Methods, apparatus, equipment and media for assessing the health status of lithium-ion battery modules

Abstract

This invention belongs to the field of lithium-ion battery technology, and specifically relates to a method, apparatus, device, and medium for assessing the health status of lithium-ion battery modules. The method includes: acquiring charging data of the lithium-ion battery module; the charging data includes the voltage change within a set time after the lithium-ion battery module reaches a set voltage during charging; inputting the obtained voltage change into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module; and outputting the health status of the lithium-ion battery module. Based on the characteristic values ​​of voltage changes during charging and the charging capacity of the battery module each time, this invention constructs a health status assessment model for energy storage batteries, which can quickly assess the health status of battery modules with an assessment error of less than 3%. The data required for the assessment can be directly obtained from engineering applications without the need for additional data acquisition devices. This method is easily implemented in engineering and has high application value.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery technology, specifically relating to a method, apparatus, equipment, and medium for assessing the health status of lithium-ion battery modules. Background Technology

[0002] Electric bicycles, with their advantages of convenience, compact design, and low cost, have become the preferred choice for short-distance commuting and delivery personnel, maintaining rapid growth over the past decade. By the end of 2021, my country's electric bicycle ownership had exceeded 300 million, with an annual increase of over 30 million units expected in the coming years. Beijing's electric bicycle ownership has also maintained rapid growth, reaching nearly 5 million units by the end of 2021, with an estimated annual increase of around 500,000 units.

[0003] Early electric bicycles primarily used lead-acid batteries as their power source. In recent years, with the rapid development of lithium-ion battery technology, lithium-ion batteries have unparalleled advantages over lead-acid batteries in terms of lightweight design, long range, and long lifespan. In 2020, over 50% of new electric bicycle models released by major electric vehicle brands were equipped with lithium-ion batteries. However, the frequent occurrence of safety accidents involving electric bicycles has also drawn widespread attention. Among these concerns, the health status of lithium-ion batteries is one of the key indicators. The health status of a lithium-ion battery typically refers to the ratio of its current discharge capacity within the specified charge / discharge voltage range to its rated capacity. During actual use, as battery capacity continuously decreases, its health status gradually declines. To accurately monitor the usage of lithium-ion battery packs and ensure safety during operation, regular assessments of the lithium-ion battery's health status are necessary.

[0004] Currently, the main methods for assessing the health status of lithium-ion batteries include the definition method and the impedance method. The definition method involves performing a complete charge-discharge cycle on the lithium-ion battery according to testing standards to obtain the current discharge capacity and calculate the health status. While this method can accurately determine the battery's health status, it requires several hours of charging and discharging, which is time-consuming, and it requires high-precision charging and discharging equipment, which is bulky and difficult to apply in the field. The impedance method involves testing the AC impedance of batteries in different health states to establish a correlation between battery health status and AC impedance. The health status of the lithium-ion battery is then estimated based on the AC impedance. Compared to the definition method, the AC impedance test time is shorter (around ten minutes). However, AC impedance testing equipment is usually expensive, and because the test requires connection to the positive and negative terminals of the lithium-ion battery, its adaptability to different modules is poor.

[0005] Existing methods for assessing the health status of lithium-ion batteries are difficult to perform online assessments under normal operating conditions of lithium-ion battery modules, resulting in poor practicality. Summary of the Invention

[0006] The purpose of this invention is to provide a method, apparatus, device, and medium for assessing the health status of lithium-ion battery modules. For battery modules with a total voltage of 48V, parameters during the charging process are extracted as model inputs to establish a health status assessment model, enabling online assessment of the health status of lithium-ion battery modules and ensuring their safe and reliable operation. This method has broad application prospects in fields such as electric bicycles and battery energy storage.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for assessing the health status of a lithium-ion battery module, comprising:

[0009] Acquire charging data of the lithium-ion battery module; the charging data includes the voltage change within a set time after the lithium-ion battery module reaches a set voltage during charging;

[0010] The obtained voltage change is input into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module.

[0011] Output the health status of the lithium-ion battery module.

[0012] A further improvement of this invention is that the pre-established health status assessment model is established through the following steps:

[0013] The lithium-ion battery module was charged at a rate of 1 / 4 to 1 / 8C. During the charging process, the voltage was recorded at 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively. The voltage change within the following set time period was recorded as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5n , where n represents the number of different charging cycles;

[0014] The capacity of a lithium-ion battery module per charge is denoted as C. n The state of health (SOH) of the lithium-ion battery module after the nth charge. n =C n / C, where C is the rated capacity of the lithium-ion battery module; the obtained voltage change and health status are normalized, the normalized voltage change is used as the input parameter, and the normalized health status is used as the output parameter, and the health status assessment model is established using the support vector regression algorithm.

[0015] A further improvement of the present invention is that the set time is 2-30 minutes.

