Method, device, equipment, storage medium and program product for predicting battery life decay curve

Abstract

The application relates to the battery technical field and discloses a battery life attenuation curve prediction method, device, equipment, storage medium and program product. The method comprises the following steps: determining each life attenuation stage of a battery based on a battery parameter of the battery; determining performance parameters of the battery at boundary points between adjacent stages in each life attenuation stage; and generating a life attenuation curve of the battery based on cycle test data of the battery in a first stage and the performance parameters at the boundary points, the first stage being any stage in the life attenuation stages. Before predicting the life attenuation curve, the application determines the life attenuation stages included in the battery and the performance parameters at the boundary points between adjacent stages, and on this basis, the life attenuation curve of the whole life cycle of the battery can be predicted based on the cycle test data of any life attenuation stage, the workload of the cycle test and the required cycle test data amount are reduced, and the efficiency and accuracy of the life prediction are improved.

Description

Technical Field

[0001] This application relates to the field of battery technology, specifically to a method, apparatus, device, storage medium, and program product for predicting battery life degradation curves. Background Technology

[0002] Lithium-ion batteries exhibit a certain degree of performance degradation with increasing cycle count during use. By fitting the battery life degradation curve of lithium-ion batteries, it is possible to accurately understand their lifespan characteristics, use them rationally, extend their lifespan, improve battery safety, and promptly replace batteries in poor condition.

[0003] Related technologies typically measure actual data during battery cycling and then fit a battery life degradation curve based on this data. However, since the degradation characteristics of a battery vary significantly across different stages of its lifespan, and the accuracy of the fitted battery life degradation curve depends on the richness of the collected data, these technologies require the collection of actual data from all stages of the battery's lifespan. This results in a large amount of data collection, high costs, and poor accuracy in fitting the battery life degradation curve.

[0004] The above statements are for the purpose of providing background information in relation to this application only and do not necessarily constitute prior art. Summary of the Invention

[0005] In view of the technical problems of fitting battery life degradation curves in the aforementioned related technologies, such as the need to collect a large amount of measured data, high cost, and poor accuracy, this application provides a method, apparatus, device, storage medium, and program product for predicting battery life degradation curves. This method only requires collecting cyclic test data from any stage of battery life degradation to predict the life degradation curve, greatly reducing the amount of data collected, lowering costs, and improving the accuracy of lifespan prediction.

[0006] A first aspect of this application provides a method for predicting battery life degradation curves, comprising:

[0007] Based on the battery parameters, determine the various lifespan degradation stages of the battery;

[0008] Determine the battery performance parameters at the boundary points between adjacent stages in each of the lifespan decay stages;

[0009] Based on the battery's cycle test data in the first stage and the performance parameters at each of the boundary points, a life decay curve for the battery is generated, wherein the first stage is any one of the life decay stages.

[0010] The above embodiments automatically determine the actual lifespan degradation stages of the battery based on its own battery parameters. The division of the lifespan degradation stages for the battery to be predicted is more consistent with the actual situation of the battery, resulting in high accuracy. The battery's lifespan degradation curve can be obtained using cyclic test data from any lifespan degradation stage and performance parameters at the boundary points between any two adjacent stages. The amount of measured data required for the entire prediction process is very small, significantly reducing actual testing time and improving battery lifespan prediction efficiency, while also reducing the cost of actual testing. By dividing the actual lifespan degradation stages and determining the performance parameters at the boundary points between any two adjacent stages in the process of predicting the battery's lifespan degradation curve, the final obtained lifespan degradation curve includes the lifespan degradation situation of each stage of the battery, realizing a staged description of the battery's lifespan degradation curve. This reduces the dependence of the accuracy of the lifespan degradation curve on measured data and effectively improves the accuracy of the lifespan degradation curve.

[0011] In some embodiments of this application, determining the various lifespan degradation stages of the battery based on battery parameters includes:

[0012] Based on the battery parameters, including the initial electrolyte filling amount and / or performance lower limit threshold, each stage of battery life degradation is determined.

[0013] The performance lower limit threshold is used to characterize the situation where the battery is discontinued when its performance parameters reach the performance lower limit threshold.

[0014] The initial electrolyte fill amount has a significant impact on battery performance degradation. Determining the battery's lifespan degradation stages based on the initial electrolyte fill amount fully considers its influence, resulting in degradation stages that more closely reflect the battery's actual performance and improve the accuracy of degradation stage determination. The performance lower limit threshold also affects the determination of the battery's lifespan degradation stages in later stages of use. Determining the battery's lifespan degradation stages based on the performance lower limit threshold improves the accuracy of these stages. Combining the initial electrolyte fill amount and the performance lower limit threshold to determine the battery's lifespan degradation curves considers both the impact of the initial electrolyte fill amount on performance degradation throughout the battery's lifespan and the influence of the performance lower limit threshold on later stages of use, providing a more comprehensive consideration and resulting in higher accuracy in determining the battery's lifespan degradation stages.

[0015] In some embodiments of this application, the battery's lifespan degradation stages are determined based on the initial electrolyte filling amount, including:

[0016] Based on the initial electrolyte filling amount and the preset electrolyte consumption rate of the battery, determine the target number of cycles required for the initial electrolyte filling amount to be completely consumed;

[0017] Based on the target number of cycles, the various lifespan degradation stages of the battery are determined.

[0018] By using the initial electrolyte filling amount and the preset electrolyte consumption rate, the target number of cycles the battery will undergo when the electrolyte dries up is estimated. Based on this target number of cycles, the various life decay stages of the battery are determined. This fully considers the impact of electrolyte drying on the battery's life decay stages throughout its entire life cycle, and can accurately determine the actual life decay stages of the battery.

[0019] In some embodiments of this application, determining the various lifespan degradation stages of the battery based on the target number of cycles includes:

[0020] If the target number of cycles is less than or equal to the first number of cycles required for the battery to reach the starting point of the linear decay phase, then the battery is determined to include an initial rapid decay phase, a middle slow decay phase, and a final accelerated decay phase.

[0021] If the target number of cycles is greater than the first number of cycles and less than or equal to the second number of cycles required for the battery to reach the end point of the linear decay stage, then the battery is determined to include the initial rapid decay stage, the intermediate slow decay stage, the linear decay stage, and the final accelerated decay stage.

[0022] If the target number of cycles is greater than the number of cycles corresponding to the lower performance threshold, then the battery is determined to include the initial rapid degradation stage, the intermediate slow degradation stage, and the linear degradation stage.

[0023] By using the initial electrolyte fill level and a preset electrolyte consumption rate, the target number of battery cycles before the electrolyte dries up is estimated. Based on this target number of cycles, the number of cycles required to reach the first point of linear degradation, the number of cycles required to reach the end point of linear degradation, and the number of cycles required to reach the lower performance threshold, a simple numerical comparison can determine the actual battery lifespan degradation stages. This approach fully considers the impact of electrolyte drying at different stages of the battery's lifespan on the degradation stages, allowing for an accurate determination of the actual battery lifespan degradation stages. Compared to related technologies that require fitting a life decay curve based on a large amount of measured data throughout the battery's life cycle before determining which life decay stages the battery includes, the embodiments of this application can predetermine each life decay stage of the battery based on the initial electrolyte filling amount before obtaining the life decay curve. Then, the life decay stages of the battery are used to assist in predicting the battery's life decay curve, providing a scheme for predicting the life decay curve that is substantially different from related technologies. Furthermore, since the battery's life decay stages are known before prediction, it also helps to improve the accuracy of the predicted life decay curve.

[0024] In some embodiments of this application, based on the performance lower limit threshold, each stage of battery life degradation is determined, including:

[0025] Based on the performance parameters at the starting point of the battery entering the linear degradation stage, which are less than or equal to the lower performance threshold, the battery is determined to include an initial rapid degradation stage and a mid-term slow degradation stage.

[0026] Based on the fact that the performance parameter at the starting point is greater than the performance parameter at the end point of the linear decay stage, the battery is determined to include the initial rapid decay stage, the intermediate slow decay stage, and the linear decay stage.

[0027] Based on the performance parameters at the end point being greater than the lower performance threshold, the battery is determined to include the initial rapid decay stage, the intermediate slow decay stage, the linear decay stage, and the final accelerated decay stage.

[0028] The above embodiments fully consider the impact of the battery's performance lower limit threshold on the battery's lifespan degradation stages, and can accurately determine each lifespan degradation stage actually included in the battery. Before obtaining the lifespan degradation curve, the battery's lifespan degradation stages can be predetermined based on the battery's performance lower limit threshold, and then the battery's lifespan degradation stages can be used to assist in predicting the battery's lifespan degradation curve. Since the battery's lifespan degradation stages are known before prediction, it helps to improve the accuracy of predicting the lifespan degradation curve.

[0029] In some embodiments of this application, based on the initial electrolyte filling amount and the performance lower limit threshold, the various lifespan degradation stages of the battery are determined, including:

[0030] The battery's lifespan decay stages are determined by a first-stage combination of lifespan decay stages determined based on the initial electrolyte filling amount and a second-stage combination of lifespan decay stages determined based on the performance lower limit threshold.

[0031] The battery's lifespan degradation stages are determined by combining the first-stage and second-stage combinations. This comprehensive approach, which considers the impact of electrolyte and performance lower limit thresholds on the battery's lifespan degradation stages, effectively improves the accuracy of the final determined lifespan degradation stages, thereby contributing to the accuracy of subsequent lifespan degradation curve predictions.

[0032] In some embodiments of this application, determining the various lifespan degradation stages of the battery based on battery parameters includes:

[0033] Based on the battery model included in the battery parameters, each life decay stage of the battery is obtained from the first mapping relationship between the pre-stored battery model and the life decay stage.

[0034] The first mapping relationship is determined based on the initial electrolyte filling amount and / or performance lower limit threshold corresponding to the battery model. The performance lower limit threshold is used to characterize the battery model to stop being used when its performance parameters reach the performance lower limit threshold.

[0035] After the first mapping relationship is pre-configured, when predicting the life degradation curve of a battery to be predicted, it is only necessary to query the corresponding life degradation stage from the first mapping relationship based on the battery model. This can shorten the time spent determining the various life degradation stages included in the battery and improve processing efficiency. Moreover, the first mapping relationship is determined based on the initial electrolyte filling amount and / or performance lower limit threshold corresponding to the battery model, which fully considers the impact of electrolyte and / or performance lower limit threshold on battery life degradation and improves the accuracy of determining the life degradation stage.

[0036] In some embodiments of this application, determining the battery performance parameters at the boundary points between adjacent stages in each lifetime decay stage includes:

[0037] Based on the battery model included in the battery parameters, the battery performance parameters at the boundary point of each life decay stage are obtained from the second mapping relationship between the pre-stored battery model and the performance parameters at the boundary point.

[0038] The second mapping relationship is determined based on the cycle test data of sample batteries of the battery model.

