Online determination method, device and equipment of battery health state, medium and program product

Abstract

The embodiment of the invention relates to the technical field of battery management, in particular to a battery health state online determination method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring current data, current charging duration data and voltage data of a target battery in a charging process, and a battery type of the target battery; determining an IC curve of the target battery in the charging process according to the voltage data, the current data and the current charging duration data; according to the IC curve, determining interval area characteristics of voltage change of the target battery in the charging process; determining a health state reference curve of the target battery according to the battery type; and determining the current health state of the target battery according to the health state reference curve and the interval area characteristics. Therefore, the estimation of the health state does not need a harsh measurement environment, and the method is suitable for batteries of different types and under different working conditions.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of battery management, and in particular to a method, device, equipment, medium and program product for online determination of a battery health status. Background Art

[0002] Lithium-ion batteries are important energy storage carriers for electric vehicles. The research on lithium-ion batteries is in line with the needs of world development. Not only in the electric vehicle industry, lithium-ion batteries have been widely used in many fields. However, due to the influence of various factors during the use of lithium-ion batteries, their performance gradually decreases, which may cause failures and even cause uncontrolled explosion of lithium ions. In order to maintain battery performance, ensure safety and extend service life, a reasonable battery management strategy is designed. It is very important to accurately estimate the health status of the battery and master the relevant information, which is of great significance to improving the safety and reliability of the battery.

[0003] In actual automotive applications, users may not fully charge or discharge the battery, resulting in a battery IC curve that is not necessarily complete. This has a certain impact on the extraction of relevant features and will affect the estimation results. In addition, the limitation of the battery state of health (SOH) estimation method based on machine learning is that it is only effective within the range of training data. To ensure the accuracy and reliability of the estimation, it is necessary to obtain data covering the entire life range from new batteries to retirement in various battery operating modes. Therefore, its practicality is greatly reduced. Summary of the invention

[0004] The purpose of the present invention is to at least provide a method, device, equipment, medium and program product for online determination of battery health status, which can at least solve the problems in the prior art that battery health status assessment has low practicality and the assessment results are easily affected by incomplete charging and discharging, so that the estimation of health status does not require a strict measurement environment, but is suitable for batteries of different types and under different working conditions, greatly improving practicality and reducing the requirements for the measurement environment.

[0005] To solve the above technical problems, this application proposes five aspects.

[0006] In a first aspect, the present application provides an online method for determining a battery health state, comprising: obtaining current data, current charging time data and battery voltage data of a target battery during a charging process, and a battery type of the target battery; determining an IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging time data; determining an interval area characteristic of voltage change of the target battery during the charging process according to the IC curve; determining a health state baseline curve of the target battery according to the battery type, the health state baseline curve being able to characterize the correspondence between a health state and an interval area; and determining the current health state of the target battery according to the health state baseline curve and the interval area characteristic.

[0007] In some embodiments, determining the IC curve of the target battery during the charging process based on the voltage data, the current data and the current charging duration data includes: determining the charged capacity data of the target battery at the current charging duration based on the current data and the current charging duration; determining the IC curve of the target battery based on the voltage data and the charged capacity data.

[0008] In some embodiments, determining the interval area characteristics of the voltage change of the target battery during the charging process according to the IC curve includes: determining the effective starting voltage and the charging termination voltage of the target battery during the charging process according to the IC curve; determining the effective voltage interval according to the effective starting voltage and the charging termination voltage; dividing the effective voltage curve into multiple sub-intervals according to preset rules; calculating the sub-interval area of ​​each sub-interval; and determining the interval area characteristics according to the sub-interval areas of multiple sub-intervals.

[0009] In some embodiments, determining the effective starting voltage and charging termination voltage of the target battery during the charging process based on the IC curve includes: performing Gaussian smoothing filtering and Kalman filtering operations on the IC curve to finally obtain a filtered smooth curve; determining the intersection of the smooth curve and the horizontal axis as the effective starting voltage; and determining the voltage value at the tail end of the smooth curve as the charging termination voltage.

[0010] In some embodiments, dividing the effective voltage curve into a plurality of sub-intervals according to a preset rule includes: dividing the effective voltage interval between the effective starting voltage and the charging termination voltage into a plurality of complete sub-intervals according to a preset voltage span.

[0011] In some embodiments, determining the health status baseline curve of the target battery according to the battery type includes: obtaining a database with preset health status baseline curves, wherein the database has preset health status baseline curves corresponding to different types of batteries; and determining a health status baseline curve matching the target battery in the database according to the battery type.

