Battery health assessment method and assessment equipment

Abstract

The invention discloses a battery health assessment method and assessment equipment, and the method comprises the steps: classifying a plurality of batteries into N battery types according to the voltage change in unit time, and taking the index data of a second single battery in the first battery type as the training data of a first prediction model, a second prediction model and a third prediction model, obtaining a first health value, a second health value and a preset health value of the first single battery through a trained first prediction model, a trained second prediction model and a trained third prediction model, and obtaining a target health value of the first single battery at a first moment in a first working step based on the health values, the battery category of the second single battery is the first battery category; the first working step comprises a discharging working step or a charging working step, whether the first single battery is abnormal at the first moment in the first working step is evaluated through the target health value, and fault diagnosis, prediction and early warning of the battery can be achieved.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery health assessment method and assessment device. Background Art

[0002] With the rapid development of battery technology in recent years, batteries are widely used in new energy vehicles and consumer electronic products. Currently, batteries on the market have poor safety and are prone to fire or explosion. Therefore, it is of great significance to analyze and detect the battery's operating data to predict the battery's performance degradation and future lifespan.

[0003] In the prior art, the health status of a battery is often analyzed by collecting the operating parameters of the battery through a battery monitoring device, and the operating parameters are transmitted to a display device for corresponding display, so that the staff can perform predictive analysis on the above operating parameters. However, the analysis efficiency is low. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a battery health assessment method and assessment device, wherein the method includes: after classifying the batteries according to the voltage change per unit time, combining the indicator data of the second single cell in the first battery category as training data for the first prediction model, the second prediction model, and the third prediction model, obtaining the first health value, the second health value, and the preset health value of the first single cell by obtaining the trained first prediction model, the second prediction model, and the third prediction model, obtaining the target health value of the first single cell at the first moment in the first process step, so as to evaluate whether the first single cell is abnormal at the first moment in the first process step, and realize battery fault diagnosis, prediction, and early warning.

[0005] In a first aspect, the present application provides a method for evaluating battery health, comprising: Obtaining index data of N single cells corresponding to N channels at various moments in the first process step, wherein the index data of the single cells at various moments in the first process step includes any one or more of the following: voltage, current, internal resistance, pressure, temperature, and capacity of the single cells at various moments in the first process step; wherein one channel corresponds to one single cell, and the first process step includes: a discharge process step or a charge process step; According to the obtained voltage of the single cell at each moment in the first step, the voltage change of the single cell per unit time in the first step is obtained, and based on the voltage change per unit time in the first step, the N single cells are divided into M different battery categories, wherein each battery category corresponds to one or more single cells, M is less than or equal to N, and both M and N are positive integers; Outputting a predicted value of the internal resistance of the first single cell at the first moment in the first step through the trained first prediction model, and outputting a predicted value of the pressure of the first single cell at the first moment in the first step through the trained second prediction model, wherein the first single cell is any single cell in a first battery category; the first battery category is any battery category in the M different battery categories; Performing a ratio operation on the internal resistance of the first single cell at the first moment in the first step and the predicted value of the internal resistance at the first moment to obtain a first ratio, and using the first ratio as a first health value of the first single cell; performing a ratio operation on the pressure of the first single cell at the first moment in the first step and the predicted value of the pressure at the first moment to obtain a second ratio, and using the second ratio as a second health value of the first single cell; The first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model are calculated and processed to obtain the target health value of the first single cell battery at the first moment in the first step to evaluate whether the first single cell battery is abnormal at the first moment in the first step, wherein the target health value of the first single cell battery is used to indicate whether the first single cell battery is abnormal.

[0006] In an optional implementation, the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step output by the trained third prediction model are calculated and processed to obtain the target health value of the first single cell at the first moment in the first step, specifically including: The weight of the first health value of the first single cell is set to a first value, the weight of the second health value of the first single cell is set to a second value, and the weight of the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step output by the trained third prediction model is set to a third value, wherein the sum of the first value, the second value and the third value is 1; Perform a product operation on the first health value and the first value to obtain a fourth value, perform a product operation on the second health value and the second value to obtain a fifth value, and perform a product operation on the preset health value and the third value to obtain a sixth value; The sum of the fourth value, the fifth value and the sixth value is used as the target health value of the first single cell at the first moment in the first step.