[0039] After the second mapping relationship is pre-configured, when it is necessary to predict the life degradation curve of the battery to be predicted, it is only necessary to query the performance parameters of each boundary point from the second mapping relationship based on the battery model. This can shorten the time spent determining the performance parameters of each boundary point and improve processing efficiency.

[0040] In some embodiments of this application, the process of constructing the second mapping relationship includes:

[0041] The sample battery is subjected to a cycle test at a preset temperature and at the nominal rate to obtain the cycle test data of the sample battery.

[0042] Based on the battery parameters of the sample battery, determine each stage of the sample battery's lifespan degradation.

[0043] Based on the cyclic test data, the performance parameters at the boundary points between adjacent stages in each lifetime decay stage of the sample battery are determined respectively.

[0044] The battery model and the performance parameters at each of the dividing points are stored as the second mapping relationship.

[0045] By using the above method, the second mapping relationship can be obtained simply by performing cycle tests on the sample battery and determining the life decay stage. There is no need to temporarily determine the second mapping relationship during the life prediction process of the battery to be predicted, which can improve the life prediction efficiency of the battery to be predicted.

[0046] In some embodiments of this application, determining the performance parameters at the boundary points between adjacent stages in each lifetime degradation stage of the sample battery based on the cyclic test data includes:

[0047] Based on the cycle test data, a curve of the decay slope as a function of battery performance parameters is fitted; the decay slope is used to characterize the amount of decay of battery performance parameters corresponding to a preset number of cycles.

[0048] Based on the aforementioned variation curves, the performance parameters at the boundary points between adjacent stages in each lifetime decay stage of the sample battery are determined.

[0049] Since this variation curve can accurately and intuitively depict the life decay law of the sample battery, it can accurately identify the performance parameters at the boundary points between adjacent stages in each life decay stage of the sample battery, thus improving the accuracy of identifying the performance parameters at the boundary points.

[0050] In some embodiments of this application, determining the performance parameters at the boundary points between adjacent stages in each lifetime degradation stage of the sample battery based on the variation curve includes:

[0051] In the case where the sample battery includes an initial rapid decay phase and a mid-term slow decay phase, the performance parameters at the first boundary point between the initial rapid decay phase and the mid-term slow decay phase are determined based on the change curve.

[0052] In the case where the sample battery includes a linear decay phase, the performance parameters at the second boundary point between the linear decay phase and the adjacent previous decay phase are determined based on the change curve.

[0053] In the case where the sample battery includes a final accelerated decay phase, the electrolyte consumption rate of the sample battery is determined based on the cycle test data; based on the electrolyte consumption rate and the initial electrolyte filling amount of the sample battery, a third boundary point for electrolyte depletion of the sample battery is determined, and the performance parameters at the third boundary point are used as the performance parameters at the boundary point between the final accelerated decay phase and the adjacent previous decay phase.

[0054] Because this variation curve accurately and intuitively depicts the lifespan degradation pattern of the sample battery, it can accurately identify the first boundary point where the sample battery transitions from the initial rapid degradation stage to the intermediate slow degradation stage, improving the accuracy of performance parameter identification at the boundary between the initial rapid degradation stage and the intermediate slow degradation stage. The linear degradation stage occurs because after the SEI film grows to a certain extent, the dissolution rate and growth rate of the SEI film become consistent, and the SEI film thickness no longer changes. At this point, the battery degradation rate remains constant, and degradation follows a linear progression. However, due to the high difficulty in measuring the SEI film, the degradation slope variation curve is fitted using the cyclic test data of the sample battery. This curve is then used to identify the second boundary point between the linear degradation stage and the adjacent previous degradation stage. This transforms the difficult method of measuring the SEI film into a simple and easy-to-operate method of fitting a variation curve, improving the efficiency and accuracy of identifying the second boundary point. For the final accelerated decay stage, the above process fully considers the impact of electrolyte on battery life decay. The electrolyte consumption rate of the sample battery is determined using cycle test data, the timing of electrolyte drying of the sample battery is estimated, and then the performance parameters at the third boundary point between the final accelerated decay stage and the adjacent previous decay stage are determined, so as to quickly and accurately identify the performance parameters at the boundary point between the final accelerated decay stage and the adjacent previous decay stage.

[0055] In some embodiments of this application, generating the battery's lifespan degradation curve based on the battery's cycle test data in the first stage and the performance parameters at each of the threshold points includes:

[0056] Based on the battery's cycle test data in the first stage, fit the first lifetime function relationship of the first stage;

[0057] Based on the first lifetime function relationship and the performance parameters at each of the boundary points, the lifetime function relationship of each stage other than the first stage in the lifetime decay stage of the battery is obtained.

[0058] Based on the lifetime function relationship of each lifetime decay stage of the battery, the lifetime decay curve of the battery is plotted.

[0059] In the embodiments of this application, only the cycle test data of the battery in the first stage is needed, without the need to obtain cycle test data for each stage of the battery's entire life cycle. This reduces the workload of cycle testing and the amount of cycle test data to be collected, thereby lowering costs. By using the cycle test data from the first stage and combining it with the performance parameters at the boundary points between adjacent stages in each stage of battery life degradation, the battery's life degradation curve can be obtained, which helps to improve the efficiency and accuracy of predicting the life degradation curve.

[0060] In some embodiments of this application, obtaining the lifetime function relationship for each stage of the battery's lifetime degradation stages other than the first stage, based on the first lifetime function relationship and the performance parameters at each of the boundary points, includes:

[0061] Based on the first lifetime function relationship, the slopes of the lifetime decay curves of the first stage and the second stage are equal at the first boundary point between the first stage and the second stage, and the lifetime values ​​of the first stage and the second stage are both equal to the performance parameters at the first boundary point, the second lifetime function relationship of the second stage is obtained; the second stage is any stage adjacent to the first stage among the lifetime decay stages included in the battery.

[0062] For adjacent lifespan degradation stages, if the lifespan function relationship of one of the degradation stages is known, the correlation between adjacent degradation stages at their boundary points can be used to solve for the unknown lifespan function relationship of the remaining degradation stage. Therefore, for all lifespan degradation stages of a battery, after obtaining the lifespan function relationship of any one stage, the correlation between adjacent degradation stages can be used to quickly obtain the lifespan function relationships for each degradation stage. Thus, by simply collecting cyclic test data for one lifespan degradation stage and fitting the lifespan function relationship for that stage, the lifespan function relationships for each degradation stage can be obtained, leading to the lifespan degradation curve for the entire battery lifecycle. This significantly improves the efficiency of battery lifespan prediction, reduces the amount of data collection required for lifespan prediction, lowers costs, and simultaneously improves the accuracy of lifespan prediction.

[0063] A second aspect of this application provides a device for predicting battery life degradation curves, comprising:

[0064] The first determining module is used to determine each stage of battery life degradation based on the battery parameters.

[0065] The second determining module is used to determine the performance parameters of the battery at the boundary point between adjacent stages in each of the life decay stages.

[0066] The generation module is used to generate the battery's life decay curve based on the battery's cycle test data in the first stage and the performance parameters at each of the boundary points, wherein the first stage is any one of the life decay stages.

[0067] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect above.

[0068] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the method described in the first aspect above.

[0069] An embodiment of the fifth aspect of this application provides a computer program product including a computer program that is executed by a processor to implement the method described in the first aspect.

[0070] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0071] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the embodiments described below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0072] Figure 1 A flowchart illustrating a method for predicting battery life degradation curves according to some embodiments of this application;

[0073] Figure 2 A flowchart of another method for predicting battery life degradation curves according to some embodiments of this application;

[0074] Figure 3 This is a schematic diagram of the process for determining the various stages of battery life degradation according to some embodiments of this application;

[0075] Figure 4 A flowchart of another method for predicting battery life degradation curves according to some embodiments of this application;

[0076] Figure 5 This is a flowchart illustrating the process of determining performance parameters at the boundary point between adjacent lifetime decay stages according to some embodiments of this application.

[0077] Figure 6 This is a schematic diagram of the prediction process for the life degradation curve of a lithium iron phosphate battery according to some embodiments of this application.

[0078] Figure 7 This is a schematic diagram of the battery degradation curve provided according to some embodiments of this application;

[0079] Figure 8 This is a schematic diagram of a device for predicting battery life degradation curves according to some embodiments of this application;

[0080] Figure 9 This is a schematic diagram of the structure of an electronic device according to some embodiments of this application. Detailed Implementation

[0081] The embodiments of the technical solution of this application will now be described in detail with reference to the accompanying drawings. These embodiments are only used to more clearly illustrate the technical solution of this application and are therefore merely examples, and should not be used to limit the scope of protection of this application.

[0082] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0083] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0084] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0085] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists, A and B exist simultaneously, and B exists. In addition, the character " / " in this document generally indicates that the related objects before and after it have an "or" relationship.

[0086] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0087] A battery life degradation curve is a curve depicting how battery performance changes over time or with usage (such as the number of charge-discharge cycles). The horizontal axis of the battery life degradation curve typically represents the degree of battery usage, which can be the number of charge-discharge cycles, time, depth of discharge, etc. The vertical axis of the battery life degradation curve represents the key performance indicators of the battery. In the embodiments of this application, the vertical axis is represented by performance parameters, which can be any one of, but is not limited to, State of Health (SOH), capacity, energy, energy efficiency, etc.

[0088] The battery life degradation curve shows the entire process of a battery from its initial state to a significant decline in performance. It is a tool for assessing battery life and health. Analyzing the battery life degradation curve helps to accurately understand the life characteristics of lithium batteries, use lithium batteries rationally, extend their service life, improve the safety of battery use, and replace batteries with poor health in a timely manner.

[0089] One related technology provides a scheme for obtaining a battery life degradation curve. This scheme measures actual data during battery cycling and fits the battery life degradation curve based on the measured data. However, since the performance of lithium batteries degrades with the increase of the number of cycles, the battery life degradation characteristics vary significantly at different stages of the battery's life cycle. The accuracy of the battery life degradation curve fitted by related technologies depends on the richness of the collected data. Therefore, to improve the accuracy of the fitted battery life degradation curve, related technologies need to collect a large amount of actual data at different stages of the battery's life cycle, resulting in a large amount of data collection and high costs.

[0090] Based on the aforementioned problems in related technologies, some embodiments of this application propose a method, apparatus, device, storage medium, and program product for predicting battery life degradation curves. This prediction method determines each stage of battery life degradation based on battery parameters; it determines the battery performance parameters at the boundary points between adjacent stages in each life degradation stage; and it generates a battery life degradation curve based on the battery's cycle test data in the first stage and the battery performance parameters at each boundary point, where the first stage is any one of the various life degradation stages.

[0091] This method first determines the actual degradation stages of the battery's lifespan, as well as the battery's performance parameters at the boundary points between adjacent stages. Then, based on the cycle test data of any stage and the performance parameters at the boundary points between adjacent stages, the battery's lifespan degradation curve can be generated. This process only requires collecting cycle test data from one stage of the battery's lifespan degradation; it eliminates the need to collect cycle test data from all stages throughout the battery's entire lifespan. The required data volume is small, resulting in low cost. By generating the battery's lifespan degradation curve based on the limited data collected and the performance parameters at the boundary points between adjacent stages, the accuracy of the generated lifespan degradation curve is minimally affected by the amount of data collected, effectively improving the efficiency and accuracy of predicting the lifespan degradation curve.

