Battery thermal management control method, device, equipment and medium

Abstract

The invention discloses a battery thermal management control method and device, equipment and a medium. The method comprises the steps that current driving data and battery state data of a target vehicle are acquired; a user driving habit database is inquired according to the current driving data, whether current driving conforms to a user driving habit rule or not is determined, and when the current driving conforms to the user driving habit rule, target user driving habit data are obtained, and the target user driving habit data comprise at least one of habit driving power and habit driving time; according to the driving habit data of the target user and the battery state data, determining a battery adaptive thermal management strategy; and performing thermal management control on the battery of the target vehicle according to the battery self-adaptive thermal management strategy. According to the method, the battery self-adaptive thermal management strategy can be dynamically adjusted and implemented according to driving habits of different users and actual vehicle using scenes, unnecessary energy consumption under a traditional fixed threshold strategy is effectively avoided, the energy utilization efficiency is remarkably improved, and the driving range of the vehicle is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal management control, and particularly to a battery thermal management control method, device, equipment and medium. Background Art

[0002] At present, with the booming development of the electric vehicle industry, as the core energy source of electric vehicles, the performance stability and efficiency of power batteries are directly related to the overall performance of vehicles and user experience. Among them, the thermal management of power batteries plays a crucial role in ensuring battery performance, extending battery service life and ensuring the safe operation of vehicles.

[0003] Currently, the commonly adopted power battery thermal management control strategy in the industry is to control battery heating and cooling based on fixed thresholds calibrated for pre-set scenarios. In terms of temperature control, temperature limits for turning on and off cooling and heating are set. For example, heating is turned on when the lowest battery temperature is below 0°C. However, the actual user driving scenarios are complex and diverse, and factors such as driving conditions, environmental temperature and user needs will all affect the battery thermal management requirements. For example, when commuting short distances in winter on urban roads at a low speed, the battery discharge power required by the vehicle is low, and the performance of the battery at low temperature does not decrease significantly, so there is no need to turn on heating. However, the existing fixed threshold strategy will still start heating when the battery temperature is below 0°C, resulting in unnecessary energy consumption, shortening the driving range and reducing energy utilization efficiency.

[0004] Therefore, how to optimize the power battery thermal management control strategy for actual complex and variable driving scenarios to achieve more efficient and energy-saving thermal management has become an urgent technical problem in the current electric vehicle industry. Summary of the Invention

[0005] The main purpose of the present invention is to provide a battery thermal management control method, device, equipment and medium, aiming to solve the technical problems such as energy waste, shortened driving range and low energy utilization efficiency brought by the existing fixed threshold strategy.

[0006] In a first aspect, a battery thermal management control method is provided, including:

[0007] Obtain the current driving data and battery state data of the target vehicle, where the current driving data includes the identity information of the current driver, the current driving time and the current driving route, and the battery state data includes the state of charge of the battery, the battery temperature and the battery voltage;

[0008] Query the user driving habit database according to the current driving data to determine whether the current driving conforms to the user driving habit pattern, and when it conforms to the user driving habit pattern, obtain the target user driving habit data, where the target user driving habit data includes at least one of the habit driving power and the habit driving time;

[0009] Determine a battery adaptive thermal management strategy according to the target user driving habit data and the battery state data;

[0010] Perform thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy.

[0011] In some implementation manners, the querying the user driving habit database according to the current driving data to determine whether the current driving conforms to the user driving habit pattern, and when it conforms to the user driving habit pattern, obtaining the target user driving habit data includes:

[0012] Taking the current driving data as an index, query whether there is historical driving data matching the current driving data in the user driving habit database;

[0013] When there is the historical driving data matching the current driving data in the user driving habit database, determine that the current driving conforms to the user driving habit pattern, and obtain the historical user driving habit data corresponding to the historical driving data matching the current driving data as the target user driving habit data;

[0014] Wherein, the user driving habit database includes multiple pieces of the historical driving data and the historical user driving habit data corresponding to the multiple pieces of the historical driving data, and each piece of the historical user driving habit data includes a habitual driving power and a habitual driving time under the corresponding historical driving data.

[0015] In some implementation manners, before querying the user driving habit database according to the current driving data to determine the target user driving habit data, the method further includes:

[0016] Obtain the historical driving data of the target vehicle in multiple consecutive historical periods, as well as the historical driving power and historical driving time corresponding to the historical driving data, where the historical driving data includes the identity information of the historical driver, the historical driving time, and the historical driving route;

[0017] Select the historical driving data for constructing the user driving habit database from the historical driving data of the multiple historical periods, denoted as target historical driving data, the target historical driving data is the historical driving data in the target historical period, and there is historical driving data matching the target historical driving data in each of the other historical periods except the target historical period, and the target historical period is any one of the multiple historical periods;

[0018] Determine the habitual driving power and habitual driving time corresponding to the target historical driving data based on the historical driving power and historical driving time corresponding to the target historical driving data, as well as the historical driving power and historical driving time corresponding to the historical driving data that matches the target historical driving data in other historical periods;

[0019] Construct the user driving habit database based on the target historical driving data, as well as the habitual driving power and the habitual driving time corresponding to the target historical driving data.

[0020] In some implementation manners, the battery adaptive thermal management strategy includes the battery heating adaptive management strategy; determining the battery adaptive thermal management strategy according to the target user driving habit data and the battery state data includes:

[0021] Determine the target power threshold according to the habitual driving power;

[0022] Determine the heating temperature shutdown threshold according to the habitual driving power and the state of charge of the battery;

[0023] Determine the battery dischargeable power value according to the state of charge of the battery and the battery temperature;

[0024] Determine the battery heating adaptive management strategy according to the target power threshold, the heating temperature shutdown threshold and the battery dischargeable power value;

[0025] Wherein, the battery heating adaptive management strategy includes: when the battery dischargeable power value is less than the target power threshold, turn on the battery heating; when the battery temperature is greater than or equal to the heating temperature shutdown threshold, exit the battery heating.

