Battery health status acquisition method, device, equipment and computer program product

Abstract

The present application discloses a method, device, equipment and computer program product for obtaining the health status of a battery. The method includes: receiving the operating condition data of a first electric device, the operating condition data of the first electric device including at least one of the charging condition data, discharging condition data and static condition data of the first electric device; determining a first characteristic parameter according to the operating condition data of the first electric device; inputting the first characteristic parameter into a target model that matches the first characteristic parameter to obtain a first maximum available capacity of the first battery; wherein the first maximum available capacity is used to indicate the current health status of the first battery, and the first battery is the battery of the first electric device. The present application can utilize the charging condition data, discharging condition data or static condition data of the electric device, and with the help of the target model, realize an accurate prediction of the current health status of the battery of the electric device, thereby improving the reliability of the use of the electric device.

Description

Technical Field

[0001] The present application belongs to the field of battery technology, and in particular, relates to a method, device, equipment and computer program product for obtaining the health status of a battery. Background Art

[0002] With the promotion of the environmental protection concept of energy conservation and emission reduction, more and more electric devices are put into use. Lithium-ion batteries are widely used as energy storage devices in electric devices due to their advantages in energy density and cycle life. However, during the use of batteries, their performance will inevitably decline over time. In order to ensure their working reliability, accurately predicting their state of health (SOH) has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide a method, apparatus, device and computer program product for obtaining the health status of a battery, which can solve the problem of obtaining the health status of a battery.

[0004] In a first aspect, an embodiment of the present application provides a method for obtaining a battery health status, comprising: receiving operating condition data of a first electric device, the operating condition data of the first electric device including at least one of charging condition data, discharging condition data and static condition data of the first electric device; determining a first characteristic parameter based on the operating condition data of the first electric device; inputting the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of a first battery; wherein the first maximum available capacity is used to indicate a current health status of the first battery, which is a battery of the first electric device.

[0005] In some embodiments, the operating condition data of the first electric device includes the charging condition data of the first electric device; the first characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the first battery; the slope of the open circuit voltage curve of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value; a first time duration, the starting time of the first time duration is the moment when the first battery switches from constant current charging to constant voltage charging, and the ending time of the first time duration is the moment when the first battery is fully charged; the target model is a first model, and the first model is trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery, wherein the first battery, the second battery and the battery of the second electric device are all target model batteries.

[0006] In some embodiments, before the first characteristic parameter is input into the target model matching the first characteristic parameter to obtain the first maximum available capacity of the first battery, the method further includes: obtaining aging cycle test data of the second battery; obtaining charging condition data of the second electric device; performing statistics on the charging condition data of the second electric device to obtain statistical results, wherein the statistical results include the actual charging interval and data sampling frequency of the target model battery; segmenting the aging cycle test data according to the actual charging interval to obtain first test data; resampling the first test data according to the data sampling frequency to obtain second test data; determining a second characteristic parameter according to the second test data, wherein the second characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the second battery; the slope of the open circuit voltage curve of the second battery; the maximum value of the differential capacity curve of the second battery and the voltage corresponding to the maximum value; a second duration, wherein the starting time of the second duration is the moment when the second battery switches from constant current charging to constant voltage charging, and the ending time of the second duration is the moment when the second battery is fully charged; and training the first model according to the second characteristic parameter and the calibrated maximum available capacity of the second battery.

[0007] In some embodiments, the operating condition data of the first electric device is the discharge condition data or the static condition data of the first electric device; the first characteristic parameter includes at least one of the following: the accumulated mileage of the first electric device; the accumulated charge amount of the first battery; the accumulated discharge amount of the first battery; the sum of the accumulated charge amount and the accumulated discharge amount of the first battery; the temperature of the first battery; the target model is a second model, and the second model is trained based on the charging condition data of the third electric device and the first model; wherein the first model is trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery; the first battery, the second battery, the battery of the second electric device and the battery of the third electric device are all target model batteries.

[0008] In some embodiments, before the first characteristic parameter is input into a target model matching the first characteristic parameter to obtain the first maximum available capacity of the first battery, the method further includes: obtaining operating condition data of the third electric device, the operating condition data of the third electric device at least including charging condition data of the third electric device; determining a third characteristic parameter and a fourth characteristic parameter based on the operating condition data of the third electric device; inputting the third characteristic information into the first model to obtain the second maximum available capacity of the third battery, the third battery being the battery of the third electric device; training the second model based on the fourth characteristic parameter and the second maximum available capacity ; Wherein, the third characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the third battery; the slope of the open circuit voltage curve of the third battery; the maximum value of the differential capacity curve of the third battery and the voltage corresponding to the maximum value; a third time duration, the starting time of the third time duration is the moment when the third battery switches from constant current charging to constant voltage charging, and the ending time of the third time duration is the moment when the third battery is fully charged; the fourth characteristic parameter includes at least one of the following: the cumulative mileage of the third electric equipment, the cumulative charge amount of the third battery, the cumulative discharge amount of the third battery, the sum of the cumulative charge amount and the cumulative discharge amount of the third battery, and the temperature of the third battery.

[0009] In a second aspect, an embodiment of the present application provides a battery health status acquisition device, comprising: a first receiving module, used to receive operating condition data of a first electric device, the operating condition data of the first electric device including at least one of charging condition data, discharging condition data and static condition data of the first electric device; a first determination module, used to determine a first characteristic parameter based on the operating condition data of the first electric device; a first operation module, used to input the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery; wherein the first maximum available capacity is used to indicate the current health status of the first battery, and the first battery is the battery of the first electric device.

[0010] In some embodiments, the operating condition data of the first electric device includes the charging condition data of the first electric device; the first characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the first battery; the slope of the open circuit voltage curve of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value; a first time duration, the starting time of the first time duration is the moment when the first battery switches from constant current charging to constant voltage charging, and the ending time of the first time duration is the moment when the first battery is fully charged; the target model is a first model, and the first model is trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery, wherein the first battery, the second battery and the battery of the second electric device are all target model batteries.

