Battery health degree estimation method and device, vehicle and storage medium

Abstract

The invention relates to the technical field of batteries, in particular to a battery health degree estimation method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining the charging segment data of the vehicle; generating a charging data list according to the charging fragment data, and calculating the optimal battery electric quantity of each single battery in the current charging fragment and the corresponding battery health degree value according to the charging data list; and estimating the health degree value of the whole battery pack of the vehicle according to the optimal battery electric quantity of each single battery in the current charging segment and the corresponding battery health degree value. Therefore, the problems that in the prior art, when the health degree of the battery pack is calculated in real time through a machine learning algorithm, the calculation result is prone to being affected by the network or the data scale, and the accuracy of battery health degree calculation is poor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly to a method and device for estimating battery health, a vehicle, and a storage medium. Background Art

[0002] With the rapid development of electric vehicle technology and the increasing perfection of big data technology, cloud big data platforms can quickly, timely, and accurately process a large amount of backhaul data. The battery is the most important component of an electric vehicle. During long-term use, the battery will inevitably age, so it is necessary to estimate the condition of the battery.

[0003] In related technologies, generally, multiple battery health assessment models are used to estimate the battery health; or machine learning algorithms are used to calculate the battery health in real time online. However, too many assessment models increase the computational complexity, and with real-time calculation, the calculation results are easily affected by the network or data scale, resulting in low accuracy. Summary of the Invention

[0004] One of the objectives of the present invention is to provide a method for estimating battery health to solve the problem in the prior art that when using machine learning algorithms to calculate the health of a battery pack in real time, the calculation results are easily affected by the network or data scale, resulting in poor accuracy of battery health calculation; the second objective is to provide a device for estimating battery health; the third objective is to provide a vehicle; the fourth objective is to provide a computer-readable storage medium.

[0005] To achieve the above objectives, the technical solutions adopted by the present invention are as follows:

[0006] A method for estimating battery health, which is applied to a server and includes the following steps: obtaining charging segment data of a vehicle; generating a charging data list according to the charging segment data, and calculating the optimal battery power and the corresponding battery health value of each battery cell during this charging segment according to the charging data list; estimating the health value of the entire battery pack of the vehicle according to the optimal battery power and the corresponding battery health value of each battery cell during this charging segment.

[0007] According to the above technical means, embodiments of the present application can obtain the charging segment data of the vehicle, generate a charging data list according to the charging segment data, traverse the charging data list to calculate the optimal battery power and the corresponding battery health value of each battery cell during this charging segment, and traverse the optimal battery power and battery health value of all battery cells, so as to obtain the optimal battery power and battery health value of the entire battery pack. Thus, the health value of the entire battery pack can be obtained without using multiple battery health assessment models, greatly reducing the amount of calculation, saving calculation costs, and ensuring the accuracy of battery health estimation.

[0008] Further, the obtaining of the charging segment data of the vehicle includes: obtaining the vehicle charging data transmitted back by the vehicle end; performing data cleaning and charging segment division on the vehicle charging data; encoding and grouping the charging segments according to the vehicle identifier, sorting them according to the time of the charging segments, and eliminating the charging segments that do not meet the pre-designed calculation conditions, and the remaining charging segments form the charging segment data.

[0009] According to the above technical means, the embodiments of the present application can eliminate the charging segments that do not meet the pre-designed calculation conditions, improving the accuracy of the subsequent calculation results.

[0010] Further, after performing data cleaning and charging segment division on the vehicle charging data, it further includes: reading various parameter data required for calculation from the dimension table; broadcasting the various parameter data to each node of the calculation.

[0011] According to the above technical means, the embodiments of the present application can use the broadcast variable technology to broadcast various parameter data to each node of the calculation for subsequent calculation of the resistance value.

[0012] Further, the generating of the charging data list according to the charging segment data includes: traversing the entire charging process, extracting the battery voltage list, current, and median battery temperature in each frame of data; calculating the ampere-hour integral of each frame of data, and combining the battery voltage list, current, and median battery temperature to form the charging data list.

[0013] According to the above technical means, the embodiments of the present application can traverse the entire charging process, extract the battery voltage list, current, median battery temperature in each frame of data, calculate the ampere-hour integral of each frame of data, and form a charging data list.

