Method for predicting heat generation amount of vehicle battery, storage medium, controller, and vehicle

Abstract

The application discloses a vehicle battery heat production prediction method, a storage medium, a controller and a vehicle. The method comprises the following steps: acquiring current driving data of the vehicle; determining a target driving route of the vehicle according to the current driving data of the vehicle; obtaining a target road spectrum according to a driving time and a driving distance corresponding to the target driving route; and obtaining a target battery heat production according to the target road spectrum. The method can predict the battery heat production in the driving process, and provides conditions for fine battery thermal management and energy management of the electric vehicle.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and in particular to a method for predicting the heat generated by a vehicle battery, a storage medium, a controller, and a vehicle. Background Technology

[0002] Because the power batteries of new energy vehicles have internal resistance, the temperature of the battery rises during charging and discharging. Long-term use of the battery in a high-temperature environment will increase the risk of thermal runaway. Therefore, in a high-temperature environment, additional power needs to be consumed to actively cool the battery, which leads to increased energy consumption during driving.

[0003] In related technologies, battery cooling does not consider the amount of heat generated by the battery during driving, but only refers to the highest and lowest battery temperatures to turn the battery cooling on or off. In urban driving conditions, during short-distance, low-load driving, there are often situations where the battery has not finished cooling before the driving activity ends, which exacerbates energy waste. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the object of this invention is to provide a method, storage medium, controller, and vehicle for predicting battery heat generation during driving, thereby providing conditions for refined battery thermal management and energy management of electric vehicles.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for predicting the heat generation of a vehicle battery. The method includes: acquiring current driving data of the vehicle; determining a target driving route of the vehicle based on the current driving data; obtaining a target road spectrum based on the driving time and driving distance corresponding to the target driving route; and obtaining the target battery heat generation based on the target road spectrum.

[0006] In addition, the method for predicting the heat generation of a vehicle battery according to the above embodiments of the present invention may also have the following additional technical features:

[0007] According to one embodiment of the present invention, obtaining the target road spectrum based on the driving time and driving distance corresponding to the target driving route includes: obtaining the target road spectrum using a preset road spectrum database based on the driving time and driving distance corresponding to the target driving route, wherein the preset road spectrum database is obtained based on driving big data and includes road spectra under different driving times and different driving distances.

[0008] According to one embodiment of the present invention, obtaining the target battery heat generation based on the target road spectrum includes: obtaining the target battery heat generation based on the target road spectrum using a preset battery heat generation database, wherein the preset battery heat generation database is obtained by calculating the corresponding battery heat generation for each road spectrum in the road spectrum database.

[0009] According to an embodiment of the present invention, the process of obtaining the preset road spectrum database includes: fitting multiple standard road spectra using the driving big data, wherein the multiple standard road spectra correspond one-to-one with multiple congestion levels; forming road spectra corresponding to different driving times and different driving distances based on the multiple standard road spectra, thereby obtaining the road spectrum database.

[0010] According to one embodiment of the present invention, the standard road spectrum is a curve showing the relationship between vehicle speed and driving time. The step of forming road spectra corresponding to different driving times and different driving distances based on multiple standard road spectra includes: cutting and splicing the standard road spectrum according to driving time, and / or scaling the standard road spectrum proportionally according to vehicle speed to obtain the road spectra corresponding to different driving times and different driving distances.

[0011] According to one embodiment of the present invention, the calculation of battery heat generation corresponding to each road spectrum in the road spectrum database includes: establishing a vehicle power economy model; calculating the vehicle resistance using the vehicle power economy model; and calculating the battery heat generation corresponding to each road spectrum based on the vehicle resistance, the battery internal resistance of the vehicle, voltage parameters, and the vehicle speed corresponding to each road spectrum in the road spectrum database.

[0012] According to one embodiment of the present invention, determining the target driving route of the vehicle based on the current driving data of the vehicle includes: predicting the target driving route of the vehicle based on the current driving data of the vehicle using a preset habitual route database, wherein the preset habitual route database is obtained based on the historical driving data of the vehicle.

[0013] To achieve the above objectives, a second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting heat generation in a vehicle battery.

[0014] To achieve the above objectives, a third aspect of the present invention provides a controller, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the above-described method for predicting heat generation in a vehicle battery.

[0015] To achieve the above objectives, a fourth aspect of the present invention provides a vehicle comprising the aforementioned controller.