[0092] In some embodiments of this application, the battery can be, but is not limited to, a cell, a single battery, a battery module, a battery pack, an energy storage cabinet, an energy storage container, etc. The battery can be a lithium-ion battery, including but not limited to lithium cobalt oxide batteries, lithium manganese oxide batteries, lithium nickel oxide batteries, lithium iron phosphate batteries, etc. For other batteries with any chemical system that does not contain lithium, if the battery's lifespan degradation characteristics differ significantly at different stages of its lifespan, and can be divided into multiple lifespan degradation stages, then the battery lifespan degradation curve prediction method provided in some embodiments of this application can also be applied. The batteries mentioned below are all lithium-ion batteries used as examples for illustration.

[0093] In some embodiments of this application, the battery can be of any shape and structure, such as a cylindrical battery, a flat battery, a pouch battery, a prismatic battery, etc. The battery can be applied in any application scenario that requires battery use. It can be used as a consumer electronics battery, such as in mobile phones and laptops. The battery can also be used as an energy storage battery, and as a power battery, such as in electric vehicles, electric bicycles, electric aircraft, and electric ships.

[0094] The battery life degradation curve prediction method provided in some embodiments of this application can predict the battery life degradation curve at any point in the battery's entire life cycle, regardless of whether the battery is in use or not. For example, the battery life degradation curve can be predicted for newly manufactured batteries, batteries that have just been put into use, and batteries that have been used for a period of time.

[0095] Some embodiments of this application provide a method for predicting battery life degradation curves, see [link to relevant documentation]. Figure 1 The method includes the following steps 101-103.

[0096] Step 101: Based on the battery parameters, determine the various lifespan degradation stages of the battery.

[0097] Step 102: Determine the battery performance parameters at the boundary points between adjacent stages in each lifespan decay stage.

[0098] Step 103: Based on the battery's cycle test data in the first stage and the performance parameters at each boundary point, generate the battery's life decay curve. The first stage is any one of the life decay stages.

[0099] In some embodiments of this application, the execution subject for performing the prediction method of battery life degradation curve can be any device with computing power, including but not limited to physical servers, server clusters, cloud servers, mobile phones, computers, vehicle terminals, etc.

[0100] Battery performance degrades to some extent with the increase of the number of cycles. The battery life degradation characteristics vary significantly throughout the battery's life cycle. Based on these differences, the embodiments of this application divide the battery into possible life degradation stages, specifically into at least an initial rapid degradation stage, a mid-term slow degradation stage, a linear degradation stage, and a final accelerated degradation stage.

[0101] The aforementioned classification of battery life degradation stages is primarily based on the growth and consumption patterns of the SEI (Solid Electrolyte Interphase) film on the negative electrode. In lithium-ion batteries, the most significant cause of battery life degradation is the formation and growth of the SEI film on the negative electrode. The formation of the SEI film consumes active lithium within the battery, and this reduction directly leads to capacity decay. As the SEI film continues to grow, this consumption continues, resulting in a gradual decline in the overall performance of the battery.

[0102] In the early stages of battery use, the SEI film forms and grows rapidly. At this time, the SEI film is relatively thin and unstable. This instability makes the SEI film more prone to cracking and rebuilding. Due to the instability of the SEI film, the active lithium in the battery is consumed more quickly. This rapid consumption of active lithium leads to a rapid decrease in the battery's usable capacity and a rapid decline in the battery's State of Health (SOH), resulting in a significant deterioration in battery performance within a short period. The embodiments of this application define an initial rapid degradation stage based on the rapid degradation characteristics of the early stages of battery use.

[0103] After a period of battery use, following the initial rapid degradation phase described above, the SEI film structure becomes relatively stable, and its growth rate slows down. The SEI film's consumption of active lithium is no longer as intense as in the early stages. Due to the slower growth of the SEI film, the rate of consumption of active lithium also decreases. Therefore, the rate of battery capacity loss also slows down, entering a relatively stable, slow degradation phase, which is referred to as the intermediate slow degradation phase in the embodiments of this application.

[0104] As battery usage time increases, the SEI film grows to a certain extent, and the dissolution rate and growth rate of the SEI film reach a dynamic equilibrium. The dissolution rate refers to the process by which some components of the SEI film may dissolve into the electrolyte under the electrochemical environment of battery operation. The growth rate refers to the rate at which new SEI film material is still being generated due to the battery's charge-discharge reactions. When the dissolution rate equals the growth rate, the thickness of the SEI film no longer changes. This is because, macroscopically, the generated portion is exactly offset by the dissolved portion. Once the SEI film thickness stabilizes, the battery degradation rate remains constant, meaning the battery life declines linearly. Linear degradation can be understood as the battery's performance (such as capacity, energy, energy efficiency, etc.) decreasing at a relatively stable rate over time. The embodiments of this application refer to this stage as the linear degradation stage.

[0105] As batteries age, the electrolyte is gradually consumed during the long-term electrochemical reaction process, potentially leading to electrolyte insufficiency. During charging and discharging, ions migrate between the positive and negative electrodes via the electrolyte. When the electrolyte is insufficient, the migration channels for ions are restricted, hindering the normal electrochemical reactions within the battery. Furthermore, insufficient electrolyte can also cause localized overheating within the battery. Because the electrolyte also plays a role in heat dissipation, its insufficiency prevents timely heat dissipation, accelerating the aging and performance degradation of internal battery materials. Therefore, insufficient electrolyte in the later stages of battery use causes a sharp acceleration in battery performance degradation. The embodiments of this application refer to this stage as the final accelerated degradation stage.

[0106] Due to differences in structure, chemical system, and usage, the actual life decay stages of different batteries may vary. The actual life decay stages of a battery may be some or all of the stages defined above.

[0107] For the battery to be predicted, some embodiments of this application determine the actual lifespan degradation stage of the battery based on its own battery parameters, and then determine the battery performance parameters at the boundary point between any two adjacent stages in each lifespan degradation stage. The performance parameters can be any one of SOH, capacity, energy, energy efficiency, etc. Using the cycle test data of any lifespan degradation stage of the battery and the performance parameters at the boundary point between any two adjacent stages, the lifespan degradation curve of the battery for its entire lifespan can be obtained.

[0108] The above embodiments automatically determine the actual lifespan degradation stages of the battery based on its own battery parameters. The division of the lifespan degradation stages for the battery to be predicted is more consistent with the actual situation of the battery, resulting in high accuracy. The battery's lifespan degradation curve can be obtained using cyclic test data from any lifespan degradation stage and performance parameters at the boundary points between any two adjacent stages. The amount of measured data required for the entire prediction process is very small, significantly reducing actual testing time and improving battery lifespan prediction efficiency, while also reducing the cost of actual testing. By dividing the actual lifespan degradation stages and determining the performance parameters at the boundary points between any two adjacent stages in the process of predicting the battery's lifespan degradation curve, the final obtained lifespan degradation curve includes the lifespan degradation situation of each stage of the battery, realizing a staged description of the battery's lifespan degradation curve. This reduces the dependence of the accuracy of the lifespan degradation curve on measured data and effectively improves the accuracy of the lifespan degradation curve.

[0109] In some embodiments of this application, the actual life decay stages of the battery can be determined by: determining each life decay stage of the battery based on the initial electrolyte filling amount and / or performance lower limit threshold, including battery parameters; wherein the performance lower limit threshold is used to characterize the battery to be discontinued when the battery performance parameters reach the performance lower limit threshold.

[0110] The initial electrolyte fill amount of a battery refers to the amount of electrolyte initially filled into the battery during the manufacturing process. The initial electrolyte fill amount can be determined based on factors such as battery specifications, design requirements, and electrode materials. The initial electrolyte fill amount affects the battery's initial performance and subsequent performance during use.

[0111] The lower performance threshold of a battery refers to the level at which its performance degrades during use, at which point the battery should be discontinued. This performance level is typically set as the standard for the battery's "end of life." The lower performance threshold can be any lower limit of performance indicators such as State of Health (SOH), capacity, energy, and energy efficiency. The lower performance threshold uses the same performance indicator as the performance parameter in step 102. For example, if the performance parameter in step 102 is capacity, then the lower performance threshold is the lower limit of capacity.

[0112] The initial electrolyte fill amount has a significant impact on the battery's lifespan degradation stages. In the initial stages, the electrolyte amount is sufficient to adequately wet the electrode materials, ensuring good ion transport and electrochemical reactions. As the battery is used, the electrolyte is gradually consumed, especially the electrolyte used to form and maintain the SEI film. Reduced electrolyte leads to insufficient wetting of the electrode material surfaces, affecting ion transport efficiency and causing a decline in battery performance, accelerating the degradation rate. When the electrolyte amount decreases to a point where it can no longer meet the wetting and ion transport requirements of the electrode materials, battery performance will drop sharply. Therefore, the initial electrolyte fill amount has a crucial impact on battery performance degradation. Determining the battery's lifespan degradation stages based on the initial electrolyte fill amount fully considers the impact of the initial electrolyte fill amount on battery lifespan degradation, resulting in a more accurate assessment of the actual battery conditions and improving the accuracy of lifespan degradation stage determination.

[0113] The performance lower limit threshold refers to the minimum performance level a battery reaches when its performance deteriorates to the point where it becomes unusable and should be discontinued. When battery performance falls below this threshold, it is considered to have reached the end of its lifespan. Continued use will accelerate the degradation process, leading to a rapid decline in performance. Therefore, the battery must be replaced when its performance parameters reach the lower limit threshold. Once the battery is replaced and its use ceases, further degradation can be disregarded. Thus, the lower limit threshold significantly impacts the determination of the battery's lifespan degradation stages in later stages of use. Using the performance lower limit threshold to determine each stage of battery lifespan degradation improves the accuracy of this determination.

[0114] In some of the above embodiments, the initial electrolyte filling amount and the performance lower limit threshold can be combined to determine the battery's various life decay curves. This takes into account both the impact of the initial electrolyte filling amount on the performance decay throughout the battery's entire life cycle and the impact of the performance lower limit threshold on the life decay stage in the later stages of battery use. This more comprehensive consideration of factors makes the determination of each life decay stage of the battery more accurate.

[0115] In some embodiments of this application, such as Figure 2 As shown, based on the initial electrolyte filling amount, the various lifespan degradation stages of the battery can be determined in the following ways:

[0116] Step 1011: Based on the initial electrolyte filling amount and the battery's preset electrolyte consumption rate, determine the target number of cycles required for the initial electrolyte filling amount to be completely consumed.

[0117] Step 1012: Based on the target number of cycles, determine the various lifespan degradation stages included in the battery.