[0011] In some embodiments, the device further includes: a first acquisition module for acquiring aging cycle test data of the second battery; a second acquisition module for acquiring charging condition data of the second electric device; a statistical module for performing statistics on the charging condition data of the second electric device to obtain statistical results, wherein the statistical results include the actual charging interval and data sampling frequency of the target model battery; a segmentation module for segmenting the aging cycle test data according to the actual charging interval to obtain first test data; a sampling module for resampling the first test data according to the data sampling frequency to obtain second test data; a second determination module for determining a second characteristic parameter according to the second test data, wherein the second characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the second battery; the slope of the open circuit voltage curve of the second battery; the maximum value of the differential capacity curve of the second battery and the voltage corresponding to the maximum value; a second duration, wherein the starting time of the second duration is the time when the second battery switches from constant current charging to constant voltage charging, and the ending time of the second duration is the time when the second battery is fully charged; a first training module for training the first model according to the second characteristic parameter and the calibrated maximum available capacity of the second battery.

[0012] In some embodiments, the operating condition data of the first electric device is the discharge condition data or the stationary condition data of the first electric device; the first characteristic parameter includes at least one of the following: the cumulative mileage of the first electric device, the cumulative charge amount of the first battery, the cumulative discharge amount of the first battery, the sum of the cumulative charge amount and the cumulative discharge amount of the first battery, and the temperature of the first battery; the target model is a second model, and the second model is trained based on the charging condition data of the third electric device and the first model; wherein, the first model is trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery; the first battery, the second battery, the battery of the second electric device and the battery of the third electric device are all target model batteries.

[0013] In some embodiments, the device further includes: a third acquisition module, used to acquire the operating condition data of the third electric device, the operating condition data of the third electric device at least including the charging condition data of the third electric device; a third determination module, used to determine the third characteristic parameter and the fourth characteristic parameter according to the operating condition data of the third electric device; a second operation module, used to input the third characteristic information into the first model to obtain the second maximum available capacity of the third battery, the third battery being the battery of the third electric device; a second training module, used to train the second model according to the fourth characteristic parameter and the second maximum available capacity; wherein, The third characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the third battery; the slope of the open circuit voltage curve of the third battery; the maximum value of the differential capacity curve of the third battery and the voltage corresponding to the maximum value; a third time duration, wherein the starting time of the third time duration is the time when the third battery switches from constant current charging to constant voltage charging, and the ending time of the third time duration is the time when the third battery is fully charged; the fourth characteristic parameter includes at least one of the following: the accumulated mileage of the third electric device, the accumulated charge amount of the third battery, the accumulated discharge amount of the third battery, the sum of the accumulated charge amount and the accumulated discharge amount of the third battery, and the temperature of the third battery.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the battery health status acquisition method as described in any one of the above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, a battery health status acquisition method as described in any one of the above is implemented.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes any one of the battery health status acquisition methods described above.

[0017] In an embodiment of the present application, a first characteristic parameter can be determined based on the received operating condition data of the first electric device, wherein the operating condition data of the first electric device includes at least one of the charging condition data, the discharging condition data, and the static condition data of the first electric device; thereafter, the first characteristic parameter is input into a matching target model, and the maximum available capacity of the battery of the first electric device is predicted by the target model, and the maximum available capacity is used to indicate the current health state of the battery of the first electric device. It can be seen that the embodiment of the present application can utilize the charging condition data, the discharging condition data, or the static condition data of the electric device, and with the help of the target model, an accurate prediction of the current health state of the battery of the electric device can be achieved, thereby improving the reliability of the use of the electric device. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application.

[0019] Figure 1 This is one of the flow charts of the method for obtaining the battery health status provided in the embodiment of the present application;

[0020] Figure 2 This is a diagram of the idea of ​​obtaining the battery health status provided in the embodiment of the present application;

[0021] Figure 3 This is the second flow chart of the method for obtaining the battery health status provided in the embodiment of the present application;

[0022] Figure 4 is a structural schematic diagram of a battery health status acquisition device provided in an embodiment of the present application;

[0023] Figure 5 It is a structural diagram of a battery health status acquisition device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0025] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0026] The following is a detailed description of the battery health status acquisition method provided by the embodiment of the present application through some embodiments and their application scenarios in combination with the accompanying drawings.

[0027] The battery health status acquisition method of the embodiment of the present application can be executed by a battery health status acquisition device. In a specific implementation, the battery health status acquisition device can be a cloud platform or a server, etc.

[0028] See also Figure 1 , Figure 1 This is one of the flow charts of the battery health status acquisition method provided in the embodiment of the present application. Figure 1 As shown, the battery health status acquisition method may include the following steps:

[0029] Step 101: Receive operating condition data of a first electric device, where the operating condition data of the first electric device includes at least one of charging condition data, discharging condition data, and stationary condition data of the first electric device.

[0030] The first electric device may be any electric device in actual operation, such as an electric car, an electric two-wheeled vehicle, an electric motorcycle, or a drone.

[0031] In an optional implementation, the first electric device may periodically send its current operating condition data to the battery health status acquisition device. In this implementation, the first electric device may send its current operating condition data to the battery health status acquisition device when the periodic transmission time of the operating condition data is reached.

[0032] In another optional implementation, the first electric device may send its current operating condition data to the battery health status acquisition device in response to a user instruction. In this implementation, if the user of the first electric device wants to know the current health status of the battery of the first electric device, the user may input an instruction information in the first electric device to instruct the acquisition of the current health status of the battery of the first electric device. The first electric device may send its current operating condition data to the battery health status acquisition device in response to the instruction information.

