Vehicle, vehicle control method, thermal management system, storage medium, and program product

Abstract

The embodiment of the invention provides a vehicle, a vehicle control method, a thermal management system, a storage medium and a program product. The method comprises the following steps: acquiring an environment temperature of a vehicle, a battery cell temperature of a power battery and an expected battery power demand map of the travel of a user in response to reserved vehicle use time, and determining a target temperature interval of preheating of a battery cell based on the expected battery power demand map and an electrochemical model of the battery; and determining heating starting time and a heating power curve on the basis of the environment temperature, the battery cell temperature, the reserved vehicle use time and the target temperature interval, so as to heat the power battery on the basis of the heating power curve when the heating starting time is up. According to the method, the vehicle heating requirement and the influence of the environment temperature on heating of the power battery are considered, it is ensured that the temperature of the power battery meets the quick starting requirement when the reserved vehicle using time set by the user is reached, and therefore the defects in the aspects of environment adaptability, energy efficiency and user experience in the prior art are overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, and in particular to a vehicle, a vehicle control method, a thermal management system, a storage medium and a program product. BACKGROUND

[0002] Thermal management of new energy vehicle power batteries is a key technology to ensure normal operation of the vehicle in extreme temperature environments. Existing thermal management technologies generally use a pre-booking heating mode with a fixed start time.

[0003] For example, after a user sets a pre-booking vehicle use time, the vehicle control system (VCU, Vehicle Control Unit) sends the pre-booking vehicle use time to the thermal management system, which sets a fixed pre-heating time (e.g., 1 hour in advance) based on historical heating time, and then sends a heating instruction to the heat pump or PTC (Positive Temperature Coefficient) heating device to make the heat pump or PTC heating device heat the battery pack 1 hour in advance.

[0004] However, the existing technology lacks real-time monitoring and dynamic analysis of the battery cell temperature and the ambient temperature, resulting in a disconnection between the thermal management strategy and the actual demand, and failing to balance the energy efficiency and user experience. SUMMARY

[0005] The vehicle, vehicle control method, thermal management system, storage medium and program product provided by the embodiments of the present application are used to monitor and dynamically analyze the battery cell temperature and the ambient temperature of the power battery, provide a thermal management strategy that takes into account the energy efficiency and the actual demand of the vehicle, and thus balance the energy efficiency and user experience.

[0006] In a first aspect, the embodiments of the present application provide a vehicle control method, and a vehicle uses a power battery to provide power. The method comprises:

[0007] In response to a pre-booking vehicle use time set by a user, obtaining an ambient temperature of the vehicle, a battery cell temperature of the power battery, and an expected battery power demand profile of the user for this trip;

[0008] Based on the expected battery power demand profile and an electrochemical model of the battery, determining a target temperature range required for preheating of the battery cell;

[0009] Based on the ambient temperature, the battery cell temperature, the pre-booking vehicle use time, and the target temperature range, determining a heating start time and a heating power curve of the power battery;

[0010] When the heating start time is reached, heating the power battery based on the heating power curve.

[0011] In a possible implementation, the obtaining the expected battery power requirement for the trip of the user this time includes:

[0012] predicting trip feature information for the trip according to historical navigation data of the user, the reservation vehicle time, and a date type to which the reservation vehicle time belongs; the trip feature information includes: predicted mileage, predicted vehicle speed data, and altitude change condition;

[0013] predicting the expected battery power requirement graph based on the trip feature information.

[0014] In a possible implementation, the determining the heating start time and the heating power curve of the power battery based on the ambient temperature, the cell temperature, the reservation vehicle time, and the target temperature interval includes:

[0015] predicting an ambient temperature change trend before the reservation vehicle time based on the ambient temperature, a location where the vehicle is located, and the reservation vehicle time;

[0016] determining the heating start time and the heating power curve of the power battery based on the ambient temperature change trend, the cell temperature, the reservation vehicle time, a target temperature interval of the cell, and a thermal inertia parameter of the cell; the thermal inertia parameter is used to represent a temperature rise rate of the cell.

[0017] In a possible implementation, the method further includes:

[0018] obtaining sampling data of the cell;

[0019] obtaining health state data of the cell based on the sampling data of the cell; the health state data at least includes: capacity attenuation data and internal resistance change data;

[0020] determining the thermal inertia parameter based on the health state data.

[0021] In a possible implementation, the method further includes:

[0022] adjusting, in a process of heating the power battery, the heating power and the time length of each heating stage in the heating power curve based on the cell temperature, the thermal inertia parameter of the cell, and a heatable time length; the heatable time length is a difference between the reservation vehicle time and the heating start time.

[0023] In a possible implementation, the determining, based on the ambient temperature, the battery cell temperature, the pre-reserved vehicle use time, and the target temperature interval, of the heating start time and the heating power curve of the power battery comprises:

[0024] The heating start time and the heating power curve of the power battery are predicted by using a prediction model based on the ambient temperature, the battery cell temperature, the pre-reserved vehicle use time, and the target temperature interval.

[0025] The prediction model is obtained by fine-tuning a basic prediction model by using historical data of the vehicle, the basic prediction model is obtained by training based on historical heating data of a plurality of sample vehicles of the same model by using a federated learning manner, and the historical data at least includes historical heating data of the vehicle.

[0026] In a possible implementation, the method further comprises:

[0027] The external ambient temperature collected by an ambient temperature sensor of the vehicle, the local temperature around the vehicle detected by an infrared thermal imaging sensor, and the meteorological data of a location where the vehicle is located based on the location of the vehicle are obtained.

[0028] The ambient temperature of the vehicle is obtained based on the external ambient temperature, the local temperature, and the meteorological data.

[0029] In a second aspect, an embodiment of the present application provides a vehicle control device, and the vehicle uses a power battery to provide power, and the device comprises:

[0030] The acquisition module is configured to acquire the ambient temperature of the vehicle, the battery cell temperature of the power battery, and an expected battery power demand graph of the user for this trip in response to a pre-reserved vehicle use time set by the user.

[0031] The determination module is configured to determine a target temperature interval required for preheating of the battery cell based on the expected battery power demand graph and an electrochemical model of the battery.