[0007] In an optional implementation, after calculating and processing the first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model to obtain the target health value of the first single cell battery at the first moment in the first step, the method further includes: Determine whether the target health value of the first single cell at the first moment in the first process step is within a preset range. If the target health value is not within the preset range, determine that the first single cell is abnormal at the first moment in the first process step, and perform an early warning and control to cut off the circuit connected to the first single cell.

[0008] In an optional implementation, before outputting the predicted value of the internal resistance of the first single cell at the first moment in the first step through the trained first prediction model, the method further includes: Acquire first training data for training the first prediction model, wherein: The first training data includes any one or more of the following: voltage and current of multiple second single cells at each moment in the charging step, voltage and current of multiple second single cells at each moment in the discharging step, voltage change within a preset time in the charging step, current within a preset time in the charging step, voltage change within a preset time in the discharging step, and current within a preset time in the discharging step; wherein the battery categories of the second single cells are all the first battery categories; Inputting the first training data into a first prediction model for training to obtain a trained first prediction model; The first moment in the first process step is input into the trained first prediction model to obtain a predicted value of the internal resistance of the first single cell at the first moment in the first process step.

[0009] In an optional implementation, before outputting the predicted value of the pressure of the first single cell at the first moment in the first step through the trained second prediction model, the method further includes: Acquire second training data for training the second prediction model, wherein: The second training data includes any one or more of the following: the pressure of each second single cell in a plurality of second single cells at each moment in a charging step, and the pressure of each second single cell in a plurality of second single cells at each moment in a discharging step; wherein the battery categories of the plurality of second single cells are all the first battery categories; Inputting the second training data into a second prediction model for training to obtain a trained second prediction model; The first moment in the first process step is input into the trained second prediction model to obtain a predicted value of the pressure of the first single cell at the first moment in the first process step.

[0010] In an optional implementation, before calculating and processing the first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model to obtain the target health value of the first single cell battery at the first moment in the first step, the method further includes: The predicted value of the full discharge capacity of the first single cell at the first moment in the first step is output by the trained third prediction model, and a ratio operation is performed on the predicted value of the full discharge capacity and a preset value of the full discharge capacity of the first single cell at the first moment in the first step to obtain a preset health value of the first single cell, wherein the preset value of the full discharge capacity of the first single cell at the first moment in the first step is set by the manufacturer of the first single cell when the first single cell leaves the factory.

[0011] In an optional implementation manner, before the predicted value of the full discharge capacity of the first single battery at the first moment in the first step is outputted by the trained third prediction model, the method further includes: Obtain third training data for training a third prediction model, wherein: The third training data includes: the discharge capacity and the discharge temperature of each second single cell in the plurality of second single cells at the discharge cut-off voltage moment; wherein the battery categories of the plurality of second single cells are the first battery category; Inputting the third training data into a third prediction model for training to obtain a trained third prediction model; The first moment in the first process step is input into the trained third prediction model to obtain a predicted value of the full discharge capacity of the first single cell at the first moment in the first process step.

[0012] In an optional implementation manner, the N single cells are divided into M different battery categories based on the voltage change per unit time in the first step, including: Based on the voltage variation per unit time in the discharge step, the N single cells are classified based on the voltage variation per unit time in the discharge step, so as to classify the N single cells into M different battery categories; or, Based on the voltage variation per unit time in the charging step, the N single cells are classified based on the voltage variation per unit time in the charging step, so as to classify the N single cells into M different battery categories.

[0013] In an optional implementation, the training data of the first prediction model includes: the index data of multiple second single cells under the first battery category; the training data of the second prediction model includes: the index data of multiple second single cells under the first battery category; or, The training data of the third prediction model includes: indicator data of a plurality of second single cells under the first battery category.

