Data updating method, battery residual charging duration prediction method, device, equipment, vehicle, medium and program product

Abstract

The application provides a data updating method, a battery residual charging duration prediction method and device, equipment, a vehicle, a medium and a program product. The method comprises the following steps: obtaining target data of a vehicle; the target data comprises data related to temperature rise change of a battery; obtaining a mapping relationship of a corresponding temperature rise rate of the battery according to the target data; and updating a corresponding temperature rise rate mapping relationship table according to the obtained mapping relationship of the corresponding temperature rise rate of the battery, wherein the mapping relationship table is used for predicting a residual charging duration of the battery. The application can improve the accuracy of battery residual charging duration prediction.

Description

Technical Field

[0001] This application relates to the field of battery charging technology, and in particular to a data update method, a method for predicting remaining battery charging time, an apparatus, equipment, vehicle, medium, and program product. Background Technology

[0002] As the core power source of electric vehicles, batteries need to be charged regularly to maintain their driving range. Currently, when charging electric vehicle batteries, the remaining charging time is usually predicted and provided to users, allowing them to plan their travel time accordingly.

[0003] The remaining charging time of the battery is predicted by combining the temperature rise rate mapping relationship recorded in the battery temperature rise rate mapping relationship table. Currently, the mapping relationship in this temperature rise rate mapping relationship table is mainly constructed by pre-calibration, resulting in low accuracy of the prediction of the remaining charging time of the battery. Summary of the Invention

[0004] This application provides a data update method, a battery remaining charging time prediction method, an apparatus, equipment, vehicle, medium, and program product to improve the accuracy of battery remaining charging time prediction.

[0005] Firstly, this application provides a data updating method, including:

[0006] Acquire target data for the vehicle; the target data includes data related to changes in battery temperature rise.

[0007] Based on the target data, obtain the mapping relationship of the temperature rise rate corresponding to the battery;

[0008] Based on the obtained mapping relationship of the battery's corresponding temperature rise rate, the corresponding temperature rise rate mapping relationship table is updated, and the mapping relationship table is used to predict the remaining charging time of the battery.

[0009] In one possible approach, the battery's corresponding temperature rise rate includes: self-heating temperature rise rate, thermal management temperature rise rate, and natural heat exchange temperature rise rate; the target data includes: the battery's charging process data, the battery's thermal management data, and the battery's static data;

[0010] The step of obtaining the mapping relationship of the battery's temperature rise rate based on the target data includes:

[0011] Based on the charging process data of the battery, a first mapping relationship between battery temperature, state of charge and self-heating temperature rise rate is obtained;

[0012] Based on the thermal management data of the battery, a second mapping relationship between the thermal management strategy and the thermal management temperature rise rate is obtained;

[0013] Based on the static data of the battery, a third mapping relationship between battery temperature, ambient temperature, and natural heat exchange temperature rise rate is obtained.

[0014] In one possible approach, the charging process data includes: charging current, battery temperature, and ambient temperature within each state of charge interval;

[0015] The step of obtaining a first mapping relationship between battery temperature, state of charge, and self-heating temperature rise rate based on the battery charging process data includes:

[0016] Obtain the charging process data corresponding to the effective state of charge interval from the charging process data;

[0017] Using the charging process data corresponding to the effective state of charge interval, the battery temperature at the beginning of the effective state of charge interval, the battery temperature at the end of the effective state of charge interval, and the average charging current are obtained.

[0018] Based on the battery temperature at the beginning of the effective state of charge interval, the battery temperature at the end of the effective state of charge interval, and the average charging current, calculate the self-heating temperature rise rate corresponding to the effective state of charge interval and the battery temperature interval.

[0019] The first mapping relationship is obtained based on the self-heating temperature rise rate corresponding to the effective state of charge range and the battery temperature range.

[0020] In one possible approach, obtaining the charging process data corresponding to the effective state of charge interval from the charging process data includes:

[0021] Based on the effective data filtering criteria, the charging process data corresponding to the effective state of charge interval is obtained from the charging process data; the effective data filtering criteria include: the change in charging current within the state of charge interval is less than a preset threshold, and the thermal management strategy remains unchanged.

[0022] In one possible approach, the thermal management data of the battery includes: a first temperature, activation time, thermal management strategy, and ambient temperature when the battery activates thermal management, and a second temperature and termination time when the battery terminates thermal management.

[0023] The step of obtaining a second mapping relationship between the thermal management strategy and the thermal management temperature rise rate based on the thermal management data of the battery includes:

[0024] Based on the thermal management data of the battery and the natural heat transfer temperature rise rate during battery preheating, the thermal management temperature rise rate corresponding to the thermal management strategy is obtained;

[0025] The second mapping relationship is obtained based on the thermal management temperature rise rate corresponding to the thermal management strategy.

[0026] In one possible approach, the static data includes: a third temperature of the battery, power-off time, and ambient temperature when the vehicle is powered off, and a fourth temperature of the battery and power-on time when the vehicle is powered off and then powered on again.

[0027] The step of obtaining a third mapping relationship between battery temperature, ambient temperature, and natural heat transfer temperature rise rate based on the battery's static data includes:

[0028] Based on the third temperature, the power-off time, the ambient temperature, the fourth temperature, and the power-on time, determine the self-heating temperature rise rate corresponding to the ambient temperature range;

[0029] The third mapping relationship is obtained based on the self-heating temperature rise rate corresponding to the environmental temperature range.

[0030] In one possible approach, updating the corresponding temperature rise rate mapping table based on the acquired mapping relationship of the battery's corresponding temperature rise rate includes:

[0031] If the difference in temperature rise rate between the mapping relationship obtained N times consecutively and the corresponding mapping relationship in the mapping relationship table is greater than a first preset value, and the difference in temperature rise rate between the mapping relationships obtained N times consecutively is less than a second preset value, then the mapping relationship obtained N times is used to update the corresponding mapping relationship in the mapping relationship table.

[0032] Secondly, this application provides a method for predicting the remaining charging time of a battery, the method comprising:

[0033] Obtain the battery cell temperature, initial state of charge, and ambient temperature;

[0034] The remaining charging time of the battery is predicted based on the battery cell temperature, initial state of charge, ambient temperature, and a temperature rise rate mapping table updated using the method described in any of the first aspects.