[0014] Further, the calculating of the best battery power and the corresponding battery health value of each battery cell in the current charging segment according to the charging data list includes: obtaining the power range and battery capacity required by the current algorithm, determining the battery health calculation range according to the battery capacity, and setting the step sizes of the power range and the battery health calculation range; traversing the charging data list, performing interpolation calculation to obtain the resistance value according to the temperature, current, and various parameter data broadcast in each frame, and calculating the cumulative sum of the absolute values of the difference between the actual electromotive force value and the interpolated electromotive force value in each frame; traversing the battery power in the power range and the battery health in the battery health calculation range according to the step size, and taking the battery power and the battery health value corresponding to the smallest cumulative sum of the absolute values as the best battery power value and the best battery health value of this cell.

[0015] Further, for each battery cell, the current and temperature in each frame are the same.

[0016] According to the above technical means, embodiments of the present application can calculate key values such as resistance, battery capacity, and battery electromotive force by means of linear interpolation, and calculate the optimal battery power value and the optimal battery health value of each battery cell by setting the step size and traversing. Moreover, the current, temperature, etc. of each frame of the battery cell are the same, and the corresponding resistance interpolation and electromotive force interpolation can be obtained at one time. Each frame of data is only calculated once, avoiding calculating each cell once, saving the calculation cost and accelerating the calculation speed.

[0017] Further, the obtaining of the power range and battery capacity required for this algorithm includes: taking the battery power of the first frame at the start of charging as the initial power value; determining the power range required for this algorithm according to the initial power value, taking the end data of each charging segment, and calculating the battery capacity required for this algorithm according to the end data, the corresponding model and capacity of the battery.

[0018] According to the above technical means, embodiments of the present application can calculate the battery capacity and the battery power range for subsequent calculation of the battery health of the battery cell.

[0019] A battery health estimation device, which is applied to a server, includes: an acquisition module, configured to acquire charging segment data of a vehicle; a generation module, configured to generate a charging data list according to the charging segment data, and calculate the optimal battery power and the corresponding battery health value of each battery cell in this charging segment according to the charging data list; an estimation module, configured to estimate the health value of the entire battery pack of the vehicle according to the optimal battery power and the corresponding battery health value of each battery cell in this charging segment.

[0020] Further, the acquisition module is further configured to acquire the vehicle charging data transmitted back from the vehicle end; perform data cleaning and charging segment division on the vehicle charging data; encode and group the charging segments according to the vehicle identifier, sort them according to the time of the charging segments, and remove the charging segments that do not meet the pre-designed calculation conditions, and the remaining charging segments constitute the charging segment data.

[0021] Further, the battery health estimation device further includes: a calculation module, configured to read various parameter data required for calculation from a dimension table after performing data cleaning and charging segment division on the vehicle charging data; a broadcast module, configured to broadcast the various parameter data to each node of the calculation.

[0022] Further, the generation module is further configured to traverse the entire charging process, extract the battery voltage list, current, and median battery temperature in each frame of data; calculate the ampere-hour integral of each frame of data, and combine the battery voltage list, current, and median battery temperature to form a charging data list.

[0023] Further, the generating module is further configured to obtain the power range and battery capacity required for the current algorithm, determine the battery health calculation range according to the battery capacity, and set the step sizes of the power range and the battery health calculation range; traverse the charging data list, perform interpolation calculations based on the temperature, current, and various parameter data broadcast in each frame to obtain the resistance value, and calculate the cumulative sum of the absolute values of the differences between the actual electromotive force value and the interpolated electromotive force value in each frame; traverse the battery power within the power range and the battery health within the battery health calculation range according to the step sizes, and use the battery power and battery health value corresponding to the smallest cumulative sum of the absolute values as the optimal battery power value and the optimal battery health value of this single cell.

[0024] Further, the generating module is further configured to take the battery power of the first frame at the start of charging as the initial power value; determine the power range required for the current algorithm according to the initial power value, take the end data of each charging segment, and calculate the battery capacity required for the current algorithm according to the end data, the corresponding model and capacity of the battery.

[0025] Further, for each battery cell, the current and temperature in each frame are the same.

[0026] A vehicle includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the battery health estimation method according to any one of the above embodiments.

[0027] A computer-readable storage medium stores a computer program thereon, and the program is executed by a processor to implement the battery health estimation method according to any one of the above embodiments.