[0014] In a second aspect, the present application provides an evaluation device, comprising: a memory and a processor connected to the memory, the memory being used to store application code, and the processor being configured to call the program code to execute the battery health evaluation method described in the first aspect.

[0015] The present application discloses a battery health assessment method and assessment device, including: after classifying multiple batteries into N battery categories according to the voltage change size per unit time, combining the indicator data of the second single battery in the first battery category as the training data of the first prediction model, the second prediction model, and the third prediction model, the first health value, the second health value, and the preset health value of the first single battery are obtained by the trained first prediction model, the second prediction model, and the third prediction model, and based on the aforementioned health values, the target health value of the first single battery at the first moment in the first step is obtained, wherein the battery category of the second single battery is the first battery category; the first step includes: a discharge step, or a charging step, and the target health value is used to assess whether the first single battery is abnormal at the first moment in the first step, so as to realize battery fault diagnosis, prediction and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flowchart of a battery health assessment method provided by the present application; Figure 2 It is a structural schematic diagram of an evaluation device provided in this application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in this application to clearly and completely describe the technical solutions in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] It should be noted that the first, second, and third in this application are only used to distinguish different prediction models, single cells, health values, training data, etc., and have no other special meanings and should not limit the scope of protection of this application.

[0020] Figure 1 This is a flowchart of a battery health assessment method provided by this application, such as Figure 1 As shown, the battery health assessment method may include but is not limited to the following steps: S101, obtaining index data of N single cells corresponding to N channels at various moments in the first process step.

[0021] In the present application, the index data of the aforementioned single cell at each moment in the first step may include but are not limited to any one or more of the following: the voltage of the single cell at each moment in the first step, the current at each moment in the first step, the internal resistance of the single cell at each moment in the first step, the pressure of the single cell at each moment in the first step, the temperature of the single cell at each moment in the first step, and the capacity of the single cell at each moment in the first step; Among them, one channel corresponds to one single cell, that is, one channel is used to charge or discharge one single cell; the first step may include but is not limited to: a discharging step or a charging step.

[0022] The various moments in the first step may be various moments in the discharge process, or various moments in the charge process; Preferably, the internal resistance of the above-mentioned single cell at each moment in the first step may include, but is not limited to: the DC internal resistance of the single cell at each moment in the first step.

[0023] It should be noted that the above-mentioned channels are channels for charging or discharging single cells.

[0024] It should be noted that the index data of the N single cells at each moment in the first process step are the index data of each single cell in the N single cells at each moment in the first process step.

[0025] Specifically, the device for obtaining the index data of N single cells corresponding to N channels at various moments in the first process step may be a battery detection device.

[0026] It should be noted that the single cell may include but is not limited to: a single cell used or integrated in a drone, a single cell used or integrated in an electric vehicle, a single cell used or integrated in a two-wheeled vehicle, or a single cell used or integrated in a consumer electronic product (such as a notebook, tablet, etc.). S102, obtaining a voltage change per unit time of the single cell in the first step according to the voltage of the single cell at each moment in the first step, and dividing the N single cells into M different battery categories based on the voltage change per unit time in the first step.

[0027] In the present application, each battery category may include one or more single cells, M is less than or equal to N, where M and N are both positive integers.

[0028] For example, the M different battery categories may include, but are not limited to: a first battery category, a second battery category, . . . , an (M-1)th battery category, and an Mth battery category.

[0029] For example, the first battery category may be: the battery category to which the voltage change per unit time during the discharge process of the single cell is 500mv-600mv; for example, the second battery category may be: the battery category to which the voltage change per unit time during the discharge process of the single cell is 600mv-700mv.

[0030] It should be noted that N single cells are divided into M different battery categories, that is, each of the M battery categories may include one or more single cells, and the M battery categories include N single cells in total.