[0032] The determination module is further configured to determine the heating start time and the heating power curve of the power battery based on the ambient temperature, the battery cell temperature, the pre-reserved vehicle use time, and the target temperature interval.

[0033] The heating module is configured to heat the power battery based on the heating power curve when the heating start time is reached.

[0034] In a possible implementation, the device further comprises a prediction module.

[0035] predict a trip feature information of the trip according to the historical navigation data of the user, the reservation time of use of the vehicle, and a date type to which the reservation time of use of the vehicle belongs; the trip feature information comprises: predicted mileage, predicted vehicle speed data, and altitude change condition;

[0036] The prediction module is further configured to predict the expected battery power requirement graph based on the trip feature information.

[0037] In a possible implementation, the prediction module is further configured to predict a temperature change trend of the environment before the reservation time of use of the vehicle based on the environment temperature, the location where the vehicle is located, and the reservation time of use of the vehicle.

[0038] The determination module is further configured to determine a heating start time and a heating power curve of the power battery based on the temperature change trend of the environment, the cell temperature, the reservation time of use of the vehicle, a target temperature interval of the cell, and a thermal inertia parameter of the cell; the thermal inertia parameter is used to represent a temperature rise rate of the cell.

[0039] In a possible implementation, the acquisition module is further configured to acquire sampling data of the cell.

[0040] The acquisition module is further configured to acquire health state data of the cell based on the sampling data of the cell; the health state data at least comprises: capacity attenuation data and internal resistance change data.

[0041] The determination module is further configured to determine the thermal inertia parameter based on the health state data.

[0042] In a possible implementation, the apparatus further comprises an adjustment module.

[0043] The adjustment module is configured to adjust, in a process of heating the power battery, a heating power and a time length corresponding to each heating stage in the heating power curve based on the cell temperature, the thermal inertia parameter of the cell, and a heatable time length; the heatable time length is a difference between the reservation time of use of the vehicle and the heating start time.

[0044] In a possible implementation, the prediction module is further configured to predict the heating start time and the heating power curve of the power battery by using a prediction model based on the environment temperature, the cell temperature, the reservation time of use of the vehicle, and the target temperature interval.

[0045] The prediction model is obtained by fine-tuning a basic prediction model based on historical data of the vehicle, the basic prediction model is obtained by training based on historical heating data of a plurality of sample vehicles of the same type through federated learning, and the historical data at least includes historical heating data of the vehicle.

[0046] In a possible implementation, the acquisition module is further configured to acquire an external environment temperature collected by an environment temperature sensor of the vehicle, a local temperature around the vehicle detected by an infrared thermal imaging sensor, and weather data of a location where the vehicle is located based on a position of the vehicle.

[0047] The acquisition module is further configured to obtain an environment temperature of the vehicle based on the external environment temperature, the local temperature, and the weather data.

[0048] In a third aspect, an electronic device is provided, including a memory and a processor.

[0049] The memory stores computer-executable instructions.

[0050] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect.

[0051] In a fourth aspect, an electronic device is provided, including a memory and a processor.

[0052] In a fifth aspect, a vehicle is provided, including a power battery and a thermal management system.

[0053] The thermal management system is configured to heat the power battery by using the first aspect and / or various possible implementation manners of the first aspect.

[0054] In a sixth aspect, a computer-readable storage medium is provided, which stores computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the second aspect and / or various possible implementation manners of the second aspect.

[0055] In a seventh aspect, a computer program product is provided, which includes a computer program, and the computer program is executed by a processor to implement the second aspect and / or various possible implementation manners of the second aspect.

[0056] The vehicle control method provided by the embodiment of the present application comprises the following steps: in response to a user setting a reservation vehicle time, obtaining an ambient temperature of a vehicle, a temperature of an electric core of a power battery, and an expected battery power demand graph of a user's trip this time; determining a target temperature interval required for preheating of the electric core based on the expected battery power demand graph and an electrochemical model of the battery; and determining a heating start time and a heating power curve of the power battery based on the ambient temperature, the temperature of the electric core, the reservation vehicle time, and the target temperature interval, so as to heat the power battery based on the heating power curve when the heating start time is reached. The method takes into account the heating demand of the vehicle and the influence of the ambient temperature on the heating of the power battery, so that the obtained heating start time and heating power curve are adapted to the current environment and matched with the battery power demand, so as to ensure that the temperature of the power battery meets the rapid start demand when the user sets the reservation vehicle time, thereby solving the deficiencies of the prior art in terms of environmental adaptability, energy efficiency, and user experience. BRIEF DESCRIPTION OF DRAWINGS

[0057] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the present application.

[0058] Figure 1 A structural schematic diagram of a vehicle provided by the present application is shown in the following figure.

[0059] Figure 2 A flowchart of a vehicle control method provided by the present application is shown in the following figure. Figure One ;

[0060] Figure 3 A flowchart of a vehicle control method provided by the present application is shown in the following figure. Figure Two ;

[0061] Figure 4 A flowchart of a vehicle control method provided by the present application is shown in the following figure. Figure Three ;

[0062] Figure 5 A structural schematic diagram of a vehicle control device provided by the present application is shown in the following figure.

[0063] Figure 6 A structural schematic diagram of an electronic device provided by the present application is shown in the following figure.

[0064] The specific embodiments of the present application have been shown in the above figures, and will be described in more detail hereinafter. These figures and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0065] The exemplary embodiments will be described in detail below with reference to the drawings. In the following description, the same numbers are used to denote the same elements throughout the several views. The embodiments described in the following exemplary embodiments are not meant to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present application as detailed in the appended claims.

[0066] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for user to choose authorization or refusal.

[0067] And the present application involves big data analysis of user information (including but not limited to personal biological characteristics, identity data, consumption data, asset data, electronic terminal operation data, etc.), and uses artificial intelligence technology for automatic decision-making, and makes technical solutions based on automatic decision-making results that have a significant impact on personal rights and interests, provides corresponding operation portal for users to choose to agree or refuse automatic decision-making results; if the user chooses to refuse, the expert decision-making process is entered.