[0118] The preset electrolyte consumption rate of a battery refers to the rate at which the electrolyte is consumed during battery use, as predetermined during the battery design and manufacturing process based on factors such as the battery's chemical system, electrode materials, and usage conditions.

[0119] The aforementioned preset electrolyte consumption rate refers to the amount of electrolyte consumed by the battery in each cycle. The ratio of the initial electrolyte fill level to the preset electrolyte consumption rate can be calculated, and this ratio is used as the target number of cycles required for the initial electrolyte fill level to be completely consumed. In some embodiments, to prevent the battery from suddenly failing due to electrolyte depletion during use, a margin factor can be introduced. The margin factor characterizes the number of cycles reached when the battery's electrolyte is consumed at a margin factor. For example, a margin factor of 90% can be set, indicating that the battery reaches the target number of cycles when 90% of the initial electrolyte fill level is consumed. When introducing a margin factor, the product of the initial electrolyte fill level and the margin factor can be calculated first, and then the ratio of this product to the preset electrolyte consumption rate can be calculated. This ratio is used as the target number of cycles required for the initial electrolyte fill level to be completely consumed.

[0120] By using the initial electrolyte filling amount and the preset electrolyte consumption rate, the target number of cycles the battery will undergo when the electrolyte dries up is estimated. Based on this target number of cycles, the various life decay stages of the battery are determined. This fully considers the impact of electrolyte drying on the battery's life decay stages throughout its entire life cycle, and can accurately determine the actual life decay stages of the battery.

[0121] In some embodiments of this application, such as Figure 3 As shown, based on the target number of cycles, the specific lifespan degradation stages of the battery can be determined in the following way:

[0122] Step 10121: If the target number of cycles is less than or equal to the number of cycles required for the battery to reach the starting point of the linear decay phase, then the battery is determined to include an initial rapid decay phase, a middle slow decay phase, and a final accelerated decay phase.

[0123] Step 10122: If the target number of cycles is greater than the number of cycles in the first cycle and less than or equal to the number of cycles required for the battery to reach the end of the linear decay stage, then the battery is determined to include the initial rapid decay stage, the middle slow decay stage, the linear decay stage, and the final accelerated decay stage.

[0124] Step 10123: If the target number of cycles is greater than the number of cycles corresponding to the lower limit threshold of battery performance, then the battery is determined to include an initial rapid degradation stage, a mid-term slow degradation stage, and a linear degradation stage.

[0125] The number of first cycles required for the battery to reach the beginning of the linear degradation phase, and the number of second cycles required to reach the end of the linear degradation phase, can be obtained in various ways. As an example, sample batteries of the same model and batch as the battery to be predicted can be selected, and cyclic tests can be performed on the sample batteries using battery testing equipment to obtain test data. The battery testing equipment can include, but is not limited to, battery cycle testers, charge-discharge testers, etc. The test data can include, but is not limited to, the initial parameters of the sample battery, such as initial capacity, internal resistance, and voltage, as well as the parameters after each cycle, such as capacity, internal resistance, and voltage. Then, based on the test data, a life degradation curve of the sample battery is fitted, and the number of first cycles corresponding to the beginning of the linear degradation phase and the number of second cycles corresponding to the end of the linear degradation phase are identified from the life degradation curve of the sample battery. The above-mentioned fitting of the life degradation curve of the sample battery based on the test data can adopt fitting methods in related technologies, which will not be elaborated here. As another example, known empirical formulas (such as the Arrhenius equation) can be used to predict battery life and degradation behavior, thereby estimating the number of cycles corresponding to the start and end points of the linear degradation phase of the sample battery. The specific processing steps for using empirical formulas can be found in relevant technical methods and will not be elaborated upon here.

[0126] After obtaining the target number of cycles, the first number of cycles required for the battery to reach the beginning of the linear decay phase, and the second number of cycles required for the battery to reach the end of the linear decay phase, the target number of cycles is compared with the first and second number of cycles. If the target number of cycles is less than or equal to the first number of cycles, it indicates that the electrolyte has dried up before the battery has entered the linear decay phase. Therefore, after experiencing the initial rapid decay phase and the middle slow decay phase, the battery will enter the final accelerated decay phase due to electrolyte drying up. Thus, the actual life decay phases of the battery include the initial rapid decay phase, the middle slow decay phase, and the final accelerated decay phase.

[0127] If the target number of cycles is greater than the number of cycles in the first cycle and less than or equal to the number of cycles in the second cycle, it indicates that the electrolyte dries up after the battery enters the linear decay stage. Therefore, the battery will experience an initial rapid decay stage, a middle slow decay stage, and a linear decay stage. After that, due to the drying up of the electrolyte, it enters the final accelerated decay stage. Therefore, the actual life decay stages of the battery include the initial rapid decay stage, the middle slow decay stage, the linear decay stage, and the final accelerated decay stage.

[0128] If the target cycle count is greater than the cycle count corresponding to the performance lower limit threshold, it means that the electrolyte in the battery has not dried out when the battery is discontinued due to performance degradation affecting its usability. In this case, the battery will not experience a rapid performance degradation due to drying out, thus confirming that the battery does not have a final accelerated degradation stage. In other words, the actual battery lifespan degradation stages include the initial rapid degradation stage, the middle slow degradation stage, and the linear degradation stage.

[0129] By using the initial electrolyte fill level and a preset electrolyte consumption rate, the target number of battery cycles before the electrolyte dries up is estimated. Based on this target number of cycles, the number of cycles required to reach the first point of linear degradation, the number of cycles required to reach the end point of linear degradation, and the number of cycles required to reach the lower performance threshold, a simple numerical comparison can determine the actual battery lifespan degradation stages. This approach fully considers the impact of electrolyte drying at different stages of the battery's lifespan on the degradation stages, allowing for an accurate determination of the actual battery lifespan degradation stages. Compared to related technologies that require fitting a life decay curve based on a large amount of measured data throughout the battery's life cycle before determining which life decay stages the battery includes, the embodiments of this application can predetermine each life decay stage of the battery based on the initial electrolyte filling amount before obtaining the life decay curve. Then, the life decay stages of the battery are used to assist in predicting the battery's life decay curve, providing a scheme for predicting the life decay curve that is substantially different from related technologies. Furthermore, since the battery's life decay stages are known before prediction, it also helps to improve the accuracy of the predicted life decay curve.

[0130] In some embodiments of this application, such as Figure 4 As shown, based on the battery's performance lower limit threshold, the various stages of battery life degradation can be determined in the following ways:

[0131] Step 10131: Based on the performance parameters at the starting point when the battery enters the linear degradation stage (the lower limit threshold for performance is less than or equal to the threshold for performance), determine whether the battery includes an initial rapid degradation stage and a mid-term slow degradation stage.

[0132] Step 10132: Based on the performance parameters at the starting point being greater than the performance lower limit threshold and at the end point of the linear decay stage being less than or equal to the performance parameters at the end point of the linear decay stage, determine that the battery includes an initial rapid decay stage, a mid-term slow decay stage, and a linear decay stage.

[0133] Step 10133: Based on the performance parameters at the end point where the lower performance threshold is greater than the end point, determine that the battery includes an initial rapid degradation stage, a middle slow degradation stage, a linear degradation stage, and a final accelerated degradation stage.

[0134] The performance parameters at the start and end of the linear degradation phase of the battery can be obtained in a similar way to those at the start and end of the linear degradation phase. Specifically, this involves experimentally testing sample batteries of the same model and batch, fitting a lifespan degradation curve based on the measured data, and then determining the performance parameters at the start and end of the linear degradation phase based on the curve. Alternatively, known empirical formulas can be used to analyze and obtain the performance parameters at the start and end of the linear degradation phase. The specific process for obtaining these parameters will not be elaborated upon here.

[0135] The performance parameters mentioned above can be any one of, but not limited to, battery health status (SOH), capacity, energy, or energy efficiency. Correspondingly, the performance lower limit threshold can also include battery health status (SOH), capacity, energy, or energy efficiency. The performance parameters and performance lower limit thresholds are represented using the same parameter, such as both using battery health status (SOH) or both using capacity.

[0136] If the performance lower limit threshold is less than or equal to the performance parameters at the starting point of the battery entering the linear degradation stage, it means that the battery has reached the performance lower limit threshold before entering the linear degradation stage and is therefore stopped from use. In this case, the battery does not include the linear degradation stage and the subsequent stages. Therefore, the actual life degradation stages of the battery include the initial rapid degradation stage and the intermediate slow degradation stage.

[0137] If the performance lower limit threshold is greater than the performance parameter at the start point of the linear degradation phase and less than or equal to the performance parameter at the end point of the linear degradation phase, it indicates that the battery reaches the performance lower limit threshold only after entering the linear degradation phase. Therefore, before being taken out of use, the battery actually experiences the following life degradation phases: the initial rapid degradation phase, the intermediate slow degradation phase, and the linear degradation phase.

[0138] If the performance lower limit threshold is greater than the performance parameter at the end of the linear decay stage, it indicates that the battery will only reach the performance lower limit threshold after the linear decay stage ends. Therefore, after the linear decay stage ends, the battery will enter the final accelerated decay stage. Thus, the battery actually includes an initial rapid decay stage, a middle slow decay stage, a linear decay stage, and a final accelerated decay stage.

[0139] The above embodiments fully consider the impact of the battery's performance lower limit threshold on the battery's lifespan degradation stages, and can accurately determine each lifespan degradation stage actually included in the battery. Before obtaining the lifespan degradation curve, the battery's lifespan degradation stages can be predetermined based on the battery's performance lower limit threshold, and then the battery's lifespan degradation stages can be used to assist in predicting the battery's lifespan degradation curve. Since the battery's lifespan degradation stages are known before prediction, it helps to improve the accuracy of predicting the lifespan degradation curve.

[0140] In some embodiments of this application, the battery's lifespan decay stages can also be determined based on the initial electrolyte filling amount and the performance lower limit threshold. Specifically, this includes: a first-stage combination of lifespan decay stages determined based on the initial electrolyte filling amount, and a second-stage combination of lifespan decay stages determined based on the performance lower limit threshold, to determine the battery's lifespan decay stages.

[0141] In one implementation, the intersection of the first-stage combination and the second-stage combination can be taken, and the lifetime decay stages included in the intersection can be taken as the respective lifetime decay stages of the battery.

[0142] In another implementation, the combination with the fewest stages is determined from the first stage combination and the second stage combination; each life decay stage in the combination with the fewest stages is determined as the life decay stage of the battery.

[0143] Following the method described above for determining the lifespan degradation stages based on the initial electrolyte fill amount, the first stage combination was determined. Following the method described above for determining the lifespan degradation stages based on the performance lower limit threshold, the second stage combination was determined. Then, the first and second stage combinations were combined to determine the various lifespan degradation stages of the battery. This comprehensive consideration of the influence of electrolyte and performance lower limit threshold on the battery's lifespan degradation stages effectively improves the accuracy of the final determined lifespan degradation stages, thereby contributing to the accuracy of subsequent lifespan degradation curve prediction.