[0031] It should be noted that the device that obtains the voltage of the single cell at each moment in the first process step, obtains the voltage change per unit time of the single cell in the first process step, and divides the N single cells into M different battery categories based on the voltage change per unit time in the first process step can be a battery detection device.

[0032] S103, outputting a predicted value of the internal resistance of the first single cell at the first moment in the first step through the trained first prediction model, and outputting a predicted value of the pressure of the first single cell at the first moment in the first step through the trained second prediction model.

[0033] In the present application, the first single cell is any single cell in the first battery category; the first battery category is any battery category in the M different battery categories; The first moment in the first process step may include but is not limited to: the first moment in the discharge process step, or the first moment in the charge process step; The training data of the first prediction model includes: indicator data of a plurality of second single cells under the first battery category; the training data of the first prediction model is also the first training data; The training data of the second prediction model includes: indicator data of a plurality of second single cells under the first battery category; the training data of the second prediction model is also the second training data.

[0034] The first moment in the first step may be the first moment in the discharge process, and the first moment in the first step may also be the first moment in the charging process; It should also be noted that the first moment in the first step may include but is not limited to: the first moment in the discharge step, or the first moment in the charge step; that is, the first moment is any moment in the discharge process, or the first moment is any moment in the charge process; It should be noted that before outputting the predicted value of the internal resistance of the first single cell at the first moment in the first process step through the trained first prediction model, the following steps may also be included but not limited to: Acquire first training data for training the first prediction model, wherein: The first training data may include but is not limited to any one or more of the following: the voltage and current of each second single cell in a plurality of second single cells at each moment in the charging step, the voltage and current of each second single cell in a plurality of second single cells at each moment in the discharging step, the voltage change of each second single cell in a plurality of second single cells within a preset time in the charging step, the current of each second single cell in a plurality of second single cells within a preset time in the charging step, the voltage change of each second single cell in a plurality of second single cells within a preset time in the discharging step, and the current of each second single cell in a plurality of second single cells within a preset time in the discharging step; wherein the battery categories of the second single cells are all the first battery categories; Inputting the first training data into a first prediction model for training to obtain a trained first prediction model; The first moment in the first process step is input into the trained first prediction model to obtain a predicted value of the internal resistance of the first single cell at the first moment in the first process step.

[0035] It should be noted that the battery detection equipment can output the predicted value of the internal resistance of the first single cell at the first moment in the first process step through the trained first prediction model, and output the predicted value of the pressure of the first single cell at the first moment in the first process step through the trained second prediction model.

[0036] S104, performing a ratio operation on the internal resistance of the first single cell at the first moment in the first process step and the predicted value of the internal resistance at the first moment to obtain a first ratio, and using the first ratio as the first health value of the first single cell, performing a ratio operation on the pressure of the first single cell at the first moment in the first process step and the predicted value of the pressure at the first moment to obtain a second ratio, and using the second ratio as the second health value of the first single cell.

[0037] In the present application, a ratio operation is performed on the internal resistance of the first single cell at the first moment in the first process step and the predicted value of the internal resistance at the first moment to obtain a first ratio, which may specifically include but is not limited to: A ratio operation is performed between the internal resistance of the first single cell at the first moment in the first step and the predicted value of the internal resistance of the first single cell at the first moment in the first step to obtain a first ratio.

[0038] The pressure of the first single cell at the first moment in the first process step is ratioed to the predicted value of the pressure at the first moment to obtain a second ratio, which may specifically include but is not limited to: A ratio operation is performed between the pressure of the first single cell at the first moment in the first step and the predicted value of the pressure of the first single cell at the first moment in the first step to obtain a second ratio.

[0039] In the present application, after calculating and processing the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step output by the trained third prediction model to obtain the target health value of the first single cell at the first moment in the first step, the following steps may also be included but not limited to: Determine whether the target health value of the first single cell at the first moment in the first process step is within a preset range. If the target health value is not within the preset range, determine that the first single cell is abnormal at the first moment in the first process step, and issue an early warning and control to cut off the circuit connected to the first single cell. Alternatively, if the target health value is not within the preset range, determine that the first single cell is abnormal, issue an early warning and bypass the first single cell.