[0068] The thermal management of new energy vehicle power battery is a key technology to ensure the normal operation of the vehicle in extreme temperature environment. For example, under low temperature working condition, the chemical activity of power battery decreases significantly, the internal resistance of the battery increases, which leads to battery capacity attenuation, charging efficiency decline and even safety risk.

[0069] The existing thermal management technology generally uses a pre-booking heating mode with fixed time start. Specifically, after the user sets the pre-booking time of the vehicle, the vehicle control system sets a fixed pre-heating time (for example, 1 hour in advance) based on experience, and heats the battery pack through a heat pump or a PTC heating device.

[0070] However, when the ambient temperature is high, pre-heating may cause the battery temperature to be overheated, and then naturally cooled to a temperature close to the time of use, which requires secondary heating, increasing energy consumption. In extremely cold environments, a fixed pre-heating time may not be enough to heat the battery to the optimal working temperature (e.g., 15-35℃), resulting in limited performance when the vehicle starts.

[0071] Therefore, the prior art lacks real-time monitoring and dynamic analysis of the temperature of the battery cell and the ambient temperature, resulting in a provided thermal management strategy that is out of touch with actual needs and cannot balance energy efficiency and user experience.

[0072] To solve the above problems, the present application provides a vehicle control method, which dynamically monitors the ambient temperature and the temperature of the battery cell of the power battery, and dynamically calculates the heating start time and the heating power curve in combination with the expected battery power demand of the user's trip, to achieve adaptive optimization of the thermal management of the power battery. In this method, the thermal management strategy is flexibly adjusted according to the ambient temperature, the temperature of the battery cell, and the change in the battery power demand, taking into account the energy efficiency and user experience of the thermal start.

[0073] The vehicle management method provided by the present application is applicable to application scenarios that require pre-heating of the battery before use, such as scenarios in cold regions where the ambient temperature in winter is as low as-30℃ or below.

[0074] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0075] Figure 1 A schematic structural diagram of a vehicle provided by the present application is shown in Figure 1 The vehicle 10 can include a power battery 11 and a thermal management system 12.

[0076] The thermal management system 12 uses a thermal management strategy to heat the power battery 11. The power battery 11 may, for example, be a lithium ion battery, a nickel-hydrogen battery, a solid-state battery, or a fuel cell, etc.

[0077] The thermal management system 12 includes but is not limited to a data acquisition device, a processor, and a heating execution device. The data acquisition device includes a first temperature sensor arranged on the power battery 11, a second temperature sensor for acquiring the ambient temperature, and a positioning module. A communication protocol is configured between the processor and the data acquisition device, and the data acquisition device uploads the acquired ambient temperature, the temperature of the battery cell of the power battery 11, and the position data of the vehicle 10 to the processor according to the pre-configured communication protocol.

[0078] The processor receives the ambient temperature, the temperature of the battery cell, and the position data, and determines the heating start time and the heating power curve of the power battery 11 in combination with the user's pre-set trip reservation information, and drives the heating execution device to heat the power battery according to the heating power curve when the heating start time is reached.

[0079] The heating execution device can be a PTC heater, a vehicle heat pump system, or a battery self-heating unit.

[0080] Optionally, the vehicle 10 is configured with a battery management system (BMS) to monitor the state parameters (such as temperature, voltage, and current) of the power battery. The processor of the thermal management system 12 can also obtain the battery temperature from the BMS system. The battery temperature can be collected by a battery temperature sensor arranged on the power battery.

[0081] The thermal management system described above can be configured to perform a vehicle control method. Figure 2 A flowchart of a vehicle control method provided in the present application Figure One As shown in Figure 2 , the vehicle control method comprises:

[0082] S201, in response to the user setting the reservation time of the vehicle, obtaining the ambient temperature of the vehicle, the battery temperature of the power battery, and the expected battery power demand profile of the user's trip.

[0083] The user reserves the trip through the vehicle terminal or the mobile phone APP (application), and after the vehicle control system (VCU) receives the trip reservation information, the reservation information is synchronized to the thermal management system to trigger the thermal management system to determine the thermal management strategy. The trip reservation information includes the user setting the reservation time of the vehicle.

[0084] The ambient temperature of the vehicle is used to indicate the external environment temperature of the vehicle parking position, which is obtained through the vehicle sensor or positioning information. For example, based on the GPS (Global Positioning System) positioning, the real-time weather data of the vehicle parking position is obtained.

[0085] The battery temperature can be obtained by the BMS collecting the temperature data of multiple batteries in the power battery and taking the average value as the battery temperature.

[0086] In addition, from the vehicle historical trip data or the historical trip data of the same type of vehicle in the cloud, the target historical trip data matching the user's trip reservation information is determined, and the target historical trip data includes the battery power demand of the vehicle in at least one historical period. Based on the battery power demand corresponding to each historical period, the expected battery power demand profile of the user's trip is generated.

[0087] For example, the user sets the travel appointment information as "going to work at 8 am", and multiple historical commuting travel data are matched from the vehicle historical travel data, and the historical commuting travel data in the last 7 days are statistically analyzed to obtain the user's early morning commuting time length of 15 minutes, 0-5 minutes passing through the climbing road section, the historical power of 150kw, and 5-15 minutes for the flat road section, the historical power of 60kw-80kw.

[0088] Therefore, based on the historical power of 150kw corresponding to 0-5 minutes and the historical power of 60kw-80kw corresponding to 5-15 minutes, an expected battery power demand graph for this trip of the user is constructed. The expected battery power demand graph takes time as the horizontal axis and power as the vertical axis, for example.

[0089] S202, based on the expected battery power demand graph and the electrochemical model of the battery, determining a target temperature interval required for preheating of the battery cell.

[0090] The electrochemical model is used to indicate the mapping relationship between the battery power and the minimum temperature value that the battery should maintain. The electrochemical model is determined by the battery cell specification parameters of the power battery, the vehicle operation data of the power battery of the same type, and the laboratory accelerated aging test data, and is adjusted based on the aging degree of the battery cell during use. The trigger time of the adjustment may be, for example, a preset adjustment period, a cumulative charge-discharge cycle number reaching a preset threshold, etc. The key indicators representing the aging degree of the battery cell include but are not limited to the cycle life mileage and the change rate of the battery cell internal resistance.