[0028] Advantages of the present invention:

[0029] (1) The embodiments of the present application can obtain the charging segment data of the vehicle, generate a charging data list according to the charging segment data, traverse the charging data list to calculate the optimal battery power and the corresponding battery health value of each battery cell in the current charging segment, and traverse the optimal battery power and battery health values of all battery cells, so as to obtain the optimal battery power and battery health value of the entire battery pack. Thus, the health value of the entire battery pack can be obtained without using multiple battery health evaluation models, greatly reducing the amount of calculation, saving the calculation cost, and ensuring the accuracy of the battery health estimation.

[0030] (2) The embodiments of the present application can eliminate the charging segments that do not meet the pre-designed calculation conditions, improving the accuracy of the subsequent calculation results.

[0031] (3) The embodiments of the present application can utilize the broadcast variable technology to broadcast various parameter data to each node of the calculation for subsequent calculation of the resistance value.

[0032] (4) The embodiments of the present application can traverse the entire charging process, extract the battery voltage list, current, median battery temperature in each frame of data, calculate the ampere-hour integral of each frame of data, and form a charging data list.

[0033] (5) The embodiments of the present application can calculate key values such as resistance, battery capacity, and battery electromotive force by using linear interpolation, and calculate the optimal battery charge value and the optimal battery health value of a single cell by setting the step size and traversing. Moreover, the current, temperature, etc. values of each frame of a single battery cell are the same, so the corresponding resistance interpolation and electromotive force interpolation can be obtained at one time. Each frame of data is only calculated once, avoiding calculating for each single cell, saving the calculation cost and accelerating the calculation speed.

[0034] (6) The embodiments of the present application can calculate the battery capacity and the battery charge interval for subsequent calculation of the battery health of a single battery cell.

[0035] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0036] Figure 1 is a flowchart of the battery health estimation method provided by the embodiment of the present invention;

[0037] Figure 2 is a detailed schematic diagram of the battery health estimation provided by an embodiment of the present invention;

[0038] Figure 3 is a block schematic diagram of the battery health estimation device provided by the embodiment of the present invention;

[0039] Figure 4 is a structural schematic diagram of the vehicle provided by the embodiment of the present invention. Detailed Embodiments

[0040] The following will illustrate the embodiments of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0041] It should be noted that the illustrations provided in the following embodiments only schematically illustrate the basic concept of the present invention. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and proportion of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0042] Specifically, Figure 1 is a schematic flowchart of a method for estimating battery health provided by an embodiment of the present application.

[0043] As Figure 1 shown, the method for estimating battery health includes the following steps:

[0044] In step S101, charging segment data of the vehicle is acquired.

[0045] In an embodiment of the present application, acquiring the charging segment data of the vehicle includes: acquiring the vehicle charging data transmitted back from the vehicle end; performing data cleaning and charging segment division on the vehicle charging data; encoding and grouping the charging segments according to the vehicle identifier, sorting them according to the time of the charging segments, and excluding the charging segments that do not meet the pre-designed calculation conditions. The remaining charging segments form the charging segment data.

[0046] It can be understood that the vehicle charging data transmitted back by the vehicle end TBOX includes data such as current, battery cell voltage, battery cell temperature, battery power, and time. Together with other vehicle data collected, it is uploaded to the cloud log server and finally landed in hdfs through flume to read the log.

[0047] Embodiments of the present application can use the Spark distributed computing method of the big data platform to pull the vehicle charging data from the data warehouse table of the big data platform. According to the charging data cleaning rules, clean the data with empty key fields such as current and time, and the number of battery cells does not meet the requirements, and can complement the data such as speed and mileage whose time difference between the upper and lower frames is within the specified time. Subsequently, the cleaned charging data is divided into charging segments according to the change of the charging state and permanently saved to disk.

[0048] Furthermore, embodiments of the present application can pull the vehicle charging data including slow charging or fast charging from the data warehouse table of the big data platform by day, map one frame of data into a case class in Spark, group it according to the unique vehicle code, sort it according to the time of the charging segments, and exclude those charging segments that do not meet the calculation conditions according to the algorithm calculation rules, and only keep the segments that meet the conditions for subsequent calculation.