[0026] In some implementation manners, the battery adaptive thermal management strategy includes the battery cooling adaptive management strategy, and the battery state data further includes the battery safety temperature calibration value, the battery heat exchange power, the battery specific heat capacity and the battery mass; determining the battery adaptive thermal management strategy according to the target user driving habit data and the battery state data includes:

[0027] Determine the cooling temperature activation threshold according to the target user driving habit data and the battery state data;

[0028] Determine the cooling temperature shutdown threshold according to the battery safety temperature calibration value;

[0029] Determine the cooling time activation threshold according to the battery temperature, the cooling temperature activation threshold, the battery heat exchange power and the habitual driving time;

[0030] Determine the battery cooling adaptive management strategy according to the cooling temperature activation threshold, the cooling temperature deactivation threshold, and the cooling time activation threshold;

[0031] Wherein, the battery cooling adaptive management strategy includes: activating battery cooling when the battery temperature exceeds the cooling temperature activation threshold and the driving time of the target vehicle exceeds the cooling time activation threshold; deactivating battery cooling when the battery temperature is less than the cooling temperature deactivation threshold.

[0032] In some implementation manners, when it is determined that the current driving does not conform to the user's driving habit pattern, the method further includes:

[0033] Perform thermal management control on the battery of the target vehicle according to the battery fixed threshold thermal management strategy;

[0034] The battery fixed threshold thermal management strategy includes: activating battery cooling when the battery temperature exceeds the first temperature threshold; deactivating battery cooling when the battery temperature is lower than the second temperature threshold; activating battery heating when the battery temperature is lower than the third temperature threshold; deactivating battery heating when the battery temperature exceeds the fourth temperature threshold; wherein, the first temperature threshold is greater than the second temperature threshold, the second temperature threshold is greater than the fourth temperature threshold, and the fourth temperature threshold is greater than the third temperature threshold.

[0035] In some implementation manners, after performing thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy, the method further includes:

[0036] Record the driving data, driving power, and driving time of the current driving;

[0037] Update the habitual driving power and / or habitual driving time in the user driving habit database based on the driving data, the driving power, and the driving time.

[0038] In a second aspect, a battery thermal management control device is provided, including:

[0039] An acquisition module, configured to acquire the current driving data and battery state data of the target vehicle, where the current driving data includes the identity information of the current driver, the current driving time, and the current driving path, and the battery state data includes the state of charge of the battery, the battery temperature, and the battery voltage;

[0040] A first determination module, where the user queries a user driving habit database according to the current driving data, determines whether the current driving conforms to the user driving habit pattern, and when it conforms to the user driving habit pattern, obtains target user driving habit data, where the target user driving habit data includes at least one of a habitual driving power and a habitual driving time;

[0041] A second determination module, configured to determine a battery adaptive thermal management strategy according to the target user driving habit data and the battery state data;

[0042] A control module, configured to perform thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy.

[0043] In a third aspect, an electronic device is provided, including: a memory and a processor, which are communicatively connected to each other, where computer instructions are stored in the memory, and the processor executes the computer instructions to execute the battery thermal management control method as described in the first aspect.

[0044] In a fourth aspect, a computer-readable storage medium is provided, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the battery thermal management control method as described in the first aspect.

[0045] The technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0046] A battery thermal management control, device, equipment and medium provided in the embodiments of the present invention accurately determine a battery adaptive thermal management strategy by obtaining the current driving data and battery state data of a target vehicle and combining the user driving habit pattern, so as to perform thermal management control on the battery. Compared with the existing fixed threshold strategy, this method can dynamically adjust and implement the battery adaptive thermal management strategy according to different user driving habits and actual vehicle use scenarios, effectively avoid the generation of unnecessary energy consumption under the traditional fixed threshold strategy, significantly improve the energy utilization efficiency, extend the vehicle's cruising range, while ensuring the stable performance of the battery, extend the battery's service life, improve the overall performance of the electric vehicle and the user experience, and provide a more efficient and energy-saving power battery thermal management solution for the development of the electric vehicle industry.

[0047] The above description is only an overview of the technical solutions of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically given below. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Various other advantages and benefits will become clear to those of ordinary skill in the art by reading the following detailed description of the preferred embodiments. The drawings are only for the purpose of illustrating the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0049] Figure 1 is a flowchart of a battery thermal management control method provided by an embodiment of the present invention;

[0050] Figure 2 is a structural block diagram of a battery thermal management control device provided by an embodiment of the present invention. Detailed Embodiments

[0051] To better understand the above technical solutions, the following will combine the specification drawings and specific embodiments to elaborate on the above technical solutions in detail. It should be understood that the embodiments of the present disclosure and the specific features in the embodiments are detailed descriptions of the technical solutions of the present application, rather than limitations on the technical solutions of the present application. Without conflict, the technical features in the embodiments of the present application and the embodiments can be combined with each other.

[0052] Figure 1 is a flowchart of a battery thermal management control method provided by an embodiment of the present invention, as Figure 1 shown, the method includes:

[0053] Step S110, obtain the current driving data and battery state data of the target vehicle. The current driving data includes the identity information of the current driver, the current driving time, and the current driving path. The battery state data includes the state of charge of the battery, the battery temperature, and the battery voltage.

[0054] In some implementation manners, different drivers may have different driving habits and requirements. The identity information of the current driver can be obtained through the user identification system of the target vehicle (such as smart keys, fingerprint recognition, face recognition, etc.). At the same time, the current driving time and driving path can be obtained by using the vehicle's navigation system and time recording module. The driving time can reflect the usage period of the vehicle, while the driving path can reflect the road conditions and driving environment where the vehicle is located. These information are crucial for subsequent judgment of the user's driving habit pattern. Specifically, the longitude and latitude information of the starting position and the ending position of the current driving path can be obtained through the navigation system.

[0055] In some implementations, the state of charge of the battery can be monitored in real time through a battery management system (BMS), that is, the percentage of the remaining battery power in the total battery power. SOC is a key parameter during battery usage, which directly affects the vehicle's endurance and driving range. Similarly, the BMS collects the temperature and voltage data of the battery. The battery temperature has a significant impact on the performance and lifespan of the battery. Both too high and too low temperatures will reduce the battery efficiency and lifespan; the battery voltage can reflect the current working state and energy output ability of the battery.

[0056] Step S120: Query the user driving habit database according to the current driving data to determine whether the current driving conforms to the user driving habit pattern. When it conforms to the user driving habit pattern, obtain the target user driving habit data, where the target user driving habit data includes at least one of the habitual driving power and the habitual driving time.

