Thermal management method and device based on historical data of electric vehicle and storage medium

Abstract

The invention discloses a thermal management method and device based on historical data of an electric vehicle and a storage medium, and relates to the field of intelligent control of the electric vehicle. The method comprises the following steps: determining inherent driving mode information of a user according to historical vehicle dynamic operation data, charging behavior characteristic data and vehicle setting preference data; acquiring battery data matched with the inherent driving mode information of the user and corresponding to the thermal management characteristic working condition to form thermal management information; forming a thermal management strategy under the thermal management characteristic working condition according to the thermal management information; and when the running information of the vehicle is matched with the inherent driving mode information of the user, performing thermal management according to a thermal management strategy. According to the method, the thermal management strategy can be planned in advance according to the predicted user behavior, the battery can work in the optimal temperature interval all the time, performance reduction and energy loss caused by abnormal battery temperature are reduced, and therefore the energy utilization efficiency can be improved, the service life of the battery can be prolonged, and the endurance mileage of the vehicle can be increased.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent control of electric vehicles, in particular to a thermal management method and device based on historical data of electric vehicles and a storage medium. BACKGROUND

[0002] In the field of electric vehicles, the thermal management system is crucial to battery performance, safety and range. The traditional thermal management system mostly uses a fixed threshold control strategy (such as battery temperature threshold triggering cooling / heating), which has the following problems: (1) When the battery temperature reaches the threshold, the power consumption is large; (2) The threshold of battery temperature is generally set according to the "zero point" that may affect the battery, and the battery life is affected when the battery temperature reaches the threshold; (3) For scenarios where users need to quickly implement thermal management (such as starting the vehicle in extremely low temperatures in winter), the speed of thermal management is slow, reducing user experience.

[0003] Chinese invention patent with publication number CN118494113A proposes an electric vehicle thermal management system and method based on intelligent control technology, which acquires real-time time series data of battery temperature and fan speed through battery temperature data acquisition module and fan speed data acquisition module, calculates covariance matrix through cooling parameter time series autocorrelation information capture module, extracts feature map through cooling parameter time series mode feature extraction module, fuses and processes through battery temperature-fan speed time series interaction fusion module, and finally adjusts fan speed according to the interaction feature map through the fan speed control module, realizes intelligent thermal management, and aims to improve the intelligent degree of the battery cooling control system and prolong the service life of the battery.

[0004] The performance of the above method depends on the accuracy and real-time performance of the sensor data, and the data acquisition problem will affect the control effect. Moreover, the input variables considered by the patent are too local, and the parameter changes of the battery, the driving habits of the driver, the location and other information are not considered, resulting in that the overall algorithm is not intelligent, and the thermal management cannot realize intelligent control; a large amount of historical data is required for model training, and data acquisition and processing may be limited. In addition, the above method does not explicitly adapt the system to different environments, and further verification of its stability and reliability is required. Finally, the patent does not provide sufficient experimental data, making it difficult to evaluate the actual application effect. SUMMARY

[0005] In view of the defects in the prior art, the technical problem solved by the present application is how to realize the thermal management of electric vehicles under the condition of suitable temperature and energy consumption of the battery.

[0006] To achieve the above object, in a first aspect, the embodiments of the present application provide a thermal management method based on historical data of an electric vehicle, which comprises the following steps: acquiring historical vehicle dynamic running data, charging behavior characteristic data and vehicle setting preference data; obtaining driving behavior mode information according to the vehicle dynamic running data, obtaining charging behavior mode information according to the charging behavior characteristic data, and obtaining vehicle setting preference information according to the vehicle setting preference data; taking the driving behavior mode information, the charging behavior mode information and the vehicle setting preference information as user inherent driving mode information; obtaining battery data corresponding to the user inherent driving mode information and corresponding to a thermal management characteristic working condition, to form thermal management information; and forming a thermal management strategy under the thermal management characteristic working condition according to the thermal management information; when the running information of the vehicle matches the user inherent driving mode information, performing thermal management according to the thermal management strategy.

[0007] In combination with the first aspect, in an implementation mode, the vehicle dynamic running data comprises: a time period of vehicle running, a vehicle driving route, an average vehicle speed, a battery output power, a frequency and intensity of vehicle acceleration and deceleration; The process of obtaining driving behavior mode information according to the vehicle dynamic running data comprises: after analyzing the periodicity of the time period of vehicle running, the vehicle driving route and the average vehicle speed and other data, obtaining a commonly used route of the user and a vehicle running setting preference; determining a high-frequency driving route and a stay point of the user; predicting a driving style of the user according to the battery output power, the frequency and intensity of vehicle acceleration and deceleration.