[0040] Preferably, the preset range can be set by the user. For example, the preset range can be set to 0.7-1; It should be noted that before outputting the predicted value of the pressure of the first single cell at the first moment in the first process step through the trained second prediction model, the following steps may also be included but not limited to: Obtaining second training data for training a second prediction model, wherein: The second training data may include but is not limited to any one or more of the following: the pressure of each second single cell in the plurality of second single cells at each moment in the charging step, the pressure of each second single cell in the plurality of second single cells at each moment in the discharging step; wherein the battery categories of the plurality of second single cells are all the first battery category; Inputting the second training data into the second prediction model for training to obtain a trained second prediction model; The first moment in the first process step is input into the trained second prediction model to obtain a predicted value of the pressure of the first single cell at the first moment in the first process step.

[0041] It should be noted that the battery detection equipment performs a ratio operation on the internal resistance of the first single cell at the first moment in the first process step and the predicted value of the internal resistance at the first moment to obtain a first ratio, and uses the first ratio as the first health value of the first single cell, performs a ratio operation on the pressure of the first single cell at the first moment in the first process step and the predicted value of the pressure at the first moment to obtain a second ratio, and uses the second ratio as the second health value of the first single cell.

[0042] S105. Calculate and process the first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model to obtain the target health value of the first single cell battery at the first moment in the first step to evaluate whether the first single cell battery is abnormal at the first moment in the first step.

[0043] In the present application, the target health value of the first single cell is used to indicate whether the first single cell is abnormal; the training data of the third prediction model includes: indicator data of multiple second single cells under the first battery category.

[0044] In the present application, the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step output by the trained third prediction model are calculated and processed to obtain the target health value of the first single cell at the first moment in the first step, which may specifically include but is not limited to the following steps: Step 1: setting the weight of the first health value of the first single cell battery to a first value, setting the weight of the second health value of the first single cell battery to a second value, and setting the weight of the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model to a third value, wherein the sum of the first value, the second value and the third value is 1; The aforementioned preset health value is obtained based on the predicted value of the full discharge capacity of the first single battery at the first moment in the first step output by the trained third prediction model. The specific obtaining process is detailed below; Step 2: multiplying the first health value by the first value to obtain a fourth value, multiplying the second health value by the second value to obtain a fifth value, and multiplying the preset health value by the third value to obtain a sixth value; Step 3: taking the sum of the fourth value, the fifth value and the sixth value as the target health value of the first single cell at the first moment in the first process step.

[0045] Regarding the above steps 1-3, the first value is represented as x1, the second value is represented as x2, and the third value is represented as x3, that is, x1+x2+x3=1; the first health value is represented as s1, the second health value is represented as s2, and the preset health value is represented as s3, then the fourth value can be represented as s1*x1, the fifth health value can be represented as s2*x2, the sixth health value can be represented as s3*x3, and the target health value is represented as SOH. For example, the calculation process of SOH is as follows: SOH=s1*x1+s2*x2+s3*x3, where x1+x2+x3=1.

[0046] It should be noted that before calculating and processing the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step output by the trained third prediction model to obtain the target health value of the first single cell at the first moment in the first step, the following steps may also be included but not limited to: The predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step is output by the trained third prediction model, and a ratio operation is performed on the obtained predicted value of the full discharge capacity and the preset value of the full discharge capacity of the first single cell battery at the first moment in the first step to obtain the preset health value of the first single cell battery, wherein the preset value of the full discharge capacity of the first single cell battery at the first moment in the first step can be set by the manufacturer of the first single cell battery when the first single cell battery leaves the factory.