[0012] In the second aspect, the present application proposes an online determination device for the health status of a battery, comprising: a first acquisition module, used to acquire the current data, current charging time data and battery voltage data of a target battery during a charging process, and the battery type of the target battery; a first determination module, used to determine the IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging time data; a second determination module, used to determine the interval area characteristics of the voltage change of the target battery during the charging process according to the IC curve; a third determination module, used to determine a health status baseline curve of the target battery according to the battery type, the health status baseline curve being able to characterize the correspondence between the health status and the interval area; a fourth determination module, used to determine the current health status of the target battery according to the health status baseline curve and the interval area characteristics.

[0013] In a third aspect, the present application proposes a computer electronic production device, characterized in that it includes a memory, a processor, and a computer program stored in the memory, and the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.

[0014] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0015] In a fifth aspect, the present application proposes a computer program product, comprising a computer program, which implements the steps of any one of the methods described in the first aspect when executed by a processor.

[0016] The present application obtains the current data, current charging time data and battery voltage data of the target battery during the charging process, as well as the battery type of the target battery; determines the IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging time data; determines the interval area characteristics of the voltage change of the target battery during the charging process according to the IC curve; determines the health status reference curve of the target battery according to the battery type, and the health status reference curve can characterize the corresponding relationship between the health status and the interval area; determines the current health status of the target battery according to the health status reference curve and the interval area characteristics. The problem of low practicality in the prior art for evaluating the health status of the battery and the evaluation results being easily affected by incomplete charging and discharging is solved, so that the estimation of the health status does not require a strict measurement environment, but is applicable to batteries of different types and under different working conditions, which greatly improves the practicality and reduces the requirements for the measurement environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0018] Figure 1 A main flow chart of a method for online determination of battery health status provided in an embodiment of the present application;

[0019] Figure 2 This is a main structural block diagram of a device for online determination of battery health status provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0021] Lithium-ion batteries are important energy storage carriers for electric vehicles. The research on lithium-ion batteries is in line with the needs of world development. Not only in the electric vehicle industry, lithium-ion batteries have been widely used in many fields. However, due to the influence of various factors during the use of lithium-ion batteries, their performance gradually decreases, which may cause failures and even cause uncontrolled explosion of lithium ions. In order to maintain battery performance, ensure safety and extend service life, a reasonable battery management strategy is designed. It is very important to accurately estimate the health status of the battery and master the relevant information, which is of great significance to improving the safety and reliability of the battery.

[0022] In actual automotive applications, users may not fully charge or discharge the battery, resulting in a battery IC curve that is not necessarily complete. This has a certain impact on the extraction of relevant features and will affect the estimation results. In addition, the limitation of the battery state of health (SOH) estimation method based on machine learning is that it is only effective within the range of training data. To ensure the accuracy and reliability of the estimation, it is necessary to obtain data covering the entire life range from new batteries to retirement in various battery operating modes. Therefore, its practicality is greatly reduced.

[0023] It is very difficult to estimate the SOH of the battery directly, because the battery is affected by many factors during the aging process, including current charge and discharge rate, discharge cut-off voltage and temperature. The constant voltage charging curve has a strong regularity in the battery degradation process. Many researchers have realized the estimation of SOH in the constant voltage charging stage of the battery. However, there are many uncertain factors in the constant voltage charging process that will affect the SOH estimation results. If the discharge conditions before charging are different, the constant voltage charging capacity will also be quite different. This is caused by the discharge depth. The deeper the discharge depth, the more electricity is charged in the constant voltage charging stage. Therefore, there are still some problems to be solved in estimating SOH by the changes in the constant voltage charging process. Among the current SOH online estimation methods, there are two main categories: the first category is the model-based estimation method, including data-driven models and physical models. The data-driven method has strong nonlinear processing capabilities, and due to its strong scalability, the data-driven model can also continuously optimize itself when the amount of data increases, showing higher accuracy and robustness, but this type of method has a large amount of calculation and high limitations, so it is difficult to actually apply it to the vehicle battery management system. Physical models are usually combined with filtering algorithms. The physical model-based method uses SOH as a state variable or model parameter. The filtering algorithm can adjust and modify the parameters of the model according to real-time data input and results to cope with the complex changes of the battery over time. By constantly adapting to the battery, the accuracy of SOH estimation can be improved. However, the need for a large number of matrix operations is a major feature of this type of method. The more clearly the model can describe the electrochemical reaction mechanism, the more operations are required, which also makes it difficult to actually apply it to the vehicle battery management system. The second is the indirect analysis method, which is characterized by multi-step derivation. The SOH is estimated by establishing the relationship between the relevant parameters of the battery capacity decline and the health index, such as incremental capacity analysis (ICA), differential voltage analysis (DVA), etc. This type of method has the advantages of small amount of calculation and easy engineering implementation. Among them, incremental capacity analysis is the representative. Compared with the former, this type of method can be better implemented on electric vehicles. On this basis, the present invention proposes a lithium-ion power battery SOH online estimation method based on voltage block analysis, which does not require a harsh measurement environment and is suitable for battery SOH estimation of different types and under different working conditions.