[0008] In combination with the first aspect, in an implementation mode, the charging behavior characteristic data comprises: a charging time period, a single charging duration, a charging location, a charging frequency and a charging mode; The process of obtaining charging behavior mode information according to the charging behavior characteristic data comprises: after associating the charging duration, the charging location and the charging mode of the same time period, analyzing to obtain a charging rule.

[0009] In combination with the first aspect, in an implementation mode, the vehicle setting preference data comprises a kinetic energy recovery mode and a passenger compartment temperature setting preference. The process of obtaining vehicle setting preference information according to the vehicle setting preference data comprises: determining the vehicle setting preference information according to the relationship between the kinetic energy recovery mode and driving efficiency.

[0010] In combination with the first aspect, in an implementation, the thermal management feature working conditions include charging heating, charging cooling, discharging heating, and discharging cooling; and the battery data include time, battery temperature, battery SOC, ambient temperature, vehicle state, charging mode, and battery power.

[0011] In combination with the first aspect, in an implementation, the thermal management strategy under the thermal management feature working conditions includes: If the vehicle is in a charging state when charging heating or charging cooling, the start time of thermal management is advanced. If the vehicle is in a high-power discharging state when charging heating, the start time of thermal management is delayed. If the vehicle is in a high-power discharging state when charging cooling, the start time of thermal management is advanced. In a driving process, when thermal management is started and the vehicle enters a powered-off state after a specified time period: The specified time period is defined as t, the safe time period is defined as x, and the powered-off time period is defined as y. When t≤x, thermal management is not started. When x<t≤y, thermal management is closed in advance.

[0012] In combination with the first aspect, in an implementation, the thermal management strategy further includes a thermal management strategy when the vehicle starts, which is formed according to thermal management information, and includes: If thermal management is started when the vehicle starts and is in a low-temperature state, the start time of thermal management is advanced.

[0013] In combination with the first aspect, in an implementation, the trigger condition for thermal management according to the thermal management strategy further includes that a temperature difference between a current ambient temperature and a historical ambient temperature corresponding to user inherent driving mode information is less than an identification threshold.

[0014] Secondly, the embodiment of the present application provides a thermal management device based on historical data of an electric vehicle, which includes a processor, a memory, and a thermal management program based on historical data of an electric vehicle stored in the memory and executable by the processor, wherein the thermal management program based on historical data of an electric vehicle is executed by the processor to implement the method provided in the first aspect.

[0015] Thirdly, the embodiment of the present application provides a computer readable storage medium, which stores a thermal management program based on historical data of an electric vehicle, and the computer program is executed to implement the method provided in the first aspect.

[0016] Compared with the prior art, the present application has the following advantages: The application analyzes the inherent driving mode of the user according to the historical data of the vehicle, and on this basis, the application can plan a thermal management strategy in advance according to the predicted user behavior, thereby realizing that the battery always works in the best temperature range, reducing the performance decline and energy loss caused by abnormal battery temperature, and therefore, the application can improve the energy utilization efficiency, prolong the service life of the battery, and increase the cruising range of the vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0018] Figure 1 The flowchart of the thermal management method based on the historical data of the electric vehicle in the embodiment of the application is shown. Figure 2 The hardware structure of the thermal management device based on the historical data of the electric vehicle involved in the embodiment of the application is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the following will combine the drawings in the embodiments of the application to clearly and completely describe the technical scheme in the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0020] The flowchart shown in the drawings is only an example, not necessarily including all the contents and operations / steps, and not necessarily executed in the described order. For example, some operations / steps can be decomposed, combined or partially merged, so the actual execution order may be changed according to the actual situation.

[0021] In order to make the purpose, technical scheme and advantages of the embodiments of the application more clear, the following will combine the drawings to further describe the embodiments of the application in detail.

[0022] In a first aspect, the embodiments of the application provide a thermal management method based on historical data of an electric vehicle, as shown in Figure 1 The steps of the method include: Step A, in the historical driving data in the specified period, obtain the vehicle dynamic running data, charging behavior characteristic data and vehicle setting preference data; store the vehicle dynamic running data, charging behavior characteristic data and vehicle setting preference data to the historical feature database.

[0023] The specified period in step A is at least 30 days, and if the specified period is too short, the data accuracy is not enough, and if the specified period is too long, the data matching degree is not enough (i.e. the driving habit may change if the period is too long).