[0057] When it is determined that the current driving conforms to the user driving habit pattern, data related to the user's habit can be extracted from the user driving habit database, such as the habitual driving power and the habitual driving time. Among them, the habitual driving power can reflect the range of power outputs commonly used by different users during different trips; the habitual driving time can reflect the commonly used driving durations of different users during different trips.

[0058] Step S130: Determine the battery adaptive thermal management strategy according to the target user driving habit data and the battery state data.

[0059] Among them, the battery adaptive thermal management strategy can include a battery heating adaptive management strategy and / or a battery cooling adaptive management strategy. The battery heating adaptive management strategy includes the conditions for turning on and turning off the battery heating, and the battery cooling adaptive management strategy includes the conditions for turning on and turning off the battery cooling.

[0060] Step S140: Perform thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy.

[0061] Exemplarily, according to the battery adaptive thermal management strategy, heating or cooling of the battery of the target vehicle can be controlled.

[0062] In some implementations, step S120 may include:

[0063] Using the current driving data as an index, query whether there is historical driving data in the user driving habit database that matches the current driving data; when there is historical driving data in the user driving habit database that matches the current driving data, determine that the current driving conforms to the user driving habit pattern, and obtain the historical user driving habit data corresponding to the historical driving data that matches the current driving data as the target user driving habit data.

[0064] Among them, the user driving habit database includes multiple pieces of historical driving data and historical user driving habit data corresponding to the multiple pieces of historical driving data. Each piece of historical user driving habit data includes the habitual driving power and habitual driving time under the corresponding historical driving data.

[0065] By comparing the current driving data with the historical driving data, the system can accurately identify whether the current driving conforms to the user's regular driving habits, so as to judge the particularity or typicality of the current driving scenario. For example, if the current driving data highly matches the historical driving data, it indicates that the current driving conforms to the user's driving habit pattern. At this time, the habitual driving power and habitual driving time of the user in the same driving scenario can be called through the user driving habit database to formulate corresponding battery adaptive thermal management strategies. For example, if the user is used to commuting at low speeds in the city and the driving time is short, the system can appropriately reduce the frequency and intensity of heating or cooling according to this habitual data to avoid unnecessary energy consumption and achieve energy-saving effects. This dynamic adjustment mechanism based on user driving habits enables the thermal management system to flexibly adjust the thermal management strategy according to different users' driving behaviors and scenarios, improving the adaptability and flexibility of the system, better meeting the personalized needs of different users, and enhancing the user experience.

[0066] In one implementation, when the similarity of each data in the current driving data and the historical driving data is greater than the set similarity threshold (such as 98%), it indicates that the current driving data matches the historical driving data. For example, when the similarity of the identity information of the current driver and the historical driver reaches 99%, and the similarity of the current driving time and the historical driving time reaches 99%, and the similarity of the longitude and latitude of the starting point and the ending point of the current driving path and the longitude and latitude of the starting point and the ending point of the historical driving path reaches 99%, it indicates that the current driving data matches the historical driving data.

[0067] In another implementation, when the difference threshold of each data in the current driving data and the historical driving data is less than the set difference threshold, it indicates that the current driving data matches the historical driving data. For example, when the time difference between the current driving time and the historical driving time is less than the set time difference threshold, and based on the longitude and latitude of the starting point of the current driving path and the longitude and latitude of the starting point of the historical driving path, the calculated distance between the two starting points is less than the set distance difference threshold, and based on the longitude and latitude of the ending point of the current driving path and the longitude and latitude of the ending point of the historical driving path, the calculated distance between the two ending points is less than the set distance difference threshold, it indicates that the current driving data matches the historical driving data.

[0068] In some implementations, before executing step S120, the method further includes:

[0069] Step 1: Obtain the historical driving data of the target vehicle in multiple consecutive historical periods, as well as the historical driving power and historical driving time corresponding to the historical driving data. The historical driving data includes the identity information of the historical driver, the historical driving time, and the historical driving route.

[0070] Exemplarily, in the embodiments of the present invention, a month, a week, a day, etc. can be used as a single historical period to statistically analyze the driving habits of users. At the same time, multiple consecutive historical periods with a relatively close driving time distance to the current driving can be selected. For example, if the current driving is in the last week of June, the historical driving data of the first three weeks of June can be obtained for constructing the database.

[0071] Step 2: Select the historical driving data used to construct the user driving habit database from the historical driving data of multiple historical periods, denoted as the target historical driving data. The target historical driving data is the historical driving data in the target historical period, and there is historical driving data matching the target historical driving data in each of the other historical periods except the target historical period. The target historical period is any one of the multiple historical periods.

[0072] Specifically, when selecting the target historical driving data, it can be determined whether there is historical driving data matching the target historical driving data in each of the other historical periods by similarity or difference magnitude. The specific judgment method can refer to the description of the matching process between the current driving data and the historical driving data above, and will not be elaborated here.

[0073] Step 3: Determine the habitual driving power and habitual driving time corresponding to the target historical driving data according to the historical driving power and historical driving time corresponding to the target historical driving data, as well as the historical driving power and historical driving time corresponding to the historical driving data matching the target historical driving data in other historical periods.

[0074] Exemplarily, the average value of the historical driving power corresponding to the target historical driving data and the historical driving power corresponding to the historical driving data matching the target historical driving data in other historical periods can be calculated as the habitual driving power corresponding to the target historical driving data; the average value of the historical driving time corresponding to the target historical driving data and the historical driving time corresponding to the historical driving data matching the target historical driving data in other historical periods can be calculated as the habitual driving time corresponding to the target historical driving data.

[0075] Step 4: Construct a user driving habit database according to the target historical driving data, as well as the habitual driving power and habitual driving time corresponding to the target historical driving data.

[0076] In the above implementation, by constructing a user driving habit database to record the user's driving habit data, the thermal management system can automatically adjust the thermal management strategy according to the user's driving habits without manual intervention from the user. This intelligent automatic adjustment mechanism not only improves the convenience of the system but also provides a more comfortable driving experience according to the actual needs of the user, enhancing the user's satisfaction with the vehicle. For example, for users who are accustomed to driving in high-temperature environments, the system can optimize the cooling strategy; for users who are accustomed to short-distance driving in low-temperature environments, the system can reduce unnecessary heating, thereby achieving more energy-efficient and efficient thermal management.