[0016] A further improvement of the present invention is that the nominal total voltage of the lithium-ion battery module is 48V.

[0017] A further improvement of the present invention is that, in the step of acquiring the charging data of the lithium-ion battery module, specifically:

[0018] The voltage change within a set time period is obtained when the voltage of the lithium-ion battery module reaches 45.5V, 46.8V, 48.1V, 49.4V and 50.7V respectively during the charging process.

[0019] A further improvement of this invention lies in the step of normalizing the obtained voltage change and health status, wherein the normalization formula is:

[0020]

[0021] x i For the normalized data, x min x is the minimum value of the collected data. max The maximum value of the collected data is denoted by x, where x represents the directly collected voltage change or health status.

[0022] A further improvement of this invention is that the step of inputting the obtained voltage change into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module specifically includes:

[0023] The voltage change of the lithium-ion battery module during the charging process is normalized and input into a pre-established health status assessment model to obtain the normalized health status of the lithium-ion battery module. Then, reverse normalization is performed to obtain the health status of the lithium-ion battery module.

[0024] Secondly, the present invention provides a lithium-ion battery module health status assessment device, comprising:

[0025] The acquisition module is used to acquire charging data of the lithium-ion battery module; the charging data includes the voltage change within a set time after the voltage of the lithium-ion battery module reaches a set voltage during charging.

[0026] The evaluation module is used to input the obtained voltage change into a pre-established health status evaluation model to obtain the health status of the lithium-ion battery module.

[0027] The output module is used to output the health status of the lithium-ion battery module;

[0028] The pre-established health status assessment model in the assessment module is established through the following steps: The lithium-ion battery module is charged at a rate of 1 / 4 to 1 / 8C; during the charging process, the voltage is recorded at 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively, and the voltage change within the specified time period is recorded as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5n Where n represents the number of different charging cycles; the capacity of the lithium-ion battery module per charge is denoted as C. n The state of health (SOH) of the lithium-ion battery module after the nth charge. n =C n / C, where C is the rated capacity of the lithium-ion battery module; the obtained voltage change and health status are normalized, the normalized voltage change is used as the input parameter, and the normalized health status is used as the output parameter, and the health status assessment model is established using the support vector regression algorithm.

[0029] The specific steps in the acquisition module for obtaining charging data of the lithium-ion battery module are as follows: when the voltage of the lithium-ion battery module reaches 45.5V, 46.8V, 48.1V, 49.4V and 50.7V respectively during the charging process, the voltage change within a set time period is then obtained.

[0030] Thirdly, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the lithium-ion battery module health status assessment method.

[0031] Fourthly, the present invention provides a computer-readable storage medium storing at least one instruction, which, when executed by a processor, implements the lithium-ion battery module health status assessment method.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This invention provides a method, apparatus, device, and medium for assessing the health status of lithium-ion battery modules. Based on the characteristic values ​​of voltage changes during charging and the charging capacity of the battery module each time, a health status assessment model for energy storage batteries is constructed, which can quickly assess the health status of battery modules with an assessment error of less than 3%. The data required for the assessment can be directly obtained from engineering applications without the need for additional data acquisition devices. This method is easy to implement in engineering and has high application value. Attached Figure Description

[0034] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0035] Figure 1 This is a flowchart illustrating a method for assessing the health status of a lithium-ion battery module according to the present invention.

[0036] Figure 2 This is a structural block diagram of a lithium-ion battery module health status assessment device according to the present invention;

[0037] Figure 3 This is a structural block diagram of an electronic device according to the present invention. Detailed Implementation

[0038] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0039] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0040] Example 1

[0041] This invention provides a method for assessing the health status of lithium-ion battery modules. Based on the voltage change characteristics during charging and the charging capacity per charge, an assessment model for the battery's health status is established. Then, feature values ​​are extracted from actual battery operating data to achieve rapid assessment of the health status of in-service lithium-ion batteries. Specifically, the method includes the following steps:

[0042] S101. Constructing a lithium-ion battery module: The battery module consists of multiple individual cells connected in series, with a nominal total voltage of 48V.

[0043] S102. Feature Extraction: Under room temperature conditions, the battery module is charged at a rate of 1 / 4 to 1 / 8C and discharged at a rate of 1 / 3 to 1 / 5C. During the charging process, the voltage change over the next 2 to 30 minutes when the voltage reaches 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively is recorded as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5n , where n represents the number of different charging cycles.

[0044] S103. Construction of Health Status Assessment Model: The capacity for each charge is denoted as C. n Let the rated capacity of the battery be denoted as C, then the current health status of the battery module is SOH. n =C n / C, where n represents the number of different charging cycles. The feature values ​​and health status are normalized, with the feature values ​​as input parameters and the health status as output parameters. A health status assessment model is established using the support vector regression algorithm.