[0049] In an embodiment of the present application, after cleaning the vehicle charging data and dividing the charging segments, the following steps are further included: reading various parameter data required for calculation from the dimension table; broadcasting the various parameter data to each node of the calculation.

[0050] It can be understood that the embodiment of the present application can read various parameter data required for calculation from the dimension table, including resistance values at different vehicle models, different temperatures, and different battery levels; battery capacity values at different vehicle models and different temperatures, etc. And use the unique broadcast variable technology of Spark to broadcast to each node of the calculation for subsequent calculation use.

[0051] For example, putting the temperature and battery level corresponding to the resistance value into separate broadcast variables respectively; taking the temperature value corresponding to the battery capacity as a separate broadcast variable. The embodiment of the present application can broadcast the above three broadcast variables to each node of the calculation for algorithm use.

[0052] In step S102, a charging data list is generated according to the charging segment data, and the optimal battery level and the corresponding battery health value of each battery cell in this charging segment are calculated according to the charging data list.

[0053] It can be understood that the embodiment of the present application can traverse the entire charging process, extract the battery voltage list, current, and median battery temperature in each frame of data; calculate the ampere-hour integral of each frame of data, and combine the battery voltage list, current, and median battery temperature to form a charging data list. After obtaining the charging data list, the embodiment of the present application can traverse and calculate each battery cell in the new charging data to obtain the optimal battery level value and the corresponding battery health value of each battery cell in this charging segment.

[0054] In the actual execution process, since the data volume of a vehicle's one-time charging may be very large and the algorithm itself is relatively complex. The embodiment of the present application can use the frame sampling method to select appropriate step-size charging data, reorganize these appropriate data into a new charging data list, which greatly reduces the calculation amount and saves the calculation cost while ensuring the algorithm accuracy.

[0055] In an embodiment of the present application, calculating the optimal battery power and the corresponding battery health value of each battery cell in the current charging segment according to the charging data list includes: obtaining the power range and battery capacity required for the current algorithm, determining the battery health calculation range according to the battery capacity, and setting the step sizes of the power range and the battery health calculation range; traversing the charging data list, performing interpolation calculation according to the temperature, current, and various parameter data broadcast in each frame to obtain the resistance value, and calculating the cumulative sum of the absolute values of the difference between the actual electromotive force value and the interpolated electromotive force value in each frame; traversing the battery power in the power range and the battery health in the battery health calculation range according to the step size, and taking the battery power and the battery health value corresponding to the smallest cumulative sum of absolute values as the optimal battery power value and the optimal battery health value of this cell.

[0056] For ease of understanding, the embodiments of the present application can calculate the optimal battery power and the corresponding battery health value of each battery cell in the current charging segment in combination with the following steps, including:

[0057] Step 1: Start traversing the cells and calculate the battery health of each cell respectively. Set the step size corresponding to the power range, and the reference range is approximately 1, 1.5, 2, etc., depending on the specific situation; set the battery health calculation range, and also set the corresponding step size, and the reference range can be approximately 1, 2, 3, etc., depending on the specific situation. Each power value corresponds to a battery health value, and each pair of power and battery health values is brought into the calculation formula to obtain subsequent calculation data;

[0058] Step 2: Traverse the charging data list, take the median of the battery temperature, the absolute value of the current, the three-dimensional interpolation into the data in Step 1, and the broadcast variable values in the above embodiments to find the corresponding resistance value, use one-dimensional interpolation into the broadcast variable value to find the corresponding electromotive force value, and each frame of data retains the battery cell voltage, current, interpolated resistance value, interpolated electromotive force value, and ampere-hour integral value to form new charging data;

[0059] Step 3: Traverse the new charging data obtained in Step 2, calculate the actual electromotive force value of each frame of data by subtracting the product of the current and the interpolated resistance value from the cell voltage, and find the cumulative sum of the absolute values of the difference between the actual electromotive force value and the interpolated electromotive force value to obtain the theoretical electromotive force value;

[0060] Step 4: Loop from Step 2 to Step 3, traverse all battery powers and battery healths for comparison to find the minimum value in Step 3, record the corresponding battery power and battery health value, and take the pair of power value and health value with the smallest cumulative sum as the optimal battery health of this battery cell;

[0061] Step 5: Loop from Step 1 to Step 4 to find the optimal battery power and the corresponding battery health value of each battery cell in the current charging segment.