[0077] Specifically, by obtaining the historical driving data of the target vehicle in multiple consecutive historical periods and selecting the target historical driving data from them to construct the user driving habit database, the user's driving habits can be accurately identified. This method ensures that the driving habit data stored in the database is representative and stable, avoiding misjudgment of driving habits due to single accidental driving behaviors. For example, if a user shows a similar driving pattern in multiple historical periods (such as commuting at a low speed on urban roads at a fixed time every morning), then this data will be selected as the basis for constructing the database, thereby ensuring that the system can accurately identify the user's regular driving habits.

[0078] To better understand the present invention, the following takes a weekly statistics of the user's driving habits as an example to obtain the historical driving data for three consecutive weeks and the historical driving power corresponding to the historical driving data, and illustrates the specific construction process of the user driving habit database:

[0079] The first week:

[0080] (1) Monday

[0081] Trip (historical driving data A11): Driver identity information w1_K1_1, driving time w1_t1_1, starting point location longitude and latitude (w1_star_x1_1, w1_star_y1_1), ending point location longitude and latitude (w1_end_x1_1, w1_end_y1_1), driving power w1_p1_1;

[0082] Trip (historical driving data A12): Driver identity information w1_K1_2, driving time w1_t1_2, starting point location longitude and latitude (w1_star_x1_2, w1_star_y1_2), ending point location longitude and latitude (w1_end_x1_2, w1_end_y1_2), driving power w1_p1_2;

[0083] …

[0084] Trip (historical driving data A1n): driver identity information w1_K1_n, driving time w1_t1_n, starting location longitude and latitude (w1_star_x1_n, w1_star_y1_n), ending location longitude and latitude (w1_end_x1_n, w1_end_y1_n), driving power w1_p1_n.

[0085] (2) Tuesday

[0086] Trip (historical driving data A21): driver identity information w1_K2_1, driving time w1_t2_1, starting location longitude and latitude (w1_star_x2_1, w1_star_y2_1), ending location longitude and latitude (w1_end_x2_1, w1_end_y2_1), driving power w1_p2_1;

[0087] Trip (historical driving data A22): driver identity information w1_K2_2, driving time w1_t2_2, starting location longitude and latitude (w1_star_x2_2, w1_star_y2_2), ending location longitude and latitude (w1_end_x2_2, w1_end_y2_2), driving power w1_p2_2;

[0088] Trip (historical driving data A2n): driver identity information w1_K2_n, driving time w1_t2_n, starting location longitude and latitude (w1_star_x2_n, w1_star_y2_n), ending location longitude and latitude (w1_end_x2_n, w1_end_y2_n), driving power w1_p2_n.

[0089] (3) Wednesday, and so on by analogy

[0090] (4) Thursday, and so on by analogy

[0091] (5) Friday, and so on by analogy

[0092] (6) Saturday, and so on by analogy

[0093] (7) Sunday, and so on by analogy

[0094] Statistically analyze each trip in the second week and the third week according to the above method to obtain multiple pieces of historical driving data.

[0095] Next, taking each piece of historical driving data in the first week as a reference, compare in turn whether there is matching historical data in each piece of historical data in the second week and the third week. For example, there is historical driving data A11 in the first week, which includes driver identity information w1_K1_1, driving time w1_t1_1, starting point position longitude and latitude (w1_star_x1_1, w1_star_y1_1), ending point position longitude and latitude (w1_end_x1_1, w1_end_y1_1), and driving power w1_p1_1; there is historical driving data B11 in the second week, which includes driver identity information w2_K1_1, driving time w2_t1_1, starting point position longitude and latitude (w2_star_x1_1, w2_star_y1_1), ending point position longitude and latitude (w2_end_x1_1, w2_end_y1_1), and driving power w2_p1_1; there is historical driving data C11 in the third week, which includes driver identity information w3_K1_1, driving time w3_t1_1, starting point position longitude and latitude (w3_star_x1_1, w3_star_y1_1), ending point position longitude and latitude (w3_end_x1_1, w3_end_y1_1), and driving power w3_p1_1.

[0096] Taking the difference threshold as an example of the matching determination condition, if the driver identity information of A11 is the same as that of B11, and the difference between the driving time w1_t1_1 of A11 and the driving time w2_t1_1 of B becomes less than the set threshold, and the distance difference between the starting point positions of A11 and B11 calculated based on the starting point position longitude and latitude (w1_star_x1_1, w1_star_y1_1) of A11 and the starting point position longitude and latitude (w2_star_x1_1, w2_star_y1_1) of B11 is less than the set difference threshold, and the distance difference between the ending point positions of A11 and B11 calculated based on the ending point position longitude and latitude (w1_end_x1_1, w1_end_y1_1) of A11 and the ending point position longitude and latitude (w2_end_x1_1, w2_end_y1_1) of B11 is less than the set difference threshold, then it is determined that data A11 and data B11 match. Similarly, if it is determined that data A11 and data C11 match, then data A11 can be selected as the target historical driving data.

[0097] Next, the average value of the driving power w1_p1_1 corresponding to A11, the driving power w2_p1_1 corresponding to B11, and the driving power w1_p1_1 corresponding to C11 can be calculated, and this average value is used as the habitual driving power corresponding to data A11.

[0098] It should be noted that during the process of calculating the habitual driving power, if the difference between the driving power w2_p1_1 corresponding to B11 and the driving power w1_p1_1 corresponding to A11 is greater than the set power difference threshold (for example, 5 kw), then the driving power w2_p1_1 corresponding to B11 needs to be excluded as abnormal data to avoid affecting the accuracy of the habitual driving power. For example, if the driving power shows an abnormally high value during a certain driving process due to sudden situations (such as emergency acceleration or braking), including it in the calculation may cause the habitual driving power to be too high, thus affecting the formulation of subsequent thermal management strategies. After excluding the abnormal data, the calculated habitual driving power can more truly reflect the power demand of the user in the conventional driving scenario.

[0099] In some implementation manners, after executing step S140, the method further includes:

[0100] Recording the driving data, driving power, and driving time of this trip;

[0101] Based on the driving data, driving power, and driving time, updating the habitual driving power and / or habitual driving time in the user driving habit database.