[0144] In some embodiments of this application, the various life decay stages of the battery can also be determined by the following method: based on the battery model included in the battery parameters, each life decay stage of the battery is obtained from a first mapping relationship between the battery model and the life decay stage that is stored in advance; wherein, the first mapping relationship is determined based on the initial electrolyte filling amount and / or the performance lower limit threshold corresponding to the battery model, and the performance lower limit threshold is used to characterize the battery model to stop being used when the battery performance parameters reach the performance lower limit threshold.

[0145] In these embodiments, the various lifespan degradation stages of the battery model are determined in advance based on the initial electrolyte filling amount and / or performance lower limit threshold corresponding to the battery model to which the prediction belongs, in accordance with the method provided in the preceding embodiments. Then, in the device used to perform the prediction method provided in the embodiments of this application, a first mapping relationship between the battery model and the various lifespan degradation stages of the battery belonging to that battery model is pre-configured.

[0146] After the first mapping relationship is pre-configured, when predicting the life degradation curve of a battery to be predicted, it is only necessary to query the corresponding life degradation stage from the first mapping relationship based on the battery model. This can shorten the time spent determining the various life degradation stages included in the battery and improve processing efficiency. Moreover, the first mapping relationship is determined based on the initial electrolyte filling amount and / or performance lower limit threshold corresponding to the battery model, which fully considers the impact of electrolyte and / or performance lower limit threshold on battery life degradation and improves the accuracy of determining the life degradation stage.

[0147] In some embodiments of this application, the battery performance parameters at the boundary point between adjacent stages in each life decay stage can be determined by the following method: based on the battery model included in the battery parameters, the battery performance parameters at the boundary point are obtained from a pre-stored second mapping relationship between battery model and performance parameters at the boundary point; wherein, the second mapping relationship is determined based on the cycle test data of sample batteries of the battery model.

[0148] In these embodiments, a sample battery of the same model as the battery to be predicted is pre-selected, and the sample battery is subjected to cyclic testing to obtain cyclic test data. Based on the cyclic test data, the performance parameters at the boundary points of adjacent life decay stages of the battery model are determined, and a second mapping relationship between the battery model and the performance parameters at each boundary point is pre-configured in the device used to execute the prediction method of the embodiments of this application.

[0149] After the second mapping relationship is pre-configured, when it is necessary to predict the life degradation curve of the battery to be predicted, it is only necessary to query the performance parameters of each boundary point from the second mapping relationship based on the battery model. This can shorten the time spent determining the performance parameters of each boundary point and improve processing efficiency.

[0150] In some embodiments of this application, the process of constructing the second mapping relationship described above may include:

[0151] The sample battery is subjected to a cycle test at a preset temperature and at the nominal rate of the sample battery to obtain the cycle test data of the sample battery; based on the battery parameters of the sample battery, the life decay stages of the sample battery are determined; based on the cycle test data, the performance parameters at the boundary points between adjacent stages in each life decay stage of the sample battery are determined; the battery model and the performance parameters at each boundary point are stored as a second mapping relationship.

[0152] The nominal rate of the battery mentioned above is the recommended discharge or charge current intensity during battery design. The preset temperature can be a relatively high temperature, which can accelerate battery aging and shorten the cycle test time. The preset temperature range can be, but is not limited to, [55℃, 60℃] or [65℃, 80℃]. The two temperature ranges given here are only examples; in actual applications, the preset temperature can be any temperature.

[0153] Using preset temperature and nominal rate as cyclic test conditions, a sample battery of the same model as the battery to be predicted is subjected to cyclic testing to obtain cyclic test data for the sample battery. This cyclic test data may include parameters such as battery capacity, voltage, and resistance after each cycle.

[0154] For the sample batteries, the method for determining the life decay stages described above was also used to determine each life decay stage of the sample batteries. The specific determination process will not be repeated here.

[0155] Based on the cyclic testing data of the sample batteries, the performance parameters at the boundary points between adjacent stages in each lifespan degradation phase of the sample batteries are determined. Then, the battery model of the sample batteries and the performance parameters at each boundary point are pre-stored as a second mapping relationship. In this way, the second mapping relationship can be obtained only by performing cyclic testing on the sample batteries and determining the lifespan degradation stages, without having to determine the second mapping relationship on the fly during the lifespan prediction process of the batteries to be predicted, thus improving the efficiency of lifespan prediction for the batteries to be predicted.

[0156] In some embodiments of this application, during the process of generating the second mapping relationship, the performance parameters at the boundary points between adjacent stages in each lifetime decay stage of the sample battery can be determined in the following ways:

[0157] Based on the cyclic test data, a curve of the decay slope as a function of battery performance parameters is fitted. The decay slope is used to characterize the amount of decay of battery performance parameters corresponding to a preset number of cycles. Based on this curve, the performance parameters at the boundary points between adjacent stages in each life decay stage of the sample battery are determined.

[0158] The aforementioned battery performance parameters can include, but are not limited to, any one of the following: battery health status, capacity, energy, and energy efficiency. Taking battery health status as an example, the formula for expressing the degradation slope can be: Where k is the attenuation slope, This represents the amount of degradation in battery health. This is the preset number of loops. The value can be, but is not limited to, 10, 15, 20, etc.

[0159] The cycle test data of the sample battery can include parameters such as capacity, voltage, and resistance after each cycle. The state of health (SOH) of the sample battery after each cycle can be obtained by calculating the ratio between the battery's capacity after each cycle and its rated capacity. Then, the state of health is calculated after each preset number of cycles. The amount of degradation in the battery health status of the sample cells after cycling For example, suppose =10, then calculate the difference between the initial state of health (SOH) of the sample battery and the SOH of the sample battery after the 10th cycle, calculate the difference between the SOH of the sample battery after the 20th cycle and the SOH of the sample battery after the 10th cycle, and so on, to obtain multiple... Substituting the values ​​of into the above formula for expressing the attenuation slope, we can obtain multiple sets of ( An array of (k).

[0160] Multiple groups were obtained in the above manner. After obtaining the array (k), we can plot the decay slope k as a function of the battery health state SOH with the horizontal axis as SOH and the vertical axis as k. This curve can well reflect the battery decay law.

[0161] The degradation of the above battery health status Other methods can also be used. For example, the voltage change curve can be fitted based on the voltage of the sample battery after each cycle in the cyclic test data, and the voltage change curve can be analyzed to determine the health status of the battery. The specific analysis method can be based on relevant technologies, which will not be elaborated here.

[0162] The degradation slope described above is expressed based on the amount of degradation in the battery's health state. Alternatively, it can be expressed using battery performance parameters such as capacity, energy, and energy efficiency. The methods for expressing the degradation slope using other battery performance parameters are similar to those using the amount of degradation in the battery's health state, and will not be elaborated upon here.

[0163] Since this variation curve can accurately and intuitively depict the life decay law of the sample battery, it can accurately identify the performance parameters at the boundary points between adjacent stages in each life decay stage of the sample battery, thus improving the accuracy of identifying the performance parameters at the boundary points.

[0164] In some embodiments of this application, specifically through Figure 5 The following method is used to determine the performance parameters at the boundary points between adjacent stages in each lifetime degradation phase of the sample battery:

[0165] Step A1: Based on the cyclic test data, fit the curve of the decay slope as a function of the battery performance parameters; the decay slope is used to characterize the amount of decay of the battery performance parameters corresponding to the preset number of cycles.

[0166] Step A2: Given that the sample battery includes an initial rapid decay phase and a mid-term slow decay phase, determine the performance parameters at the first dividing point between the initial rapid decay phase and the mid-term slow decay phase based on the change curve.

[0167] Step A3: In the case that the sample cell includes a linear decay phase, determine the performance parameters at the second boundary point between the linear decay phase and the adjacent previous decay phase based on the change curve.

[0168] Step A4: In the case that the sample battery includes the final accelerated decay stage, determine the electrolyte consumption rate of the sample battery based on the cycle test data; based on the electrolyte consumption rate and the initial electrolyte filling amount of the sample battery, determine the third boundary point of electrolyte drying of the sample battery, and take the performance parameters at the third boundary point as the performance parameters at the boundary point between the final accelerated decay stage and the adjacent previous decay stage.

[0169] Based on this change curve, the boundary point where the decay slope k changes from rapid to slow is identified. This boundary point is the first dividing point between the initial rapid decay stage and the intermediate slow decay stage. The x-coordinate of this first dividing point on the change curve is taken as the performance parameter at the first dividing point. Since this change curve can accurately and intuitively depict the life decay law of the sample battery, it can accurately identify the first dividing point where the sample battery switches from the initial rapid decay stage to the intermediate slow decay stage, thus improving the accuracy of identifying the performance parameter at the dividing point between the initial rapid decay stage and the intermediate slow decay stage.

[0170] In the case where the sample battery includes a linear decay stage, since the decay slope of the linear decay stage is a fixed value, the linear decay stage in the above change curve will be a line segment parallel to the horizontal axis. Therefore, the second dividing point between the linear decay stage and the adjacent previous decay stage can be easily identified from the above change curve, and the horizontal axis value at the second dividing point is taken as the performance parameter at the second dividing point.

[0171] The linear decay stage occurs because after the SEI film grows to a certain extent, its dissolution rate matches its growth rate, and the SEI film thickness no longer changes. At this point, the battery decay rate remains constant, and the decay follows a linear pattern. However, due to the difficulty in measuring the SEI film, a curve representing the change in the decay slope is fitted using cyclic test data from sample batteries. This curve is then used to identify the second boundary point between the linear decay stage and the adjacent previous decay stage. This transforms the difficult method of measuring the SEI film into a simple and easy-to-operate method of fitting a curve, improving the efficiency and accuracy of identifying the second boundary point.

[0172] The final accelerated degradation stage is a period of rapid performance decline caused by the drying out of the electrolyte. During cycling, electrolyte consumption leads to the loss of active lithium and an increase in side reactions in the electrode materials, thus affecting battery capacity. Therefore, capacity decay can be used as an indirect indicator of electrolyte consumption. Furthermore, as electrolyte is consumed, its conductivity decreases, increasing the resistance to ion transport within the battery and consequently increasing its internal resistance. Therefore, increased internal resistance can also be used as an indirect indicator of electrolyte consumption. The electrolyte consumption rate described above can be determined based on the capacity and / or resistance after each cycle in the cycling test data.

[0173] As an example, a curve showing the relationship between resistance and the number of cycles can be plotted based on the resistance after each cycle in the cyclic test data. The slope of the resistance increase can then be calculated from this curve to infer the electrolyte consumption rate. For instance, if the internal resistance increases linearly with the number of cycles, the slope of the resistance change can be used to estimate the resistance change caused by electrolyte consumption in each cycle, thereby determining the electrolyte consumption rate.

[0174] As another example, a capacity versus cycle count curve can be plotted based on the capacity after each cycle in the cyclic test data. The slope of capacity decay can then be calculated from this curve. This slope can be correlated with the electrolyte consumption rate, as electrolyte consumption is a significant factor contributing to capacity decay. For instance, if the capacity decays linearly with the number of cycles, the electrolyte consumption rate can be estimated using the capacity decay slope.