[0045] In one specific implementation, the normalization formula is:

[0046]

[0047] x i For the normalized data, x min x is the minimum value of the collected data. max The maximum value of the collected data is denoted by x, where x represents the directly collected voltage change or health status.

[0048] S104. Health Status Assessment: Battery charging data is obtained from actual operating data. When the voltage reaches 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively, the voltage changes in the next 15 minutes are 1.105V, 1.035V, 1.065V, 0.449V, and 0.714V respectively. The data is input into the assessment model, and the current health status of the battery module is obtained as 93.3%. The actual health status of the calibrated battery module is 94.1%, with a relative error of 0.74%, which is low.

[0049] Example 2

[0050] Please see Figure 1 As shown, the present invention provides a method for assessing the health status of a lithium-ion battery module, comprising:

[0051] S1. Obtain charging data of the lithium-ion battery module; the charging data includes the voltage change within a set time after the lithium-ion battery module reaches a set voltage during charging.

[0052] S2. Input the obtained voltage change into the pre-established health status assessment model to obtain the health status of the lithium-ion battery module.

[0053] S3. Output the health status of the lithium-ion battery module.

[0054] In one specific implementation, the pre-established health status assessment model is established through the following steps:

[0055] The lithium-ion battery module was charged at a rate of 1 / 4 to 1 / 8C. During the charging process, the voltage was recorded at 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively. The voltage change within the following set time period was recorded as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5n , where n represents the number of different charging cycles;

[0056] The capacity of a lithium-ion battery module per charge is denoted as C. n The state of health (SOH) of the lithium-ion battery module after the nth charge. n =C n / C, where C is the rated capacity of the lithium-ion battery module; the obtained voltage change and health status are normalized, the normalized voltage change is used as the input parameter, and the normalized health status is used as the output parameter, and the health status assessment model is established using the support vector regression algorithm.

[0057] In one specific implementation, the set time is 2-30 minutes.

[0058] In one specific embodiment, the nominal total voltage of the lithium-ion battery module is 48V.

[0059] In one specific embodiment, the step of acquiring the charging data of the lithium-ion battery module specifically includes:

[0060] The system acquires the voltage change of a lithium-ion battery module during the charging process, when the voltage reaches 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively, and then calculates the voltage change over a set time period (2-30 minutes).

[0061] In one specific embodiment, the normalization formula for the obtained voltage change and health status is as follows:

[0062]

[0063] x i For the normalized data, x min x is the minimum value of the collected data. max The maximum value of the collected data is denoted by x, where x represents the directly collected voltage change or health status.

[0064] In one specific embodiment, the step of inputting the obtained voltage change into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module specifically includes: normalizing the obtained voltage change of the lithium-ion battery module during the charging process, inputting it into the pre-established health status assessment model to obtain the normalized health status of the lithium-ion battery module, and performing reverse normalization to obtain the health status of the lithium-ion battery module.

[0065] In one specific implementation, reverse normalization specifically includes:

[0066] x = x i (x max -x min )+x min

[0067] x represents the health status of the lithium-ion battery module after reverse normalization; x i The normalized health status of the lithium-ion battery module; x min To determine the minimum health status of the ion battery module collected during the establishment of the health status assessment model, x max The maximum health status value of the ion battery module collected when establishing the health status assessment model.

[0068] Example 3

[0069] Please see Figure 2 As shown, the present invention provides a lithium-ion battery module health status assessment device, comprising:

[0070] The acquisition module is used to acquire charging data of the lithium-ion battery module; the charging data includes the voltage change within a set time after the voltage of the lithium-ion battery module reaches a set voltage during charging.

[0071] The evaluation module is used to input the obtained voltage change into a pre-established health status evaluation model to obtain the health status of the lithium-ion battery module.

[0072] The output module is used to output the health status of the lithium-ion battery module;

[0073] The pre-established health status assessment model in the assessment module is established through the following steps: The lithium-ion battery module is charged at a rate of 1 / 4 to 1 / 8C; during the charging process, the voltage is recorded at 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively, and the voltage change within the specified time period is recorded as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5nWhere n represents the number of different charging cycles; the capacity of the lithium-ion battery module per charge is denoted as C. n The state of health (SOH) of the lithium-ion battery module after the nth charge. n =C n / C, where C is the rated capacity of the lithium-ion battery module; the obtained voltage change and health status are normalized, the normalized voltage change is used as the input parameter, and the normalized health status is used as the output parameter, and the health status assessment model is established using the support vector regression algorithm.

[0074] The specific steps in the acquisition module for obtaining charging data of the lithium-ion battery module are as follows: when the voltage of the lithium-ion battery module reaches 45.5V, 46.8V, 48.1V, 49.4V and 50.7V respectively during the charging process, the voltage change within a set time period is then obtained.