[0102] At this time, since the driving data of this trip matches the historical driving data, the driving data of this trip can be selected as the target historical driving data for constructing the user driving habit database. Correspondingly, the habitual driving power and habitual driving time can be updated accordingly based on the driving power and driving time corresponding to this trip to ensure the real-time performance and accuracy of the habitual driving power and / or habitual driving time in the database. By setting the above update rules, if the user changes their driving habit (such as road conditions change) within a certain historical period, the system can adjust the thermal management strategy in a timely manner by updating the data in the database to ensure that the battery is always in the best working state. This dynamic adjustment mechanism improves the adaptability and flexibility of the thermal management strategy, enabling it to better cope with the dynamic changes in the user's driving habits.

[0103] In some implementation manners, when the battery adaptive thermal management strategy includes a battery heating adaptive management strategy, step S130 includes:

[0104] Step S1301: Determine the target power threshold according to the habitual driving power.

[0105] In one implementation manner, the habitual driving power can be set as the target power threshold P 阈 , if the habitual driving power is 22 kw, at this time the target power threshold P 阈 = 22 kw.

[0106] In another implementation, the target power threshold can also be set as the habitual driving power + a preset power margin. The preset power margin can be set to, for example, 3 kw. At this time, the target power threshold can be set as 22 kw + 3 kw, that is, P 阈 = 25 kw. By setting the power margin, it can help to start battery heating in advance when the battery dischargeable power is close to but has not reached the habitual driving power.

[0107] Step S1302: Determine the heating temperature shutdown threshold according to the habitual driving power and the state of charge of the battery.

[0108] Exemplarily, the battery discharge characteristic-temperature mapping table can be queried to determine the minimum battery temperature T3 corresponding to the current state of charge of the battery when the dischargeable power value exceeds the habitual driving power; according to the minimum battery temperature T3 and the preset temperature margin value T0, determine the heating temperature shutdown threshold T 阈 . That is, T 阈 = T3 + T0.

[0109] Among them, the battery discharge characteristic-temperature mapping table can be pre-calibrated through bench tests and is used to characterize the battery temperature corresponding to different battery SOCs and different battery dischargeable power values.

[0110] Step S1303: Determine the battery dischargeable power value according to the state of charge of the battery and the battery temperature.

[0111] Exemplarily, the battery continuous discharge power table can be queried to determine the battery dischargeable power value P0 corresponding to the current state of charge of the battery and the current battery temperature.

[0112] Among them, the battery continuous discharge power table can be pre-calibrated through bench tests and is used to characterize the battery dischargeable power value corresponding to different battery SOCs and different battery temperatures.

[0113] Step S1304: Determine the battery heating adaptive management strategy according to the target power threshold, the heating temperature shutdown threshold, and the battery dischargeable power value.

[0114] Among them, the battery heating adaptive management strategy includes: when the battery dischargeable power value P0 is less than the target power threshold P 阈 , turn on the battery heating; when the battery temperature T is greater than or equal to the heating temperature shutdown threshold T 热关阈 , exit the battery heating.

[0115] The above implementation method dynamically determines the battery heating adaptive management strategy by comprehensively considering multiple factors such as the habitual driving power, the state of charge of the battery, and the battery temperature, achieving precise control of battery heating, improving energy utilization efficiency, extending battery life, enhancing user experience, strengthening the adaptability and flexibility of the system, reducing the risk of system failures, optimizing the performance of the thermal management system, improving the adaptive ability of the system, and enhancing the predictability and stability of the system. These effects together improve the overall performance of the electric vehicle and the user experience.

[0116] Based on the above implementation method, in step S140, performing thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy may include:

[0117] When the dischargeable power value P0 of the battery is less than the target power threshold P 阈 , turn on the battery heating;

[0118] When the battery temperature T is greater than or equal to the heating temperature off threshold T 热关阈 , exit the battery heating.

[0119] In some implementation methods, the battery state data further includes the battery safety temperature calibration value, the battery heat exchange power, the battery specific heat capacity, and the battery mass. When the battery adaptive thermal management strategy includes a battery cooling adaptive management strategy, step S130 includes:

[0120] Step S1311: Determine the cooling temperature on threshold according to the target user driving habit data and the battery state data.

[0121] In some implementation methods, step S1311 includes:

[0122] Query the battery DC internal resistance table according to the state of charge of the battery and the battery temperature to determine the current battery internal resistance R; determine the expected heat generation Q of the battery based on the current battery internal resistance R, the battery voltage u, the habitual driving power P, and the habitual driving time t; determine the expected temperature rise ΔT of the battery based on the expected heat generation Q of the battery, the battery specific heat capacity c, and the battery mass m; determine the cooling temperature on threshold T 冷开阈 .

[0123] Exemplarily, the expected heat generation Q of the battery can be calculated by the following formula (1):

[0124] Q = (P / u) * (P / u) * R * t; (1)

[0125] where P represents the habitual driving power, u represents the battery voltage, R represents the current battery internal resistance, and t represents the habitual driving time.

[0126] The predicted temperature rise ΔT of the battery can be calculated by the following formula (2):

[0127] ΔT = Q / (c * m); (2)

[0128] Where c represents the specific heat capacity of the battery, and m represents the mass m of the battery.

[0129] Exemplarily, T 冷开阈 = T0 - ΔT.

[0130] Step S1312: Determine the cooling temperature off threshold according to the battery safety temperature calibration value.

[0131] In one implementation, the cooling temperature off threshold T 冷关阈 can be set to the battery safety temperature calibration value T0. If the battery safety temperature calibration value T0 is 45 °C, the target power threshold T 冷关阈 = 45 °C.

[0132] In another implementation, the cooling temperature off threshold T 冷关阈 can also be set to = the battery safety temperature calibration value T0 + a preset temperature threshold margin. The preset temperature threshold margin can be set to, for example, 2 - 3 °C. At this time, the cooling temperature off threshold T 冷关阈 can be set to 45 - 2 = 43 °C. Since the frequent on and off of the battery heating system will not only increase energy consumption but also may cause system failures. Therefore, in this embodiment, by setting the temperature margin, it is possible to avoid frequent switching caused by small fluctuations in temperature, improving the stability and reliability of the system.