[0091] In the expected battery power demand graph, each time period corresponds to at least one battery power demand value. Based on the electrochemical model, the minimum temperature value corresponding to the battery power demand in each time period is determined, and from the multiple minimum temperature values, the lower limit value and the upper limit value of the temperature are determined to obtain the target temperature interval.

[0092] For example, the expected battery power demand graph includes a first time period and a second time period. The first time period corresponds to a first battery power demand value, and the first battery power demand value is input into the electrochemical model to obtain a first minimum temperature value; the second time period corresponds to a second battery power demand value and a third battery power demand value, and based on the electrochemical model, the second battery power demand value and the third battery power demand value correspond to a second minimum temperature value and a third minimum temperature value, respectively. The second battery power demand value is less than the third battery power demand value, and the second minimum temperature value is less than the third minimum temperature value.

[0093] Comparing the first minimum temperature value, the second minimum temperature value and the third minimum temperature value, it is found that the first minimum temperature value is the smallest and the third minimum temperature value is the largest, so the target temperature interval is [the first minimum temperature value, the third minimum temperature value].

[0094] This step realizes accurate matching of the expected battery power demand and the target temperature range through the electrochemical model of the battery, which is beneficial to guarantee the stability of power output during vehicle starting and driving.

[0095] In some embodiments, the lower limit value of the target temperature range is increased by a preset increment in consideration of heat loss during heating when the ambient temperature is lower than a first temperature threshold. For example, the lower limit value T1 of the target temperature range is increased by 3℃ when the ambient temperature is lower than -20℃.

[0096] It can be understood that the first temperature threshold is a critical value of the influence degree of the ambient temperature on the heating of the battery cell. When the ambient temperature is greater than or equal to the first temperature threshold, the ambient temperature is too low, which significantly slows down the heating efficiency of the battery cell. The preset increment is used to compensate for the heat loss of the battery cell at extremely low temperature. The first temperature threshold and the preset increment can be calibrated by experiments, and the specific values of the first temperature threshold and the preset increment are not limited in the present application.

[0097] S203, determining the heating start time and the heating power curve of the power battery based on the ambient temperature, the battery cell temperature, the pre-booking time, and the target temperature range.

[0098] The purpose of this step is to determine a heating strategy that adapts to environmental changes and battery cell states and accurately matches the pre-booking time, so that the power battery is maintained in the target temperature range at the pre-booking time.

[0099] Specifically, the heating start time and the heating power curve are determined based on the ambient temperature, the battery cell temperature, the pre-booking time, and the target temperature range according to a pre-calibrated mapping relationship.

[0100] In an implementation manner, the battery thermodynamic equation and the heat transfer equation are taken as physical constraints to construct the mapping relationship through a large number of calibration experiments. The calibration experiments include: taking the battery thermodynamic equation and the heat transfer equation as physical constraints, setting the ambient temperature, the battery cell temperature, the pre-booking time, the target temperature range, and setting the upper limit value of the heating power; using the orthogonal experiment method, simulating more than one hundred working condition combinations in an environmental chamber, calculating the theoretical heating time and the heating start time. Conducting real test experiments to obtain the heating start time and the heating power curve corresponding to each working condition combination under the condition that the pre-booking time and the battery cell temperature are stable in the target temperature range, and constructing a mapping relationship database of “input parameters-output control quantities” through multivariate nonlinear fitting.

[0101] Further, through real vehicle road test verification under different climates and battery aging states, the deviation of the real vehicle data and the output parameters of the mapping relationship database is compared, and if the deviation is more than 8%, the fitting coefficient is updated by supplementing the experiment, and a dynamic optimization mechanism every 1000h is established.

[0102] S204, when the heating start time is reached, heating the power battery based on the heating power curve.

[0103] When the timing of the on-board clock reaches the heating start time, the processor of the thermal management system sends a heat start instruction to the heating execution device, the heat start instruction being generated based on the heat start identifier, the heating start time and the heating power curve, and being used to instruct the heating execution device to heat the power battery according to the heating power curve at the heating start time.

[0104] The vehicle control method provided by the embodiment of the present application responds to the user-set reservation vehicle time, acquires the ambient temperature of the vehicle, the cell temperature of the power battery, and the expected battery power demand graph of the user's this trip, and determines the target temperature interval required for preheating of the cell based on the expected battery power demand graph and the electrochemical model of the battery, and then determines the heating start time and the heating power curve of the power battery based on the ambient temperature, the cell temperature, the reservation vehicle time and the target temperature interval, so as to heat the power battery based on the heating power curve when the heating start time is reached. This method takes into account the heating demand of the vehicle and the influence of the ambient temperature on the heating of the power battery, so that the obtained heating start time and heating power curve are adapted to the current environment and matched with the battery power demand, so as to ensure that the temperature of the power battery meets the rapid start demand when the user-set reservation vehicle time is reached, thereby solving the deficiencies of the prior art in environmental adaptability, energy efficiency and user experience.

[0105] In an implementation manner, in order to avoid excessive heating or repeated heating, reduce energy consumption, and at the same time ensure that the cell temperature reaches the target temperature interval at the reservation vehicle time, the heating power and the time length of each heating stage in the heating power curve are adjusted based on the cell temperature, the thermal inertia parameter of the cell and the heatable time length during the heating of the power battery.

[0106] The heatable time length is the difference between the reservation vehicle time and the heating start time. The thermal inertia parameter is used to represent the temperature rising rate of the cell.

[0107] The heating power curve includes a preheating stage, a main heating stage and a maintenance heating stage. In the preheating stage, heating is started at a lower power to avoid a sudden rise in the cell temperature; in the main heating stage, the heating power is increased to make the cell temperature reach a target temperature value; and in the maintenance heating stage, the cell temperature reaches the lower limit value of the target temperature interval through intermittent heating, and the battery temperature is maintained stable in the target temperature interval.

[0108] Specifically, the battery cell temperature and thermal inertia parameter are collected in real time, and the heatable duration is calculated based on the thermal starting time and the current time. Based on the real-time battery cell temperature and thermal inertia parameter, it is determined whether the battery cell temperature can reach the lower limit value of the target temperature interval within the heatable duration, and the target temperature interval is maintained at the reservation time.