[0035] Thirdly, this application provides a data updating device, comprising:

[0036] The first acquisition module is used to acquire target data of the vehicle; the target data includes data related to the temperature rise of the battery.

[0037] The second acquisition module is used to acquire the mapping relationship of the temperature rise rate of the battery based on the target data;

[0038] The update module is used to update the corresponding temperature rise rate mapping table according to the obtained mapping relationship of the battery temperature rise rate. The mapping relationship table is used to predict the remaining charging time of the battery.

[0039] Fourthly, this application provides a battery remaining charging time prediction device, the device comprising:

[0040] The acquisition module is used to acquire the battery cell temperature, initial state of charge, and ambient temperature.

[0041] The prediction module is used to predict the remaining charging time of the battery based on the battery cell temperature, initial state of charge, ambient temperature, and a temperature rise rate mapping table updated by the method described in any of the first aspects.

[0042] Fifthly, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0043] The memory stores computer-executed instructions;

[0044] The processor executes computer execution instructions stored in the memory to implement the method described in either the first aspect or the second aspect.

[0045] In a sixth aspect, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method described in any of the first or second aspects.

[0046] In a seventh aspect, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the first or second aspects.

[0047] Eighthly, this application provides a vehicle including electronic devices and a battery; wherein the electronic devices are configured to perform the method as described in either the first or second aspect.

[0048] The data updating method, battery remaining charging time prediction method, apparatus, device, vehicle, medium, and program product provided in this application can obtain data reflecting the actual temperature rise of the battery in the vehicle based on the vehicle's actual operating data. This data is then used to obtain a mapping relationship of the temperature rise rate that closely matches the actual battery situation in the vehicle, and updated to a mapping relationship table. This results in more accurate predictions of the remaining charging time of the battery based on this mapping relationship table, improving the accuracy of the prediction. Attached Figure Description

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0050] Figure 1 A flowchart illustrating a data update method provided in an embodiment of this application;

[0051] Figure 2 This is a schematic flowchart illustrating a process for obtaining a mapping relationship of the temperature rise rate of a battery, as provided in an embodiment of this application.

[0052] Figure 3 A flowchart illustrating a method for predicting remaining battery charging time provided in an embodiment of this application;

[0053] Figure 4 A flowchart illustrating another method for predicting remaining battery charging time provided in an embodiment of this application;

[0054] Figure 5 This is a schematic diagram of the structure of a data update device provided in an embodiment of this application;

[0055] Figure 6 This is a schematic diagram of a battery remaining charging time prediction device provided in an embodiment of this application;

[0056] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0057] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0058] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0059] The execution subject of the method provided in this application can be any device on the vehicle used to predict the remaining charging time of the battery. For example, it can be a Battery Management System (BMS), other devices on the vehicle capable of implementing the method, or a cloud server connected to the vehicle. The following embodiments use a BMS as an example for illustration.

[0060] As the core power source of electric vehicles (hereinafter referred to as vehicles for ease of description), batteries need to be charged regularly to maintain their driving range. During the battery charging process, the prediction of remaining charging time directly affects users' daily travel plans and driving experience. An accurate method for estimating remaining charging time can help users know and reasonably arrange charging time, avoiding inconvenience caused by excessive charging time.

[0061] During battery charging, the BMS predicts the remaining charging time. To accurately predict the remaining charging time, the prediction process needs to consider not only the battery's inherent charging properties, such as battery capacity and charging current, but also the impact of the battery's temperature rise rate on the remaining charging time.

[0062] During charging, the battery temperature rise rate refers to the rate at which the battery temperature rises per unit time. The battery temperature rise rate can include one or more aspects, such as the self-heating temperature rise rate of the battery current, the thermal management temperature rise rate, the natural heat transfer temperature rise rate, etc.

[0063] The self-heating temperature rise rate characterizes the rate at which the battery temperature rises due to charging current. The thermal management temperature rise rate characterizes the rate at which the battery temperature rises or falls due to different thermal management strategies. The thermal management strategies mentioned here are related to the thermal management methods used in the vehicle and are not limited thereto. The natural heat transfer temperature rise rate characterizes the rate at which the battery temperature rises or falls due to ambient temperature.

[0064] A mapping table of battery temperature rise rate is usually pre-set in the BMS for use when predicting the remaining charging time of the battery. This mapping table includes the mapping relationship of the battery temperature rise rate to the corresponding battery.

[0065] Taking the battery temperature rise rate, which includes the battery's self-heating temperature rise rate, thermal management temperature rise rate, and natural heat transfer temperature rise rate, as an example, the BMS can pre-set the following three tables:

[0066] (1) Mapping table of self-heating temperature rise rate:

[0067] A self-heating temperature rise rate mapping table could include, for example, a first mapping relationship between battery temperature, state of charge (SOC), and self-heating temperature rise rate. This table could be, for example, shown in Table 1 below:

[0068] Table 1

[0069]

[0070] It should be understood that in Table 1 above, the horizontal axis represents the State of Charge (SOC) value, and the vertical axis represents the battery temperature value. For example, assuming the battery temperature is 30℃ and the SOC is 20%, the table shows that the battery's self-heating temperature rise rate is 0.00026℃ / min / A. 2 Where A is the unit of current, ampere. The battery temperature is 30℃, the state of charge (SOC) is 20%, and the battery's self-heating temperature rise rate is 0.00026℃ / min / A. 2 These three elements constitute a first mapping relationship.

[0071] It should be noted that Table 1 above is only an example of how the first mapping relationship in the self-heating temperature rise rate mapping relationship table is represented. This embodiment does not limit the structure of the table, nor the division of battery temperature and SOC in the table. For example, a finer granular division or a coarser granular division can be used.