[0133] Step S1313: Determine the cooling time on threshold according to the battery temperature, the cooling temperature on threshold, the battery heat exchange power, and the habitual driving time.

[0134] In some implementations, step S1313 includes:

[0135] Determine the heat exchange time according to the battery temperature, the cooling temperature on threshold, and the battery heat exchange power;

[0136] Determine the cooling time on threshold according to the heat exchange time and the habitual driving time.

[0137] Exemplarily, the heat exchange time t2 can be determined according to the following formula (3):

[0138] t2 = (T - T 冷开阈 ) / q; (3)

[0139] Where T 冷开阈 represents the cooling temperature on threshold, T represents the battery temperature, and q represents the battery heat exchange power.

[0140] Exemplarily, the cooling time activation threshold t3 can be determined according to the following formula (4):

[0141] t3 = t1 - t2; (4)

[0142] Wherein, t1 represents the habitual driving time. For example, when t1 = 30 min, t1 can be converted to s, that is, t3 = 30 * 60 - t2 = 1800 - t2.

[0143] Step S1314, determine the battery cooling adaptive management strategy according to the cooling temperature activation threshold, the cooling temperature deactivation threshold, and the cooling time activation threshold.

[0144] Among them, the battery cooling adaptive management strategy includes: when the battery temperature exceeds the cooling temperature activation threshold and the driving time of the target vehicle exceeds the cooling time activation threshold, turn on the battery cooling; when the battery temperature is less than the cooling temperature deactivation threshold, exit the battery cooling.

[0145] The above implementation method dynamically determines the battery cooling adaptive management strategy by comprehensively considering the target user's driving habit data and the battery state data, realizing precise control of battery cooling, improving energy utilization efficiency, extending battery life, enhancing user experience, enhancing the adaptability and flexibility of the system, reducing the risk of system failures, optimizing the performance of the thermal management system, improving the adaptive ability of the system, enhancing the predictability and stability of the system, reducing the wear of the thermal management system, adapting to different environmental conditions, and improving the overall performance of the system. These effects together improve the overall performance of the electric vehicle and the user's usage experience.

[0146] Based on the above implementation method, in step S140, the thermal management control of the battery of the target vehicle according to the battery adaptive thermal management strategy may include:

[0147] When the battery temperature T exceeds the cooling temperature activation threshold T 冷开阈 , and the driving time t of the target vehicle exceeds the cooling time activation threshold t3, turn on the battery cooling;

[0148] When the battery temperature T is less than the cooling temperature deactivation threshold T 冷关阈 , exit the battery cooling.

[0149] It should be noted that the battery cooling adaptive management strategy in the embodiments of the present invention mainly considers from the aspects of battery life and battery safety, and the battery heating adaptive management strategy mainly considers from the aspect of battery power. This targeted design can comprehensively optimize battery management, ensure that the battery can maintain the best performance under various conditions, extend battery life, ensure battery safety, optimize power output, improve energy utilization efficiency, enhance the adaptability and flexibility of the system, enhance user experience, and improve the overall performance of the system.

[0150] In some implementations, when it is determined that the current driving does not conform to the user's driving habit pattern, the method further includes:

[0151] Performing thermal management control on the battery of the target vehicle according to a fixed battery threshold thermal management strategy;

[0152] The fixed battery threshold thermal management strategy includes: turning on battery cooling when the battery temperature exceeds the first temperature threshold; exiting battery cooling when the battery temperature is lower than the second temperature threshold; turning on battery heating when the battery temperature is lower than the third temperature threshold; and exiting battery heating when the battery temperature exceeds the fourth temperature threshold; wherein, the first temperature threshold is greater than the second temperature threshold, the second temperature threshold is greater than the fourth temperature threshold, and the fourth temperature threshold is greater than the third temperature threshold.

[0153] Exemplarily, the first temperature threshold is 38 °C, the second temperature threshold is 36 °C, the third temperature threshold is 0 °C, and the fourth temperature threshold is 5 °C.

[0154] In the above implementation, when the driving scenario does not conform to the user's normal habits, adopting a fixed battery threshold thermal management strategy can ensure the normal operation of the basic thermal management function of the battery. This strategy provides a stable thermal management benchmark for the battery, ensuring the basic performance and safety of the battery even under atypical driving conditions.

[0155] Based on the same inventive concept, an embodiment of the present invention further provides a battery thermal management control device for performing thermal management control on the battery of a target vehicle. Figure 2 is a structural block diagram of a battery thermal management control device provided by an embodiment of the present invention, as Figure 2 shown, the device 200 includes:

[0156] An acquisition module 210, configured to acquire current driving data and battery state data of the target vehicle, where the current driving data includes the identity information of the current driver, the current driving time, and the current driving path, and the battery state data includes the state of charge of the battery, the battery temperature, and the battery voltage.

[0157] A first determination module 220, configured to query the user driving habit database according to the current driving data to determine whether the current driving conforms to the user's driving habit pattern, and when it conforms to the user's driving habit pattern, acquire target user driving habit data, where the target user driving habit data includes at least one of a habitual driving power and a habitual driving time.

[0158] A second determination module 230, configured to determine a battery adaptive thermal management strategy according to the target user driving habit data and the battery state data.

[0159] The control module 240 is configured to perform thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy.

[0160] In some implementation manners, the first determination module 220 is configured to:

[0161] Query whether there is historical driving data matching the current driving data in the user driving habit database with the current driving data as the index;

[0162] When there is historical driving data matching the current driving data in the user driving habit database, determine that the current driving conforms to the user driving habit pattern, and obtain the historical user driving habit data corresponding to the historical driving data matching the current driving data as the target user driving habit data;

[0163] Wherein, the user driving habit database includes multiple pieces of historical driving data and the historical user driving habit data corresponding to the multiple pieces of historical driving data, and each piece of historical user driving habit data includes the habitual driving power and the habitual driving time under the corresponding historical driving data.