[0024] Step B, obtain driving behavior mode information according to the vehicle dynamic running data in the historical feature database; obtain charging behavior mode information according to the charging behavior characteristic data in the historical feature database; obtain vehicle setting preference information according to the vehicle setting preference data in the historical feature database; take the driving behavior mode information, the charging behavior mode information and the vehicle setting preference information as user inherent driving mode information, and store them to the user inherent driving mode database.

[0025] Step C, obtain battery data matching the user inherent driving mode information and corresponding to the thermal management feature working condition, form thermal management information and store it to the thermal management information database.

[0026] Step D, form the thermal management strategy under the thermal management feature working condition according to the thermal management information in the thermal management information database.

[0027] Step E, when it is monitored that the running information of the vehicle matches the user inherent driving mode information, perform thermal management according to the thermal management strategy in step D.

[0028] Step F, to cope with the changes of user behavior, battery aging and environmental temperature, update the information in the historical feature database regularly (this embodiment updates it in monthly units) to ensure the accuracy of the intelligent thermal management strategy.

[0029] As can be seen, the application analyzes the historical data of the vehicle to obtain the inherent driving mode of the user, and on this basis, the application can plan the thermal management strategy in advance according to the predicted user behavior, thereby realizing that the battery always works in the best temperature range, reducing the performance decline and energy loss caused by abnormal battery temperature; this not only can improve energy utilization efficiency, prolong battery service life and increase vehicle cruising range, but also can realize intelligent temperature control of the passenger cabin air conditioner and the power system, avoid excessive refrigeration or heating, and reduce the energy consumption of the whole vehicle, thereby effectively prolonging the cruising range of the electric vehicle.

[0030] In an embodiment, the vehicle dynamic running data in step A includes: time period of vehicle running (such as morning and evening peak hours / peak period), vehicle driving route (including city road / highway / suburb scene label), average vehicle speed, battery output power, frequency and intensity of vehicle acceleration and deceleration (through acceleration sensor to identify sudden acceleration / deceleration events), and other dynamic driving parameters.

[0031] On this basis, the process of obtaining driving behavior mode information according to the vehicle dynamic running data in the historical feature database in step B includes: Through time series clustering algorithm (such as K-means), the periodicity of the data such as time period of vehicle running, vehicle driving route and average vehicle speed is analyzed, and the user's common route (daily commuting route) and vehicle running setting preference are obtained.

[0032] Through GPS trajectory data clustering, the user's high-frequency driving route and stopping point (such as home, company, charging station) are determined.

[0033] Through deep learning time series model (such as LSTM, based on long short-term memory network algorithm), the user's driving style (aggressive / conservative) is predicted according to the battery output power, frequency and intensity of vehicle acceleration and deceleration.

[0034] In an embodiment, the charging behavior feature data in step A includes: charging time period (including time label such as early morning valley power period / diurnal peak period), single charging duration, charging location (reflected by geographic coordinates to distinguish home charging pile / public fast charging station / commercial parking lot and other scenes), charging frequency, and charging method (such as fast charging and slow charging, reminded by charging power level and charging mode switching habit), and other charging related behavior data.

[0035] On this basis, the process of obtaining charging behavior mode information according to the charging behavior feature data in the historical feature database in step B includes: Through association rule mining algorithm (such as Apriori, based on candidate set generation association rule mining algorithm), the charging duration, charging location and charging method in the same period are associated, and the charging rule is analyzed, such as "night home charging is the main charging method during weekdays, and fast charging is the main charging method during weekends".

[0036] In an embodiment, the vehicle setting preference data in step A includes: regenerative energy recovery mode (such as strong / medium / weak recovery gear usage frequency), passenger compartment temperature setting preference (including air conditioner automatic / manual mode, winter heating / summer cooling temperature threshold setting), and other personalized setting parameters.

[0037] On this basis, the process of obtaining vehicle setting preference information according to the vehicle setting preference data in the historical feature database in step B includes: Determine vehicle setting preference information, such as high recovery intensity to extend the endurance, according to the relationship between the kinetic energy recovery mode and the driving efficiency through a classification algorithm (for example, XGBoost, an integrated learning algorithm).

[0038] In an embodiment, the process of forming the historical feature database includes: uploading the vehicle dynamic operation data, the charging behavior feature data and the vehicle setting preference data to the server (cloud data center) after encryption processing by the vehicle terminal, and forming the historical feature database after data decryption, data cleaning, data feature extraction and data structuring processing by the server; providing bottom layer data support for subsequent battery thermal management strategy optimization, endurance mileage prediction and charging planning service.