[0033] In the embodiment of the present application, the operating condition data of the first electric device may be expressed in the following forms:

[0034] Expression form 1: the operating condition data of the first electric device may only include charging condition data of the first electric device;

[0035] Expression form 2: the operating condition data of the first electric device may only include the discharging condition data or the static condition data of the first electric device;

[0036] Expression form 3: The operating condition data of the first electric device may include charging condition data, discharging condition data and stationary condition data of the first electric device.

[0037] It can be understood that the reason why the operating condition data of the first electric device is expressed in expression form 2 may include at least one of the following:

[0038] The first electric device has no charging condition data uploaded, such as: when charging the battery of the first electric device (hereinafter referred to as the first battery), it needs to be taken out from the first electric device and charged in a dedicated charging device;

[0039] The first electric device is prohibited from uploading the charging condition data, for example, for privacy protection considerations, the user prohibits the first electric device from uploading the charging condition data;

[0040] The first electric device is powered off, resulting in the charging condition data not being uploaded;

[0041] The first electric device misses transmitting charging condition data.

[0042] In practical applications, the charging condition data of the first electric device may include but is not limited to at least one of the following: the charging current of the first battery; the charging voltage of the first battery; the state of charge (SOC) range of the first battery, which may also be called the charging range; the accumulated mileage of the first electric device; the temperature of the first battery, etc.

[0043] The discharge operating condition data or the stationary operating condition data of the first electric device may include, but is not limited to, at least one of the following: the accumulated mileage of the first electric device; the temperature of the first battery, etc.

[0044] From the above content, it can be seen that the same type of data may exist in the charging condition data, the discharging condition data and the static condition data, such as: accumulated mileage, battery temperature, etc.

[0045] Step 102: Determine a first characteristic parameter according to the operating condition data of the first electric device.

[0046] In one specific implementation, the first characteristic parameter can be obtained by extracting or processing the operating condition data of the first electric device. It can be understood that for the operating condition data of the first electric device in different forms, the first characteristic parameter can be expressed in the same or different forms, specifically as follows:

[0047] For the operating condition data of the first electric device in the above-mentioned expression form 1, the first characteristic parameter is determined based on the charging condition data of the first electric device.

[0048] For the operating condition data of the first electric device in the above-mentioned expression form 2, the first characteristic parameter is determined based on the discharge condition data or the static condition data of the first electric device.

[0049] For the operating condition data of the first electric device in the above-mentioned expression form 3, the first characteristic parameter may be determined based on at least one of the charging condition data, the discharging condition data, and the static condition data of the first electric device. It is understandable that, in the case where the first characteristic parameter is determined based on the charging condition data, the discharging condition data, and the static condition data of the first electric device, the first characteristic parameter may include a first sub-characteristic parameter determined based on the charging condition data of the first electric device, and a second sub-characteristic parameter determined based on the discharging condition data or the static condition data of the first electric device.

[0050] In the embodiment of the present application, the characteristic parameter determined based on the charging condition data of the first electric device may include but is not limited to at least one of the following:

[0051] At least one charging voltage sampling value of the first battery (also referred to as the voltage sequence of the first battery); the slope of the open circuit voltage (OCV) curve of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value (also referred to as the peak position of the differential capacity curve of the first battery); a first time duration, wherein the starting time of the first time duration is the time when the first battery switches from constant current charging to constant voltage charging (also referred to as a constant current constant voltage (CCCV) change point), and the ending time of the first time duration is the time when the first battery is fully charged, etc.

[0052] In one specific implementation manner, the OCV curve may be a SOC-OCV curve.

[0053] The characteristic parameters determined based on the discharge condition data or the static condition data of the first electric device may include, but are not limited to, at least one of the following:

[0054] The accumulated mileage of the first electric device; the accumulated charge amount of the first battery; the accumulated discharge amount of the first battery; the sum of the accumulated charge amount and the accumulated discharge amount of the first battery; the temperature of the first battery, etc. The accumulated charge amount, the accumulated discharge amount, and the sum of the accumulated charge amount and the accumulated discharge amount can be collectively referred to as capacity throughput.

[0055] It can be understood that both the discharge condition data and the stationary condition data belong to the case where there is no charging condition data. Therefore, the method of determining the characteristic parameters based on the discharge condition data or the stationary condition data of the first electric device is the same.

[0056] Step 103: input the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery.

[0057] The first maximum available capacity is used to indicate the current health status of the first battery, and the first battery is the battery of the first electric device.

[0058] In an embodiment of the present application, the battery health status acquisition device can pre-train two models, namely a first model and a second model. The outputs of the first model and the second model are both the maximum available capacity of the battery, but the inputs are different.

[0059] The input of the first model is a characteristic parameter determined based on the charging condition data of the electric device; the input of the second model is a characteristic parameter determined based on the discharge condition data or the static condition data of the electric device. Based on the input of the model, it can be understood that the first model can be used to predict the maximum available capacity of the battery of the electric device that has uploaded the charging condition data; the second model can be used to predict the maximum available capacity of the battery of the electric device that has not uploaded the charging condition data, that is, uploaded the discharge condition data or the static condition data.

[0060] Therefore, when the first characteristic parameter is determined based on the charging condition data of the first electric device, the target model may be the first model mentioned above. In this case, the output of the first model is the maximum available capacity of the first battery, that is, the first maximum available capacity.

[0061] When the first characteristic parameter is determined based on the discharge condition data or the stationary condition data of the first electric device, the target model may be the second model mentioned above. In this case, the output of the second model is the first maximum available capacity.