[0109] If yes, the heating execution device is controlled according to the current heating power curve to heat the power battery.

[0110] If no, the current ambient temperature is collected, and the ambient temperature, the battery cell temperature at the current time, the thermal inertia parameter and the heatable duration are input into the pre-trained optimization model to obtain the heating power and the market of the multiple heating stages.

[0111] It can be understood that, in order to ensure that the battery cell temperature reaches the lower limit value of the target temperature interval within the heatable duration, the heating power curve and the duration of the main heating stage are determined first, and then the heating power and the duration corresponding to the pre-heating stage and the maintenance heating stage are determined based on the remaining duration.

[0112] In some embodiments, the battery cell temperature and the heatable duration are updated every 3 minutes, and the adaptability of the duration and the power of each stage is rechecked. If the user temporarily changes the reservation time, resulting in a sharp decrease in the heatable duration (≤15 minutes), the pre-heating stage is skipped and the main heating high-power mode is started; if the battery cell temperature rising rate deviates from the thermal inertia parameter by >20%, the power curve is corrected in real time; after each heating cycle, the deviation between the actual effect and the model prediction is recorded, and the thermal inertia parameter and the power adjustment coefficient are updated to improve the adjustment accuracy in the subsequent period.

[0113] It can be understood that the method for determining the thermal inertia parameter of the battery cell comprises: obtaining sampling data of the battery cell; obtaining health state data of the battery cell based on the sampling data of the battery cell; and determining the thermal inertia parameter based on the health state data.

[0114] The health state data at least includes capacity attenuation data and internal resistance change data. For example, the battery capacity attenuation rate is 20% and the internal resistance increase rate is 15% through BMS collection.

[0115] Specifically, the BMS collects the health state data of the battery in real time, and outputs the thermal inertia parameter of the battery cell based on the health state-thermal inertia parameter calibration matrix. The health state-thermal inertia parameter calibration matrix is trained and generated based on the accelerated aging experiment data of the whole life cycle of the power battery, and the quantitative mapping relationship between the health state data such as the capacity attenuation rate and the internal resistance change rate of the battery cell and the thermal inertia parameter is established.

[0116] For example, the internal resistance of the aged battery is high, resulting in a slow temperature rising rate, and the model will automatically adjust the heating starting time to compensate for the thermal inertia.

[0117] The method determines the thermal inertia parameter through the health state data of the power battery, solves the problem that the battery aging difference is not considered in the prior art, and enables the thermal management strategy to adapt to batteries with different aging degrees, for example, shortens the heating time when a new battery heats up quickly, and prolongs the heating time when an aged battery heats up slowly. The method significantly improves the personalized adaptation capability of the thermal management strategy, avoids excessive heating or insufficient heating caused by a unified strategy, and prolongs the battery life.

[0118] Figure 3 A flowchart of a vehicle control method provided in the present application Figure Two As shown in Figure 3 The specific process of obtaining the expected battery power demand of the user's trip this time includes:

[0119] S301, according to the historical navigation data of the user, the reservation vehicle time, and the date type to which the reservation vehicle time belongs, the trip feature information of this trip is predicted.

[0120] The trip feature information includes: predicted mileage, predicted vehicle speed data, and altitude change.

[0121] It can be understood that the historical navigation data can be collected from the vehicle navigation system or the mobile phone APP, for example. The historical navigation data may, for example, include the user's historical trip data in the past 5 months.

[0122] In some embodiments, the user sets a destination, at least one historical target scene similar to the current user scene is matched from the historical navigation data based on the reservation vehicle time, the date type and the destination, and the historical driving route with the highest driving frequency is taken as the trip route based on the historical driving route corresponding to the at least one historical target scene, and the trip feature information of this trip is predicted in combination with the historical driving data corresponding to the trip route and the reservation vehicle time.

[0123] For example, the historical driving data is statistically analyzed to obtain historical trip statistical characteristics, which include but are not limited to historical mileage average, historical vehicle speed distribution and historical altitude change range. Based on the reservation vehicle time, the trip feature data of the trip route is obtained from the cloud. The trip feature data includes but is not limited to: road conditions, mileage data, vehicle speed distribution data of the same type of vehicle in the trip route, and altitude change data corresponding to the trip route. Further, the trip feature information of this trip is predicted in combination with the historical trip statistical characteristics and the trip feature data.

[0124] The historical trip statistics of the example "workday morning peak - home to company" use scenario are: average mileage 12 km, average speed 60 km / h, and cumulative elevation gain 30 m. The trip feature data obtained from the cloud are: congestion index 20%, average speed of the same type of vehicle on the congested road section 35 km / h, and the route needs to detour 500 m due to construction.

[0125] Based on the historical trip statistics and the trip feature data, the predicted mileage is 12 km + 500 m, the predicted speed data is adjusted according to the congestion, the speed on the congested road section is corrected to 35 km / h, and the elevation change is cumulative elevation gain 30 m. Finally, the trip feature information that fits the actual scenario is obtained.

[0126] In some embodiments, the user does not set a destination, and at least one similar scenario is matched from the historical navigation data based on the reservation use time and date type. For example, 8:30 on weekdays corresponds to a historical commuting scenario, and 17:00 on weekends corresponds to a shopping or travel scenario.

[0127] Each similar scenario corresponds to a destination, and the destination with the highest frequency is taken as the destination of this trip. Then, the trip feature information corresponding to the destination of this trip is predicted based on the use scenarios in the historical navigation data that match the reservation use time and the date type to which the reservation use time belongs.

[0128] S302, based on the trip feature information, predict the expected battery power demand graph.

[0129] The trip feature information is input into the prediction model to obtain the battery power demand graph. The prediction model may, for example, be a CNN-LSTM hybrid neural network. CNN is used to extract the spatial correlation in the trip feature information (such as the coupling relationship between elevation and speed), and LSTM is used to capture the time sequence dependence of the influence of speed change on battery power.

[0130] The prediction model described above is trained based on a historical trip data set. The historical trip feature data in the historical trip data set corresponds to an actual battery power curve. The actual battery power curve may, for example, be recorded in real time by the vehicle-mounted system during vehicle travel.