[0039] In an embodiment, the thermal management feature working conditions in step C include charging heating, charging cooling, discharging heating and discharging cooling, and the principle of forming the thermal management information database according to the thermal management working conditions is: some thermal management related feature points need to be identified to prepare for the subsequent intelligent thermal management strategy; based on the analysis of the longest running time data of the existing electric vehicle thermal management system single opening, it is found that the longest running time of different vehicle thermal management systems in the four working conditions of "charging heating", "charging cooling", "discharging heating" and "discharging cooling" has obvious difference (all less than 1h).

[0040] Further, the specific battery data in step C and the reasons for its selection are: 1. Time: as a data basis; 2. Battery temperature: battery maximum temperature, minimum temperature, temperature difference, these data are important input conditions for evaluating whether the thermal management system is opened; 3. Battery SOC (State of charge): if the SOC is too low (such as <5%), the battery power is used for vehicle power output, and the thermal management is not started; 4. Ambient temperature: air is an important heat transfer path for the power battery, and the ambient temperature will greatly affect the actual temperature of the battery; 5. Vehicle state: in the power-on or power-off state, the overall energy consumption and thermal management effect of the vehicle will be greatly affected; 6. Charging mode: according to the actual charging situation, the matching of energy consumption and charging time is considered; 7. Battery power: the battery itself has resistance, and when the charging and discharging power is high (more than 50KW), the polarization phenomenon is serious, the heat generation rate is fast, and the battery temperature performance is directly affected.

[0041] At the same time, the thermal management strategy under the thermal management feature working condition in step D includes: (1) If the vehicle is in a charging state when the charging heating is being carried out, the energy consumption problem can be ignored to a certain extent. In order to speed up the charging speed, the start time of thermal management is advanced. The advance time is set according to the needs. In this embodiment, it is 10 minutes.

[0042] (2) If the vehicle is in the charging state when it is cooling down, the energy consumption problem can be ignored to a certain extent. In order to speed up the charging speed, the start time of thermal management is advanced. The advance time is set according to the needs. In this embodiment, it is 5 minutes.

[0043] (3) If the vehicle is in a high-power discharge state during charging and heating, the battery will heat up rapidly due to the heat generated by itself. Therefore, the start time of thermal management is delayed. The delay time is set according to the requirements. In this embodiment, it is 5 minutes.

[0044] (4) If the vehicle is in a high-power discharge state when charging and cooling, in order to prevent the battery from overheating, the start time of thermal management is advanced. The advance time is set according to the needs. In this embodiment, it is 5 minutes.

[0045] (5) When thermal management is activated during driving (i.e., it falls under any thermal management characteristic condition), and the vehicle will enter a power-off state after a specified period of time: Define the specified duration as t, the safety duration as x, and the power-off duration as y. The specific durations of x and y are set according to requirements. In this embodiment, x is 5 minutes and y is 20 minutes. When t≤x, it is determined that high / low temperature will not affect the battery in the short term, and thermal management is not enabled in order to save power. When x < t ≤ y, thermal management is turned off in advance, with the advance time being less than y. The specific advance time is set according to the requirements. In this embodiment, the advance time is 10 minutes.

[0046] In one embodiment, step D further includes the following step: forming a thermal management strategy for vehicle startup based on thermal management information in the thermal management information database.

[0047] Specifically, the thermal management strategy during vehicle startup is as follows: If thermal management is activated when the vehicle starts and it is in a low-temperature state (below zero degrees Celsius in this embodiment), the battery will have difficulty releasing its charging and discharging performance at excessively low temperatures, which will affect the customer experience. Therefore, the activation time of thermal management is advanced so that the vehicle is in a suitable temperature when it starts. The advance time is set according to the requirements, and in this embodiment it is 10 minutes.

[0048] In one embodiment, see Figure 1As shown, step E specifically includes: when it is monitored that the running information of the vehicle matches the user inherent driving mode information, and the temperature difference between the current environment temperature and the historical environment temperature corresponding to the user inherent driving mode information is less than the identification threshold (the identification threshold is 15° in this embodiment), the thermal management is performed according to the thermal management strategy in step D.