[0062] It should be noted that for each battery cell, the current and temperature are the same for each frame. In the embodiments of the present application, the corresponding resistance interpolation and electromotive force interpolation can be obtained at one time. Each frame of data is only calculated once, avoiding calculating for each cell once, saving the calculation cost and accelerating the calculation speed.

[0063] In an embodiment of the present application, obtaining the power range and battery capacity required for the current algorithm includes: taking the battery power of the first frame at the start of charging as the initial power value; determining the power range required for the current algorithm according to the initial power value, taking the end data of each charging segment, and calculating the battery capacity required for the current algorithm according to the end data, the corresponding model and capacity of the battery.

[0064] It can be understood that in the embodiments of the present application, the battery power of the first frame at the start of charging can be taken as the initial power value, subtracting a pre-given value from the initial power as the starting power. If this starting variable value is greater than 0, take itself, otherwise set it to 0; adding a pre-given value to the initial power as the ending power. If this ending power value is less than 100, take itself, otherwise set it to 100. The calculated initial power and ending power are used as the power calculation range.

[0065] Furthermore, in the embodiments of the present application, the end data of the charging data can also be taken, and the battery capacity required for the current algorithm can be calculated using the interpolation method according to the end data, the corresponding model and capacity of the battery; for example, in the embodiments of the present application, the last frame of data in the charging process can be taken, and the median of the absolute value of the charging current and the charging battery temperature can be taken out. Extract the broadcast variables in the above embodiments, and use the two-dimensional linear interpolation algorithm to calculate the battery capacity value in the current charging process.

[0066] In step S103, estimate the health value of the entire battery pack of the vehicle according to the best battery power and the corresponding battery health value of each battery cell in the current charging segment.

[0067] It can be understood that in the embodiments of the present application, the health value of the entire battery pack can be calculated by traversing and multiplying the best battery power and battery health value of all battery cells pairwise, and taking the minimum result. This pair of battery power and battery health value is used as the best battery power and battery health value of the entire battery pack.

[0068] In summary, as Figure 2 shown, the embodiments of the present application can combine a specific embodiment to detail the battery health estimation method.

[0069] Step 1: The vehicle transmits data, including data such as current, battery cell voltage, battery cell temperature, battery power, and time.

[0070] Step 2: Data cleaning, cleaning out data with keyword fields such as empty current and time, and data with the number of battery cells not meeting the requirements.

[0071] Step 3: Divide the charging segments and eliminate those charging segments that do not meet the calculation conditions.

[0072] Step 4: Extract the charging data of a certain vehicle for a certain charge to calculate the battery health. First, load the dimension information of the corresponding vehicle model from the relational database, including the battery capacity interpolation table, the resistance interpolation table, and the electromotive force interpolation table. Use the Spark broadcast variable to broadcast the dimension information to each node of the calculation and provide it for subsequent calculations of the algorithm.

[0073] Step 5: Extract the charging segments that meet the algorithm requirements from the data warehouse as the charging data for this calculation.

[0074] Sort the charging data in chronological order, and take the median value of the battery temperature in the last piece of the charging data. For example, for [1, 2, 3, 4, 5, 6, 7], take 4, and the current is -xx.xA. Combine the above battery capacity interpolation broadcast variable to calculate the battery capacity for this time.

[0075] Step 6: Traverse the charging data, and extract the battery cell voltage list [x.xx, x.xx, x.xx, x.xx, x.xx......], the battery temperature list [x, x, x, x, x,.....], the current, and the ampere-hour integral value for each frame of data to form new charging data.

[0076] Step 7: Calculate the battery power range. Generally, take the battery power in the first frame of data, add and subtract the pre-given range value from the initial power to obtain the start and end ranges of the power, and set the battery health range.

[0077] Step 8: Traverse the power range and the battery health range. Each power value corresponds to a battery health value. Traverse the new charging data, substitute each pair of power and battery health values, calculate the three-dimensional interpolation of the resistance and the one-dimensional interpolation of the electromotive force for each frame, and calculate the actual electromotive force value. Accumulate and sum the actual electromotive force value and the interpolated electromotive force value for each frame, and take the pair of power value and health value with the smallest accumulated sum as the optimal value for this cell. Loop through each cell to find the optimal values for all cells.