[0024] In order to solve the above technical problems, the present invention proposes an online determination method of the battery health status. The implementation details of the bandwidth determination method of this embodiment are specifically described below. The following content is only the implementation details provided for easy understanding and is not necessary for the implementation of this solution.

[0025] Embodiment 1:

[0026] like Figure 1As shown, the present application provides an online method for determining the health status of a battery, and the method system is applied to electronic production equipment, and the electronic production equipment can be a server, a mobile terminal, a computer, a cloud platform, etc. The functions implemented by the production equipment data processing provided in the embodiment of the present application can be implemented by calling a program code by a processor of the electronic production equipment, wherein the program code can be stored in a computer storage medium. The online method for determining the health status of a battery includes:

[0027] Step S1: Acquire current data, current charging duration data, and battery voltage data of a target battery during charging, as well as the battery type of the target battery.

[0028] The health of a battery is mainly reflected in its ability to accommodate electricity at different voltages. The healthier the battery, the more electricity it can charge or release for every unit change in voltage. During the charging process, the amount of electricity charged is mainly determined by the charging current and the charging time. Of course, during charging, as the battery is charged with electricity, the battery voltage will gradually increase, and the corresponding relationship between voltage and electricity in a healthy state is different for different types of batteries. Therefore, in this application, if you want to evaluate the health of the target battery, you need to obtain the current data of the target battery during the charging process, the charging time, the voltage changes of the battery at each moment during the charging process, and the battery type of the target battery.

[0029] Step S2: determining an IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging duration data.

[0030] In some embodiments, step S2 "determining the IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging duration data" includes:

[0031] Step S21: determining the charge data of the target battery at the current charging duration according to the current data and the current charging duration.

[0032] Step S22: determining the IC curve of the target battery according to the voltage data and the charged power data.

[0033] After obtaining the voltage data, current data and current charging time of the target battery during the charging process, the charged power data can be calculated based on the current data and charging time. Since the general battery will be charged with a constant current source in the early stage of charging, when the battery is charged to a certain degree, it will be changed to a constant voltage source for charging. Since the battery voltage will gradually increase during the charging process, the charging current will decrease as the battery voltage increases during constant voltage charging. Therefore, when calculating the charging power, it is necessary to consider the current data at each moment to obtain more accurate charged power data. For example, in this application, the voltage and current data can be collected with a period of 10s and a sampling frequency of 0.1Hz, and the power change in each sampling interval can be calculated. The original IC between each two sampling points is calculated based on the voltage data at the corresponding moment, and the original IC curve is drawn. The calculation formula for calculating the original IC is as follows:

[0034] In the formula, U is voltage and Q is charge.

[0035] Step S3: determining the interval area characteristics of the voltage change of the target battery during the charging process according to the IC curve.

[0036] In some embodiments, step S3 of “determining the interval area characteristics of the voltage change of the target battery during the charging process according to the IC curve” includes:

[0037] Step S31: determining the effective starting voltage and charging termination voltage of the target battery during the charging process according to the IC curve.

[0038] In some embodiments, step S31 "determining the effective starting voltage and charging termination voltage of the target battery during the charging process according to the IC curve" includes:

[0039] Step S311: performing Gaussian smoothing filtering and Kalman filtering operations on the IC curve to finally obtain a filtered smooth curve.

[0040] Step S312: determining the intersection point of the smooth curve and the abscissa as the effective starting voltage.

[0041] Step S313: determining the voltage value at the tail end of the smooth curve as the charging termination voltage.