[0175] As another example, a comprehensive analysis of the capacity and resistance for each cycle allows for the establishment of a multiple linear regression model, with the number of cycles as the independent variable and capacity and resistance as the dependent variables. By fitting the model, the capacity decay coefficient and resistance increase coefficient are obtained. Then, based on the relationship between these two coefficients and the electrolyte consumption rate, the electrolyte consumption rate can be determined. This method, because it simultaneously considers the relationship between capacity and resistance and the electrolyte consumption rate, can improve the accuracy of estimating the electrolyte consumption rate.

[0176] In addition to the various examples given above, electrolyte consumption rates can also be obtained using methods from related technologies, which will not be elaborated here.

[0177] After obtaining the electrolyte consumption rate of the sample battery, the ratio between the initial electrolyte fill amount and the electrolyte consumption rate can be calculated. This ratio represents the number of cycles required for the initial electrolyte fill amount to be consumed and the battery to dry out. The boundary point corresponding to this number of cycles is taken as the third boundary point for electrolyte drying in the sample battery. From the cycle test data of the sample battery, parameters such as capacity, resistance, or voltage corresponding to this number of cycles are obtained. Based on these parameters corresponding to this number of cycles, the performance parameter value corresponding to this number of cycles is calculated, and this performance parameter value is taken as the performance parameter at the third boundary point.

[0178] The above process fully considers the impact of electrolyte on battery life degradation. It uses cycle test data to determine the electrolyte consumption rate of the sample battery, estimates the timing of electrolyte drying in the sample battery, and then determines the performance parameters at the third boundary point between the final accelerated degradation stage and the adjacent previous degradation stage. This enables rapid and accurate identification of the performance parameters at the boundary point between the final accelerated degradation stage and the adjacent previous degradation stage.

[0179] Using the above method, performance parameters at the boundary points between any two adjacent stages in each lifespan degradation phase of the sample battery are identified in advance based on the cyclic test data of the sample battery. A second mapping relationship between battery model and the performance parameters at each boundary point is pre-stored. Thus, when predicting the lifespan of the battery to be predicted, the corresponding performance parameters at each boundary point can be directly retrieved from the second mapping relationship based on the battery model. Then, based on the battery's cyclic test data in the first stage and the retrieved performance parameters at each boundary point, the battery's lifespan degradation curve is generated. The first stage can be any lifespan degradation stage of the battery to be predicted.

[0180] In some embodiments of this application, the battery life degradation curve can be generated in the following ways: fitting a first life function relationship of the first stage based on the battery's cycle test data in the first stage; obtaining the life function relationship of each stage of the battery's life degradation stages other than the first stage based on the first life function relationship and the performance parameters at each boundary point; and plotting the battery life degradation curve based on the life function relationship of each life degradation stage of the battery.

[0181] The cycle test data from the first stage mentioned above can be obtained by cyclically testing the battery at a preset temperature and the battery's nominal rate. The meanings of the preset temperature and nominal rate have been described above and will not be repeated here.

[0182] The first lifetime function relationship of the first stage can be expressed as the following formula (1):

[0183] ...(1)

[0184] in, Let n be the remaining battery life in the first stage, and n be the number of cycles. , , The coefficients to be identified are denoted as .

[0185] Based on the battery's first-stage cycle test data, multiple sets of (S, n) were obtained. Substituting these multiple sets of (S, n) into formula (1) yielded a set of equations. Solving this set of equations yielded the following results. , , The value to be obtained. , , Substituting into formula (1) yields the lifetime function relationship for the first stage.

[0186] After obtaining the first lifetime function relationship, the lifetime function relationships of the battery in other stages besides the first stage are obtained by combining the performance parameters at each boundary point, and then the battery lifetime degradation curve is plotted.

[0187] In the embodiments of this application, only the cycle test data of the battery in the first stage is needed, without the need to obtain cycle test data for each stage of the battery's entire life cycle. This reduces the workload of cycle testing and the amount of cycle test data to be collected, thereby lowering costs. By using the cycle test data from the first stage and combining it with the performance parameters at the boundary points between adjacent stages in each stage of battery life degradation, the battery's life degradation curve can be obtained, which helps to improve the efficiency and accuracy of predicting the life degradation curve.

[0188] In some embodiments of this application, the lifetime function relationship for lifetime decay stages other than the first stage can be obtained in the following ways:

[0189] Based on the first lifetime function relationship, the slopes of the lifetime decay curves of the first stage and the second stage are equal at the first boundary point between the first stage and the second stage, and the lifetime values ​​of the first stage and the second stage are both equal to the performance parameters at the first boundary point, the second lifetime function relationship of the second stage is obtained; the second stage is any stage adjacent to the first stage among the lifetime decay stages included in the battery.

[0190] Since battery lifespan degradation is continuous over time, the boundary point between any two adjacent degradation stages is also the intersection point of those two adjacent degradation stages. At this intersection point, the slopes of the degradation curves corresponding to the two adjacent stages are equal, and the lifetime values ​​of the two adjacent stages at this intersection point are also the same. Based on this correlation between adjacent degradation stages, if the lifetime function relationship of one of the two adjacent degradation stages is known, a system of equations can be established based on the identical slope and lifetime value at the boundary point. This allows us to identify the coefficients to be identified in the lifetime function relationship of the unknown stage among the two adjacent degradation stages, thereby obtaining the lifetime function relationship of the unknown stage.

[0191] As an example, let's assume the lifetime function relationship of the first stage mentioned above. It is known that the first stage is a medium-term slow decay stage. The battery includes a second stage adjacent to the first stage, which is a linear decay stage. The lifetime function relationship of the second stage can be expressed as the following formula (2):

[0192] ...(2)

[0193] In formula (2), To represent the remaining battery life in the second stage, where n is the number of cycles. , The coefficients to be identified are denoted as .

[0194] Based on the fact that the slopes of the first and second stages are the same at the dividing point, the formula is constructed as follows:

[0195] ...(3)

[0196] In formula (3), Let f(x) represent the derivative of formula (1) at the boundary between the first and second stages, which is equal to the slope of the first stage at that boundary.

[0197] Based on the fact that the lifetime values ​​at the boundary point are the same for both the first and second stages, the formula is constructed as follows:

[0198] = ...(4)

[0199] In formula (4), This indicates the number of cycles at the boundary between the first and second phases.

[0200] In formulas (3) and (4), only the coefficients to be identified are present. , It is unknown; the coefficients are obtained by solving formulas (3) and (4). , Substituting this into formula (2), we obtain the lifetime function relationship for the second stage.

[0201] For adjacent lifespan degradation stages, if the lifespan function relationship of one stage is known, the correlation between adjacent degradation stages at their boundary point can be used to solve for the unknown lifespan function relationship of the remaining stage. Therefore, for all lifespan degradation stages of a battery, after obtaining the lifespan function relationship of any one stage, the correlation between adjacent degradation stages can be used to quickly obtain the lifespan function relationships for each stage. Thus, by collecting cyclic test data for one lifespan degradation stage and fitting the lifespan function relationship for that stage, the lifespan function relationships for all stages of battery lifespan can be obtained, leading to the lifespan degradation curve for the entire battery lifespan. This significantly improves the efficiency of battery lifespan prediction, reduces the amount of data collection required for lifespan prediction, lowers costs, and simultaneously improves the accuracy of lifespan prediction.

[0202] To facilitate understanding of the battery life degradation curve prediction method provided in the embodiments of this application, examples are given below. For instance... Figure 6 As shown, taking lithium iron phosphate batteries as an example, the complete process of generating a life prediction curve for lithium iron phosphate batteries is illustrated.

[0203] S1: Based on the battery model, determine the various lifespan degradation stages of the battery from the pre-stored first mapping relationship.

[0204] The first mapping relationship includes the mapping relationship between battery model and life degradation stage.

[0205] S2: Based on the battery model, determine the battery performance parameters at the boundary points between adjacent life decay stages from the pre-stored second mapping relationship.

[0206] The second mapping relationship includes the mapping relationship between the battery model and the performance parameters at each dividing point.

[0207] S3: Obtain cycle test data for the battery during the mid-term slow degradation phase.

[0208] For any type of lithium iron phosphate battery, there are typically at least two phases: an initial rapid degradation phase and a mid-term slow degradation phase. In the initial stage of use, the SEI film is thin, and its formation and growth are rapid, accelerating the consumption of active lithium in the battery; this is the initial rapid degradation phase. As usage time increases, the SEI film structure becomes relatively stable, and its growth rate slows down, leading to a decrease in the rate of active lithium consumption; this is called the mid-term slow degradation phase.

[0209] In this example, we will first determine the lifetime function relationship during the mid-term slow degradation phase. The battery can be cycled at 50°C using its nominal rate during the mid-term slow degradation phase to obtain cycle test data.

[0210] The mid-term slow degradation stage can be the stage entered when the battery's remaining lifespan reaches a preset value. This preset value can be represented by performance parameters such as battery health, capacity, energy, and energy efficiency. Taking battery health as an example, the preset value can be, but is not limited to, 95% SOH, 93% SOH, etc. This example uses a preset value of 95% SOH.

[0211] S4: Based on the cycle test data of the battery in the mid-term slow degradation stage, fit the lifetime function relationship in the mid-term slow degradation stage.

[0212] In this example, taking the battery's state of health (SOH) as the performance parameter, the lifetime function relationship during the mid-term slow degradation phase can be expressed as:

[0213] —Equation 1

[0214] Equation 1 describes the degradation trend of battery health during the medium-term slow degradation phase, where The parameter to be identified is SOH, which is the vertical axis of the decay curve (which can represent the capacity retention rate), and n is the horizontal axis of the decay curve (which can represent the number of cycles).

[0215] Substituting multiple sets of cycle counts and capacity data from the cyclic test data of the mid-term slow decay phase into Equation 1, we can fit the result. These three parameters complete the description of the lifetime function relationship during the mid-term slow degradation phase. Based on the lifetime function relationship during the mid-term slow degradation phase, the lifetime degradation curve segment of the battery during this phase can be plotted.

[0216] S5: Utilize the lifetime function relationship of the mid-term slow decay stage, and the battery performance parameters at the boundary between the mid-term slow decay stage and the initial rapid decay stage, to obtain the lifetime function relationship of the initial rapid decay stage.

[0217] The lifetime function relationship during the initial rapid decay phase can be described as follows:

[0218] —Equation 2

[0219] Equation 2 describes the degradation trend of the battery's health state during the initial rapid degradation phase, where... The parameters to be identified.

[0220] Since the lifetime decay curves corresponding to the initial rapid decay phase and the intermediate slow decay phase intersect at the dividing point, their slopes are equal at the dividing point. The slope of the initial rapid decay phase at the dividing point is the derivative. The slope of the slow decay phase at the boundary point is the derivative. Construct the equation:

[0221] —Equation 3

[0222] The lifetime decay curves corresponding to the initial rapid decay phase and the intermediate slow decay phase have the same lifetime value at the dividing point, and the number of cycles at the dividing point is... Construct the equation:

[0223] —Equation 4

[0224] —Equation 5

[0225] Solve equations 1-5 and identify Substituting this into Equation 2, we obtain the lifetime function relationship for the initial rapid decay stage.