[0016] The vehicle battery heat generation prediction method, storage medium, controller, and vehicle of the present invention can predict battery heat generation during driving, providing conditions for refined battery thermal management and energy management of electric vehicles. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for predicting heat generation from a vehicle battery according to the first embodiment of the present invention;

[0018] Figure 2 This is a flowchart of a method for predicting heat generation from a vehicle battery according to a second embodiment of the present invention;

[0019] Figure 3 This is a flowchart of a method for obtaining a preset habitual route database based on historical driving data of a vehicle, according to an embodiment of the present invention;

[0020] Figure 4 This is a flowchart of a threshold filtering method according to an embodiment of the present invention;

[0021] Figure 5 This is a flowchart of a method for clustering historical driving data according to an embodiment of the present invention;

[0022] Figure 6 This is a flowchart of another embodiment of the present invention for a method of clustering historical driving data;

[0023] Figure 7 This is a schematic diagram of a preset habitual route database according to an embodiment of the present invention;

[0024] Figure 8 This is a flowchart of a method for predicting and obtaining a target driving route for a vehicle according to an embodiment of the present invention;

[0025] Figure 9 This is a flowchart of a method for predicting and obtaining a target driving route for a vehicle according to another embodiment of the present invention;

[0026] Figure 10 This is a schematic diagram of a standard road spectrum according to an embodiment of the present invention;

[0027] Figure 11 This is a road map corresponding to a driving time of 10 minutes and a driving distance of 10 km in one embodiment of the present invention;

[0028] Figure 12 This is a road map corresponding to a driving time of 90 minutes and a driving distance of 30 km in one embodiment of the present invention;

[0029] Figure 13This is a flowchart of a method for calculating the battery heat generation corresponding to each path spectrum in the path spectrum database according to an embodiment of the present invention;

[0030] Figure 14 This is a structural block diagram of a vehicle powertrain system according to an embodiment of the present invention;

[0031] Figure 15 This is a structural block diagram of a controller according to an embodiment of the present invention;

[0032] Figure 16 This is a structural block diagram of a vehicle according to an embodiment of the present invention. Detailed Implementation

[0033] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0034] The following description, with reference to the accompanying drawings, outlines an embodiment of the present invention, including a method for predicting heat generation from a vehicle battery, a storage medium, a controller, and a vehicle.

[0035] Figure 1 This is a flowchart of a method for predicting the heat generation of a vehicle battery according to an embodiment of the present invention.

[0036] like Figure 1 As shown, the methods for predicting heat generation from vehicle batteries include:

[0037] S11, Obtain the vehicle's current driving data.

[0038] S12 determines the target driving route of the vehicle based on the vehicle's current driving data.

[0039] S13. Obtain the target road map based on the driving time and driving distance corresponding to the target driving route.

[0040] S14, obtain the target battery heat generation based on the target road spectrum.

[0041] Specifically, such as Figure 2 As shown, the method for predicting vehicle battery heat generation includes two parts: obtaining the target road spectrum and obtaining the target battery heat generation. Obtaining the target road spectrum includes: acquiring the vehicle's current driving data and predicting the current driving conditions. The current driving condition prediction includes: determining the vehicle's target driving route based on the vehicle's current driving data, and obtaining the target road spectrum based on the driving time and distance corresponding to the target driving route.

[0042] The method for predicting vehicle battery heat generation according to embodiments of the present invention can predict battery heat generation during driving, providing conditions for refined battery thermal management and energy management of electric vehicles.

[0043] In some embodiments, determining the target driving route of the vehicle based on the vehicle's current driving data includes: predicting the target driving route of the vehicle based on the vehicle's current driving data using a preset habitual route database, wherein the preset habitual route database is obtained based on the vehicle's historical driving data.

[0044] Specifically, the vehicle's historical driving data includes: the starting point of the driving trip, the destination of the driving trip, the start time of the driving trip, the end time of the driving trip, the driving distance, the driving time, the peak battery discharge power of a single trip, the average vehicle speed of a single trip, and the longitudinal acceleration of the vehicle during a single trip. Among these, the driving time is defined as the closing time of the high-voltage relay of a single trip, the driving distance is the distance traveled inside the vehicle during the driving time, and trips with a driving time of less than 180 seconds are not included in the statistics.