[0072] (2) Mapping table of thermal management temperature rise rate

[0073] A mapping table for thermal management temperature rise rate may include, for example, a second mapping relationship between thermal management strategy and thermal management temperature rise rate, as shown in Table 2 below:

[0074] Table 2

[0075] Thermal management strategy Rate of temperature rise Heating gear 1 0.5°C / min Heating gear 2 0.2°C / min Cooling gear 1 -0.3°C / min Cooling gear 2 -0.6°C / min

[0076] It should be understood that the first column of Table 2 above represents the thermal management strategy, and the second column represents the temperature rise rate. For example, when the thermal management mode is heating level 1, the thermal management temperature rise rate is 0.5℃ / min, and the two constitute a second mapping relationship.

[0077] It should be noted that Table 2 above is merely an example of how the second mapping relationships in a thermal management temperature rise rate mapping table are represented. This embodiment does not limit the structure of the table, nor the division of thermal management modes and levels in the table. For example, the thermal management strategy may include heating level 1, heating level 2, heating level n (n≥1), cooling level 1, cooling level 2, cooling level n (n≥1).

[0078] (3) Mapping table of natural heat transfer temperature rise rate

[0079] A natural heat transfer temperature rise rate mapping table may include, for example, a third mapping relationship between battery temperature, ambient temperature, and natural heat transfer temperature rise rate. This natural heat transfer temperature rise rate mapping table may be shown in Table 3 below:

[0080] Table 3

[0081]

[0082] It should be understood that in Table 3 above, the horizontal axis represents the battery temperature, and the vertical axis represents the ambient temperature. For example, when the ambient temperature is 30°C and the battery temperature is 15°C, the natural heat transfer temperature rise rate is 0.01°C / min, forming a mapping relationship among the three. Here, °C / min is the unit for the natural heat transfer temperature rise rate.

[0083] It should be noted that Table 3 above is only an example of how the third mapping relationship in the natural heat transfer temperature rise rate mapping relationship table is presented. This embodiment does not limit the structure of the table, nor the division of battery temperature and ambient temperature in the table.

[0084] As mentioned earlier, these temperature rise rate mapping tables are typically pre-configured in the BMS. The data in these temperature rise rate mapping tables is usually constructed through pre-calibration. For example, the tables can be constructed using the aforementioned data from offline battery testing.

[0085] However, as vehicles are used, issues such as battery aging, thermal management system aging, and vehicle insulation performance aging will occur, causing changes in the battery's temperature rise rate. This results in a significant error between the remaining charging time calculated based on the temperature rise rate mapping table constructed through pre-calibration and the actual remaining charging time of the battery.

[0086] In view of this, this application proposes a data update method that updates the corresponding temperature rise rate mapping table based on the actual operating data of the vehicle, so that the temperature rise rate mapping table matches the actual temperature rise of the battery in the vehicle. This results in more accurate predictions of the remaining charging time of the battery based on the mapping table, thus improving the accuracy of the remaining charging time prediction.

[0087] The following description, still using BMS as an example, details the technical solution of this application and how it solves the aforementioned technical problems through specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0088] Figure 1 This is a flowchart illustrating a data update method provided in an embodiment of this application. Figure 1As shown, the method includes:

[0089] S101. Obtain target data for the vehicle; the target data includes data related to changes in battery temperature rise.

[0090] Data related to battery temperature rise changes can specifically relate to the rate of temperature rise used when predicting the remaining charging time of the battery. For example, target data may include data related to at least one of the following: battery temperature rise changes caused by current self-heating, battery temperature rise changes caused by changes in the thermal management system, and battery temperature rise changes caused by changes in ambient temperature.

[0091] Taking the battery's corresponding temperature rise rate as an example, which includes: self-heating temperature rise rate, thermal management temperature rise rate, and natural heat exchange temperature rise rate, the target data can include: battery charging process data, battery thermal management data, and battery static data.

[0092] The battery charging process data may include: charging current, battery temperature, and ambient temperature within each SOC range.

[0093] Battery thermal management data may include: the first temperature when battery thermal management is activated, the activation time, the thermal management strategy and the ambient temperature, and the second temperature and the end time when battery thermal management is terminated.

[0094] Battery static data can include: the battery's third temperature, power-off time, and ambient temperature when the vehicle is powered off; and the battery's fourth temperature and power-on time when the vehicle is powered off and then powered on again.

[0095] Optionally, the target data mentioned above can be collected and recorded in real time during vehicle use, or it can be extracted from the vehicle's logs used to record operational data.

[0096] S102. Based on the target data, obtain the mapping relationship of the battery's corresponding temperature rise rate.

[0097] The mapping relationship between the battery's temperature rise rate and the specific battery parameters used depends on the battery parameters.

[0098] Taking the battery's corresponding temperature rise rate, which includes: self-heating temperature rise rate, thermal management temperature rise rate, and natural heat transfer temperature rise rate, as an example, the mapping relationship of the battery's corresponding temperature rise rate can include, for example:

[0099] Corresponding to the self-heating temperature rise rate, the corresponding mapping relationship can be, for example, the mapping relationship between battery temperature, charging current and self-heating temperature rise rate, or the first mapping relationship between battery temperature, SOC and self-heating temperature rise rate mentioned above.

[0100] Corresponding to the thermal management temperature rise rate, the corresponding mapping relationship can be, for example, a mapping relationship between the thermal management strategy, the cooling medium flow rate and the battery temperature, or a second mapping relationship between the thermal management strategy and the thermal management temperature rise rate.

[0101] For the natural heat exchange temperature rise rate, the corresponding mapping relationship can be a third mapping relationship between battery temperature, ambient temperature and natural heat exchange temperature rise rate.

[0102] It should be understood that the above mapping relationship is only an example to provide some possible implementation methods, and the embodiments of this application are not limited thereto. The specific configuration is related to the parameters used to predict the remaining charging time of the battery.

[0103] For example, the BMS can calculate the parameters covered in the corresponding mapping relationship and the corresponding temperature rise rate based on the target data, thereby obtaining the mapping relationship.

[0104] S103. Update the corresponding temperature rise rate mapping table according to the obtained mapping relationship of the battery temperature rise rate. The mapping relationship table is used to predict the remaining charging time of the battery.