[0078] Step 9: Estimate the health value of the entire battery pack of the vehicle based on the best battery power and the corresponding battery health value of each battery cell in this charging segment.

[0079] Step 10: Save the calculation results for this time to the data warehouse, which can be partitioned by day for subsequent use by the algorithm.

[0080] The battery health estimation method proposed according to the embodiments of the present application obtains the charging segment data of the vehicle, generates a charging data list based on the charging segment data, traverses the charging data list to calculate the optimal battery power and the corresponding battery health value of each battery cell in this charging segment, and traverses the optimal battery power and battery health values of all battery cells, so as to obtain the optimal battery power and battery health value of the entire battery pack. Thus, it solves the problem in the prior art that when using a machine learning algorithm to calculate the battery health in real time, the calculation result is easily affected by the network or the data scale, resulting in poor accuracy of the battery health calculation.

[0081] Next, a battery health estimation device proposed according to the embodiments of the present application will be described with reference to the accompanying drawings.

[0082] Figure 3 It is a block diagram of the battery health estimation device according to the embodiments of the present application.

[0083] As Figure 3 shown, the battery health estimation device 10 includes: an acquisition module 100, a generation module 200, and an estimation module 300.

[0084] Among them, the acquisition module 100 is used to acquire the charging segment data of the vehicle; the generation module 200 is used to generate a charging data list based on the charging segment data, and calculate the optimal battery power and the corresponding battery health value of each battery cell in this charging segment according to the charging data list; the estimation module 300 is used to estimate the health value of the entire battery pack of the vehicle according to the optimal battery power and the corresponding battery health value of each battery cell in this charging segment.

[0085] In an embodiment of the present application, the acquisition module 100 is further used to acquire the vehicle charging data transmitted back from the vehicle end; perform data cleaning and charging segment division on the vehicle charging data; encode and group the charging segments according to the vehicle identifier, sort them according to the time of the charging segments, and eliminate the charging segments that do not meet the pre-designed calculation conditions. The remaining charging segments form the charging segment data.

[0086] In an embodiment of the present application, the battery health estimation device 10 further includes: a calculation module and a broadcast module.

[0087] Among them, the calculation module is used to read various parameter data required for calculation from the dimension table after performing data cleaning and charging segment division on the vehicle charging data; the broadcast module is used to broadcast various parameter data to each node of the calculation.

[0088] In one embodiment of the present application, the generation module 200 is further configured to traverse the entire charging process, extract the battery voltage list, current, and median battery temperature in each frame of data; calculate the ampere-hour integral of each frame of data, and combine the battery voltage list, current, and median battery temperature to form a charging data list.

[0089] In one embodiment of the present application, the generation module 200 is further configured to obtain the power range and battery capacity required for this algorithm, determine the battery health calculation range according to the battery capacity, and set the step sizes of the power range and the battery health calculation range; traverse the charging data list, perform interpolation calculations based on the temperature, current, and various parameter data broadcast in each frame to obtain the resistance value, and calculate the cumulative sum of the absolute values of the difference between the actual electromotive force value and the interpolated electromotive force value in each frame; traverse the battery power within the power range and the battery health within the battery health calculation range according to the step size, and use the battery power and the battery health value corresponding to the smallest cumulative sum of absolute values as the best battery power value and the best battery health value of this single cell.

[0090] In one embodiment of the present application, the generation module 200 is further configured to take the battery power of the first frame at the start of charging as the initial power value; determine the power range required for this algorithm according to the initial power value, take the end data of each charging segment, and calculate the battery capacity required for this algorithm according to the end data, the corresponding model and capacity of the battery.

[0091] In one embodiment of the present application, for each battery cell, the current and temperature in each frame are the same.

[0092] It should be noted that the foregoing explanation of the embodiments of the battery health estimation method also applies to the battery health estimation device of this embodiment, and will not be elaborated here.

[0093] According to the battery health estimation device proposed in the embodiments of the present application, by obtaining the charging segment data of the vehicle, generating a charging data list according to the charging segment data, traversing the charging data list to calculate the best battery power and the corresponding battery health value of each battery cell in this charging segment, and traversing the best battery power and battery health values of all battery cells, thereby obtaining the best battery power and battery health value of the entire battery pack. Thus, the problem that in the prior art, when using a machine learning algorithm to calculate the health of a battery in real time, the calculation result is easily affected by the network or the data scale, resulting in poor accuracy of the battery health calculation is solved.