[0047] It should be noted that before the predicted value of the full discharge capacity of the first single battery at the first moment in the first step is outputted by the trained third prediction model, the following steps may also be included but not limited to: Step 1: Obtain third training data for training a third prediction model, wherein: The third training data includes: the discharge capacity and the discharge temperature of each second single cell in the plurality of second single cells at the discharge cut-off voltage moment; wherein the battery categories of the plurality of second single cells are all the first battery category; Step 2: Input the third training data into the third prediction model for training to obtain the trained third prediction model; Step 3: Input the first moment in the first process step into the trained third prediction model to obtain the predicted value of the full discharge capacity of the first single cell at the first moment in the first process step.

[0048] For example, input the discharge cut-off voltage U1 moment into the third prediction model to obtain the discharge capacity C1 and discharge temperature T1 corresponding to the discharge cut-off voltage moment U1; according to the temperature compensation formula, C11 = C1 - (T1 - C) * D, obtain the temperature-compensated discharge capacity C11, and then according to the formula C22 = A * C11 + B, obtain the predicted value of the full discharge capacity of the first single cell. Wherein, C is the average discharge temperature of the above-mentioned multiple second single cells during the discharge process, D is the capacity difference of the above-mentioned multiple second single cells compared with the average discharge temperature C; wherein, * represents the multiplication sign, C11 is the discharge capacity after temperature compensation, C22 is the predicted value of the predicted full discharge capacity, A and B are constants, A represents the capacity prediction coefficient, and B is the capacity difference.

[0049] In this application, based on the magnitude of the voltage change per unit time in the first process step, N single cells are divided into M different cell categories, which may include but are not limited to the following process: Based on the magnitude of the voltage change per unit time in the discharge process step, classify N single cells based on the magnitude of the voltage change per unit time in the discharge process step to divide N single cells into M different cell categories; or, Based on the magnitude of the voltage change per unit time in the charging process step, classify N single cells based on the magnitude of the voltage change per unit time in the charging process step to divide N single cells into M different cell categories.

[0050] It should be noted that the battery detection device calculates and processes the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first process step output by the trained third prediction model to obtain the target health value of the first single cell at the first moment in the first process step to evaluate whether the first single cell is abnormal at the first moment in the first process step.

[0051] It should be noted that the devices for implementing the battery health assessment method in this application may include but are not limited to: battery detection devices, battery management devices, or other battery health monitoring devices.

[0052] Figure 1 It is only used to illustrate the embodiments of this application and should not limit the protection scope of this application.

[0053] The present application provides an evaluation device, Figure 2 is a schematic diagram of the structure of the evaluation device provided in this application. In the embodiment of this application, Figure 2 The evaluation equipment in the Figure 1 A battery testing device, a battery management device or other battery health monitoring device for a battery health assessment method, specifically, like Figure 2 As shown, the evaluation device 20 may include, but is not limited to: a processor 21 and a memory 22; It should be noted that the memory 22 is coupled to the processor 21 and can be used to store Figure 1 The application code of the battery health assessment method in the embodiment, the processor 21 is configured to call Figure 1 The application code of the battery health assessment method in the embodiment is used to execute Figure 1 The battery health assessment method in the embodiment. Specifically, The processor 21 may be configured to: Obtaining index data of N single cells corresponding to N channels at various moments in the first process step; According to the obtained voltage of the single cell at each moment in the first step, the voltage change of the single cell per unit time in the first step is obtained, and based on the voltage change per unit time in the first step, the N single cells are divided into M different battery categories; Outputting a predicted value of the internal resistance of the first single cell at the first moment in the first step through the trained first prediction model, and outputting a predicted value of the pressure of the first single cell at the first moment in the first step through the trained second prediction model; Performing a ratio operation on the internal resistance of the first single cell at the first moment in the first step and the predicted value of the internal resistance at the first moment to obtain a first ratio, and using the first ratio as a first health value of the first single cell; performing a ratio operation on the pressure of the first single cell at the first moment in the first step and the predicted value of the pressure at the first moment to obtain a second ratio, and using the second ratio as a second health value of the first single cell; The first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model are calculated and processed to obtain the target health value of the first single cell battery at the first moment in the first step to evaluate whether the first single cell battery is abnormal at the first moment in the first step.