[0062] In the case where the first characteristic parameter includes the above-mentioned first sub-characteristic parameter and the second sub-characteristic parameter, the target model may include the first model and the second model. In specific implementation, the first sub-characteristic parameter is input into the first model to obtain the first maximum available capacity; the second sub-characteristic parameter is input into the second model to obtain the second maximum available capacity. In one implementation, the average value of the above-mentioned two maximum available capacities may be determined as the first maximum available capacity; in another implementation, the weighted sum of the above-mentioned two maximum available capacities may be determined as the first maximum available capacity, and the weight value of the maximum available capacity predicted based on different models may be pre-set based on actual needs, such as: the weight value of the maximum available capacity predicted based on the first model may be set to 0.8; the weight value of the maximum available capacity predicted based on the first model may be set to 0.2.

[0063] The battery health status acquisition method of this embodiment can determine the first characteristic parameter based on the received operating condition data of the first electric device, wherein the operating condition data of the first electric device includes at least one of the charging condition data, discharging condition data and static condition data of the first electric device; then, the first characteristic parameter is input into the matching target model, and the maximum available capacity of the battery of the first electric device is predicted by the target model, and the maximum available capacity is used to indicate the current health status of the battery of the first electric device. It can be seen that the embodiment of the present application can use the charging condition data or the discharging condition data or the static condition data of the electric device, and use the target model to accurately predict the current health status of the battery of the electric device, thereby improving the reliability of the use of the electric device.

[0064] In some embodiments, the operating condition data of the first electric device includes charging condition data of the first electric device;

[0065] The first characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the first battery; the slope of the open circuit voltage curve (such as the SOC-OCV curve) of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value; a first duration, wherein the starting time of the first duration is the time when the first battery switches from constant current charging to constant voltage charging, and the ending time of the first duration is the time when the first battery is fully charged;

[0066] The target model is a first model, which is trained based on aging cycle test data of a second battery, charging condition data of a second electric device, and a calibrated maximum available capacity of the second battery, wherein the first battery, the second battery, and the battery of the second electric device are all target model batteries.

[0067] In this embodiment, the first characteristic parameter is determined based on the charging condition data of the first electric device, and the first characteristic parameter can be regarded as a health factor of the first battery.

[0068] The second battery is a battery used for aging cycle testing; the second electric device is an electric device used for training the first model, and the second electric device is an electric device in actual operation.

[0069] Furthermore, the battery of the second electric device, the battery of the first electric device and the second battery can be batteries of the same model, namely, target model batteries. In this way, the maximum available capacity of the first battery can be predicted by using the first model trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery, thereby improving the reliability of obtaining the battery health status of the first battery.

[0070] The same battery model can be manifested as the same performance parameter of at least one of the following batteries: capacity; battery cell material; chemical composition; chemical material, etc. For example, the first battery, the second battery, and the battery of the second electric device are all lithium iron phosphate batteries, ternary batteries, lithium cobalt oxide batteries, sodium ion batteries, or solid-state batteries. In one specific implementation, the first battery, the second battery, and the battery of the second electric device can be specifically manifested as: batteries of the same electrochemical system, or batteries of the same batch on the same production line, or batteries of different batches but with similar or consistent performance parameters.

[0071] The training of the first model is described in detail below.

[0072] In some embodiments, before inputting the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery, the method further includes:

[0073] Acquiring aging cycle test data of the second battery;

[0074] Acquiring charging condition data of the second electric device;

[0075] Collecting statistics on the charging condition data of the second electric device to obtain statistical results, wherein the statistical results include an actual charging interval and a data sampling frequency of the target model battery;

[0076] According to the actual charging interval, the aging cycle test data is segmented to obtain first test data;

[0077] Resampling the first test data according to the data sampling frequency to obtain second test data;

[0078] Determine a second characteristic parameter according to the second test data, wherein the second characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the second battery; the slope of the open circuit voltage curve (such as the SOC-OCV curve) of the second battery; the maximum value of the differential capacity curve of the second battery and the voltage corresponding to the maximum value; a second duration, wherein the start time of the second duration is the time when the second battery switches from constant current charging to constant voltage charging, and the end time of the second duration is the time when the second battery is fully charged;

[0079] The first model is trained according to the second characteristic parameter and the calibrated maximum available capacity of the second battery.

[0080] The aging cycle test data is the data collected by the aging cycle test conducted in the laboratory. In the aging cycle test, the charging conditions are accurate and consistent, and the battery is fully charged and discharged, that is, the SOC range is 0-100%, and the data sampling frequency is relatively large, such as collecting data once every 100 milliseconds (ms).

[0081] However, for electric equipment in actual operation, the charging conditions of the batteries vary greatly and there is a lot of noise, so the batteries cannot generally be fully charged and discharged, and the data sampling frequency is relatively small compared to the aging cycle test, such as: the SOC range is 30% to 90%, and the sampling frequency is 10 seconds (s) or 30s to collect data.

[0082] In this embodiment, in order to enable the first model to accurately predict the battery health status of the actually running electric device, the charging condition data of the second electric device can be counted to obtain the actual charging interval and data sampling frequency of the actually running electric device.

[0083] Afterwards, the actual charging interval of the actually running electric device can be used to segment the aging cycle test data to obtain the first test data. It can be understood that the first test data only includes the test data in the aging cycle test data that is located in the actual charging interval. Then, the first test data is resampled using the data sampling frequency of the actually running electric device to obtain the second test data. In this way, it can be ensured that the charging interval and data sampling frequency of the second test data are consistent with the charging interval and data sampling frequency of the actually running electric device, and further it can be ensured that the first model trained using the second test data can accurately predict the battery health status of the actually running electric device.

[0084] After acquiring the second test data, the second test data may be processed to obtain a second characteristic parameter, which may be regarded as a health factor of the second battery. To improve the prediction reliability of the first model, the data type included in the second characteristic parameter may be the same as the data type included in the first characteristic parameter determined based on the charging condition data of the first electric device.

[0085] Afterwards, the second characteristic parameter can be used as the input of the first model, and the calibrated maximum available capacity of the second battery can be used as the output of the first model to train the first model until the first model converges to obtain a trained first model. The trained first model is then used to predict the battery health status of the electric device in actual operation.

