: performing energy consumption minimization processing on the electric energy information based on the first calculation model, the second calculation model, the third calculation model, the fourth calculation model, the fifth calculation model, the sixth calculation model and the constraints to determine the target heating data.

[0089] In this embodiment, based on the obtained first calculation model, second calculation model, third calculation model, fourth calculation model, fifth calculation model, sixth calculation model, and constraints, the battery's power information can be processed with minimal energy consumption to determine the target heating data.

[0090] Optionally, the constraints may include constraints on the battery's state of charge, constraints on the battery's instantaneous discharge power, constraints on the battery's instantaneous charging power, constraints on the battery's target temperature, and constraints on the electric water pump's speed. The above constraints can be expressed by the following formula:

[0091] Among them, SOC max Indicates the maximum state of charge of the current battery pack, SOC min It indicates the minimum state of charge of the current battery pack. It represents the maximum instantaneous discharge power of the battery at every moment in the entire road section. It represents the minimum instantaneous discharge power of the battery at every moment in the entire road section. It represents the maximum instantaneous charging power of the battery at every moment in the entire road section. It represents the minimum instantaneous charging power of the battery at every moment in the entire road section. It represents the maximum target temperature for battery pack heating. It represents the minimum target temperature for battery pack heating. It indicates the maximum speed of the electric water pump. Indicates the minimum speed of the electric water pump.

[0092] Optionally, based on the constraints of the battery state of charge, the instantaneous discharge power at the battery terminal, the instantaneous charging power at the battery terminal, the target temperature of the battery and the speed of the electric water pump, the energy consumption ΔE heat The minimum principle is solved through a dynamic programming algorithm to obtain the optimal battery pack heating target temperature.

[0093] As an optional embodiment, step S104 performs a heating operation on the battery based on the control strategy corresponding to the target heating data, including: in response to the actual temperature of the battery being lower than the target heating data, controlling the heating device of the battery to operate at a target load and a target speed so that the heating device performs the heating operation; in response to the actual temperature of the battery being equal to the target heating data, controlling the heating device to operate at half the target speed for a target time so that the heating device performs the heating operation.

[0094] In this embodiment, when a heating operation is performed on the battery based on the control strategy corresponding to the target heating data, if the actual temperature of the battery obtained is lower than the target heating data, the heating state of the battery needs to be controlled to operate at the target load and target speed to achieve the purpose of making the heating device perform the heating operation; if the actual temperature of the battery is equal to the target heating data, the heating device needs to be controlled to operate at half the target speed for the target time.

[0095] For example, when the vehicle controller controls the electric water pump and the heating device to heat the battery, before the battery pack temperature reaches the target temperature, the heating device operates at maximum load and the electric water pump operates at the highest speed; after the battery pack temperature reaches the target temperature, the heating device load is 0, and the electric water pump operates at 50% * the maximum speed for a period of T seconds (s), and then the water pump stops working.

[0096] In the disclosed embodiment, during vehicle operation, vehicle driving condition information and battery status information can be obtained. Based on the driving condition and status information, the battery's electrical energy information is determined, thereby determining target heating data for the battery. Based on the control strategy corresponding to the target heating data, the battery heating operation can be performed. By comprehensively considering the vehicle's actual driving conditions and information such as the electrical energy consumed by various devices throughout the vehicle during operation, the battery heating control strategy is comprehensively considered, ensuring that the resulting control strategy not only meets the user's overall power requirements but also extends the vehicle's low-temperature driving range. This resolves the technical issue of poor battery heating effectiveness and achieves the technical effect of improving battery heating effectiveness.

[0097] The technical solutions of the embodiments of the present disclosure are illustrated below with reference to preferred implementations.

[0098] At present, the internal resistance of current power batteries is large at low temperatures, and both the discharge performance and the charging recovery performance are significantly reduced compared to normal temperature. The decrease in the discharge power of the power battery will lead to poor acceleration of the entire vehicle. The decrease in the recovery power of the power battery will lead to a decrease in the vehicle's braking recovery energy and increased power consumption, which directly affects the vehicle's power and cruising range. In order to solve the problem of poor performance of power batteries at low temperatures, various power battery heating methods have been proposed in the prior art. Some use the battery's own power to heat with external heating equipment, some use motor pulses and batteries to form a self-discharge and self-charging circuit to heat the battery, and some use motor stalling for heating. However, since the above methods cannot calculate the benefits of heating (for example, target heating data) and the cost (for example, electric energy information) in real time according to different working conditions to heat the battery, and since heating requires consuming the battery's own energy, there is a situation where the vehicle's cruising range is reduced, therefore, there is still a technical problem of poor battery heating effect.