[0105] For example, after obtaining the mapping relationship of the battery's corresponding temperature rise rate, the BMS can compare it with the corresponding mapping relationship in the corresponding temperature rise rate mapping relationship table. If the temperature rise rate corresponding to the same parameter in the mapping relationship is inconsistent, the temperature rise rate in the temperature rise rate mapping relationship table obtained this time will be used to update the temperature rise rate corresponding to that mapping relationship.

[0106] For example, the BMS can also integrate the mapping relationships of multiple acquired temperature rise rates to update the temperature rise rate mapping relationship table. For instance, if the difference in temperature rise rate between the mapping relationship acquired N times consecutively and the corresponding mapping relationship in the mapping relationship table is greater than a first preset value, and the difference in temperature rise rate between the mapping relationships acquired N times consecutively is less than a second preset value, then the mapping relationship acquired N times is used to update the corresponding mapping relationship in the mapping relationship table.

[0107] For example, the first preset value can be a temperature rise rate difference of 5% between the obtained mapping relationship and the corresponding mapping relationship in the mapping relationship table. The second preset value can be a temperature rise rate difference of 2% between the obtained mapping relationships. When the temperature rise rate difference between the obtained mapping relationship and the corresponding mapping relationship in the mapping relationship table is greater than 5% for N consecutive times, and the temperature rise rate difference between the obtained mapping relationships is less than 2% for N consecutive times, then the mapping relationship obtained in the Nth time is used to update the corresponding mapping relationship in the mapping relationship table.

[0108] The value of N here can be set according to actual needs. For example, it can be 3.

[0109] The data update method provided in this application can obtain data reflecting the actual temperature rise of the battery in the vehicle based on the vehicle's actual operating data. This data is then used to obtain a mapping relationship of the temperature rise rate that closely matches the actual battery condition, and the mapping relationship is updated in a mapping table. This results in more accurate predictions of the remaining charging time of the battery based on this mapping table, improving the accuracy of the remaining charging time prediction.

[0110] The following example illustrates how to obtain the mapping relationship of the battery's corresponding temperature rise rate based on the target data, which includes the battery's charging process data, the battery's thermal management data, and the battery's static data, and the battery's corresponding temperature rise rate includes the self-heating temperature rise rate, the thermal management temperature rise rate, and the natural heat exchange temperature rise rate.

[0111] Figure 2 This is a schematic flowchart illustrating a process for obtaining a mapping relationship of the temperature rise rate of a battery, as provided in an embodiment of this application. Figure 2 As shown, step S102 above may include, for example, the following steps:

[0112] S201. Based on the battery charging process data, obtain the first mapping relationship between battery temperature, SOC and self-heating temperature rise rate.

[0113] For example, charging process data may include: charging current, battery temperature, ambient temperature, etc. in each SOC range.

[0114] For example, the BMS can first obtain the charging process data corresponding to the valid SOC range from the charging process data. For example, taking an SOC range of 10%-20% as an example, the charging process data includes the charging current, battery temperature, and ambient temperature at each sampling moment during the process of SOC from 10% to 20%. The battery temperature mentioned here can be the average temperature of all or some of the battery cells, the maximum value of the cell temperature, the minimum value of the cell temperature, or the temperature of any single cell.

[0115] For example, the BMS can use the above charging process data to directly calculate the first mapping relationship between battery temperature, SOC and self-heating temperature rise rate, or it can first filter the above charging process data to remove noisy data, ensure the reliability of the extracted data, and thus ensure the accuracy of the mapping relationship obtained based on these data.

[0116] For example, the BMS can obtain charging process data corresponding to the valid SOC range from the charging process data based on valid data filtering conditions. These valid data filtering conditions can be determined according to user needs; for example, they may include at least one of the following: the charging current change within the SOC range is less than a preset threshold, and the thermal management strategy remains unchanged.

[0117] For example, the preset threshold for charging current is 5A. If the change in charging current does not exceed 5A within the SOC range of 10%-20%, then the valid data screening condition is met, and it can be used as the charging process data corresponding to the valid SOC range. The change in charging current can refer to the difference between the charging current at two adjacent sampling times, or the difference between the charging current at 10% SOC and the charging current at 20% SOC, etc.

[0118] Then, the BMS can use the charging process data corresponding to the effective SOC range to obtain the battery temperature at the beginning of the effective SOC range, the battery temperature at the end of the effective SOC range, and the average charging current.

[0119] Optionally, the average charging current can be the average of all charging currents in the charging process data corresponding to the effective SOC range, and the average charging current can be defined as Ic. Optionally, the battery temperature T5 at the beginning of the effective SOC range and the battery temperature T6 at the end of the effective SOC range can be obtained.

[0120] After obtaining the battery temperature at the beginning of the effective SOC range, the battery temperature at the end of the effective SOC range, and the average charging current, the BMS can use the acquired data to obtain the self-heating temperature rise rate corresponding to the effective SOC range and the battery temperature range.

[0121] For example, within the effective SOC range, the self-heating temperature rise rate K1 corresponding to the battery temperature range can be calculated using formula (1):

[0122] K1=[(T6-T5) / t-K2-K3] / Ic 2 (1)

[0123] Where K2 represents the thermal management temperature rise rate, K3 represents the natural heat exchange temperature rise rate, and t is the charging time of the process.

[0124] The above K2 and K3 can be obtained by using the thermal management strategy used during battery charging and querying the corresponding mapping table based on the ambient temperature, or they can be calculated in real time; there is no limitation on this.

[0125] Thus, the BMS obtains the first mapping relationship based on the self-heating temperature rise rate corresponding to the effective SOC range and battery temperature range. Taking an effective SOC range of 10%-20%, a battery temperature range of 10℃-15℃, a charging time of 5 minutes, and a charging current of 20A as an example, the obtained first mapping relationship is: SOC range of 10%-20%, battery temperature range of 10℃-15℃, and, based on these values, the self-heating temperature rise rate K1 calculated using the above formula (1) is 0.0025℃ / min / A. 2 .