[0226] Due to factors such as electrolyte filling amount and performance lower limit threshold, different batteries experience different lifespan degradation stages. If step S1 determines that the battery includes only a linear degradation stage besides the initial rapid degradation stage and the intermediate slow degradation stage, then steps S6 and S8 are executed sequentially. If, besides the initial rapid degradation stage and the intermediate slow degradation stage, the battery includes only an accelerated degradation stage at the end, then steps S7 and S8 are executed sequentially. If, besides the initial rapid degradation stage and the intermediate slow degradation stage, the battery also includes a linear degradation stage and an accelerated degradation stage at the end, then steps S6-S8 are executed sequentially.

[0227] S6: Utilize the lifetime function relationship of the mid-term slow decay stage, and the battery performance parameters at the boundary between the mid-term slow decay stage and the linear decay stage corresponding to the battery, to obtain the lifetime function relationship of the linear decay stage.

[0228] The linear decay phase can be described as follows:

[0229] —Equation 6

[0230] in, The parameter to be identified is SOH, which is the vertical axis of the decay curve (representing capacity retention rate), and n is the horizontal axis of the decay curve (representing the number of cycles).

[0231] Since the lifetime decay curves corresponding to the intermediate slow decay stage and the linear decay stage intersect at the boundary point, their slopes are equal at the boundary point. The slope of the intermediate slow decay stage at the boundary point is expressed as the derivative. The slope of the linear decay phase at the boundary point is... Construct the equation:

[0232] —Equation 7

[0233] Since the lifetime decay curves corresponding to the intermediate slow decay phase and the linear decay phase have the same lifetime value at the dividing point, the number of cycles at the dividing point is expressed as follows: Construct the equation:

[0234] —Equation 8

[0235] —Equation 9

[0236] Solve equations 1, 6~9 to complete the parameterization. The identification of the function and its substitution into Equation 6 yield the lifetime function relationship of the linear decay stage.

[0237] S7: By utilizing the lifetime function relationship of the linear decay stage and the battery performance parameters at the boundary between the linear decay stage and the final accelerated decay stage, the lifetime function relationship of the final accelerated decay stage can be obtained.

[0238] The final accelerated decay phase can be described as follows:

[0239] —Equation 10

[0240] in, The parameter to be identified is SOH, which is the vertical axis of the decay curve (representing capacity retention rate), and n is the horizontal axis of the decay curve (representing the number of cycles).

[0241] The lifetime decay curves corresponding to the linear decay phase and the accelerated decay phase at the end intersect at the dividing point, therefore their slopes are equal at the dividing point. The slope of the linear decay phase is... The slope at the boundary point of the final accelerated decay phase can be expressed as the derivative. Construct the equation:

[0242] —Equation 11

[0243] The lifetime values ​​at the boundary point are equal for the lifetime decay curves corresponding to the linear decay phase and the accelerated decay phase at the end. The number of cycles at the boundary point is expressed as follows: Construct the equation:

[0244] —Equation 12

[0245] —Equation 13

[0246] Solve equations 10-13 to complete the parameterization. The identification of the components and their substitution into Equation 10 yields the lifetime function relationship for the final accelerated decay stage.

[0247] S8: Based on the life function relationship of each life decay stage of the battery, draw the decay curve segment of each life decay stage to obtain the life decay curve of the battery throughout its entire life cycle.

[0248] When the battery's lifespan degradation includes the initial rapid degradation phase, the middle slow degradation phase, the linear degradation phase, and the final accelerated degradation phase, the generated lifespan degradation curve is as follows: Figure 7 As shown.

[0249] If the battery's lifespan degradation stages only include the initial rapid degradation stage and the intermediate slow degradation stage, then the generated lifespan degradation curve will only include... Figure 7 The blue and red curve segments are present, but there are no green or yellow curve segments.

[0250] If the battery's lifespan degradation stages only include the initial rapid degradation stage, the intermediate slow degradation stage, and the linear degradation stage, then the generated lifespan degradation curve will only include... Figure 7 The curves in the image are blue, red, and green, but there is no yellow curve segment.

[0251] If the battery's lifespan degradation stages only include the initial rapid degradation stage, the middle slow degradation stage, and the final accelerated degradation stage, then the generated lifespan degradation curve will only include... Figure 7The curves in the image are blue, red, and yellow, but there is no green curve segment. The red and yellow curve segments are continuous curves.

[0252] Figure 6 The battery life degradation curve prediction process shown is for illustrative purposes only and is based on the prediction method provided in the foregoing embodiments. Figure 6 The content of each step and the execution order of each step can be changed in other ways.

[0253] pass Figure 6 The prediction method shown can quickly determine the battery's lifespan degradation stages based on the battery model, and the performance parameters at the boundary points of each adjacent degradation stage, before generating the battery's lifespan degradation curve. Knowing this information in advance improves the convenience and accuracy of subsequent lifespan prediction. The prediction process only requires collecting cyclic test data during the mid-term slow degradation stage, eliminating the need to collect cyclic test data for each stage of the battery's entire lifespan, significantly reducing data collection volume, lowering costs, and improving processing efficiency. Based on the cyclic test data of the mid-term slow degradation stage, the lifespan function relationship for that stage is first fitted. Then, based on the correlation between adjacent degradation stages at the boundary points, the lifespan function relationship for each degradation stage can be quickly obtained through simple calculations. This reduces the overall computational load of lifespan prediction, occupies fewer computing resources, and allows for rapid and accurate prediction of the battery's lifespan degradation curve.

[0254] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0255] Some embodiments of this application also provide a battery life degradation curve prediction apparatus, which is used to execute the battery life degradation curve prediction method provided in any of the foregoing embodiments. See also Figure 8 As shown, the device includes:

[0256] The first determining module 401 is used to determine the various lifespan degradation stages of the battery based on the battery parameters.

[0257] The second determining module 402 is used to determine the battery performance parameters at the boundary points between adjacent stages in each life decay stage.

[0258] The generation module 403 is used to generate the battery's life decay curve based on the battery's cycle test data in the first stage and the performance parameters at each boundary point. The first stage is any one of the life decay stages.

[0259] The first determining module 401 is used to determine each stage of battery life degradation based on battery parameters including the initial electrolyte filling amount and / or the performance lower limit threshold; wherein the performance lower limit threshold is used to characterize the battery to be discontinued when the battery performance parameters reach the performance lower limit threshold.

[0260] The first determining module 401 is specifically used to determine the target number of cycles required for the initial electrolyte filling amount to be completely consumed based on the initial electrolyte filling amount and the battery's preset electrolyte consumption rate; and to determine the various life decay stages included in the battery based on the target number of cycles.

[0261] The first determining module 401 is specifically used to determine that if the target number of cycles is less than or equal to the first number of cycles required for the battery to reach the starting point of the linear decay stage, the battery includes an initial rapid decay stage, a mid-term slow decay stage, and a final accelerated decay stage; if the target number of cycles is greater than the first number of cycles and less than or equal to the second number of cycles required for the battery to reach the ending point of the linear decay stage, the battery includes an initial rapid decay stage, a mid-term slow decay stage, a linear decay stage, and a final accelerated decay stage; if the target number of cycles is greater than the number of cycles corresponding to the performance lower limit threshold, the first lifetime decay stage combination includes an initial rapid decay stage, a mid-term slow decay stage, and a linear decay stage.

[0262] The first determining module 401 is specifically used to determine, based on the performance parameters at the starting point when the battery enters the linear degradation stage (where the performance lower limit threshold is less than or equal to the starting point), whether the battery includes an initial rapid degradation stage and a mid-term slow degradation stage; based on the performance parameters at the ending point when the performance lower limit threshold is greater than the starting point, whether the performance parameters are less than or equal to the ending point of the linear degradation stage, whether the battery includes an initial rapid degradation stage, a mid-term slow degradation stage, a linear degradation stage, and a final accelerated degradation stage.

[0263] The first determining module 401 is specifically used to determine each life decay stage of the battery by combining a first-stage combination of each life decay stage determined based on the initial electrolyte filling amount and a second-stage combination of each life decay stage determined based on the performance lower limit threshold.

[0264] The first determining module 401 is used to obtain each life decay stage of the battery from a pre-stored first mapping relationship between battery model and life decay stage based on the battery model included in the battery parameters; wherein, the first mapping relationship is determined based on the initial electrolyte filling amount and / or performance lower limit threshold corresponding to the battery model, and the performance lower limit threshold is used to characterize the battery model to stop being used when the battery performance parameters reach the performance lower limit threshold.

[0265] The second determining module 402 is used to obtain the battery performance parameters at the boundary point of adjacent stages in each life decay stage based on the battery model included in the battery parameters, from the second mapping relationship between the battery model and the performance parameters at the boundary point that is stored in advance; wherein, the second mapping relationship is determined based on the cycle test data of the sample battery of the battery model.

[0266] The device also includes a second mapping relationship construction module, which is used to perform cyclic testing on the sample battery at a preset temperature and at the nominal rate of the sample battery to obtain cyclic test data of the sample battery; determine each life decay stage of the sample battery based on the battery parameters of the sample battery; determine the performance parameters at the boundary points of adjacent stages in each life decay stage of the sample battery based on the cyclic test data; and store the battery model and the performance parameters at each boundary point as the second mapping relationship.

[0267] The module is specifically used to fit a curve of the decay slope as a function of battery performance parameters based on cyclic test data; the decay slope is used to characterize the amount of decay of battery performance parameters corresponding to a preset number of cycles; based on the curve of change, the performance parameters at the boundary points between adjacent stages in each life decay stage of the sample battery are determined respectively.

[0268] The module is specifically designed to determine the performance parameters at the first boundary point between the initial rapid decay phase and the intermediate slow decay phase, based on the change curve, when the sample battery includes an initial rapid decay phase and an intermediate slow decay phase; when the sample battery includes a linear decay phase, it determines the performance parameters at the second boundary point between the linear decay phase and the adjacent previous decay phase, based on the change curve; when the sample battery includes a final accelerated decay phase, it determines the electrolyte consumption rate of the sample battery based on cycle test data; and based on the electrolyte consumption rate and the initial electrolyte filling amount of the sample battery, it determines the third boundary point where the electrolyte dries up, and uses the performance parameters at the third boundary point as the performance parameters at the boundary point between the final accelerated decay phase and the adjacent previous decay phase.

[0269] The generation module 403 is used to fit the lifetime function relationship of the first stage based on the cyclic test data of the battery in the first stage; based on the first lifetime function relationship and the performance parameters at each boundary point, obtain the lifetime function relationship of each stage of the battery's lifetime decay stage except for the first stage; and draw the battery's lifetime decay curve based on the lifetime function relationship of each lifetime decay stage of the battery.