[0131] It can be understood that the loss function of the prediction model may, for example, be the mean square error between the expected battery power demand graph and the actual battery power graph.

[0132] The vehicle control method provided in the embodiments of the present application predicts the trip feature information of this trip according to the historical navigation data of the user, the reserved vehicle use time, and the date type to which the reserved vehicle use time belongs, and predicts the expected battery power demand graph based on the trip feature information, so that the battery power demand graph that adapts to the vehicle use scene and matches the user's vehicle use habit is obtained, which is beneficial to accurately predict the high-power demand scene such as climbing and high speed, thereby improving the prediction accuracy of the thermal management strategy.

[0133] Figure 4 Flowchart of a vehicle control method provided in the present application Figure Three As shown in Figure 4 The step S203 may, for example, include the following steps.

[0134] S401, based on the ambient temperature, the location of the vehicle, and the reserved vehicle use time, predict the ambient temperature change trend before the reserved vehicle use time.

[0135] The ambient temperature includes the temperature value of the environment in which the vehicle is located collected by the vehicle-mounted ambient temperature sensor. The temperature change data of the location of the vehicle in a preset time period is obtained by calling a meteorological service API (Application Programming Interface). The preset time period includes the time period between the current time and the reserved vehicle use time.

[0136] The temperature change data, the ambient temperature, and the reserved vehicle use time are input into a temperature change prediction model to obtain the ambient temperature change trend before the reserved vehicle use time. The temperature change prediction model is trained based on the historical ambient temperature, the historical temperature change data of the location of the vehicle in a historical preset time period, and the historical ambient temperature change trend in the historical preset time period. The historical reservation time period is determined based on the historical reserved vehicle use time.

[0137] It can be understood that the temperature change prediction model may, for example, be a long short-term memory neural network.

[0138] In an implementation manner, the method for determining the ambient temperature of the vehicle may, for example, be: obtaining the external environment temperature collected by the ambient temperature sensor of the vehicle, the local temperature around the vehicle detected by the infrared thermal imaging sensor, and the meteorological data of the location of the vehicle based on the location of the vehicle; and obtaining the ambient temperature of the vehicle based on the external environment temperature, the local temperature, and the meteorological data.

[0139] Specifically, the external environment temperature and the local temperature around the vehicle are weighted and averaged to obtain a first ambient temperature, and a temperature correction factor corresponding to the meteorological data is determined. The first ambient temperature is corrected based on the temperature correction factor to obtain the ambient temperature of the vehicle.

[0140] It can be understood that the weather data includes but is not limited to temperature change rate, wind speed and humidity.

[0141] In some embodiments, an update time of temperature data in the weather data is acquired, and a temperature correction factor is calculated based on a time difference between the update time and a current time, and a temperature change rate in the weather data. The temperature correction factor = time difference x temperature change rate.

[0142] After obtaining the temperature correction factor, the first ambient temperature is added to the temperature correction factor to obtain the ambient temperature of the vehicle.

[0143] For example, the external environment temperature collected by the ambient temperature sensor is-15℃, the local temperature around the vehicle detected by the infrared thermal imaging sensor is-20℃ (such as parked in the shade), and the ambient temperature is corrected to-18℃ by the weighted average algorithm. The GPS positioning module acquires the weather data (such as the night cooling rate) of the parking position of the vehicle, and further optimizes the ambient temperature prediction.

[0144] The method corrects the ambient temperature data through multi-sensor fusion technology, solves the heating strategy deviation problem caused by single sensor error in the prior art, significantly improves the accuracy of ambient temperature prediction, and ensures the battery temperature control effect in different parking scenarios.

[0145] S402, based on the ambient temperature change trend, the cell temperature, the reservation time of use, the target temperature interval of the cell, and the thermal inertia parameter of the cell, determine the heating start time and the heating power curve of the power battery.

[0146] The thermal inertia parameter is used to represent the temperature rise rate of the cell.

[0147] The ambient temperature change trend, the cell temperature, the reservation time of use, the target temperature interval of the cell, and the thermal inertia parameter of the cell are input into a dynamic algorithm model to obtain the heating start time and the heating power curve of the power battery.

[0148] It can be understood that the historical heating data is acquired, the historical ambient temperature change trend, the historical cell temperature, the historical reservation time of use, the historical target temperature interval, and the historical thermal inertia parameter in the historical heating data are used to construct historical input data, the historical heating start time and the historical heating power curve in the historical heating data are used to construct training labels corresponding to the historical input data. Based on a plurality of historical input data-training labels, a training set is generated to train an initial dynamic algorithm model.

[0149] In an implementation, the dynamic algorithm model comprises, for example, a thermodynamic rule module and a machine learning prediction module. The thermodynamic rule module is constructed based on physical laws such as thermal conduction of the battery cell, heat dissipation of the environment, etc., and is used to calculate the basic heating duration and the lower limit value of the heating power. The machine learning prediction module may, for example, be an XGBoost (eXtreme Gradient Boosting) used to fit the coupling relationship of multiple parameters, correct the output of the thermodynamic rule module, and obtain the heating start time and heating power curve of the power battery.

[0150] For example, assuming that the reservation time for using the vehicle is 08:00, the current ambient temperature is -20°C, the battery cell temperature is -15°C, and the model predicts that the ambient temperature will drop to -25°C during 06:00-07:30, the optimal heating start time is calculated to be 06:30 to ensure that the battery temperature stabilizes at 20°C at 08:00.

[0151] In another implementation, the environmental temperature variation trend, the battery cell temperature, the reservation time for using the vehicle, the target temperature interval of the battery cell, and the thermal inertia parameter of the battery cell are input into the dynamic algorithm model, and the target heating duration is output via the thermodynamic rule module of the dynamic algorithm model.

[0152] Further, the target heating duration is used as a constraint condition, the minimum total energy consumption is used as an optimization target, and the pre-heating duration, the main heating duration, and the maintenance heating duration, as well as the target pre-heating temperature, the target main heating temperature, and the target maintenance temperature, are continuously adjusted in combination with the target temperature interval, the thermal inertia parameter of the battery cell, the battery cell temperature, and the ambient temperature, the heating power curve of each stage corresponding to a plurality of duration combinations and segment target temperature combinations is calculated, and the multi-stage heating power curve with the minimum energy consumption under the current ambient temperature and the battery cell state is output.