[0099] In a related technology, a power battery heating system is proposed, which includes a heat source device, a circulation pipeline and a controller; wherein the heat source device is used to provide heat energy, a circulation pump and a heating chamber are provided on the circulation pipeline, and the power battery pack of a pure electric vehicle is connected to the circulation pipeline, and the circulation pump is used to drive the condenser to circulate in the circulation pipeline. When the condenser flows through the heating chamber, it absorbs the heat energy provided by the heat source device to be heated. When the heated condenser circulates to the power battery pack, the power batteries in the power battery pack are heated; the controller controls the heat source device and the circulation pump according to the heating start signal.

[0100] Another related technology proposes a power battery self-heating control method, system, and vehicle. This method switches the power battery to self-heating mode when the power battery's real-time temperature falls below a preset low-temperature threshold. After obtaining the power battery's current open-circuit voltage and AC internal resistance based on the current SOC, the method determines the switching frequency and duty cycle required for the heating circuit to heat the power battery. Based on the determined switching frequency and duty cycle, the on-off state of transistors in a three-phase bridge assembly is controlled to connect the self-heating circuit between the three-phase bridge assembly and the power battery. The self-heating circuit continues to self-heat the power battery until the real-time power battery temperature exceeds a preset target temperature threshold, at which point the self-heating circuit is disconnected, exiting the power battery self-heating mode.

[0101] However, the above method still has the technical problem of poor heating effect on the battery.

[0102] In order to solve the above problems, the embodiment of the present disclosure proposes a battery heating method. During the operation of the vehicle, the method can obtain the vehicle's driving condition information and the status information of the battery in the vehicle, and determine the battery's electric energy information based on the above driving condition information and status information, and then determine the battery's target heating data. According to the control strategy corresponding to the above target heating data, the battery can be heated. Since the battery heating can be comprehensively considered to ensure that the user's power requirements are met as a whole and the vehicle's low-temperature cruising range is extended, the technical problem of poor battery heating effect is solved, and the technical effect of improving the battery heating effect is achieved.

[0103] The following is a further introduction to the embodiments of the present disclosure.

[0104] Figure 2 is a schematic diagram of a heating control system for a pure electric vehicle power battery of a navigation system according to an embodiment of the present disclosure. As shown in Figure 2, the heating control system may include: an on-board navigation system 201, a vehicle controller (VCU) 202, a heating device 203, a battery management system (BMS) 204 and a cloud server 205.

[0105] In this embodiment, the VCU, as the core of the entire control, can receive road traffic information sent by the navigation system, including the average speed of traffic, the distance from the starting point to the end point, the estimated time of arrival at the end point, etc. The VCU can receive battery information sent by the BMS, mainly including battery temperature, battery SOC (state of charge), battery allowable charge and discharge power, etc. The VCU can receive vehicle historical big data stored in the cloud server. The VCU controls the heating device to heat the battery, and the heating device reports its own working status (load).

[0106] FIG3 is a schematic diagram of a heating system principle diagram according to an embodiment of the present disclosure. As shown in FIG3 , the heating system may include: a power battery 301 , an electric water pump 302 , an expansion water tank 303 and a heating device 304 .

[0107] In this embodiment, a heating circuit can be formed by several components such as the power battery 301, the electric water pump 302, the expansion water tank 303 and the heating device 304. After the heating device generates heat, the heat can be transferred to the power battery through the operation of the electric water pump to heat the power battery.

[0108] FIG4 is a flow chart of a power battery heating method according to an embodiment of the present disclosure. As shown in FIG4 , the power battery heating method may include the following steps:

[0109] Step S401: The navigation system collects road condition information ahead and sends it to the VCU.

[0110] In the technical solution provided in step S401 of the present disclosure, the navigation system collects road condition information ahead and sends it to the VCU. The specific road condition information includes the distance to the destination, the estimated travel time to the destination, the average speed of the traffic flow, etc. This is only for illustration and is not a specific limitation.

[0111] In step S402, the VCU performs operating condition prediction based on navigation information and historical big data.