[0086] In this embodiment, the actual charging interval and data sampling frequency of the target model battery are determined using the charging condition data of the electric device in actual operation. After that, the aging cycle test data of the second battery is processed using the actual charging interval and data sampling frequency to obtain the second test data. Then, the first model is trained using the second characteristic parameter determined based on the second test data and the calibrated maximum available capacity of the second battery. In this way, the first model can use the charging condition data of the electric device in actual operation to accurately predict the health status of the target model battery in the electric device in actual operation.

[0087] In some embodiments, the operating condition data of the first electric device is the discharging condition data or the static condition data of the first electric device;

[0088] The first characteristic parameter includes at least one of the following: the accumulated mileage of the first electric device; the accumulated charge amount of the first battery; the accumulated discharge amount of the first battery; the sum of the accumulated charge amount and the accumulated discharge amount of the first battery; the temperature of the first battery;

[0089] The target model is the second model, which is trained based on the charging condition data of the third electric device and the first model; wherein the first model is trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery; the first battery, the second battery, the battery of the second electric device and the battery of the third electric device are all target model batteries.

[0090] In this embodiment, the first characteristic parameter is determined based on the discharge condition data or the stationary condition data of the first electric device, and the first characteristic parameter can be regarded as an influencing factor of the first battery.

[0091] The third electric device is an electric device used to train the second model, and the third electric device is an electric device in actual operation.

[0092] Furthermore, the battery of the third electric device and the battery of the first electric device may be of the same model, namely, a target model battery. In this way, the maximum available capacity of the first battery is predicted using the second model obtained by training the first model based on the charging condition data of the third electric device, thereby improving the reliability of obtaining the battery health status of the first battery.

[0093] The training of the second model is described in detail below.

[0094] In some embodiments, before inputting the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery, the method further includes:

[0095] Acquire operating condition data of the third electric device, where the operating condition data of the third electric device at least includes charging condition data of the third electric device;

[0096] Determining a third characteristic parameter and a fourth characteristic parameter according to the operating condition data of the third electric device;

[0097] Inputting the third characteristic information into the first model to obtain a second maximum available capacity of a third battery, where the third battery is a battery of the third electric device;

[0098] Training the second model according to the fourth characteristic parameter and the second maximum available capacity;

[0099] The third characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the third battery; the slope of the open circuit voltage curve (such as the SOC-OCV curve) of the third battery; the maximum value of the differential capacity curve of the third battery and the voltage corresponding to the maximum value; a third time duration, wherein the starting time of the third time duration is the time when the third battery switches from constant current charging to constant voltage charging, and the ending time of the third time duration is the time when the third battery is fully charged;

[0100] The fourth characteristic parameter includes at least one of the following: the accumulated mileage of the third electric equipment, the accumulated charge amount of the third battery, the accumulated discharge amount of the third battery, the sum of the accumulated charge amount and the accumulated discharge amount of the third battery, and the temperature of the third battery.

[0101] In this embodiment, the third characteristic parameter is determined based on the charging condition data of the third electric device, and the third characteristic parameter can be regarded as a battery of the third electric device, that is, a health factor of the third battery of the third electric device. To improve the prediction reliability of the second model, the data type included in the third characteristic parameter can be the same as the data type included in the first characteristic parameter determined based on the charging condition data of the first electric device.

[0102] Furthermore, the operating condition data of the third electric device may also include discharging condition data or stationary condition data of the third electric device.

[0103] The fourth characteristic parameter can be determined based on the discharge condition data or the static condition data of the third electric device, or can be determined based on the charging condition data, the discharge condition data and the static condition data of the third electric device. The fourth characteristic parameter can be regarded as an influencing factor of the third battery. In order to improve the prediction reliability of the second model, the data type included in the fourth characteristic parameter can be the same as the data type included in the first characteristic parameter determined based on the discharge condition data or the static condition data of the first electric device.

[0104] In this embodiment, the third characteristic information can be first input into the first model, and the current maximum available capacity of the third battery can be predicted by the first model. After that, the fourth characteristic parameter is used as the input of the second model, and the current maximum available capacity of the third battery is used as the output of the second model to train the second model. In this way, the second model can use the discharge condition data or static condition data of the actual running electric device to accurately predict the health status of the target model battery in the actual running electric device.

[0105] It can be understood that the various optional embodiments introduced in the embodiments of the present application can be implemented in combination with each other or can be implemented separately if they do not conflict with each other, and the embodiments of the present application are not limited to this.

[0106] In order to facilitate understanding of the battery health status acquisition method provided in the embodiment of the present application, the above-mentioned battery health status acquisition method is explained below using a specific scenario embodiment.

[0107] In the embodiment of this scenario, the cloud platform can estimate the battery health status by combining test data (data collected by aging cycle tests conducted in the laboratory) with actual operating condition data (data collected by sensors of electric equipment during actual operation). In case of differences between actual working conditions and test conditions, the test data is processed using the statistical results of the actual operating data, and a machine learning model 1 (i.e., the aforementioned first model) is constructed to achieve timely and accurate estimation of the health status of the battery during actual charging. In case of a large amount of missing charging data in the cloud platform (privacy is not uploaded, power is not uploaded, etc.), a machine learning model 2 (i.e., the aforementioned second model) is constructed to achieve health status estimation for all working conditions.

[0108] The actual operating condition data received by the cloud platform are statistically analyzed, and the cycle test data are sliced ​​and resampled (according to different SOC intervals, different sampling time intervals, etc.) based on the statistical results. The health factors (such as voltage series, SOC-OCV curve slope, peak position of differential capacity curve, CCCV change point, etc.) are extracted from the processed cycle data. A machine learning model is established to describe the regression relationship between the health factor and the calibrated capacity (referring to the current rated capacity calibrated in the aging cycle test), thereby achieving timely and accurate estimation of the health status of the actual charging condition.