[0049] The principle of taking the environment temperature as the trigger condition of thermal management is that the environment temperature has a huge impact on the battery temperature, and the environment temperature changes uncontrollably in a time period of 1 month, so it is very important to identify the temperature difference between the current environment temperature and the temperature recorded in the last month inherent driving mode. If the temperature difference is greater than 15℃, the identification will have a great impact on the intelligent identification of the thermal management system, so the intelligent regulation is suspended, otherwise, the intelligent regulation is performed. The vehicle driving state is basically consistent with the last month inherent driving mode state, and the environment temperature meets the preset condition, and only when both conditions are met, the intelligent regulation is started.

[0050] In a second aspect, the embodiment of the present application provides a thermal management device based on historical data of an electric vehicle. The thermal management device based on historical data of an electric vehicle can be a personal computer (PC), a notebook computer, a server, or other devices with data processing functions.

[0051] Reference Figure 2 , Figure 2 The hardware structure of the thermal management device based on historical data of an electric vehicle involved in the embodiment of the present application is shown in the figure. In the embodiment of the present application, the thermal management device based on historical data of an electric vehicle can include a processor, a memory, a communication interface, and a communication bus.

[0052] The communication bus can be of any type, used to realize the interconnection of the processor, the memory, and the communication interface.

[0053] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, and other interfaces used to realize the interconnection of devices inside the thermal management device based on historical data of an electric vehicle, and interfaces used to realize the interconnection of the thermal management device based on historical data of an electric vehicle and other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber interface, an ATM interface, etc.; the user device can be a display (Display), a keyboard (Keyboard), etc.

[0054] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), and the like.

[0055] The processor can be a general-purpose processor, which can invoke the heat management program based on historical data of an electric vehicle stored in the memory and execute the heat management method based on historical data of an electric vehicle provided by the embodiments of the present application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the heat management program based on historical data of an electric vehicle is invoked can refer to various embodiments of the heat management method based on historical data of an electric vehicle of the present application, which will not be described here.

[0056] Those skilled in the art can understand that the hardware structure shown in the above-mentioned embodiments is not a limitation of the present application, and can include more or less components than the illustrated components, or combine certain components, or different component arrangements. Figure 2 The hardware structure shown in the above-mentioned embodiments is not a limitation of the present application, and can include more or less components than the illustrated components, or combine certain components, or different component arrangements.

[0057] In a third aspect, the embodiments of the present application further provide a computer readable storage medium.

[0058] The computer readable storage medium of the present application stores a heat management program based on historical data of an electric vehicle, wherein when the heat management program based on historical data of an electric vehicle is executed by the processor, the steps of the heat management method based on historical data of an electric vehicle as described above are implemented.

[0059] The method implemented when the heat management program based on historical data of an electric vehicle is executed can refer to various embodiments of the heat management method based on historical data of an electric vehicle of the present application, which will not be described here.

[0060] It should be noted that the above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0061] The terms “include,” “comprise,” “have,” and any variations thereof, in the specification and in the claims of the present application, and the above-described drawings, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or device that includes a list of steps or units is not limited to the listed steps or units, but can optionally further include steps or units not listed, or can optionally further include other steps or units inherent to such processes, methods, products, or devices. The terms “first”, “second”, and “third” and the like descriptions are used to distinguish different objects, and do not represent the order or limit the types of “first”, “second”, and “third”.

[0062] In the description of the embodiments of the present application, “exemplary”, “for example”, or “for instance” is used to represent an example, illustration, or description. Any embodiment or design scheme described as “exemplary”, “for example”, or “for instance” in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words “exemplary”, “for example”, or “for instance” are intended to present the relevant concept in a specific manner.

[0063] In the description of the embodiments of the present application, unless otherwise specified, “ / ” represents the meaning of or, for example, A / B can represent A or B; “and / or” in the text only describes the relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can represent the three cases of A alone, A and B together, and B alone. In addition, in the description of the embodiments of the present application, “multiple” means two or more than two.

[0064] In some of the processes described in the embodiments of the present application, a plurality of operations or steps are included in a specific order, but it should be understood that these operations or steps can be executed or performed in parallel or in an order different from that in which they appear in the embodiments of the present application. The serial number of the operation is only used to distinguish different operations, and the serial number itself does not represent any execution order. In addition, these processes can include more or fewer operations, and these operations or steps can be executed in sequence or in parallel, and these operations or steps can be combined.

[0065] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and a general hardware platform as required, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk) as described above, and includes a plurality of instructions for causing a terminal device to execute the methods described in the embodiments of the present application.

[0066] The above merely provides the specific implementation of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the embodiments of the present application, and these modifications or replacements should be covered in the protection scope of the embodiments of the present application. Therefore, the protection scope of the embodiments of the present application should be subject to the protection scope of the claims.