[0126] Referring to Table 1, still taking a SOC range of 10%-20%, a battery temperature range of 10℃-15℃, and a charging time of 5 minutes as an example, after obtaining the first mapping relationship, first determine the corresponding self-heating temperature rise rate when the SOC range is 10%-20% and the battery temperature range is 10℃-15℃. If the two temperature rise rates are inconsistent, then the current rate is used to replace the original data in that position in the table.

[0127] Alternatively, the first mapping relationship obtained by combining N effective SOC ranges of 10%-20% and battery temperature ranges of 10℃-15℃ is compared with the corresponding self-heating temperature rise rate when the SOC range is 10%-20% and the battery temperature range is 10℃-15℃. If the difference in temperature rise rate between the first mapping relationship obtained in N consecutive iterations and the corresponding mapping relationship in the self-heating temperature rise rate mapping relationship table is greater than 5%, and the difference in temperature rise rate between the self-heating temperature rise rate mapping relationships obtained in N consecutive iterations is less than 2%, then the mapping relationship of the self-heating temperature rise rate obtained in the Nth iteration is used to update the corresponding self-heating temperature rise rate mapping relationship in the self-heating temperature rise rate mapping relationship table.

[0128] It should be noted that the above example is merely an illustrative illustration of an implementation method where the charging process data includes charging current, battery temperature, and ambient temperature within each SOC range, and the first mapping relationship is the mapping relationship between battery temperature, SOC, and self-heating temperature rise rate. When other mapping relationships are used, the charging process data used, and the method of obtaining the mapping relationship based on the charging process data, can be adjusted accordingly, and will not be elaborated upon here.

[0129] Battery aging affects the battery's self-heating rate. Therefore, this application's embodiments can update the self-heating rate mapping table according to the actual condition of the battery. Compared to the self-heating rate mapping table constructed using a pre-calibrated method in the prior art, the self-heating rate mapping table updated in this application matches the current actual condition of the battery, thus making the prediction of the battery's remaining charging time more accurate using the updated self-heating rate mapping table.

[0130] S202. Based on the battery's thermal management data, a second mapping relationship between the thermal management strategy and the thermal management temperature rise rate is obtained.

[0131] For example, the battery thermal management data may include: a first temperature when the battery activates thermal management, activation time, thermal management strategy and ambient temperature, and a second temperature and end time when the battery thermal management ends.

[0132] Optionally, the first temperature at which battery thermal management is activated can be defined as T3, and the activation time can be defined as t3. The second temperature at which battery thermal management ends can be defined as T4, and the end time can be defined as t4. The ambient temperature can be defined as Tc. Optionally, the thermal management strategy can be a thermal management mode of turning on the air conditioner, with the thermal management level set to heating.

[0133] For example, the BMS can obtain the thermal management temperature rise rate corresponding to the thermal management strategy based on the battery's thermal management data and the natural heat transfer temperature rise rate during battery preheating.

[0134] Optionally, the thermal management temperature rise rate K2 corresponding to the thermal management strategy can be calculated using formula (2):

[0135] K2=(T4-T3) / (t4–t3)-K3 (2)

[0136] Wherein, K3 is the natural heat exchange temperature rise rate during the battery preheating process.

[0137] The aforementioned K3 can be obtained by looking up the natural heat transfer temperature rise rate mapping table using the ambient temperature during the battery preheating process, or it can be calculated in real time; there is no limitation on which method is used.

[0138] Then, the BMS can obtain the second mapping relationship based on the thermal management temperature rise rate corresponding to the thermal management strategy. Taking the thermal management level as heating as an example, the first temperature when the battery starts thermal management can be 20℃, and the start time can be defined as 20:00. The second temperature when the battery ends thermal management can be 30℃, and the end time can be defined as 20:05. Then the natural heat exchange temperature rise rate K2 is 1.99℃ / min.

[0139] Referring to Table 2, and still taking the heating setting as an example, after obtaining the second mapping relationship, first determine the self-heating temperature rise rate corresponding to the heating setting. If the two heating settings are inconsistent, then the current one replaces the original data in that position in the table.

[0140] Alternatively, the second mapping relationship obtained by combining N thermal management strategies for the heating setting is compared with the corresponding thermal management temperature rise rate for the heating setting. If the temperature rise rate difference between the second mapping relationship obtained in N consecutive iterations and the corresponding mapping relationship in the thermal management temperature rise rate mapping relationship table is greater than 5%, and the temperature rise rate difference between the thermal management temperature rise rate mapping relationships obtained in N consecutive iterations is less than 2%, then the thermal management temperature rise rate mapping relationship obtained in the Nth iteration is used to update the corresponding thermal management temperature rise rate mapping relationship in the thermal management temperature rise rate mapping relationship table.

[0141] It should be noted that the above example is merely an illustrative illustration of the implementation method of the second mapping relationship as a mapping relationship between thermal management strategy and thermal management temperature rise rate in the battery preheating scenario. When other mapping relationships are used, the thermal management data used and the method of obtaining the mapping relationship based on the thermal management data can be adapted and adjusted, which will not be elaborated here. For example, the above second mapping relationship can be obtained based on the battery precooling scenario.

[0142] Battery aging and thermal management system aging can affect the thermal management temperature rise rate. Therefore, this application embodiment can update the thermal management temperature rise rate in the thermal management temperature rise rate mapping table according to the actual condition of the battery. Compared with the thermal management temperature rise rate mapping table constructed by pre-calibration in the prior art, the thermal management temperature rise rate in the updated thermal management temperature rise rate mapping table of this application matches the actual condition of the battery at present, thereby making the prediction of the remaining charging time of the battery more accurate using the updated thermal management temperature rise rate.

[0143] S203. Based on the battery's static data, obtain the third mapping relationship between battery temperature, ambient temperature, and natural heat exchange temperature rise rate.

[0144] For example, the static data may include: the battery's third temperature, power-off time, and ambient temperature when the vehicle is powered off, and the battery's fourth temperature and power-on time when the vehicle is powered off and then powered on again.

[0145] For example, the BMS can determine the self-heating temperature rise rate corresponding to the ambient temperature range based on the third temperature, power-off time, ambient temperature, fourth temperature, and power-on time. For instance, the battery's third temperature when the vehicle is powered off can be defined as T1, the power-off time as t1, and the ambient temperature as Tm. When the vehicle is powered off and then reactivated, the battery's fourth temperature can be defined as T2, and the reactivation time as t2.