[0054] Understandable, about Figure 2 For a specific implementation of the evaluation device 20, please refer to Figure 1Method embodiments are not elaborated herein again.

[0055] It should be understood that the evaluation device 20 is merely an example provided in the embodiments of the present application. Moreover, the evaluation device 20 may have more or fewer components than those shown, two or more components may be combined, or different configurations of components may be implemented.

[0056] Those of ordinary skill in the art can realize that the devices and algorithm steps of each example described in combination with the embodiments disclosed in the present application can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0057] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein again.

[0058] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the composition and steps of each example have been described. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0059] The embodiments of the method or device described above are merely illustrative. Additionally, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be in the form of electrical, mechanical, or other connections.

[0060] Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0061] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A battery health assessment method, characterized in that: include: Obtaining index data of N single cells corresponding to N channels at various moments in the first process step, wherein the index data of the single cells at various moments in the first process step includes any one or more of the following: voltage, current, internal resistance, pressure, temperature and capacity of the single cells at various moments in the first process step; wherein one channel corresponds to one single cell, and the first process step includes: a discharge process step or a charge process step; According to the obtained voltage of the single cell at each moment in the first step, the voltage change of the single cell per unit time in the first step is obtained, and based on the voltage change per unit time in the first step, the N single cells are divided into M different battery categories, wherein each battery category corresponds to one or more single cells, M is less than or equal to N, and both M and N are positive integers; Outputting a predicted value of the internal resistance of the first single cell at the first moment in the first step through the trained first prediction model, and outputting a predicted value of the pressure of the first single cell at the first moment in the first step through the trained second prediction model, wherein the first single cell is any single cell in a first battery category; the first battery category is any battery category in the M different battery categories; Performing a ratio operation on the internal resistance of the first single cell at the first moment in the first step and the predicted value of the internal resistance at the first moment to obtain a first ratio, and using the first ratio as a first health value of the first single cell; performing a ratio operation on the pressure of the first single cell at the first moment in the first step and the predicted value of the pressure at the first moment to obtain a second ratio, and using the second ratio as a second health value of the first single cell; The first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model are calculated and processed to obtain the target health value of the first single cell battery at the first moment in the first step to evaluate whether the first single cell battery is abnormal at the first moment in the first step, wherein the target health value of the first single cell battery is used to indicate whether the first single cell battery is abnormal.

2. The battery health assessment method according to claim 1, characterized in that: The first health value, the second health value, and the preset health value of the first single cell battery obtained according to the predicted value of the full discharge capacity of the first single cell battery at the first moment in the first step output by the trained third prediction model are calculated and processed to obtain the target health value of the first single cell battery at the first moment in the first step, specifically including: The weight of the first health value of the first single cell is set to a first value, the weight of the second health value of the first single cell is set to a second value, and the weight of the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step output by the trained third prediction model is set to a third value, wherein the sum of the first value, the second value and the third value is 1; Perform a product operation on the first health value and the first value to obtain a fourth value, perform a product operation on the second health value and the second value to obtain a fifth value, and perform a product operation on the preset health value and the third value to obtain a sixth value; The sum of the fourth value, the fifth value and the sixth value is used as the target health value of the first single cell at the first moment in the first step.

3. The battery health assessment method according to claim 2, characterized in that: After calculating and processing the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step outputted by the trained third prediction model to obtain the target health value of the first single cell at the first moment in the first step, the method further includes: Determine whether the target health value of the first single cell at the first moment in the first process step is within a preset range. If the target health value is not within the preset range, determine that the first single cell is abnormal at the first moment in the first process step, and perform an early warning and control to cut off the circuit connected to the first single cell.