[0109] For situations where charging data may not be uploaded in actual battery applications, based on the electrochemical mechanism, we select influencing factors that exist in both charging and discharging conditions, such as the accumulated mileage of electric equipment and the temperature of the battery, to construct a second machine learning model to achieve health status estimation under all conditions.

[0110] The embodiments of the present application make full use of the accuracy of the test data, fully consider the variability of actual operating conditions, and based on machine learning methods, achieve timely and accurate estimation of the health status of the battery under all operating conditions of actual application. This can effectively overcome the problems of the prior art in which there are few calculable situations in actual operating conditions and the updates are not timely and accurate.

[0111] The overall idea of ​​the embodiment of the present application is as follows Figure 2 As shown. The cycle test data is processed through actual charging conditions to extract the health factors that can be calculated in actual applications, and the first machine learning model is constructed and trained to achieve the health status estimation under actual charging conditions; the influencing factors that exist in all conditions of actual applications are extracted, and the second machine learning model is constructed and trained to achieve the health status estimation under all conditions of actual applications.

[0112] The specific implementation process is as follows Figure 3 As shown. Includes:

[0113] S1. Carry out battery aging test and obtain test data;

[0114] S2. Obtain battery data under actual vehicle charging conditions from the big data platform to obtain battery condition statistics;

[0115] S3. Slice and resample the test data in multiple dimensions according to the actual charging condition statistics, and calculate and extract the health characteristics of the charging curve;

[0116] S4, based on regression machine learning such as Xgboost and neural networks, train model 1 using the features of step 3 and the standard maximum available capacity of step 1;

[0117] S5. Model 1 big data platform deployment and application to timely and accurately estimate the maximum available capacity of batteries with charging data;

[0118] S6, extracting the battery data in step 5, and training a second machine learning model of capacity throughput, cumulative mileage, temperature, etc. and maximum available capacity;

[0119] S7. Deployment and application of big data platform to achieve timely and accurate estimation of the maximum available capacity of batteries with no charging data uploaded or missing charging data.

[0120] The embodiments of the present application can achieve the following beneficial effects for electric vehicles, electric two-wheeled vehicles, drones and other products that have deployed a cloud platform without increasing the cost of software and hardware: (1) improving the accuracy of battery health status estimation results; and (2) real-time updating of massive battery health status estimation results on the cloud platform.

[0121] Based on the battery health status acquisition method provided in the above embodiment, the present application also provides a specific implementation of a battery health status acquisition device. Please refer to the following embodiment.

[0122] See also Figure 4 , the battery health status acquisition device provided in the embodiment of the present application may include:

[0123] A first receiving module 401 is used to receive operating condition data of a first electric device, where the operating condition data of the first electric device includes at least one of charging condition data, discharging condition data and stationary condition data of the first electric device;

[0124] A first determining module 402, configured to determine a first characteristic parameter according to the operating condition data of the first electric device;

[0125] A first operating module 403 is configured to input the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of a first battery;

[0126] The first maximum available capacity is used to indicate the current health status of the first battery, and the first battery is the battery of the first electric device.

[0127] In some embodiments, the operating condition data of the first electric device includes charging condition data of the first electric device;

[0128] The first characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the first battery; the slope of the open circuit voltage curve of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value; a first duration, wherein the starting time of the first duration is the time when the first battery switches from constant current charging to constant voltage charging, and the ending time of the first duration is the time when the first battery is fully charged;

[0129] The target model is a first model, which is trained based on aging cycle test data of a second battery, charging condition data of a second electric device, and a calibrated maximum available capacity of the second battery, wherein the first battery, the second battery, and the battery of the second electric device are all target model batteries.

[0130] In some embodiments, the apparatus further comprises:

[0131] A first acquisition module, used to acquire aging cycle test data of the second battery;

[0132] A second acquisition module, used to acquire charging condition data of the second electric device;

[0133] A statistical module, used for collecting statistics on the charging condition data of the second electric device to obtain statistical results, wherein the statistical results include an actual charging interval and a data sampling frequency of the target model battery;

[0134] A segmentation module, configured to segment the aging cycle test data according to the actual charging interval to obtain first test data;

[0135] A sampling module, used for resampling the first test data according to the data sampling frequency to obtain second test data;

[0136] A second determination module is used to determine a second characteristic parameter according to the second test data, wherein the second characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the second battery; the slope of the open circuit voltage curve (such as the SOC-OCV curve) of the second battery; the maximum value of the differential capacity curve of the second battery and the voltage corresponding to the maximum value; a second duration, wherein the start time of the second duration is the time when the second battery switches from constant current charging to constant voltage charging, and the end time of the second duration is the time when the second battery is fully charged;

[0137] The first training module is used to train the first model according to the second characteristic parameter and the calibrated maximum available capacity of the second battery.

[0138] In some embodiments, the operating condition data of the first electric device is the discharging condition data or the static condition data of the first electric device;

[0139] The first characteristic parameter includes at least one of the following: the accumulated mileage of the first electric device, the accumulated charge amount of the first battery, the accumulated discharge amount of the first battery, the sum of the accumulated charge amount and the accumulated discharge amount of the first battery, and the temperature of the first battery;

[0140] The target model is the second model, which is trained based on the charging condition data of the third electric device and the first model; wherein the first model is trained based on the aging cycle test data of the second battery, the charging condition data of the second electric device and the calibrated maximum available capacity of the second battery; the first battery, the second battery, the battery of the second electric device and the battery of the third electric device are all target model batteries.