Claims

1. A thermal management method based on historical data of electric vehicles, characterized in that, The method includes the following steps: Acquire historical vehicle dynamic operation data, charging behavior characteristic data, and vehicle setting preference data; Driving behavior pattern information is obtained from vehicle dynamic operation data analysis; charging behavior pattern information is obtained from charging behavior characteristic data analysis; vehicle setting preference information is obtained from vehicle setting preference data analysis; driving behavior pattern information, charging behavior pattern information, and vehicle setting preference information are used as the user's inherent driving mode information. Obtain battery data that matches the user's inherent driving mode information and corresponds to thermal management characteristic conditions to form thermal management information; formulate thermal management strategies under thermal management characteristic conditions based on thermal management information; When the vehicle's operating information matches the user's inherent driving mode information, thermal management is performed according to the thermal management strategy.

2. The thermal management method based on historical data of electric vehicles as described in claim 1, characterized in that, The vehicle dynamic operation data includes: The time period of vehicle operation, vehicle route, average vehicle speed, battery output power, frequency and intensity of vehicle acceleration and deceleration; The process of obtaining driving behavior pattern information based on vehicle dynamic operation data analysis includes: After analyzing the periodic patterns of data such as vehicle operation time periods, vehicle routes, and average vehicle speeds, the user's commonly used routes and vehicle operation settings preferences are obtained. Determine the user's frequent driving routes and stop points; The user's driving style is predicted based on the battery output power and the frequency and intensity of vehicle acceleration and deceleration.

3. The thermal management method based on historical data of electric vehicles as described in claim 1, characterized in that, The charging behavior characteristic data includes: Charging time period, single charging duration, charging location, charging frequency, and charging method; The process of obtaining charging behavior pattern information based on charging behavior characteristic data analysis includes: By correlating the charging duration, charging location, and charging method within the same time period, the charging patterns can be analyzed.

4. The thermal management method based on historical data of electric vehicles as described in claim 1, characterized in that, The vehicle setting preference data includes: kinetic energy recovery mode and passenger compartment temperature setting preference; The process of obtaining vehicle setting preference information based on vehicle setting preference data analysis includes: Based on the relationship between kinetic energy recovery mode and driving efficiency, vehicle setting preference information is determined.

5. The thermal management method based on historical data of electric vehicles as described in claim 1, characterized in that: The thermal management characteristic operating conditions include charging heating, charging cooling, discharging heating, and discharging cooling; the battery data includes time, battery temperature, battery SOC, ambient temperature, vehicle status, charging mode, and battery power.

6. The thermal management method based on historical data of electric vehicles as described in claim 5, characterized in that, The thermal management strategies under the aforementioned thermal management characteristic operating conditions include: If the vehicle is charging while the charging heating or cooling process is underway, the activation time of thermal management will be advanced. If the vehicle is in a high-power discharge state during charging and heating, the activation time of thermal management will be delayed. If the vehicle is in a high-power discharge state during charging and cooling, the activation time of thermal management will be brought forward. When thermal management is activated while driving, and the vehicle will enter a power-down state after a specified period of time: Define the specified duration as t, the safe duration as x, and the power-off duration as y; When t≤x, thermal management is not enabled; When x < t ≤ y, turn off thermal management in advance.

7. The thermal management method based on historical data of electric vehicles as described in any one of claims 1 to 6, characterized in that: The thermal management strategy also includes a vehicle startup thermal management strategy formed based on thermal management information, which includes: If thermal management is activated when the vehicle starts and the vehicle is in a low-temperature state, the activation time of thermal management will be advanced.

8. The thermal management method based on historical data of electric vehicles as described in any one of claims 1 to 6, characterized in that, The triggering conditions for thermal management based on the thermal management strategy also include: the temperature difference between the current ambient temperature and the historical ambient temperature corresponding to the user's inherent driving mode information is less than the identification threshold.

9. A thermal management device based on historical data of electric vehicles, characterized in that, The thermal management device based on historical electric vehicle data includes a processor, a memory, and a thermal management program based on historical electric vehicle data stored in the memory and executable by the processor, wherein when the thermal management program based on historical electric vehicle data is executed by the processor, it implements the steps of the thermal management method based on historical electric vehicle data as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a thermal management program based on historical data of electric vehicles, wherein when the thermal management program based on historical data of electric vehicles is executed, it implements the steps of the thermal management method based on historical data of electric vehicles as described in any one of claims 1 to 8.

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