[0094] Figure 4 The structural schematic diagram of the vehicle provided for the embodiments of the present application. The vehicle may include:

[0095] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

[0096] When the processor 402 executes the program, it implements the battery health estimation method provided in the above embodiments.

[0097] Furthermore, the vehicle further includes:

[0098] A communication interface 403, which is used for communication between the memory 401 and the processor 402.

[0099] A memory 401, which is used to store computer programs that can run on the processor 402.

[0100] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0101] If the memory 401, the processor 402, and the communication interface 403 are implemented independently, the communication interface 403, the memory 401, and the processor 402 can be interconnected through a bus and complete communication with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 4 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0102] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 can complete communication with each other through an internal interface.

[0103] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0104] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above battery health estimation method.

[0105] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0106] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of these features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0107] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0108] It should be understood that each part of this application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following technologies well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays, field programmable gate arrays, etc.

[0109] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0110] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for estimating battery health, characterized in that, The method is applied to a server, wherein the method comprises the following steps: Obtain vehicle charging segment data; Generate a charging data list based on the charging segment data, and calculate the optimal battery power and corresponding battery health value of each battery cell in this charging segment based on the charging data list; The health value of the entire battery pack of the vehicle is estimated based on the optimal battery power of each battery cell in the current charging segment and the corresponding battery health value.

2. The method for estimating battery health according to claim 1, wherein The obtaining of the vehicle's charging segment data includes: Obtain vehicle charging data sent back by the vehicle; performing data cleaning and charging segment division on the vehicle charging data; The charging segments are coded and grouped according to the vehicle identification, sorted according to the time of the charging segments, and the charging segments that do not meet the preset calculation conditions are eliminated. The remaining charging segments constitute the charging segment data.

3. The method for estimating battery health according to claim 2, wherein After cleaning the vehicle charging data and dividing the charging segments, the method further includes: Read various parameter data required for calculation from the dimension table; The various parameter data are broadcast to each computing node.

4. The battery health estimation method according to claim 1, characterized in that, Generating a charging data list according to the charging segment data includes: Traverse the entire charging process and extract the battery voltage list, current, and battery temperature median in each frame of data; Calculate the ampere-hour integral of each frame of data and combine it with the battery voltage list, current, and battery temperature median to form a charging data list.

5. The battery health estimation method according to claim 1, characterized in that, The step of calculating the optimal battery power and the corresponding battery health value of each battery cell in the current charging segment according to the charging data list includes: Obtain the power range and battery capacity required by this algorithm, determine the battery health calculation interval based on the battery capacity, and set the power range and the step size of the battery health calculation interval; Traversing the charging data list, interpolating and calculating the resistance value based on the temperature, current, and various broadcast parameter data of each frame, and calculating the cumulative sum of the absolute values of the differences between the actual electromotive force value and the interpolated electromotive force value of each frame; The battery power within the power range and the battery health within the battery health calculation range are traversed according to the step size, and the battery power and battery health values corresponding to the minimum absolute value accumulation are taken as the optimal battery power value and optimal battery health value of the current cell.

6. The method for estimating battery health according to claim 5, wherein The method of obtaining the power range and battery capacity required by the algorithm includes: Take the battery power of the first frame when charging starts as the initial power value; The power range required by this algorithm is determined based on the initial power value, the terminal data of each charging segment is obtained, and the battery capacity required by this algorithm is calculated based on the terminal data, the corresponding battery model and capacity.

7. The battery health estimation method according to claim 5, wherein For each battery cell, the current and temperature are the same in each frame.

8. A battery health estimation device, characterized in that, The device is applied to a server, wherein the device includes: An acquisition module, used to obtain charging segment data of the vehicle; a generating module, configured to generate a charging data list according to the charging segment data, and calculate the optimal battery power and corresponding battery health value of each battery cell in the current charging segment according to the charging data list; An estimation module for estimating the health value of the entire battery pack of the vehicle based on the optimal battery power and the corresponding battery health value of each battery cell in the current charging segment.

9. A vehicle, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the battery health estimation method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the battery health estimation method according to any one of claims 1-7.

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