[0141] In some embodiments, the apparatus further comprises:

[0142] A third acquisition module, used to acquire the operating condition data of the third electric device, wherein the operating condition data of the third electric device at least includes the charging condition data of the third electric device;

[0143] A third determining module, used to determine a third characteristic parameter and a fourth characteristic parameter according to the operating condition data of the third electric device;

[0144] a second operating module, configured to input the third characteristic information into the first model to obtain a second maximum available capacity of a third battery, wherein the third battery is a battery of the third electric device;

[0145] a second training module, configured to train the second model according to the fourth characteristic parameter and the second maximum available capacity;

[0146] The third characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the third battery; the slope of the open circuit voltage curve (such as the SOC-OCV curve) of the third battery; the maximum value of the differential capacity curve of the third battery and the voltage corresponding to the maximum value; a third time duration, wherein the starting time of the third time duration is the time when the third battery switches from constant current charging to constant voltage charging, and the ending time of the third time duration is the time when the third battery is fully charged;

[0147] The fourth characteristic parameter includes at least one of the following: the accumulated mileage of the third electric equipment, the accumulated charge amount of the third battery, the accumulated discharge amount of the third battery, the sum of the accumulated charge amount and the accumulated discharge amount of the third battery, and the temperature of the third battery.

[0148] The battery health status acquisition device provided in the embodiment of the present application can achieve Figure 1 In order to avoid repetition, the various processes implemented by the battery health status acquisition device in the method embodiment are not described here.

[0149] Figure 5 A schematic diagram of the hardware structure for obtaining the battery health status provided in an embodiment of the present application is shown.

[0150] The battery health status acquisition device may include a processor 501 and a memory 502 storing computer program instructions.

[0151] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0152] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.

[0153] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present disclosure.

[0154] The processor 501 implements any one of the battery health status acquisition methods in the above embodiments by reading and executing computer program instructions stored in the memory 502 .

[0155] In one example, the battery health status acquisition device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.

[0156] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0157] The bus 510 includes hardware, software, or both, coupling the components of the battery health status acquisition device to each other. By way of example and not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industry standard architecture (EISA) bus, a front-end bus (FSB), a hypertransport (HT) interconnect, an industry standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 510 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0158] In addition, in combination with the battery health status acquisition method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the battery health status acquisition methods in the above embodiment is implemented.

[0159] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0160] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0161] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0162] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0163] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for obtaining a battery health status, characterized in that: include: Receiving operating condition data of a first electric device, the operating condition data of the first electric device comprising at least one of charging condition data, discharging condition data, and stationary condition data of the first electric device; Determine a first characteristic parameter based on the operating condition data of the first electric device; the first characteristic parameter includes at least one of the following: a first sub-characteristic parameter determined based on the charging condition data of the first electric device; a second sub-characteristic parameter determined based on the discharging condition or static condition data of the first electric device; Inputting the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery; Wherein, the first maximum available capacity is used to indicate the current health status of the first battery, and the first battery is a battery of the first electric device; In the case where the first feature parameter is the first sub-feature parameter, the target model is the first model; In the case where the first feature parameter is the second sub-feature parameter, the target model is the second model; In the case where the first feature parameter includes the first sub-feature parameter and the second sub-feature parameter, the target model includes the first model and the second model; The first model is trained based on the second characteristic parameter and the calibrated maximum available capacity of the second battery; the data type included in the second characteristic parameter is the same as the data type included in the first sub-characteristic parameter; The second model is trained based on the fourth characteristic parameter and the second maximum available capacity of the third battery of the third electric device; wherein the second maximum available capacity is obtained by inputting the third characteristic parameter into the first model; the third characteristic parameter is determined based on the charging condition data of the third electric device, and the data type included in the third characteristic parameter is the same as the data type included in the first sub-characteristic parameter; the fourth characteristic parameter is determined based on the discharging condition data or the static condition data of the third electric device, and the data type included in the fourth characteristic parameter is the same as the data type included in the second sub-characteristic parameter; The first battery, the second battery, the battery of the second electric device and the third battery are all target model batteries; The second characteristic parameter is obtained in the following manner: obtaining aging cycle test data of the second battery; obtaining charging condition data of the second electric device; performing statistics on the charging condition data of the second electric device to obtain statistical results, wherein the statistical results include an actual charging interval and a data sampling frequency of the target model battery; dividing the aging cycle test data according to the actual charging interval to obtain first test data; resampling the first test data according to the data sampling frequency to obtain second test data; and determining the second characteristic parameter according to the second test data.

2. The method according to claim 1, characterized in that The operating condition data of the first electric device includes charging condition data of the first electric device; The first characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the first battery; the slope of the open circuit voltage curve of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value; a first time duration, wherein the starting time of the first time duration is the time when the first battery switches from constant current charging to constant voltage charging, and the ending time of the first time duration is the time when the first battery is fully charged.

3. The method according to claim 1, characterized in that The second characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the second battery; the slope of the open circuit voltage curve of the second battery; the maximum value of the differential capacity curve of the second battery and the voltage corresponding to the maximum value; a second duration, wherein the start time of the second duration is the time when the second battery switches from constant current charging to constant voltage charging, and the end time of the second duration is the time when the second battery is fully charged; Before inputting the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery, the method further includes: The first model is trained according to the second characteristic parameter and the calibrated maximum available capacity of the second battery.

4. The method according to claim 1, characterized in that The operating condition data of the first electric device is the discharging condition data or the static condition data of the first electric device; The first characteristic parameter includes at least one of the following: the accumulated mileage of the first electric device; the accumulated charge amount of the first battery; the accumulated discharge amount of the first battery; the sum of the accumulated charge amount and the accumulated discharge amount of the first battery; and the temperature of the first battery.