[0164] In some implementation manners, the device further includes a database establishment module, which is configured to:

[0165] Obtain the historical driving data of the target vehicle in multiple consecutive historical periods, as well as the historical driving power and the historical driving time corresponding to the historical driving data. The historical driving data includes the identity information of the historical driver, the historical driving time, and the historical driving route;

[0166] Select the historical driving data used to construct the user driving habit database from the historical driving data of multiple historical periods, denoted as the target historical driving data. The target historical driving data is the historical driving data in the target historical period, and there is historical driving data matching the target historical driving data in each of the other historical periods except the target historical period. The target historical period is any one of the multiple historical periods;

[0167] Determine the habitual driving power and the habitual driving time corresponding to the target historical driving data according to the historical driving power and the historical driving time corresponding to the target historical driving data, as well as the historical driving power and the historical driving time corresponding to the historical driving data matching the target historical driving data in other historical periods;

[0168] Construct the user driving habit database according to the target historical driving data, as well as the habitual driving power and the habitual driving time corresponding to the target historical driving data.

[0169] In some implementation manners, after performing thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy, the database establishment module is further configured to:

[0170] Record the driving data, driving power, and driving time of this trip;

[0171] Based on the driving data, driving power, and driving time, update the habitual driving power and / or habitual driving time in the user's driving habit database.

[0172] In some implementation manners, the battery adaptive thermal management strategy includes a battery heating adaptive management strategy. The second determination module 230 is used for:

[0173] Determine a target power threshold according to the habitual driving power;

[0174] Determine a heating temperature shutdown threshold according to the habitual driving power and the state of charge of the battery;

[0175] Determine the dischargeable power value of the battery according to the state of charge of the battery and the battery temperature;

[0176] Determine a battery heating adaptive management strategy according to the target power threshold, the heating temperature shutdown threshold, and the dischargeable power value of the battery;

[0177] Among them, the battery heating adaptive management strategy includes: when the dischargeable power value of the battery is less than the target power threshold, turn on the battery heating; when the battery temperature is greater than or equal to the heating temperature shutdown threshold, exit the battery heating.

[0178] In some implementation manners, the battery adaptive thermal management strategy includes a battery cooling adaptive management strategy, and the battery state data further includes a battery safety temperature calibration value, a battery heat exchange power, a battery specific heat capacity, and a battery mass. The second determination module 230 is used for:

[0179] Determine a cooling temperature startup threshold according to the target user driving habit data and the battery state data;

[0180] Determine a cooling temperature shutdown threshold according to the battery safety temperature calibration value;

[0181] Determine a cooling time startup threshold according to the battery temperature, the cooling temperature startup threshold, the battery heat exchange power, and the habitual driving time;

[0182] Determine a battery cooling adaptive management strategy according to the cooling temperature startup threshold, the cooling temperature shutdown threshold, and the cooling time startup threshold;

[0183] Among them, the battery cooling adaptive management strategy includes: when the battery temperature exceeds the cooling temperature startup threshold and the driving time of the target vehicle exceeds the cooling time startup threshold, turn on the battery cooling; when the battery temperature is less than the cooling temperature shutdown threshold, exit the battery cooling.

[0184] In some implementations, when it is determined that the current driving does not conform to the user's driving habit pattern, the second determination module 230 is further configured to:

[0185] Perform thermal management control on the battery of the target vehicle according to the fixed battery threshold thermal management strategy;

[0186] The fixed battery threshold thermal management strategy includes: turning on battery cooling when the battery temperature exceeds the first temperature threshold; exiting battery cooling when the battery temperature is lower than the second temperature threshold; turning on battery heating when the battery temperature is lower than the third temperature threshold; exiting battery heating when the battery temperature exceeds the fourth temperature threshold; wherein, the first temperature threshold is greater than the second temperature threshold, the second temperature threshold is greater than the fourth temperature threshold, and the fourth temperature threshold is greater than the third temperature threshold.

[0187] The specific details of the battery thermal management control method adopted in the above control device can be understood corresponding to the relevant descriptions and effects in the battery thermal management control method embodiments shown above, and will not be elaborated here.

[0188] Based on the same inventive concept as the above battery thermal management control method, the present invention also provides an electronic device, which may include a processor and a memory, wherein the processor and the memory may be communicatively connected to each other through a bus or other means. The processor may be a central processing unit (CPU). The processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. chips, or combinations of the above types of chips. The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the battery thermal management control method in the embodiments of the present invention. The processor executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions, and modules stored in the memory, that is, to implement the battery thermal management control in the above method embodiments.

[0189] The memory may include a program storage area and a data storage area. The program storage area may store an operating system and application programs required for at least one function. The data storage area may store data created by the processor and the like. In addition, the memory may include a high-speed random access memory and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. The one or more modules are stored in the memory and, when executed by the processor, perform the battery thermal management control method in the embodiment as shown in Figure 1 the battery thermal management control method in the illustrated embodiment.

[0190] Specific details of the above electronic device may be understood by referring to the corresponding relevant descriptions and effects in the embodiment shown in Figure 1 and will not be elaborated herein.

[0191] Based on the same inventive concept as the battery thermal management control method, the present invention also provides a computer-readable storage medium having computer instructions stored thereon, and the computer program instructions are used to cause a computer to execute the battery thermal management control method in the above embodiments.

[0192] Those skilled in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when the program is executed, it may include the processes of the embodiments of the above methods. Among them, the storage medium may be a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium may also include a combination of the above types of memories.

[0193] The technical solutions provided in the above embodiments of the present application have at least the following technical effects or advantages:

[0194] An embodiment of the present invention provides a battery thermal management control, device, equipment and medium. By obtaining the current driving data and battery state data of the target vehicle and combining with the user's driving habit rules, the battery adaptive thermal management strategy is accurately determined, so as to control the battery thermal management. Compared with the existing fixed threshold strategy, this method can dynamically adjust and implement the battery adaptive thermal management strategy according to different user driving habits and actual vehicle use scenarios, effectively avoiding the generation of unnecessary energy consumption under the traditional fixed threshold strategy, significantly improving the energy utilization efficiency, extending the vehicle's cruising range, while ensuring the stable battery performance, prolonging the battery service life, improving the overall performance of electric vehicles and the user experience, and providing a more efficient and energy-saving power battery thermal management solution for the development of the electric vehicle industry.

[0195] In the specification provided here, a large number of specific details are described. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and technologies are not shown in detail so as not to obscure the understanding of this specification.