4. The battery health assessment method according to claim 1, characterized in that: Before outputting the predicted value of the internal resistance of the first single cell at the first moment in the first step through the trained first prediction model, the method further includes: Acquire first training data for training the first prediction model, wherein: The first training data includes any one or more of the following: the voltage and current of each second single cell in a plurality of second single cells at each moment in a charging step, the voltage and current of each second single cell in a plurality of second single cells at each moment in a discharging step, the voltage change of each second single cell in a plurality of second single cells within a preset time in a charging step, the current of each second single cell in a plurality of second single cells within a preset time in the charging step, the voltage change of each second single cell in a plurality of second single cells within a preset time in a discharging step, and the current of each second single cell in a plurality of second single cells within a preset time in the discharging step; wherein the battery categories of the second single cells are all the first battery categories; Inputting the first training data into a first prediction model for training to obtain a trained first prediction model; The first moment in the first process step is input into the trained first prediction model to obtain a predicted value of the internal resistance of the first single cell at the first moment in the first process step.

5. The battery health assessment method according to claim 1, characterized in that: Before outputting the predicted value of the pressure of the first single cell at the first moment in the first step through the trained second prediction model, the method further includes: Acquire second training data for training the second prediction model, wherein: The second training data includes any one or more of the following: the pressure of each second single cell in a plurality of second single cells at each moment in a charging step, and the pressure of each second single cell in a plurality of second single cells at each moment in a discharging step; wherein the battery categories of the plurality of second single cells are all the first battery categories; Inputting the second training data into a second prediction model for training to obtain a trained second prediction model; The first moment in the first process step is input into the trained second prediction model to obtain a predicted value of the pressure of the first single cell at the first moment in the first process step.

6. The battery health assessment method according to claim 1, characterized in that: Before calculating and processing the first health value, the second health value, and the preset health value of the first single cell obtained according to the predicted value of the full discharge capacity of the first single cell at the first moment in the first step outputted by the trained third prediction model to obtain the target health value of the first single cell at the first moment in the first step, the method further includes: The predicted value of the full discharge capacity of the first single cell at the first moment in the first step is output by the trained third prediction model, and a ratio operation is performed on the predicted value of the full discharge capacity and a preset value of the full discharge capacity of the first single cell at the first moment in the first step to obtain a preset health value of the first single cell, wherein the preset value of the full discharge capacity of the first single cell at the first moment in the first step is set by the manufacturer of the first single cell when the first single cell leaves the factory.

7. The battery health assessment method according to claim 6, characterized in that: Before the predicted value of the full discharge capacity of the first single battery at the first moment in the first step is outputted by the trained third prediction model, the method further includes: Obtain third training data for training a third prediction model, wherein: The third training data includes: the discharge capacity and the discharge temperature of each second single cell in the plurality of second single cells at the discharge cut-off voltage moment; wherein the battery categories of the plurality of second single cells are the first battery category; Inputting the third training data into a third prediction model for training to obtain a trained third prediction model; The first moment in the first process step is input into the trained third prediction model to obtain a predicted value of the full discharge capacity of the first single cell in the first process step at the first moment.

8. The battery health assessment method according to claim 1, characterized in that: The dividing the N single cells into M different battery categories based on the voltage change per unit time in the first step includes: Based on the voltage variation per unit time in the discharge step, the N single cells are classified based on the voltage variation per unit time in the discharge step, so as to classify the N single cells into M different battery categories; or, Based on the voltage variation per unit time in the charging step, the N single cells are classified based on the voltage variation per unit time in the charging step, so as to classify the N single cells into M different battery categories.

9. The battery health assessment method according to claim 1, characterized in that: The training data of the first prediction model includes: the index data of the second single cells under the first battery category; the training data of the second prediction model includes: the index data of the second single cells under the first battery category; or, The training data of the third prediction model includes: indicator data of a plurality of second single cells under the first battery category.

10. A battery health assessment device, characterized in that: include: A memory and a processor connected to the memory, wherein the memory is used to store application code, and the processor is configured to call the program code to execute the battery health assessment method as described in any one of claims 1-9.

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