[0042] Since the sampled data is too dense and the calculated IC is too dense, the generated original IC curve has more noise, so the original IC curve needs to be smoothed. In this application, without changing the effective starting voltage and the curve characteristics, Gaussian smoothing filtering (GSF) is first used. GSF will assign different weights according to the Gaussian function, and in order to maintain the length of the IC curve, the moving window width is taken as M = 10, that is, 10 adjacent data points are taken for GSF calculation. The mathematical expression of the Gaussian function is:

[0043] Will

[0044] The curve after Gaussian smoothing is then passed through a Kalman filter (KF) to finally obtain a smooth IC curve. Since the method of the present application does not require the IC curve in the entire charging process, at any time, the IC curve has an effective starting voltage and a charging termination voltage, wherein the effective starting voltage is used to indicate the voltage when the battery voltage changes for the first time during the current charging process, and the charging termination voltage is the last voltage that can be collected during the current charging process.

[0045] Step S32: determining an effective voltage range according to the effective starting voltage and the charging termination voltage.

[0046] The voltage range between the effective starting voltage and the charging termination voltage is the effective voltage range.

[0047] Step S33: Divide the effective voltage curve into a plurality of sub-intervals according to a preset rule.

[0048] In some embodiments, step S33 “dividing the effective voltage curve into a plurality of sub-intervals according to a preset rule” includes:

[0049] Step S331: dividing the effective voltage interval between the effective starting voltage and the charging termination voltage into a plurality of complete sub-intervals according to a preset voltage span.

[0050] Since the horizontal and vertical coordinates of the IC curve are U and dQ / dU respectively, the space enclosed by the IC curve and the horizontal coordinate represents the charged capacity, so we can use the IC curve to clarify the charged capacity corresponding to each voltage interval. Since the curve near the effective starting voltage of the IC curve changes greatly and the area under the curve is relatively unstable, the effective voltage interval is divided into multiple intervals according to the preset voltage span in this application. Among these intervals, there are unstable intervals with an area near the effective starting voltage and non-closed intervals near the charging termination voltage. The integrity of these intervals is low and should be discarded. Therefore, the sub-intervals of this application need to be complete sub-intervals. The complete sub-intervals represent the stability of the interval area, and the interval area is also representative.

[0051] Step S34: Calculate the sub-interval area of ​​each sub-interval.

[0052] When calculating the area of ​​a sub-interval, the sub-interval may be regarded as a trapezoid, and the area of ​​the trapezoid is then calculated. Of course, in order to reduce the error, the voltage span preset in this application is 0.1V.

[0053] Step S35: determining the interval area feature according to the sub-interval areas of a plurality of sub-intervals.

[0054] Although the area difference between the selected sub-intervals is small, it still has certain fluctuations. Therefore, the average area of ​​each sub-interval is required as the interval area feature to characterize the charged capacity corresponding to each voltage span of the target battery during the charging process.

[0055] Step S4: determining a health status reference curve of the target battery according to the battery type, wherein the health status reference curve can represent a corresponding relationship between a health status and an interval area.

[0056] In some embodiments, step S4 “determining a health status reference curve of the target battery according to the battery type” includes:

[0057] Step S41: obtaining a database with preset health status reference curves, wherein the database has preset health status reference curves corresponding to different types of batteries.

[0058] Step S42: Determine a health status reference curve matching the target battery in the database according to the battery type.

[0059] Since the ratio of voltage to charge is different in different health states for the same type of battery, if you want to determine the health state of the target battery based on the interval area characteristics, you also need to determine the health state reference curve of the target battery. Therefore, there is also a database in this application, in which health state reference curves of various types of batteries are preset, and these preset health state reference curves are formed by performing multiple charge and discharge tests on the type of battery, and determining the corresponding relationship between the interval area characteristics and the health state in each charge and discharge experiment, and then performing the health state reference curve. Therefore, after obtaining the battery type, the matching health state reference curve can be determined in the database.

[0060] Step S5: determining the current health state of the target battery according to the health state reference curve and the interval area characteristic.

[0061] After obtaining the health status baseline curve and the interval area characteristics of the target battery, the corresponding battery health status can be determined in the health status baseline curve according to the interval area characteristics, thereby realizing online evaluation of the health status of the target battery.

[0062] In summary, the present invention does not require any model, is easy to calculate, and can estimate SOH during the constant current charging stage. The GSK-KF dual filtering method is used to process the initial incremental capacity data, which reduces the number of data required to process IC noise and lowers the voltage threshold for obtaining the effective value of the IC. The use of partition analysis makes SOH estimation more convenient and realizes SOH estimation under incomplete charge / discharge conditions. Finally, verification was carried out on batteries with different aging test methods. The results show that the present invention can be applied to batteries with different usage habits, accurately estimates SOH throughout the life cycle, and has strong robustness to influencing factors such as discharge rate, charge / discharge depth, and measurement accuracy.