[0153] For example, the target pre-heating temperature is 40% of the lower limit value of the target temperature interval, and the target main heating temperature is 90% of the lower limit value of the target temperature interval.

[0154] It can be understood that the multi-stage heating power curve is determined based on the machine learning prediction module. In the prediction process, the main heating stage is preferentially allocated with a duration, then the pre-heating stage is allocated with a duration, and the heating power curve of the stage is determined. Finally, the maintenance heating duration is calculated based on the target heating duration, the main heating duration, and the pre-heating duration, and intermittent heating power is provided within the maintenance heating duration to ensure that the battery cell temperature is within the target temperature interval.

[0155] Optionally, the heating start time and the heating power curve of the power battery can also be predicted based on the ambient temperature, the battery cell temperature, the reservation time for using the vehicle, and the target temperature interval.

[0156] The prediction model is obtained by fine-tuning the basic prediction model using historical vehicle data; the basic prediction model is trained using federated learning based on historical heating data of multiple sample vehicles of the same model; the historical data includes at least the historical heating data of the vehicles.

[0157] The vehicle's historical heating data includes historical heating start time and multi-dimensional prediction parameters corresponding to the historical heating power curve. These multi-dimensional prediction parameters include at least historical ambient temperature, historical battery cell temperature, historical scheduled vehicle usage time, and historical target temperature range.

[0158] Understandably, the prediction model can be, for example, an LSTM (Long Short-Term Memory) neural network model.

[0159] This method, by introducing a multivariate prediction model, can more accurately capture the complex interaction between the environment and battery state (such as the decreased heating rate due to battery aging and the diurnal temperature variation in local microclimates), thereby dynamically adjusting the heating start-up time. For example, when a vehicle is parked in a garage in a cold region, the prediction model can identify the garage's insulation characteristics and reduce the preheating time; while when parked outdoors and experiencing rapid cooling at night, the prediction model can start heating earlier to compensate for the sudden drop in ambient temperature. This refined prediction significantly improves the adaptability of the heating strategy, avoids errors caused by a single variable (such as ambient temperature alone), and reduces the problems of repeated heating or insufficient heating caused by prediction bias.

[0160] It should be noted that the prediction model updates the training data after each heating cycle, and adapts to long-term changes in different vehicles and environments through an online learning mechanism.

[0161] The vehicle control method provided in this application predicts the ambient temperature change trend before the scheduled vehicle use time based on the ambient temperature, the vehicle's location, and the scheduled vehicle use time. This allows for dynamic adjustment of the heating start time and heating power curve based on ambient temperature change trends such as day-night temperature differences and sudden temperature drops, resulting in a thermal management strategy that is highly adaptable to changes in the vehicle's operating environment.

[0162] Furthermore, based on the ambient temperature change trend, cell temperature, scheduled vehicle usage time, target temperature range of the cell, and the cell's thermal inertia parameters, the heating start-up time and heating power curve of the power battery are determined. This method integrates multiple parameters to calculate the heating start-up time and heating power curve, improving the accuracy of both and avoiding problems such as insufficient or excessive heating caused by fixed-time strategies.

[0163] Figure 5 A schematic diagram of the vehicle control device provided in this application. The vehicle uses a hybrid power system, such as...Figure 5 As shown, the vehicle control device 50 provided by the embodiment includes:

[0164] The acquisition module 501 is configured to acquire, in response to a reservation time set by a user, an ambient temperature of a vehicle, a temperature of an electric core of a power battery, and an expected battery power demand graph of a trip of the user this time;

[0165] The determination module 502 is configured to determine a target temperature range required for preheating of the electric core based on the expected battery power demand graph and an electrochemical model of the battery.

[0166] The determination module 502 is further configured to determine a heating start time and a heating power curve of the power battery based on the ambient temperature, the temperature of the electric core, the reservation time, and the target temperature range.

[0167] The heating module 503 is configured to heat the power battery based on the heating power curve when the heating start time is reached.

[0168] In a possible implementation, the device further includes a prediction module 504.

[0169] The prediction module 504 is configured to predict travel feature information of the trip this time according to historical navigation data of the user, the reservation time, and a date type to which the reservation time belongs; the travel feature information includes expected mileage, expected speed data, and altitude change.

[0170] The prediction module 504 is further configured to predict the expected battery power demand graph based on the travel feature information.

[0171] In a possible implementation, the prediction module 504 is further configured to predict a trend of change of the ambient temperature before the reservation time based on the ambient temperature, a location of the vehicle, and the reservation time.

[0172] The determination module 502 is further configured to determine the heating start time and the heating power curve of the power battery based on the trend of change of the ambient temperature, the temperature of the electric core, the reservation time, a target temperature range of the electric core, and a thermal inertia parameter of the electric core; the thermal inertia parameter is used to represent a temperature rising rate of the electric core.

[0173] In a possible implementation, the acquisition module 501 is further configured to acquire sampling data of the electric core.

[0174] The acquisition module 501 is further configured to acquire health state data of the electric core based on the sampling data of the electric core; the health state data at least includes capacity attenuation data and internal resistance change data.

[0175] The determination module 502 is further configured to determine the thermal inertia parameter based on the health state data.

[0176] In a possible implementation, the apparatus further includes an adjusting module 505.

[0177] The adjusting module 505 is configured to, during the heating of the power battery, adjust the heating power and the time length of each heating stage in the heating power curve based on the cell temperature, the thermal inertia parameter of the cell, and the heatable time length, the heatable time length being a difference between the reservation time and the heating start time.

[0178] In a possible implementation, the prediction module 504 is further configured to predict the heating start time and the heating power curve of the power battery by using a prediction model based on the environment temperature, the cell temperature, the reservation time, and the target temperature range.

[0179] The prediction model is obtained by fine-tuning a basic prediction model using historical data of the vehicle, and the basic prediction model is obtained by training a basic prediction model based on historical heating data of a plurality of sample vehicles of the same type through federated learning.