[0112] 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.

[0113] 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.

[0114] Table 1 Historical usage data of vehicles

[0115] 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.

[0116] 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:

[0117] P i,j =P(v(k+1)=v j |v(k)=v i )

[0118] Among them, P i,jIt represents the element of row i and column j of the state transition probability matrix. i,j The value of can be obtained by the maximum likelihood estimation method, that is, by the following formula: Among them, F i,j It refers to the vehicle's speed from v i Transfer to v j The number of times, F i It refers to the vehicle's speed from v i The total number of transfers, where i, j = 0, 1, …, N.

[0119] 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:

[0120] 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

[0121] Based on the prediction model of the Markov chain and the solution of the state transfer matrix, the predicted speed value is:

[0122] v(k)=[(U k -1)+r k ] d

[0123] 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.

[0124] Step S403: Calculate the estimated electric energy consumed by the vehicle when passing through the entire road section based on the predicted operating conditions.

[0125] In this embodiment, the VCU can calculate the estimated electric energy consumed by the vehicle through the entire road section based on the predicted operating conditions. The calculation method is as follows:

[0126] E batt =∫P dischar dt-∫P char dt+ΔE heat +ΔE pump

[0127] Among them, E batt It represents the total energy consumed by the battery in the entire road section, P dischar It represents the instantaneous discharge power of the battery at every moment in the entire road section, P char It represents the instantaneous charging power of the battery at every moment in the entire road section, ΔE heat It represents the electric energy consumed by the heating battery in the entire road section, ΔE pump It represents the electric energy consumed by the electric water pump during the entire heating process.

[0128] Alternatively, the consumed electrical energy can be expressed by ΔE heat To express it, ΔE heat Related to the target temperature and initial temperature of the battery pack heating, that is, ΔE heat =f1(T0_batt,T target _batt), T target _batt represents the target temperature for battery pack heating.

[0129] Optionally, P char Related to battery pack SOC and battery temperature, P char It can be expressed by the following formula: char =f2(SOC,T_batt)=f2(SOC,T0_batt+K heat *t), where T0_batt represents the initial temperature of the battery pack, T_batt represents the temperature data of the battery pack obtained by calculation, and K heat It represents the temperature rise rate of the battery pack, t represents the time, and SOC represents the current state of charge of the battery pack.

[0130] Optionally, K heat It is related to the initial temperature of the battery pack and the target heating temperature of the battery pack, and the temperature rise rate of the battery pack can be expressed as K heat =f3(T target _batt).

[0131] Alternatively, ΔE pump It is related to the speed of the electric water pump and can be expressed by the following formula: ΔE pump=f4(n_pump), n_pump=f5(T0_batt,T target _batt), where n_pump represents the speed of the electric water pump, which can be determined by the initial temperature of the battery pack heating and the target temperature of the battery pack heating.

[0132] Step S404 : Calculate the battery heating target temperature based on the principle of minimizing power consumption.

[0133] In this embodiment, the target temperature for battery heating can be calculated by the VCU based on the principle of minimizing power consumption. The constraints are established based on the principle of minimizing power consumption, and the constraints are as follows:

[0134] Among them, SOC max Indicates the maximum state of charge of the current battery pack, SOC min It indicates the minimum state of charge of the current battery pack. It represents the maximum instantaneous discharge power of the battery at every moment in the entire road section. It represents the minimum instantaneous discharge power of the battery at every moment in the entire road section. It represents the maximum instantaneous charging power of the battery at every moment in the entire road section. It represents the minimum instantaneous charging power of the battery at every moment in the entire road section. It represents the maximum target temperature for battery pack heating. It represents the minimum target temperature for battery pack heating. It indicates the maximum speed of the electric water pump. Indicates the minimum speed of the electric water pump.

[0135] Optionally, based on the constraints of the battery state of charge, the instantaneous discharge power at the battery terminal, the instantaneous charging power at the battery terminal, the target temperature of the battery and the speed of the electric water pump, the energy consumption ΔE heat The minimum principle is solved through a dynamic programming algorithm to obtain the optimal battery pack heating target temperature.

[0136] In step S405 , the VCU controls the electric water pump and the heating device to heat the power battery.

[0137] In this embodiment, before the battery pack temperature reaches the target temperature, the heating device operates at maximum load and the electric water pump operates at the highest speed. After the battery pack temperature reaches the target temperature, the load of the heating device is 0, and the electric water pump operates at 50%*maximum speed for a period of T seconds, after which the electric water pump stops working.