[0146] For example, the self-heating temperature rise rate K3 corresponding to the ambient temperature range Tm can be calculated by the following formula (3):

[0147] K3=(T2-T1) / (t2-t1) (3)

[0148] In this way, the BMS can update the third mapping relationship based on the self-heating temperature rise rate corresponding to the ambient temperature range. Taking the battery's third temperature as 20℃ when the vehicle is powered off, the power-off time as 20:00, the battery's fourth temperature as 22℃ when the vehicle is powered off and then woken up again, the wake-up time as 21:00, and the ambient temperature as 40℃ as an example, the natural heat transfer temperature rise rate K3 is 0.033℃ / min.

[0149] Referring to Table 3, still assuming the battery's third temperature is 20℃ when the vehicle is powered off, the power-off time is 20:00, and the battery's fourth temperature is 22℃ when the vehicle is powered off and then reactivated, the reactivation time is 21:00, and the ambient temperature is 40℃, after obtaining this third mapping relationship, first determine the corresponding natural heat transfer temperature rise rate when the battery's third temperature is 20℃ when the vehicle is powered off, the battery's fourth temperature is 22℃ when the vehicle is powered off and the ambient temperature is 40℃. If the two natural heat transfer temperature rise rates are inconsistent, then the current rate is used to replace the original data in that position in the table.

[0150] Alternatively, the third mapping relationship obtained by combining the battery's third temperature of 20℃ when the vehicle is powered off N times, the fourth battery temperature of 22℃ when the vehicle is powered off and then powered on again, and the ambient temperature of 40℃, is compared with the corresponding natural heat transfer temperature rise rate. If the two temperature rise rates are inconsistent, the average of the natural heat transfer temperature rise rates is used to replace the original data in that position in the table. If the temperature rise rate difference between the third mapping relationship obtained N times and the corresponding mapping relationship in the natural heat transfer temperature rise rate mapping relationship table is greater than 5%, and the temperature rise rate difference between the natural heat transfer temperature rise rate mapping relationships obtained N times is less than 2%, then the natural heat transfer temperature rise rate mapping relationship obtained in the Nth time is used to update the corresponding natural heat transfer temperature rise rate mapping relationship in the natural heat transfer temperature rise rate mapping relationship table.

[0151] It should be noted that the above example is merely an illustrative illustration of an implementation method where the charging process data includes charging current, battery temperature, and ambient temperature within each SOC range, and the first mapping relationship is the mapping relationship between battery temperature, SOC, and self-heating temperature rise rate. When other mapping relationships are used, the charging process data used, and the method of obtaining the mapping relationship based on the charging process data, can be adjusted accordingly, and will not be elaborated upon here.

[0152] Battery aging and the aging of the vehicle's thermal insulation performance can affect the natural heat transfer rate. This application's embodiments can update the natural heat transfer rate mapping table based on the actual condition of the battery. Compared to existing technologies that use pre-calibrated natural heat transfer rate mapping tables, the natural heat transfer rate in this application's updated table matches the current actual condition of the battery, thus making the prediction of the battery's remaining charging time more accurate.

[0153] Figure 3 This is a flowchart illustrating a method for predicting remaining battery charging time provided in an embodiment of this application, as shown below. Figure 3 As shown, the method may include, for example, the following steps:

[0154] S301. Obtain the battery cell temperature, initial state of charge, and ambient temperature;

[0155] S302. Based on the battery cell temperature, initial state of charge, ambient temperature, and temperature rise rate mapping table, predict the remaining charging time of the battery.

[0156] The temperature rise rate mapping table mentioned here can be obtained by updating the method described above.

[0157] This embodiment does not limit the composition of the above temperature rise rate mapping table; the specific structure can be determined according to the mapping relationship required in actual use. Taking an example where the temperature rise rate mapping table includes a self-heating temperature rise rate mapping table, a thermal management temperature rise rate mapping table, a natural heat transfer temperature rise rate mapping table, and cell temperatures including the maximum and minimum cell temperatures, the remaining charging time of the battery can be predicted using the following method:

[0158] Figure 4 A flowchart illustrating another method for predicting remaining battery charging time provided in this application embodiment is shown below. Figure 4 As shown, the method includes:

[0159] S401. Obtain the initial maximum cell temperature Tmax_n, initial minimum cell temperature Tmin_n, initial state of charge SOC_n, and ambient temperature Tc of the battery in the nth cycle.

[0160] It should be understood that when the first iteration begins, the value of n is 0.

[0161] S402. Based on the maximum cell temperature Tmax_n, minimum cell temperature Tmin_n, SOC_n of the nth cycle, and the self-heating temperature rise rate mapping table, determine the self-heating temperature rise rate K1_n of the battery in the nth cycle, and based on the maximum cell temperature Tmax_n, minimum cell temperature Tmin_n, state of charge, and the charging current mapping table, obtain the charging current Ic_n of the battery in the nth cycle.

[0162] S403. Determine the thermal management strategy of the battery in the nth cycle based on the maximum cell temperature Tmax_n and the minimum cell temperature Tmin_n in the nth cycle.

[0163] S404. Based on the thermal management strategy of the battery in the nth cycle and the thermal management temperature rise rate mapping table, determine the thermal management temperature rise rate K2_n of the battery in the nth cycle.

[0164] S405. Based on the ambient temperature Tc of the battery, the maximum cell temperature Tmax_n and the minimum cell temperature Tmin_n of the nth cycle, and the natural heat transfer temperature rise rate mapping table, determine the natural heat transfer temperature rise rate K3_n of the battery in the nth cycle.

[0165] S406. Based on the maximum cell temperature Tmax_n, charging current Ic_n, duration of a single cycle, self-heating temperature rise rate K1_n, thermal management temperature rise rate K2_n, and natural heat exchange temperature rise rate K3_n, determine the maximum cell temperature Tmax_n+1 of the battery in the (n+1)th cycle.