[0045] like Figure 3 As shown, the method for obtaining a preset habitual route database based on the vehicle's historical driving data includes: obtaining historical driving data based on the vehicle's VIN (Vehicle Identification Number); clustering the historical driving data; obtaining multiple trip categories; and forming a preset habitual route database.

[0046] The process of threshold filtering before clustering historical driving data includes: removing historical driving data that is more than a time threshold from the current time; removing historical driving data that has an average daily driving distance greater than a first distance threshold; removing historical driving data that has a driving distance greater than a second distance threshold; removing historical driving data that has an average speed per trip greater than a speed threshold; and removing historical driving data whose starting point and / or destination is outside a preset area. Specifically, the accuracy of the input data directly affects the analysis results, necessitating threshold filtering. The threshold filtering process is as follows: Figure 4As shown, the process of removing historical driving data that is more than 45 days from the current time may include: removing historical driving data that is more than 45 days from the current time, because data that is too short is irregular and data that is too long is too large to reflect the customer's latest driving patterns; removing historical driving data that is more than 200 km from the current time; removing historical driving data that is more than 100 km from the current time, because high-mileage driving behavior is sporadic, random and irregular; removing historical driving data that is more than 300 km / h from the current time, because this data is abnormal; and removing historical driving data whose starting point and / or destination is outside the preset area, such as historical driving data whose starting point and / or destination is outside China, because this data is abnormal.

[0047] like Figure 5 As shown, historical driving data is clustered, including:

[0048] S51 assigns time labels to historical driving data. These time labels include: weekday morning rush hour label, weekday noon rush hour label, weekday evening rush hour label, other weekday time period label, and non-weekday label.

[0049] S52 clusters historical driving data based on time tags.

[0050] This includes clustering historical driving data based on time tags, specifically: for each time tag, historical driving behavior data where the difference in distance between the starting point and the destination is less than a third distance threshold, the difference in driving distance is less than a third distance threshold, and the difference in driving distance is less than a proportional threshold, are classified as the same trip category. For example... Figure 6 As shown, the third distance threshold can be 1km, the proportion threshold can be 10%, and the clustering results include: trip classification 1-5, forming a clustering pattern as follows: Figure 7 The database of preset, commonly used routes is shown.

[0051] In this embodiment, such as Figure 8 As shown, the target driving route of the vehicle is predicted based on the vehicle's current driving data using a preset habitual route database, including:

[0052] S81, retrieves the time data and departure location data from the current driving data.

[0053] S82 compares the time data in the current driving data with the time label to obtain the target time label corresponding to the current behavior data.

[0054] Specifically, for example: if the time data in the user's current driving data is 8:00, the target time label corresponding to the current driving data is the weekday morning rush hour label.

[0055] S83 compares the departure point data in the current driving data with the departure points of the driving trips under the target time label in the habitual route database to obtain the target trip category. The obtained target trip category is the vehicle's target driving route.

[0056] Specifically, for example: if the departure location data in the current driving data of a user's vehicle is 39°49'N, 116°21'E, and the latitude and longitude error with the departure location of a certain driving trip under the morning peak period label in the habitual route database is less than a preset threshold, and the trip category corresponding to the departure location of the driving trip is trip category 1, then the target trip category is determined to be trip category 1.

[0057] In some embodiments, such as Figure 9 As shown, the user driving behavior prediction data for the target trip classification includes:

[0058] The target trip category corresponds to the average driving distance, average driving duration, and maximum peak battery discharge power. The target trip category data may include only historical behavior data, or it may include both historical and current behavior data.

[0059] The user driving style for the target trip category is determined based on the average absolute value of longitudinal acceleration of the corresponding data for that target trip category and the percentage of time with a longitudinal acceleration absolute value greater than a preset acceleration. The preset acceleration can be 1 m / s². 2 The user's driving style ranges from gentle to aggressive, represented by a numerical range (0, 1), with larger numbers indicating more aggressive driving.

[0060] In some embodiments, the process of obtaining the preset road spectrum database includes: using driving big data to fit multiple standard road spectra, wherein the multiple standard road spectra correspond one-to-one with multiple congestion levels; and forming road spectra corresponding to different driving times and different driving distances based on the multiple standard road spectra, thereby obtaining the road spectrum database.