[0138] In the disclosed embodiment, the road condition information ahead can be collected through the navigation system and sent to the VCU. The VCU performs operating condition prediction based on the navigation information and historical big data. Then, based on the predicted operating condition results, the estimated electric energy consumed by the vehicle through the entire road section can be calculated. Finally, based on the principle of minimizing electric energy consumption, the target temperature for battery heating is calculated, thereby controlling the electric water pump and the heating device to heat the power battery, solving the technical problem of poor heating effect of the battery and achieving the technical effect of improving the heating effect of the battery.

[0139] According to an embodiment of the present disclosure, a battery heating device is also provided. It should be noted that the battery heating device can be used to perform the battery heating method in Example 1.

[0140] FIG5 is a schematic diagram of a battery heating device according to an embodiment of the present disclosure. As shown in FIG5 , the battery heating device 500 may include: an acquisition component 501 , a first determination component 502 , a second determination component 503 and an execution component 504 .

[0141] 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.

[0142] The first determining component 502 is configured to determine the power information of the battery based on the driving condition information and the state information, wherein the power information is used to represent the amount of power consumed by the vehicle during operation.

[0143] The second determining component 503 is configured to determine target heating data of the battery based on the electric energy information, wherein the target heating data is used to represent the working state of the battery after heating.

[0144] The execution component 504 is configured to set a control strategy corresponding to the target heating data and perform a heating operation on the battery.

[0145] Optionally, the acquisition component 501 may include: a first 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.

[0146] Optionally, the prediction component may include: an acquisition subcomponent, configured to discretize the speed data in the historical driving data and the speed data in the navigation information, respectively, to obtain corresponding discrete results; a first determination subcomponent, configured to determine the state transition probability of the vehicle based on the discrete results, wherein the state transition probability is used to represent the speed state of the vehicle at the current moment, and the transition probability of the vehicle to multiple speed states at the next moments; 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 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.

[0147] Optionally, the first determination component 502 may include: a second acquisition component, configured to perform differential processing on the discharge power information and the charging power information to obtain a differential processing result; a third acquisition component, configured to obtain a first calculation model based on the differential processing result, the first energy consumption information and the second energy consumption information; and a second determination component, configured to determine the electric energy information based on the first calculation model.

[0148] Optionally, the device may also include: a first generation component, configured to generate a second calculation model based on the first energy consumption information, the initial temperature of the battery and the target temperature of the battery, wherein the second calculation model is used to determine the first energy consumption information; a second generation component, configured to generate a third calculation model based on the charging power information, the initial temperature, the temperature rise rate of the battery and the state of charge of the battery, wherein the third calculation model is used to determine the charging power information; a third generation component, configured to generate a fourth calculation model based on the temperature rise rate, the initial temperature and the target temperature, wherein the fourth calculation model is used to determine the temperature rise rate; a fourth generation component, configured to generate a fifth calculation model based on the second energy consumption information and the water pump equipment information, wherein the fifth calculation model is used to determine the second energy consumption information; a fifth generation component, configured to generate a sixth calculation model based on the water pump equipment information, the initial temperature and the target temperature, wherein the sixth calculation model is used to determine the water pump equipment information; a third determination component, configured to determine the constraint conditions of the target heating data based on the state of charge, discharge power information, charging power information, target temperature and water pump equipment information.

[0149] Optionally, the device may also include: a fourth determination component, configured to perform energy consumption minimization processing on the electric energy information based on the first calculation model, the second calculation model, the third calculation model, the fourth calculation model, the fifth calculation model, the sixth calculation model and the constraints, and determine the target heating data.

[0150] Optionally, the execution component 504 may include: a first control component, configured to control the battery's heating device to operate at a target load and a target speed in response to the actual temperature of the battery being lower than the target heating data, so that the heating device performs a heating operation; a second control component, configured to control the heating device to operate at half the target speed for a target time in response to the actual temperature of the battery being equal to the target heating data, so that the heating device performs a heating operation.