[0063] Embodiment 2:

[0064] Based on the foregoing embodiments, an embodiment of the present application provides an online determination device for a battery health status. The modules included in the device and the units included in each module can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0065] like Figure 2 As shown, an online determination device for a battery health status includes: a first acquisition module 1, a first determination module 2, a second determination module 3, a third determination module 4 and a fourth determination module 5.

[0066] The first acquisition module 1 is used to obtain the current data, current charging time data and battery voltage data of the target battery during the charging process, as well as the battery type of the target battery. The first determination module 2 is used to determine the IC curve of the target battery during the charging process based on the voltage data, the current data and the current charging time data. The second determination module 3 is used to determine the interval area characteristics of the voltage change of the target battery during the charging process based on the IC curve. The third determination module 4 is used to determine the health status reference curve of the target battery according to the battery type, and the health status reference curve can characterize the corresponding relationship between the health status and the interval area. The fourth determination module 5 is used to determine the current health status of the target battery based on the health status reference curve and the interval area characteristics.

[0067] Each module in the above-mentioned device for online determination of battery health status can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the device in hardware form, or can be stored in the memory in the processing device in software form, so that the processor can call and execute the operations corresponding to each of the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0068] Embodiment 3:

[0069] In a third aspect, the present application provides a computer electronic production device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.

[0070] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0071] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0072] Embodiment 4:

[0073] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0074] Embodiment 5:

[0075] In a fifth aspect, the present application proposes a computer program product, comprising a computer program / instructions, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0076] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor 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 (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0077] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for determining the health status of a battery online, characterized in that: include: Acquire current data, current charging duration data, and battery voltage data of a target battery during charging, as well as the battery type of the target battery; Determine an IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging duration data; Determine, according to the IC curve, the interval area characteristics of the voltage change of the target battery during the charging process; Determine a health status reference curve of the target battery according to the battery type, wherein the health status reference curve can represent a corresponding relationship between a health status and an interval area; The current health state of the target battery is determined according to the health state reference curve and the interval area characteristic.

2. The method according to claim 1, characterized in that The step of determining the IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging duration data includes: Determine the charged power data of the target battery under the current charging time according to the current data and the current charging time; The IC curve of the target battery is determined according to the voltage data and the charged power data.

3. The method according to claim 1, characterized in that The determining, according to the IC curve, the interval area characteristic of the voltage change of the target battery during the charging process includes: Determine the effective starting voltage and charging termination voltage of the target battery during the charging process according to the IC curve; Determine an effective voltage interval according to the effective starting voltage and the charging termination voltage; Dividing the effective voltage curve into a plurality of sub-intervals according to a preset rule; Calculate the subinterval area of ​​each subinterval; The interval area feature is determined according to the sub-interval areas of a plurality of sub-intervals.

4. The method according to claim 3, characterized in that The step of determining the effective starting voltage and charging termination voltage of the target battery during the charging process according to the IC curve includes: Performing Gaussian smoothing filtering and Kalman filtering operations on the IC curve to finally obtain a filtered smooth curve; Determine the intersection of the smooth curve and the abscissa as the effective starting voltage; The voltage value at the tail end of the smooth curve is determined as the charging termination voltage.

5. The method according to claim 3, characterized in that: The dividing the effective voltage curve into a plurality of sub-intervals according to a preset rule comprises: The effective voltage interval between the effective starting voltage and the charging termination voltage is divided into a plurality of complete sub-intervals according to a preset voltage span.

6. The method according to claim 1, characterized in that The determining the health status reference curve of the target battery according to the battery type includes: Acquire a database with preset health status reference curves, wherein the database has preset health status reference curves corresponding to different types of batteries; A health status reference curve matching the target battery is determined in the database according to the battery type.

7. An online determination device for battery health status, characterized in that: include: A first acquisition module is used to acquire current data, current charging duration data and battery voltage data of a target battery during charging, as well as a battery type of the target battery; A first determination module, used to determine an IC curve of the target battery during the charging process according to the voltage data, the current data and the current charging duration data; A second determination module, configured to determine, according to the IC curve, an interval area characteristic of a voltage change of the target battery during a charging process; A third determination module is used to determine a health state reference curve of the target battery according to the battery type, wherein the health state reference curve can represent a corresponding relationship between a health state and an interval area; The fourth determination module is used to determine the current health state of the target battery according to the health state reference curve and the interval area feature.

8. A computer electronic production equipment, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the steps of the method according to any one of claims 1 to 6 when being executed by a processor.

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