[0061] Specifically, taking urban traffic conditions as an example, congestion levels can be categorized as congested, normal, and smooth. Three standard road spectra can be derived using big data analysis of driving conditions: Spectrum A, Spectrum B, and Spectrum C. Among these, for example... Figure 10 As shown, route spectrum A is the standard route spectrum corresponding to congestion, route spectrum B is the standard route spectrum corresponding to normal conditions, and route spectrum C is the standard route spectrum corresponding to smooth traffic.

[0062] In some embodiments, the standard road spectrum is a curve showing the relationship between vehicle speed and driving time. Based on multiple standard road spectra, road spectra corresponding to different driving times and different driving distances are formed, including: cutting and splicing the standard road spectrum according to the driving time, and / or scaling the standard road spectrum proportionally according to the vehicle speed to obtain road spectra corresponding to different driving times and different driving distances.

[0063] Specifically, for example: based on the standard road map corresponding to a driving time of 50 minutes and a driving distance of 30 km, the road map corresponding to a driving time of 30 minutes and a driving distance of 10 km is obtained, including: cutting the first 30 minutes of the road map corresponding to a driving time of 50 minutes and a driving distance of 30 km and reducing the vehicle speed to 5 / 9 times.

[0064] Based on the standard road map corresponding to a driving time of 50 minutes and a driving distance of 30 km, a road map corresponding to a driving time of 90 minutes and a driving distance of 30 km was obtained. This process included: connecting two identical standard road maps corresponding to a driving time of 50 minutes and a driving distance of 30 km, extracting the road map within the 90-minute period, and reducing the vehicle speed to a factor of 5 / 9. The road maps corresponding to different driving times and different driving distances are shown in Table 1, where road map 2 is as follows. Figure 11 As shown, road spectrum 36 Figure 12 As shown.

[0065] Table 1

[0066]

[0067] In some embodiments, obtaining a target road spectrum based on the driving time and driving distance corresponding to the target driving route includes: obtaining the target road spectrum using a preset road spectrum database based on the driving time and driving distance corresponding to the target driving route, wherein the preset road spectrum database is obtained based on driving big data and includes road spectra under different driving times and different driving distances.

[0068] In some embodiments, such as Figure 13 As shown, the calculation of battery heat generation corresponding to each road spectrum in the road spectrum database includes:

[0069] S131, Establish a vehicle power economy model.

[0070] S132 uses the vehicle dynamic economy model to calculate the vehicle's overall resistance.

[0071] Specifically, the vehicle's drag parameters, curb weight, tire data, motor efficiency, and motor speed ratio can be input into the vehicle dynamics and economy model to calculate the vehicle's overall drag.

[0072] S133 calculates the battery heat generation corresponding to each road spectrum based on the vehicle resistance, battery internal resistance, voltage parameters, and vehicle speed corresponding to each road spectrum in the road spectrum database.

[0073] Specifically, the vehicle powertrain system, such as Figure 14 As shown, the vehicle resistance F is the sum of the driving resistance and acceleration resistance, the battery internal resistance is R, the voltage parameter is U, and the vehicle speed corresponding to each road spectrum in the road spectrum database is V. The battery heat generation corresponding to each road spectrum is calculated, including: calculating the vehicle power demand P: P = FV, the battery discharge current I: I = P / U, and the battery heat generation Q: Q = I 2 *R. The calculated battery heat generation for each spectrum is shown in Table 2.

[0074] Table 2

[0075]

[0076] In some embodiments, obtaining the target battery heat generation based on the target road spectrum includes: obtaining the target battery heat generation based on the target road spectrum using a preset battery heat generation database, wherein the preset battery heat generation database is obtained by calculating the corresponding battery heat generation for each road spectrum in the road spectrum database.

[0077] Specifically, such as Figure 2 As shown, obtaining the target battery heat generation includes: forming road profiles corresponding to different driving times and distances based on multiple standard road profiles; calculating the battery heat generation corresponding to each road profile; forming a battery heat generation database; and comparing the driving time and distance corresponding to the target road profile with the battery heat generation database to obtain the battery heat generation corresponding to the target road profile. In some embodiments, the vehicle's overall resistance can also be calculated based on the target road profile and the established vehicle dynamic economy model. Based on the overall vehicle resistance, the vehicle's battery internal resistance, voltage parameters, and the vehicle speed corresponding to the target road profile, the battery heat generation corresponding to the target road profile can be directly calculated. The calculation method is consistent with the method described above for calculating the battery heat generation corresponding to each road profile in the road profile database.