[0166] S407. Based on the minimum cell temperature Tmin_n, charging current Ic_n, duration of a single cycle, self-heating temperature rise rate K1_n, thermal management temperature rise rate K2_n, and natural heat exchange temperature rise rate K3_n of the nth cycle, determine the minimum cell temperature Tmin_n+1 of the battery in the (n+1)th cycle.

[0167] S408. Based on the charging current Ic_n and the initial state of charge SOC_n of the battery in the nth cycle, predict the initial state of charge SOC_n+1 of the battery in the (n+1)th cycle.

[0168] S409. Determine whether the initial state of charge (SOC_n+1) of the battery in the (n+1)th cycle exceeds the preset charging cutoff SOC.

[0169] If so, proceed to step S410.

[0170] If not, then n+1 is used as the new n, and the process returns to step S402.

[0171] S410. Obtain the remaining charging time of the battery based on the iteration number n+1.

[0172] It should be noted that the above is only an example of a method for predicting the remaining charging time of a battery. The methods for using these three mapping tables to predict the remaining charging time of a battery are not limited to this, and will not be elaborated further.

[0173] The battery remaining charging time prediction method provided in this application uses a temperature rise rate mapping table updated based on data reflecting the actual temperature rise of the battery in the vehicle, ensuring that the table closely matches the actual battery conditions. This results in more accurate predictions of the remaining charging time based on this mapping table, thus improving the overall accuracy of the prediction.

[0174] The above are the method embodiments provided in this application. The apparatus provided in this application will be described below.

[0175] Figure 5 This is a schematic diagram of a data update device provided in an embodiment of this application. Figure 5 As shown, the data update device 500 may include, for example:

[0176] The first acquisition module 501 is used to acquire target data of the vehicle; the target data includes data related to the temperature rise change of the battery.

[0177] The second acquisition module 502 is used to acquire the mapping relationship of the temperature rise rate of the battery based on the target data.

[0178] The update module 503 is used to update the corresponding temperature rise rate mapping table according to the obtained mapping relationship of the battery temperature rise rate, and the mapping relationship table is used to predict the remaining charging time of the battery.

[0179] Optionally, the battery's corresponding temperature rise rate includes: self-heating temperature rise rate, thermal management temperature rise rate, and natural heat transfer temperature rise rate; the target data includes: battery charging process data, battery thermal management data, and battery resting data. In this implementation, the second acquisition module 502 is specifically used to: obtain a first mapping relationship between battery temperature, state of charge, and self-heating temperature rise rate based on the battery charging process data; obtain a second mapping relationship between thermal management strategy and thermal management temperature rise rate based on the battery thermal management data; and obtain a third mapping relationship between battery temperature, ambient temperature, and natural heat transfer temperature rise rate based on the battery resting data.

[0180] For example, the charging process data includes: charging current, battery temperature, and ambient temperature within each state of charge interval; the second acquisition module 502 is specifically used to acquire charging process data corresponding to the effective state of charge interval from the charging process data; using the charging process data corresponding to the effective state of charge interval, acquire the battery temperature at the beginning of the effective state of charge interval, the battery temperature at the end of the effective state of charge interval, and the average charging current; calculate the self-heating temperature rise rate corresponding to the effective state of charge interval and the battery temperature interval based on the battery temperature at the beginning of the effective state of charge interval, the battery temperature at the end of the effective state of charge interval, and the average charging current; and obtain the first mapping relationship based on the self-heating temperature rise rate corresponding to the effective state of charge interval and the battery temperature interval.

[0181] For example, the second acquisition module 502 can acquire charging process data corresponding to the effective state of charge interval from the charging process data according to the effective data filtering conditions; the effective data filtering conditions include: the change in charging current within the state of charge interval is less than a preset threshold and the thermal management strategy remains unchanged.

[0182] For example, the thermal management data of the battery includes: a first temperature, activation time, thermal management strategy, and ambient temperature when the battery activates thermal management, and a second temperature and termination time when the battery terminates thermal management; the second acquisition module 502 is specifically used to acquire the thermal management temperature rise rate corresponding to the thermal management strategy based on the thermal management data of the battery and the natural heat transfer temperature rise rate during battery preheating; and to obtain the second mapping relationship based on the thermal management temperature rise rate corresponding to the thermal management strategy.

[0183] For example, the static data includes: the third temperature of the battery, the power-off time, and the ambient temperature when the vehicle is powered off; and the fourth temperature of the battery and the power-on time when the vehicle is powered off and then powered on again. The second acquisition module 502 is specifically used to determine the self-heating temperature rise rate corresponding to the environmental range to which the ambient temperature belongs based on the third temperature, the power-off time, the ambient temperature, the fourth temperature, and the power-on time; and to obtain the third mapping relationship based on the self-heating temperature rise rate corresponding to the environmental range to which the ambient temperature belongs.

[0184] Optionally, the update module 503 is specifically used to update the corresponding mapping relationship in the mapping relationship table using the mapping relationship obtained in the Nth time when the difference in temperature rise rate between the mapping relationship obtained in N consecutive times and the corresponding mapping relationship in the mapping relationship table is greater than a first preset value and the difference in temperature rise rate between the mapping relationships obtained in N consecutive times is less than a second preset value.

[0185] The data update apparatus provided in this application embodiment can be used to implement the data update method of any of the foregoing embodiments. Its implementation principle and technical effect are similar, and will not be repeated here.

[0186] Figure 6 This is a schematic diagram of a battery remaining charging time prediction device provided in an embodiment of this application, as shown below. Figure 6 As shown, the device may include, for example:

[0187] The acquisition module 601 is used to acquire the battery cell temperature, initial state of charge, and ambient temperature.

[0188] The prediction module 602 is used to predict the remaining charging time of the battery based on the battery cell temperature, initial state of charge, ambient temperature, and a temperature rise rate mapping table updated by the method described in the foregoing method embodiments.

[0189] The data update device provided in this application embodiment can be used to implement the aforementioned battery remaining charging time prediction method. Its implementation principle and technical effect are similar, and will not be described again here.

[0190] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 700 can be, for example, any device used to predict the remaining charging time of the battery, such as the aforementioned BMS or a cloud server connected to the vehicle network.