[0196] Similarly, it should be understood that, in order to streamline the present disclosure and assist in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting the intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the preceding single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0197] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims.

Claims

1. A battery thermal management control method, characterized in that, Including: Obtain the current driving data and battery status data of the target vehicle. The current driving data includes the identity information of the current driver, the current driving time, and the current driving path. The battery status data includes the state of charge of the battery, the battery temperature, and the battery voltage; Query the user driving habit database according to the current driving data to determine whether the current driving conforms to the user driving habit pattern. When it conforms to the user driving habit pattern, obtain the target user driving habit data, where the target user driving habit data includes at least one of the habitual driving power and the habitual driving time; Determine the battery adaptive thermal management strategy according to the target user driving habit data and the battery status data; Perform thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy.

2. The method according to claim 1, wherein The querying the user driving habit database according to the current driving data to determine whether the current driving conforms to the user driving habit pattern and, when it conforms to the user driving habit pattern, obtaining the target user driving habit data includes: Using the current driving data as an index, query whether there is historical driving data in the user driving habit database that matches the current driving data; When there is the historical driving data in the user driving habit database that matches the current driving data, determine that the current driving conforms to the user driving habit pattern, and obtain the historical user driving habit data corresponding to the historical driving data that matches the current driving data as the target user driving habit data; Wherein, the user driving habit database includes multiple pieces of the historical driving data and the historical user driving habit data corresponding to the multiple pieces of the historical driving data. Each piece of the historical user driving habit data includes the habitual driving power and the habitual driving time under the corresponding historical driving data.

3. The method according to claim 2, wherein Before querying the user driving habit database according to the current driving data to determine the target user driving habit data, the method further includes: Obtain the historical driving data of the target vehicle in multiple consecutive historical periods, and the historical driving power and historical driving time corresponding to the historical driving data. The historical driving data includes the identity information of the historical driver, the historical driving time, and the historical driving path; Select the historical driving data used to construct the user driving habit database from the historical driving data of the multiple historical periods, denoted as the target historical driving data. The target historical driving data is the historical driving data in the target historical period, and there is historical driving data that matches the target historical driving data in each of the other historical periods except the target historical period. The target historical period is any one of the multiple historical periods; Determine the habitual driving power and habitual driving time corresponding to the target historical driving data according to the historical driving power and historical driving time corresponding to the target historical driving data, and the historical driving power and historical driving time corresponding to the historical driving data that matches the target historical driving data in other historical periods. Construct the user driving habit database based on the target historical driving data, the habitual driving power corresponding to the target historical driving data, and the habitual driving time.

4. The method according to claim 1, characterized in that The battery adaptive thermal management strategy includes a battery heating adaptive management strategy; determining the battery adaptive thermal management strategy according to the target user driving habit data and the battery state data includes: Determine a target power threshold according to the habitual driving power. Determine a heating temperature cut-off threshold according to the habitual driving power and the state of charge of the battery. Determine the battery dischargeable power value according to the state of charge of the battery and the battery temperature. Determine the battery heating adaptive management strategy according to the target power threshold, the heating temperature cut-off threshold, and the battery dischargeable power value. Among them, the battery heating adaptive management strategy includes: turning on battery heating when the battery dischargeable power value is less than the target power threshold; exiting battery heating when the battery temperature is greater than or equal to the heating temperature cut-off threshold.

5. The method according to claim 1 or 4, characterized in that, The battery adaptive thermal management strategy includes a battery cooling adaptive management strategy, and the battery state data further includes a battery safety temperature calibration value, a battery heat exchange power, a battery specific heat capacity, and a battery mass; determining the battery adaptive thermal management strategy according to the target user driving habit data and the battery state data includes: Determine a cooling temperature activation threshold according to the target user driving habit data and the battery state data. Determine a cooling temperature cut-off threshold according to the battery safety temperature calibration value. Determine a cooling time activation threshold according to the battery temperature, the cooling temperature activation threshold, the battery heat exchange power, and the habitual driving time. Determine the battery cooling adaptive management strategy according to the cooling temperature activation threshold, the cooling temperature cut-off threshold, and the cooling time activation threshold. Among them, the battery cooling adaptive management strategy includes: turning on battery cooling when the battery temperature exceeds the cooling temperature activation threshold and the driving time of the target vehicle exceeds the cooling time activation threshold; exiting battery cooling when the battery temperature is less than the cooling temperature cut-off threshold.

6. The method according to claim 1, characterized in that When it is determined that the current driving does not conform to the user driving habit pattern, the method further includes: Perform thermal management control on the battery of the target vehicle according to the battery fixed threshold thermal management strategy. The battery fixed threshold thermal management strategy includes: turning on battery cooling when the battery temperature exceeds a first temperature threshold; exiting battery cooling when the battery temperature is lower than a second temperature threshold; turning on battery heating when the battery temperature is lower than a third temperature threshold; exiting battery heating when the battery temperature exceeds a fourth temperature threshold; wherein, the first temperature threshold is greater than the second temperature threshold, the second temperature threshold is greater than the fourth temperature threshold, and the fourth temperature threshold is greater than the third temperature threshold.

7. The method according to claim 1, wherein After performing thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy, the method further includes: Record the driving data, driving power, and driving time of this trip. Update the habitual driving power and / or habitual driving time in the user driving habit database based on the driving data, the driving power, and the driving time.

8. A battery thermal management control device, characterized in that, Comprising: An acquisition module, configured to acquire current driving data and battery state data of a target vehicle, where the current driving data includes identity information of a current driver, a current driving time, and a current driving path, and the battery state data includes a state of charge of the battery, a battery temperature, and a battery voltage; A first determination module, configured to query a user driving habit database according to the current driving data to determine whether the current driving conforms to the user driving habit pattern, and when it conforms to the user driving habit pattern, acquire target user driving habit data, where the target user driving habit data includes at least one of a habitual driving power and a habitual driving time; A second determination module, configured to determine a battery adaptive thermal management strategy according to the target user driving habit data and the battery state data; A control module, configured to perform thermal management control on the battery of the target vehicle according to the battery adaptive thermal management strategy.

9. An electronic device, characterized in that: Comprising: A memory and a processor, where the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the battery thermal management control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the battery thermal management control method according to any one of claims 1 to 7.

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