[0078] In summary, the vehicle battery heat generation prediction method of this invention, which obtains the target battery heat generation based on the target road spectrum, can achieve the purpose of refined battery thermal management and enable the whole vehicle to save energy and reduce consumption.

[0079] Based on the vehicle battery heat generation prediction method of the above embodiments, the present invention also proposes a computer-readable storage medium.

[0080] In this embodiment, a computer program is stored on a computer-readable storage medium. When the computer program is executed by a processor, it implements the above-described method for predicting the heat generated by the vehicle battery.

[0081] Figure 15This is a structural block diagram of a controller according to an embodiment of the present invention.

[0082] like Figure 15 As shown, the controller 150 includes a processor 151 and a memory 153. The processor 151 and the memory 153 are connected, for example, via a bus 152. Optionally, the controller 150 may also include a transceiver 154. It should be noted that in practical applications, the transceiver 154 is not limited to one, and the structure of the controller 150 does not constitute a limitation on the embodiments of the present invention.

[0083] Processor 151 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 151 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0084] Bus 152 may include a pathway for transmitting information between the aforementioned components. Bus 152 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 152 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 15 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0085] The memory 153 stores a computer program corresponding to the vehicle battery heat generation prediction method of the above embodiments of the present invention. This computer program is controlled and executed by the processor 151. The processor 151 executes the computer program stored in the memory 153 to implement the content shown in the foregoing method embodiments.

[0086] The controller 150 includes, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 15 The controller 150 shown is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.

[0087] Figure 16 This is a structural block diagram of a vehicle according to an embodiment of the present invention.

[0088] like Figure 16 As shown, vehicle 160 includes: the aforementioned controller 150.

[0089] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0090] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0091] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0092] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0093] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0094] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0095] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0096] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for predicting heat generation from a vehicle battery, characterized in that, The method includes: Obtain the vehicle's current driving data; The target driving route of the vehicle is determined based on the vehicle's current driving data; A target road spectrum is obtained based on the driving time and driving distance corresponding to the target driving route; wherein, the target road spectrum is obtained using a preset road spectrum database based on the driving time and driving distance corresponding to the target driving route, and the preset road spectrum database is obtained based on driving big data, including road spectra under different driving times and different driving distances; The heat generation of the target battery is obtained based on the target road spectrum; The process of obtaining the preset road spectrum database includes: Multiple standard road profiles are obtained by fitting the aforementioned driving big data, and each of the multiple standard road profiles corresponds one-to-one with a different level of congestion; Based on various standard road maps, road maps corresponding to different driving times and different driving distances are formed, resulting in the road map database; The standard road spectrum is a curve showing the relationship between vehicle speed and driving time. The process of forming road spectra corresponding to different driving times and distances based on various standard road spectra includes: The standard road spectrum is cut and spliced ​​according to the driving time, and / or scaled proportionally according to the vehicle speed to obtain the road spectrum corresponding to different driving times and different driving distances.

2. The method for predicting heat generation from a vehicle battery according to claim 1, characterized in that, The step of obtaining the target battery heat generation based on the target road spectrum includes: The target battery heat generation is obtained by using a preset battery heat generation database based on the target road spectrum, wherein the preset battery heat generation database is obtained by calculating the corresponding battery heat generation for each road spectrum in the road spectrum database.

3. The method for predicting heat generation from a vehicle battery according to claim 1, characterized in that, Calculating the battery heat generation corresponding to each road spectrum in the road spectrum database includes: Establish a vehicle power economy model; The vehicle's overall drag is calculated using the aforementioned vehicle dynamic economy model; The heat generation of the battery corresponding to each road spectrum is calculated based on the overall vehicle resistance, the battery internal resistance, voltage parameters, and the vehicle speed corresponding to each road spectrum in the road spectrum database.

4. The method for predicting heat generation from a vehicle battery according to claim 1, characterized in that, Determining the target driving route of the vehicle based on the vehicle's current driving data includes: The target driving route of the vehicle is predicted using a preset habitual route database based on the vehicle's current driving data. The preset habitual route database is obtained based on the vehicle's historical driving data.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the heat generated by the vehicle battery as described in any one of claims 1-4.

6. A controller, characterized in that, The system includes a memory, a processor, and a computer program stored in the memory, which, when executed by the processor, implements the method for predicting heat generation from a vehicle battery as described in any one of claims 1-4.

7. A vehicle, characterized in that, include: The controller as described in claim 6.

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