5. The method according to claim 1, characterized in that Before inputting the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of the first battery, the method further includes: Acquire operating condition data of the third electric device, where the operating condition data of the third electric device at least includes charging condition data of the third electric device; Determining a third characteristic parameter and a fourth characteristic parameter according to the operating condition data of the third electric device; Inputting the third characteristic information into the first model to obtain a second maximum available capacity of a third battery, where the third battery is a battery of the third electric device; Training the second model according to the fourth characteristic parameter and the second maximum available capacity; The third characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the third battery; the slope of the open circuit voltage curve of the third battery; the maximum value of the differential capacity curve of the third battery and the voltage corresponding to the maximum value; a third time duration, wherein the starting time of the third time duration is the time when the third battery switches from constant current charging to constant voltage charging, and the ending time of the third time duration is the time when the third battery is fully charged; The fourth characteristic parameter includes at least one of the following: the accumulated mileage of the third electric equipment, the accumulated charge amount of the third battery, the accumulated discharge amount of the third battery, the sum of the accumulated charge amount and the accumulated discharge amount of the third battery, and the temperature of the third battery.

6. A battery health status acquisition device, characterized in that: include: A first receiving module, configured to receive operating condition data of a first electric device, wherein the operating condition data of the first electric device includes at least one of charging condition data, discharging condition data, and stationary condition data of the first electric device; a first determination module, configured to determine a first characteristic parameter according to the operating condition data of the first electric device; the first characteristic parameter includes at least one of the following: a first sub-characteristic parameter determined based on the charging condition data of the first electric device; a second sub-characteristic parameter determined based on the discharging condition or the static condition data of the first electric device; a first operating module, configured to input the first characteristic parameter into a target model matching the first characteristic parameter to obtain a first maximum available capacity of a first battery; Wherein, the first maximum available capacity is used to indicate the current health status of the first battery, and the first battery is a battery of the first electric device; In the case where the first feature parameter is the first sub-feature parameter, the target model is the first model; In the case where the first feature parameter is the second sub-feature parameter, the target model is the second model; In the case where the first feature parameter includes the first sub-feature parameter and the second sub-feature parameter, the target model includes the first model and the second model; The first model is trained based on the second characteristic parameter and the calibrated maximum available capacity of the second battery; the data type included in the second characteristic parameter is the same as the data type included in the first sub-characteristic parameter; The second model is trained based on the fourth characteristic parameter and the second maximum available capacity of the third battery of the third electric device; wherein the second maximum available capacity is obtained by inputting the third characteristic parameter into the first model; the third characteristic parameter is determined based on the charging condition data of the third electric device, and the data type included in the third characteristic parameter is the same as the data type included in the first sub-characteristic parameter; the fourth characteristic parameter is determined based on the discharging condition data or the static condition data of the third electric device, and the data type included in the fourth characteristic parameter is the same as the data type included in the second sub-characteristic parameter; The first battery, the second battery, the battery of the second electric device and the third battery are all target model batteries; The device also includes: A first acquisition module, used to acquire aging cycle test data of the second battery; A second acquisition module, used to acquire charging condition data of the second electric device; A statistical module, used for collecting statistics on the charging condition data of the second electric device to obtain statistical results, wherein the statistical results include an actual charging interval and a data sampling frequency of the target model battery; A segmentation module, configured to segment the aging cycle test data according to the actual charging interval to obtain first test data; A sampling module, used for resampling the first test data according to the data sampling frequency to obtain second test data; The second determining module is used to determine a second characteristic parameter according to the second test data.

7. The device according to claim 6, characterized in that The operating condition data of the first electric device includes charging condition data of the first electric device; The first characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the first battery; the slope of the open circuit voltage curve of the first battery; the maximum value of the differential capacity curve of the first battery and the voltage corresponding to the maximum value; a first time duration, wherein the starting time of the first time duration is the time when the first battery switches from constant current charging to constant voltage charging, and the ending time of the first time duration is the time when the first battery is fully charged.

8. The device according to claim 6, characterized in that The second characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the second battery; the slope of the open circuit voltage curve of the second battery; the maximum value of the differential capacity curve of the second battery and the voltage corresponding to the maximum value; a second duration, wherein the start time of the second duration is the time when the second battery switches from constant current charging to constant voltage charging, and the end time of the second duration is the time when the second battery is fully charged; The device also includes: The first training module is used to train the first model according to the second characteristic parameter and the calibrated maximum available capacity of the second battery.

9. The device according to claim 6, characterized in that The operating condition data of the first electric device is the discharging condition data or the static condition data of the first electric device; The first characteristic parameter includes at least one of the following: the accumulated mileage of the first electric device, the accumulated charge amount of the first battery, the accumulated discharge amount of the first battery, the sum of the accumulated charge amount and the accumulated discharge amount of the first battery, and the temperature of the first battery.

10. The device according to claim 6, characterized in that The device also includes: A third acquisition module, used to acquire the operating condition data of the third electric device, wherein the operating condition data of the third electric device at least includes the charging condition data of the third electric device; A third determining module, used to determine a third characteristic parameter and a fourth characteristic parameter according to the operating condition data of the third electric device; a second operating module, configured to input the third characteristic information into the first model to obtain a second maximum available capacity of a third battery, wherein the third battery is a battery of the third electric device; a second training module, configured to train the second model according to the fourth characteristic parameter and the second maximum available capacity; The third characteristic parameter includes at least one of the following: at least one charging voltage sampling value of the third battery; the slope of the open circuit voltage curve of the third battery; the maximum value of the differential capacity curve of the third battery and the voltage corresponding to the maximum value; a third time duration, wherein the starting time of the third time duration is the time when the third battery switches from constant current charging to constant voltage charging, and the ending time of the third time duration is the time when the third battery is fully charged; The fourth characteristic parameter includes at least one of the following: the accumulated mileage of the third electric equipment, the accumulated charge amount of the third battery, the accumulated discharge amount of the third battery, the sum of the accumulated charge amount and the accumulated discharge amount of the third battery, and the temperature of the third battery.

11. A battery health status acquisition device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the method for obtaining the battery health status as described in any one of claims 1 to 5 is implemented.

12. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the battery health status acquisition method as described in any one of claims 1 to 5.

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