[0151] In an embodiment of the present disclosure, the vehicle's driving condition information and status information of the battery in the vehicle are obtained during the operation of the vehicle through an acquisition component, wherein the status information is used to characterize the working status of the battery during the operation of the vehicle; the battery's electric energy information is determined based on the driving condition information and the status information through a first determination component, wherein the electric energy information is used to characterize the amount of electric energy consumed by the vehicle during the operation; the battery's target heating data is determined based on the electric energy information through a second determination component, wherein the target heating data is used to characterize the working status of the battery after heating; and the battery is heated based on the control strategy corresponding to the target heating data through an execution component, thereby solving the technical problem of poor heating effect of the battery and achieving the technical effect of improving the heating effect of the battery.

[0152] The various functional components provided in the embodiments of the present disclosure may be operated in a battery heating method or a similar computing device, and may also be stored as part of a computer-readable storage medium.

[0153] 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:

[0154] 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;

[0155] Determining battery power information based on the driving condition information and the state information, wherein the power information is used to represent the power consumed by the vehicle during operation;

[0156] determining target heating data of the battery based on the electric energy information, wherein the target heating data is used to represent the working state of the battery after heating;

[0157] Based on the control strategy corresponding to the target heating data, a heating operation is performed on the battery.

[0158] Optionally, the computer program also implements program codes of the following steps when executed by the processor: obtaining navigation information of the vehicle during operation; and predicting driving condition information based on historical driving data and navigation information of the vehicle.

[0159] Optionally, the computer program is further configured to implement program codes for 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 corresponding 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 speed state of the vehicle at the current moment, and the transition probability of transferring to the speed states of the vehicle at multiple next moments; generating a state transition probability matrix for 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 based on the initial speed state vector and the target speed state vector; and predicting the driving condition information based on the prediction model.

[0160] Optionally, the computer program also implements program code for the following steps when executed by the processor: determining the state transition probability based on the vehicle speed at the current moment in the discrete results and the vehicle speeds at multiple next moments in the discrete results.

[0161] Optionally, the computer program is also a degree code that implements the following steps when executed by the processor: based on discrete results, determining the number of transfers from the current driving speed to the driving speed at each next moment, and the total number of transfers from the current driving speed to the driving speed at multiple next moments; based on the number of transfers and the total number of transfers, determining the state transition probability.

[0162] Optionally, the computer program also implements program codes of the following steps when executed by the processor: performing differential processing on the discharge power information and the charging power information to obtain a differential processing result; obtaining a first calculation model based on the differential processing result, the first energy consumption information and the second energy consumption information; and determining the electric energy information based on the first calculation model.

[0163] Optionally, the computer program is also executed by the processor to implement program code for the following steps: generating a second calculation model based on the first energy consumption information, the initial temperature of the battery and the target temperature of the battery, wherein the second calculation model is used to determine the first energy consumption information; generating a third calculation model based on the charging power information, the initial temperature, the temperature rise rate of the battery and the state of charge of the battery, wherein the third calculation model is used to determine the charging power information; generating a fourth calculation model based on the temperature rise rate, the initial temperature and the target temperature, wherein the fourth calculation model is used to determine the temperature rise rate; generating a fifth calculation model based on the second energy consumption information and the water pump equipment information, wherein the fifth calculation model is used to determine the second energy consumption information; generating a sixth calculation model based on the water pump equipment information, the initial temperature and the target temperature, wherein the sixth calculation model is used to determine the water pump equipment information; determining the constraints of the target heating data based on the state of charge, discharge power information, charging power information, target temperature and water pump equipment information.

[0164] Optionally, the computer program also implements program code of the following steps when executed by the processor: based on the first calculation model, the second calculation model, the third calculation model, the fourth calculation model, the fifth calculation model, the sixth calculation model and the constraints, the electric energy information is processed to minimize energy consumption and the target heating data is determined.

[0165] Optionally, the computer program also implements program codes for the following steps when executed by the processor: in response to the actual temperature of the battery being lower than the target heating data, controlling the heating device of the battery to operate at a target load and a target speed so that the heating device performs a heating operation; in response to the actual temperature of the battery being equal to the target heating data, controlling the heating device to operate at half the target speed for a target time so that the heating device performs a heating operation.

[0166] 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 battery heating method.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 battery heating method according to the embodiment of the present disclosure.

[0171] In the embodiment of the present disclosure, the processor 70 may execute an operating program of a vehicle fault information processing method.