[0191] like Figure 7 As shown, the electronic device 700 may include: a memory 701, a processor 702, and a transceiver 703, wherein the memory 701 and the processor 702 communicate with each other; for example, the memory 701, the processor 702, and the transceiver 703 may communicate via a communication bus 704, the memory 701 is used to store a computer program, and the processor 702 executes the computer program to implement the method of the above embodiment.

[0192] Optionally, the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps in the method embodiments disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0193] This application also provides a vehicle, which includes electronic devices and a battery; wherein the electronic devices are used to perform the data update as described in the foregoing method embodiments, which will not be repeated here.

[0194] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods in any of the above method embodiments.

[0195] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods in any of the above method embodiments.

[0196] All or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0197] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0198] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0199] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0200] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

[0201] In this application, the term "comprising" and its variations can refer to non-limiting inclusion; the term "or" and its variations can refer to "and / or". The terms "first", "second", etc., in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

Claims

1. A data update method, characterized in that, The method includes: Acquire target data for the vehicle; the target data includes battery charging process data, battery thermal management data, and battery static data; Based on the target data, obtain the mapping relationship of the battery's corresponding temperature rise rate; the battery's corresponding temperature rise rate includes: self-heating temperature rise rate, thermal management temperature rise rate, and natural heat exchange temperature rise rate; Based on the charging process data of the battery, a first mapping relationship between battery temperature, state of charge and self-heating temperature rise rate is obtained; Based on the thermal management data of the battery, a second mapping relationship between the thermal management strategy and the thermal management temperature rise rate is obtained; Based on the battery's static data, a third mapping relationship is obtained between the battery temperature, ambient temperature, and natural heat exchange temperature rise rate; the static data includes: the battery's third temperature, power-off time, and ambient temperature when the vehicle is powered off, and the battery's fourth temperature and power-on time when the vehicle is powered off and then powered on again. Based on the third temperature, the power-off time, the ambient temperature, the fourth temperature, and the power-on time, determine the self-heating temperature rise rate corresponding to the ambient temperature range; The third mapping relationship is obtained based on the self-heating temperature rise rate corresponding to the environmental temperature range to which the ambient temperature belongs; Based on the obtained mapping relationship of the battery's corresponding temperature rise rate, if the difference in temperature rise rate between the obtained mapping relationship and the corresponding mapping relationship in the mapping relationship table is greater than a first preset value for N consecutive times, and the difference in temperature rise rate between the obtained mapping relationships is less than a second preset value for N consecutive times, then the corresponding temperature rise rate mapping relationship table is updated using the Nth obtained mapping relationship. The mapping relationship table is used to predict the remaining charging time of the battery.

2. The method according to claim 1, characterized in that, The charging process data includes: charging current, battery temperature, and ambient temperature within each state of charge range. The step of obtaining a first mapping relationship between battery temperature, state of charge, and self-heating temperature rise rate based on the battery charging process data includes: Obtain the charging process data corresponding to the effective state of charge interval from the charging process data; Using the charging process data corresponding to the effective state of charge interval, the battery temperature at the beginning of the effective state of charge interval, the battery temperature at the end of the effective state of charge interval, and the average charging current are obtained. Based on the battery temperature at the beginning of the effective state of charge interval, the battery temperature at the end of the effective state of charge interval, and the average charging current, calculate the self-heating temperature rise rate corresponding to the effective state of charge interval and the battery temperature interval. The first mapping relationship is obtained based on the self-heating temperature rise rate corresponding to the effective state of charge range and the battery temperature range.

3. The method according to claim 2, characterized in that, The step of obtaining the charging process data corresponding to the effective state of charge interval from the charging process data includes: Based on the effective data filtering criteria, the charging process data corresponding to the effective state of charge interval is obtained from the charging process data; the effective data filtering criteria include: the change in charging current within the state of charge interval is less than a preset threshold, and the thermal management strategy remains unchanged.

4. The method according to claim 1, characterized in that, The thermal management data of the battery includes: the first temperature, activation time, thermal management strategy and ambient temperature when the battery activates thermal management, and the second temperature and end time when the battery terminates thermal management. The step of obtaining a second mapping relationship between the thermal management strategy and the thermal management temperature rise rate based on the thermal management data of the battery includes: Based on the thermal management data of the battery and the natural heat transfer temperature rise rate during battery preheating, the thermal management temperature rise rate corresponding to the thermal management strategy is obtained; The second mapping relationship is obtained based on the thermal management temperature rise rate corresponding to the thermal management strategy.

5. A method for predicting the remaining charging time of a battery, characterized in that, The method includes: Obtain the battery cell temperature, initial state of charge, and ambient temperature; Based on the battery cell temperature, initial state of charge, ambient temperature, and a temperature rise rate mapping table updated using the method described in any one of claims 1-4, the remaining charging time of the battery is predicted.

6. A data update device, characterized in that, include: The first acquisition module is used to acquire target data of the vehicle; the target data includes data related to the temperature rise of the battery. The second acquisition module is used to acquire the mapping relationship of the temperature rise rate of the battery based on the target data; An update module is used to update the corresponding temperature rise rate mapping table based on the obtained mapping relationship of the battery's corresponding temperature rise rate. When the temperature rise rate difference between the obtained mapping relationship and the corresponding mapping relationship in the mapping relationship table is greater than a first preset value for N consecutive times, and the temperature rise rate difference between the obtained mapping relationships is less than a second preset value for N consecutive times, the corresponding temperature rise rate mapping relationship table is used to predict the remaining charging time of the battery.

7. A device for predicting remaining battery charging time, characterized in that, The device includes: The acquisition module is used to acquire the battery cell temperature, initial state of charge, and ambient temperature. The prediction module is used to predict the remaining charging time of the battery based on the battery cell temperature, initial state of charge, ambient temperature, and a temperature rise rate mapping table updated by the method described in any one of claims 1-4.

8. An electronic device, characterized in that, include: Processor; memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-5.

9. A vehicle, characterized in that, The vehicle includes electronic equipment and a battery; The electronic device is used to perform the method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-5.

11. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-5.

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