[0180] In a possible implementation, the obtaining module 501 is further configured to obtain an external environment temperature collected by an environment temperature sensor of the vehicle, a local temperature around the vehicle detected by an infrared thermal imaging sensor, and weather data of a location where the vehicle is located based on a location of the vehicle.

[0181] The obtaining module 501 is further configured to obtain the environment temperature of the vehicle based on the external environment temperature, the local temperature, and the weather data.

[0182] The vehicle control apparatus provided in this embodiment can perform the method provided in the method embodiments, and has similar implementation principles and technical effects, which will not be described here again.

[0183] Figure 6 A structural schematic diagram of an electronic device provided in this application is shown in FIG. 6. Figure 6 As shown in FIG. 6, the electronic device 60 provided in this embodiment includes at least one processor 601 and a memory 602. Optionally, the device 60 further includes a communication component 603. The processor 601, the memory 602, and the communication component 603 are connected through a bus 604.

[0184] In the specific implementation process, the at least one processor 601 executes the computer execution instructions stored in the memory 602, so that the at least one processor 601 performs the method described above.

[0185] The specific implementation process of the processor 601 can refer to the method embodiments described above, and has similar implementation principles and technical effects, which will not be described here again.

[0186] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0187] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), such as at least one disk memory.

[0188] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0189] The present application also provides a thermal management system, comprising the electronic device shown in the above Figure 6 The embodiment shown in the above vehicle control method.

[0190] The present application also provides a computer readable storage medium, the computer readable storage medium stores computer execution instructions, when the processor executes the computer execution instructions, the vehicle control method shown in the above is realized.

[0191] The above readable storage medium can be realized by any type of volatile or non-volatile storage device or their combination, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0192] An exemplary readable storage medium is coupled to the processor so that the processor can read information from the readable storage medium and can write information to the readable storage medium. Of course, the readable storage medium can also be a part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0193] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0194] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0195] In addition, the functional units in various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0196] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the present application that essentially contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0197] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. The program executes to perform the steps of the above-mentioned method embodiments; and the foregoing storage medium includes various media capable of storing program codes, such as ROM, RAM, magnetic disk, or optical disk.

[0198] Finally, it should be noted that other embodiments of the present application will readily occur to those skilled in the art upon consideration of the specification and practice of the present application disclosed herein. The present application is intended to include all such variations and modifications as fall within the scope of the present application, which is defined by the following claims, as well as the full scope of equivalents to which such claims are entitled. It is intended, therefore, that the present application be considered as including all possibilities falling within the scope of the application and their equivalents.

Claims

1. A vehicle control method characterized by, The vehicle is powered by a power battery, comprising: in response to a user setting a reservation time for using the vehicle, obtaining an ambient temperature of the vehicle, a cell temperature of the power battery, and an expected battery power demand profile of the user for this trip; based on the expected battery power demand profile and an electrochemical model of the battery, determining a target temperature interval required for preheating the cell; based on the ambient temperature, the cell temperature, the reservation time for using the vehicle, and the target temperature interval, determining a heating start time and a heating power curve of the power battery; when the heating start time is reached, heating the power battery based on the heating power curve.

2. The method of claim 1, wherein, The expected battery power demand of the user for this trip includes: according to the user's historical navigation data, the reservation time for using the vehicle, and the date type to which the reservation time for using the vehicle belongs, predicting the trip feature information of this trip; the trip feature information includes: expected mileage, expected vehicle speed data, and altitude change; based on the trip feature information, predict the expected battery power demand profile.

3. The method according to claim 1 or 2, characterized in that, The determination of the heating start time and the heating power curve of the power battery based on the ambient temperature, the cell temperature, the reservation time for using the vehicle, and the target temperature interval includes: based on the ambient temperature, the location of the vehicle, and the reservation time for using the vehicle, predict the ambient temperature change trend before the reservation time for using the vehicle; based on the ambient temperature change trend, the cell temperature, the reservation time for using the vehicle, the target temperature interval of the cell, and the thermal inertia parameter of the cell, determine the heating start time and the heating power curve of the power battery; wherein the thermal inertia parameter is used to represent the temperature rise rate of the cell.

4. The method of claim 3, wherein, The method further comprises: obtaining sampling data of the cell; based on the sampling data of the cell, obtaining health state data of the cell; the health state data at least includes: capacity attenuation data, internal resistance change data; based on the health state data, determine the thermal inertia parameter.

5. The method of claim 3, wherein, The method further comprises: during the heating process of the power battery, based on the cell temperature, the thermal inertia parameter of the cell, and the heatable duration, adjust the heating power and duration of each heating stage in the heating power curve; the heatable duration is the difference between the reservation time for using the vehicle and the heating start time.

6. The method of claim 1, wherein, The determination of the heating start time and the heating power curve of the power battery based on the ambient temperature, the cell temperature, the reservation time for using the vehicle, and the target temperature interval includes: based on the ambient temperature, the cell temperature, the reservation time for using the vehicle, and the target temperature interval, predict the heating start time and the heating power curve of the power battery by using a prediction model; The prediction model is obtained by fine-tuning a basic prediction model using historical data of the vehicle, the basic prediction model is obtained by training based on historical heating data of multiple sample vehicles of the same model through federated learning, and the historical data at least includes historical heating data of the vehicle.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: obtaining an external environment temperature collected by an environment temperature sensor of the vehicle, a local temperature around the vehicle detected by an infrared thermal imaging sensor, and meteorological data of a location where the vehicle is located based on a position of the vehicle; obtaining an environment temperature of the vehicle based on the external environment temperature, the local temperature, and the meteorological data.

8. An electronic device, comprising: comprise: a memory and a processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory, so that the processor executes the method according to any one of claims 1-7.

9. A thermal management system, characterized by, The electronic device according to claim 8 is included.

10. A vehicle characterized by comprising: The vehicle comprises a power battery and a thermal management system; the thermal management system is configured to heat the power battery by using the method according to any one of claims 1-7.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the method according to any one of claims 1-7.

12. A computer program product, characterised in that, The computer program is executed by the processor to implement the method according to any one of claims 1-7.

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