[0075] Example 4

[0076] Please see Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a method for assessing the health status of a lithium-ion battery module; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0077] The memory 101 can be used to store the computer program 103. The processor 102 implements the method steps of the lithium-ion battery module health status assessment method described in Embodiment 1 or 2 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0078] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0079] The memory 101 in the electronic device 100 stores multiple instructions to implement a health status assessment of a lithium-ion battery module, and the processor 102 can execute the multiple instructions to achieve the following:

[0080] Acquire charging data of the lithium-ion battery module; the charging data includes the voltage change within a set time after the lithium-ion battery module reaches a set voltage during charging;

[0081] The obtained voltage change is input into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module.

[0082] Output the health status of the lithium-ion battery module.

[0083] Example 5

[0084] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for assessing the health status of a lithium-ion battery module, characterized in that, include: Acquire charging data from the lithium-ion battery module; The charging data includes the voltage change within a set time after the lithium-ion battery module reaches a set voltage during charging. The obtained voltage change is input into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module. Output the health status of the lithium-ion battery module; The pre-established health status assessment model is built through the following steps: The lithium-ion battery module was charged at a rate of 1 / 4 to 1 / 8C. During the charging process, the voltage was recorded at 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively. The voltage change within the following set time period was recorded as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5n , where n represents the number of different charging cycles; The capacity of a lithium-ion battery module per charge is denoted as C. n The state of health (SOH) of the lithium-ion battery module after the nth charge. n =C n / C, where C is the rated capacity of the lithium-ion battery module; The obtained voltage change and health status are normalized. The normalized voltage change is used as the input parameter and the normalized health status is used as the output parameter. The health status assessment model is established using the support vector regression algorithm. The set time is 2-30 minutes; The nominal total voltage of the lithium-ion battery module is 48V; The specific steps for obtaining charging data from the lithium-ion battery module are as follows: The voltage change within a set time period is obtained when the voltage of the lithium-ion battery module reaches 45.5V, 46.8V, 48.1V, 49.4V and 50.7V respectively during the charging process. The step of inputting the obtained voltage change into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module specifically includes: The voltage change of the lithium-ion battery module during the charging process is normalized and input into a pre-established health status assessment model to obtain the normalized health status of the lithium-ion battery module. Then, reverse normalization is performed to obtain the health status of the lithium-ion battery module.

2. The method for assessing the health status of a lithium-ion battery module according to claim 1, characterized in that, In the step of normalizing the obtained voltage changes and health status, the normalization formula is as follows: x i For the normalized data, x min The minimum value of the collected data. x max The maximum value of the collected data. x This refers to the voltage change or health status directly collected.

3. A lithium-ion battery module health status assessment device, characterized in that, include: The acquisition module is used to acquire charging data from the lithium-ion battery module. The charging data includes the voltage change within a set time after the lithium-ion battery module reaches a set voltage during charging. The evaluation module is used to input the obtained voltage change into a pre-established health status evaluation model to obtain the health status of the lithium-ion battery module. The output module is used to output the health status of the lithium-ion battery module; The pre-established health status assessment model in the assessment module is established through the following steps: The lithium-ion battery module is charged at a rate of 1 / 4 to 1 / 8C; during the charging process, the voltage is recorded at 45.5V, 46.8V, 48.1V, 49.4V, and 50.7V respectively, and the voltage change within the specified time period is denoted as Δ. 1n Δ 2n Δ 3n Δ 4n and Δ 5n Where n represents the number of different charging cycles; the capacity of the lithium-ion battery module per charge is denoted as C. n The state of health (SOH) of the lithium-ion battery module after the nth charge. n =C n / C, where C is the rated capacity of the lithium-ion battery module; The obtained voltage change and health status are normalized. The normalized voltage change is used as the input parameter and the normalized health status is used as the output parameter. The health status assessment model is established using the support vector regression algorithm. In the steps of acquiring charging data of the lithium-ion battery module, the specific steps are as follows: when the voltage of the lithium-ion battery module reaches 45.5V, 46.8V, 48.1V, 49.4V and 50.7V respectively during the charging process, the voltage change within a set time period is then acquired. The set time is 2-30 minutes; The nominal total voltage of the lithium-ion battery module is 48V; The step of inputting the obtained voltage change into a pre-established health status assessment model to obtain the health status of the lithium-ion battery module specifically includes: The voltage change of the lithium-ion battery module during the charging process is normalized and input into a pre-established health status assessment model to obtain the normalized health status of the lithium-ion battery module. Then, reverse normalization is performed to obtain the health status of the lithium-ion battery module.

4. An electronic device, characterized in that, The electronic device includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the lithium-ion battery module health status assessment method as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the lithium-ion battery module health status assessment method as described in any one of claims 1 to 2.

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