[0172] Optionally, in the embodiment of the present disclosure, the processor 70 may be configured to perform the following steps:

[0173] 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;

[0174] Determining battery power information based on the driving condition information and the state information, wherein the power information is used to represent the power consumed by the vehicle during operation;

[0175] determining target heating data of the battery based on the electric energy information, wherein the target heating data is used to represent the working state of the battery after heating;

[0176] Based on the control strategy corresponding to the target heating data, a heating operation is performed on the battery.

[0177] Optionally, the processor 70 may be further configured to perform the following steps: obtaining navigation information of the vehicle during operation; and predicting driving condition information based on historical driving data and navigation information of the vehicle.

[0178] 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 corresponding 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 speed state of the vehicle at the current moment, and the transition probability of transferring to the speed state of the vehicle at multiple next moments; 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 based on the initial speed state vector and the target speed state vector; and predict the driving condition information based on the prediction model.

[0179] Optionally, the processor 70 can also be configured to perform the following steps: perform differential processing on the discharge power information and the charging power information to obtain a differential processing result; obtain a first calculation model based on the differential processing result, the first energy consumption information and the second energy consumption information; and determine the electric energy information based on the first calculation model.

[0180] Optionally, the processor 70 can be further configured to perform the following steps: generating a second calculation model based on the first energy consumption information, the initial temperature of the battery and the target temperature of the battery, wherein the second calculation model is used to determine the first energy consumption information; generating a third calculation model based on the charging power information, the initial temperature, the temperature rise rate of the battery and the state of charge of the battery, wherein the third calculation model is used to determine the charging power information; generating a fourth calculation model based on the temperature rise rate, the initial temperature and the target temperature, wherein the fourth calculation model is used to determine the temperature rise rate; generating a fifth calculation model based on the second energy consumption information and the water pump equipment information, wherein the fifth calculation model is used to determine the second energy consumption information; generating a sixth calculation model based on the water pump equipment information, the initial temperature and the target temperature, wherein the sixth calculation model is used to determine the water pump equipment information; determining the constraints of the target heating data based on the state of charge, the discharge power information, the charging power information, the target temperature and the water pump equipment information.

[0181] Optionally, the processor 70 can also be configured to perform the following steps: based on the first calculation model, the second calculation model, the third calculation model, the fourth calculation model, the fifth calculation model, the sixth calculation model and the constraints, perform energy consumption minimization processing on the electric energy information to determine the target heating data.

[0182] Optionally, the processor 70 may be further configured to perform the following steps: in response to the actual temperature of the battery being lower than the target heating data, controlling the heating device of the battery to operate at a target load and a target speed so that the heating device performs a heating operation; in response to the actual temperature of the battery being equal to the target heating data, controlling the heating device to operate at half the target speed for a target time so that the heating device performs a heating operation.

[0183] The processor 70 can execute various functional applications and data processing by running software programs and components stored in the memory, that is, implement the above-mentioned battery heating method.

[0184] 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 battery heating method of the present disclosure.

[0185] According to another aspect of an embodiment of the present disclosure, a computer program product is provided, including a non-volatile computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the steps of the battery heating method of the embodiment of the present disclosure are performed.

[0186] According to an embodiment of the present disclosure, a vehicle is further provided. The vehicle is configured to execute the battery heating method of the first embodiment of the present disclosure.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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

[0194] The solution provided in the embodiment of the present application can be applied to determine whether the battery needs to be heated, and when necessary, obtain the vehicle's driving condition information and the status information of the battery in the vehicle during the heating process. Based on the above driving condition information and status information, the battery's electrical energy information is determined, thereby determining the target heating data of the battery. According to the control strategy corresponding to the above target heating data, the battery heating operation can be performed. By taking into account the actual driving conditions of the vehicle and information such as the electrical energy consumed by various devices in the entire vehicle during operation, the control strategy for battery heating is comprehensively considered, thereby ensuring that the obtained control strategy not only meets the user's power requirements as a whole, but also extends the vehicle's low-temperature cruising range, thereby solving the technical problem of poor battery heating effect and achieving the technical effect of improving the battery heating effect.

Claims

1. A method for heating a battery, 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; Determining the power information of the battery based on the driving condition information and the state information, wherein the power information is used to characterize the power consumed by the vehicle during operation; Based on the electric energy information, determining target heating data of the battery, wherein the target heating data is used to characterize a working state of the battery after heating; Based on the control strategy corresponding to the target heating data, a heating operation is performed on the battery.

2. 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 of the vehicle during operation; The driving condition information is predicted based on the historical driving data of the vehicle and the navigation information.

3. The method according to claim 2, 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 corresponding discrete results; Based on the discrete results, determining a state transition probability of the vehicle, wherein the state transition probability is used to represent the transition probability of the speed state of the vehicle at a current moment to the speed states of the vehicle at a plurality of next moments; 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 the prediction model based on the initial speed state vector and the target speed state vector; The driving condition information is predicted based on the prediction model.

4. The method according to claim 3, wherein: Determining a state transition probability of the vehicle based on the discrete result includes: The state transition probability is determined based on the vehicle speed at the current moment in the discrete results and a plurality of vehicle speeds at the next moments in the discrete results.

5. The method according to claim 3, wherein: Determining a state transition probability of the vehicle based on the discrete result includes: Based on the discrete result, determining the number of transfers from the current driving speed to each of the driving speeds at the next moment, and the total number of transfers from the current driving speed to the driving speeds at the next moment; The state transition probability is determined based on the transition number and the total number.

6. The method according to claim 1, wherein: The state information at least includes discharge power information of the battery, charging power information of the battery, first energy consumption information of the battery, and second energy consumption information of the heating device in the battery, wherein the first energy consumption information is used to indicate the electric energy consumed by the battery, and the second energy consumption information is used to indicate the electric energy consumed by the water pump device in the heating device, wherein the electric energy information of the battery is determined based on the driving condition information and the state information, including: Performing differential processing on the discharge power information and the charge power information to obtain a differential processing result; Obtaining a first calculation model based on the differential processing result, the first energy consumption information, and the second energy consumption information; Based on the first calculation model, the electric energy information is determined.

7. The method according to claim 6, wherein: Before determining target heating data of the battery based on the electric energy information, the method further includes: generating a second calculation model according to the first energy consumption information, the initial temperature of the battery, and the target temperature of the battery, wherein the second calculation model is used to determine the first energy consumption information; generating a third calculation model according to the charging power information, the initial temperature, the temperature rise rate of the battery, and the state of charge of the battery, wherein the third calculation model is used to determine the charging power information; generating a fourth calculation model according to the temperature rise speed, the initial temperature and the target temperature, wherein the fourth calculation model is used to determine the temperature rise rate; generating a fifth calculation model according to the second energy consumption information and the water pump equipment information, wherein the fifth calculation model is used to determine the second energy consumption information; generating a sixth calculation model according to the water pump equipment information, the initial temperature and the target temperature, wherein the sixth calculation model is used to determine the water pump equipment information; Based on the state of charge, the discharge power information, the charge power information, the target temperature and the water pump equipment information, a constraint condition of the target heating data is determined.

8. The method according to claim 7, wherein: Determining target heating data of the battery based on the electric energy information includes: Based on the first calculation model, the second calculation model, the third calculation model, the fourth calculation model, the fifth calculation model, the sixth calculation model and the constraint conditions, the electric energy information is processed to minimize energy consumption and the target heating data is determined.

9. The method according to claim 1, wherein: Based on the control strategy corresponding to the target heating data, a heating operation is performed on the battery, including: In response to an actual temperature of the battery being lower than the target heating data, controlling a heating device of the battery to operate at a target load and a target speed so that the heating device performs the heating operation; In response to the actual temperature of the battery being equal to the target heating data, the heating device is controlled to operate at half the target speed for a target time so that the heating device performs the heating operation.

10. A battery heating device, 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; a first determination component, configured to determine the power information of the battery based on the driving condition information and the state information, wherein the power information is used to characterize the amount of power consumed by the vehicle during operation; a second determining component, configured to determine target heating data of the battery based on the electric energy information, wherein the target heating data is used to characterize a working state of the battery after heating; The execution component is configured to perform a heating operation on the battery based on a control strategy corresponding to the target heating data.

11. A processor, wherein: The processor is used to run a program, and when the program is run by the processor, the method according to any one of claims 1 to 9 is executed.

12. A vehicle configured to perform the method of any one of claims 1 to 9.

13. 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 battery heating method according to any one of claims 1 to 9.

14. An electronic device, wherein: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the battery heating method according to any one of claims 1 to 9.

15. A computer program product, wherein: The invention comprises a computer program, which, when executed by a processor, implements the battery heating method according to any one of claims 1 to 9.

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