[0270] The generation module 403 is specifically used to obtain the second lifetime function relationship of the second stage based on the first lifetime function relationship, the slope of the lifetime decay curves of the first stage and the second stage being equal at the first boundary point between the first stage and the second stage, and the lifetime values ​​of the first stage and the second stage being equal to the performance parameters at the first boundary point; the second stage is any stage adjacent to the first stage among the lifetime decay stages included in the battery.

[0271] The battery life degradation curve prediction device provided in this application embodiment is based on the same inventive concept as the method provided in this application embodiment, and has the same beneficial effects as the method used, operated or implemented.

[0272] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0273] Other embodiments of this application provide an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method for predicting the battery life degradation curve of any of the above embodiments.

[0274] like Figure 9 As shown, the electronic device 60 may include: a processor 600, a memory 601, a bus 602 and a communication interface 603. The processor 600, the communication interface 603 and the memory 601 are connected through the bus 602. The memory 601 stores a computer program that can run on the processor 600. When the processor 600 runs the computer program, it executes the method provided in any of the foregoing embodiments of this application.

[0275] The memory 601 may include high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device. Communication between the device network element and at least one other network element is achieved through at least one communication interface 603 (which may be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0276] Bus 602 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. The memory 601 is used to store programs. After receiving an execution instruction, the processor 600 executes the program. The methods disclosed in any of the foregoing embodiments of this application can be applied to the processor 600, or implemented by the processor 600.

[0277] The processor 600 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 600 or by instructions in software form. The processor 600 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), an Off-the-shelf Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 601. Processor 600 reads the information in memory 601 and, in conjunction with its hardware, completes the steps of the above method.

[0278] The electronic devices and methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0279] Other embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the methods of any of the above embodiments.

[0280] The computer-readable storage medium provided in the embodiments of this application and the method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods used, operated or implemented therein.

[0281] This application also provides a computer program product corresponding to the method provided in the foregoing embodiments. The computer program product includes a computer program that is executed by a processor to implement the method for predicting battery life degradation curves provided in the foregoing embodiments.

[0282] The computer program products provided in the above embodiments of this application and the methods provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0283] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0284] It should be noted that:

[0285] The term "module" is not intended to be limited to a particular physical form. Depending on the specific application, a module can be implemented as hardware, firmware, software, and / or a combination thereof. Furthermore, different modules may share common components or even be implemented using the same components. Clear boundaries may or may not exist between different modules.

[0286] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. Various general-purpose devices can also be used with the examples based on this. The required structure for constructing such devices is obvious from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0287] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0288] The above embodiments merely illustrate the implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting battery life degradation curves, characterized in that, include: Based on the battery parameters, determine the various lifespan degradation stages of the battery; Determine the battery performance parameters at the boundary points between adjacent stages in each of the lifespan decay stages; Based on the battery's cycle test data in the first stage and the performance parameters at each of the boundary points, a life decay curve for the battery is generated, wherein the first stage is any one of the life decay stages. The determination of each stage of battery life degradation based on battery parameters includes: Based on the initial electrolyte fill amount of the battery and the preset electrolyte consumption rate of the battery, the target number of cycles required for the initial electrolyte fill amount to be completely consumed is determined; based on the target number of cycles and the number of cycles corresponding to the first cycle point at the start of the linear degradation stage, the second cycle point at the end of the linear degradation stage, and the number of cycles corresponding to the performance lower limit threshold, the various lifespan degradation stages of the battery are determined by numerical comparison; and / or, Based on the performance lower limit threshold, the performance parameters at the starting point, and the performance parameters at the ending point, the various lifespan degradation stages of the battery are determined by comparing their numerical values; the performance lower limit threshold is used to characterize the situation where the battery is no longer used when its performance parameters reach the performance lower limit threshold.

2. The method according to claim 1, characterized in that, The battery's lifespan degradation stages are determined by comparing the target number of cycles with the first number of cycles at the start of the linear degradation stage, the second number of cycles at the end of the linear degradation stage, and the number of cycles corresponding to reaching the performance lower limit threshold. These stages include: If the target number of cycles is less than or equal to the first number of cycles required for the battery to reach the starting point of the linear decay phase, then the battery is determined to include an initial rapid decay phase, a middle slow decay phase, and a final accelerated decay phase. If the target number of cycles is greater than the first number of cycles and less than or equal to the second number of cycles required for the battery to reach the end point of the linear decay stage, then the battery is determined to include the initial rapid decay stage, the intermediate slow decay stage, the linear decay stage, and the final accelerated decay stage. If the target number of cycles is greater than the number of cycles corresponding to the lower performance threshold, then the battery is determined to include the initial rapid degradation stage, the intermediate slow degradation stage, and the linear degradation stage.

3. The method according to claim 1, characterized in that, Based on the performance lower limit threshold, the performance parameters at the starting point, and the performance parameters at the ending point, the various lifespan degradation stages of the battery are determined, including: Based on the performance parameters at the starting point of the battery entering the linear degradation stage, which are less than or equal to the lower performance threshold, the battery is determined to include an initial rapid degradation stage and a mid-term slow degradation stage. Based on the fact that the performance parameter at the starting point is greater than the performance parameter at the end point of the linear decay stage, the battery is determined to include the initial rapid decay stage, the intermediate slow decay stage, and the linear decay stage. Based on the performance parameters at the end point being greater than the lower performance threshold, the battery is determined to include the initial rapid decay stage, the intermediate slow decay stage, the linear decay stage, and the final accelerated decay stage.

4. The method according to any one of claims 1-3, characterized in that, Based on the battery parameters, the various lifespan degradation stages of the battery are determined, including: The battery's lifespan decay stages are determined by a first-stage combination based on the initial electrolyte filling amount and a second-stage combination based on the performance lower limit threshold, the performance parameters at the starting point, and the performance parameters at the ending point, determined by numerical comparison.

5. The method according to claim 1, characterized in that, The battery parameters, based on the battery itself, determine the various stages of battery life degradation, including: Based on the battery model included in the battery parameters, each life decay stage of the battery is obtained from the first mapping relationship between the pre-stored battery model and the life decay stage. The first mapping relationship is determined based on the initial electrolyte filling amount and / or performance lower limit threshold corresponding to the battery model. The performance lower limit threshold is used to characterize the battery model to stop being used when its performance parameters reach the performance lower limit threshold.

6. The method according to any one of claims 1-3 and 5, characterized in that, The step of determining the battery performance parameters at the boundary points between adjacent stages in each of the lifespan degradation stages includes: Based on the battery model included in the battery parameters, the battery performance parameters at the boundary point of each life decay stage are obtained from the second mapping relationship between the pre-stored battery model and the performance parameters at the boundary point. The second mapping relationship is determined based on the cycle test data of sample batteries of the battery model.

7. The method according to claim 6, characterized in that, The process of constructing the second mapping relationship includes: The sample battery is subjected to a cycle test at a preset temperature and at the nominal rate to obtain the cycle test data of the sample battery. Based on the battery parameters of the sample battery, determine each stage of the sample battery's lifespan degradation. Based on the cyclic test data, the performance parameters at the boundary points between adjacent stages in each lifetime decay stage of the sample battery are determined respectively. The battery model and the performance parameters at each of the dividing points are stored as the second mapping relationship.

8. The method according to claim 7, characterized in that, The step of determining the performance parameters at the boundary points between adjacent stages in each lifetime degradation stage of the sample battery based on the cyclic test data includes: Based on the cycle test data, a curve of the decay slope as a function of battery performance parameters is fitted; the decay slope is used to characterize the amount of decay of battery performance parameters corresponding to a preset number of cycles. Based on the aforementioned variation curves, the performance parameters at the boundary points between adjacent stages in each lifetime decay stage of the sample battery are determined.

9. The method according to claim 8, characterized in that, The step of determining the performance parameters at the boundary points between adjacent stages in each lifetime degradation stage of the sample battery based on the change curve includes: In the case where the sample battery includes an initial rapid decay phase and a mid-term slow decay phase, the performance parameters at the first boundary point between the initial rapid decay phase and the mid-term slow decay phase are determined based on the change curve. In the case where the sample battery includes a linear decay phase, the performance parameters at the second boundary point between the linear decay phase and the adjacent previous decay phase are determined based on the change curve. In the case where the sample battery includes a final accelerated decay phase, the electrolyte consumption rate of the sample battery is determined based on the cycle test data; based on the electrolyte consumption rate and the initial electrolyte filling amount of the sample battery, a third boundary point for electrolyte depletion of the sample battery is determined, and the performance parameters at the third boundary point are used as the performance parameters at the boundary point between the final accelerated decay phase and the adjacent previous decay phase.

10. The method according to any one of claims 1-3 and 5, characterized in that, The process of generating a lifespan degradation curve for the battery based on the battery's cycle test data in the first phase and the performance parameters at each of the threshold points includes: Based on the battery's cycle test data in the first stage, fit the first lifetime function relationship of the first stage; Based on the first lifetime function relationship and the performance parameters at each of the boundary points, the lifetime function relationship of each stage other than the first stage in the lifetime decay stage of the battery is obtained. Based on the lifetime function relationship of each lifetime decay stage of the battery, the lifetime decay curve of the battery is plotted.

11. The method according to claim 10, characterized in that, The step of obtaining the lifetime function relationship for each stage of the battery's lifespan degradation process, excluding the first stage, based on the first lifetime function relationship and the performance parameters at each of the boundary points, includes: Based on the first lifetime function relationship, the slopes of the lifetime decay curves of the first stage and the second stage are equal at the first boundary point between the first stage and the second stage, and the lifetime values ​​of the first stage and the second stage are both equal to the performance parameters at the first boundary point, the second lifetime function relationship of the second stage is obtained; the second stage is any stage adjacent to the first stage among the lifetime decay stages included in the battery.

12. A device for predicting battery life degradation curves, characterized in that, include: The first determining module is used to determine each stage of battery life degradation based on the battery parameters. The second determining module is used to determine the performance parameters of the battery at the boundary point between adjacent stages in each of the life decay stages. The generation module is used to generate the battery's life decay curve based on the battery's cycle test data in the first stage and the performance parameters at each of the boundary points, wherein the first stage is any one of the life decay stages. The first determining module is specifically used to determine the target number of cycles required for the initial electrolyte filling amount to be completely consumed, based on the initial electrolyte filling amount of the battery and the preset electrolyte consumption rate of the battery. Based on the target number of cycles, the number of cycles at the start point of the linear degradation phase, the number of cycles at the end point of the linear degradation phase, and the number of cycles at the performance lower limit threshold, the battery's lifespan degradation stages are determined by numerical comparison; and / or, based on the performance lower limit threshold, the performance parameters at the start point, and the performance parameters at the end point, the battery's lifespan degradation stages are determined by numerical comparison. The performance lower limit threshold is used to characterize the situation where the battery is discontinued when its performance parameters reach the performance lower limit threshold.

13. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the method as described in any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the method as described in any one of claims 1-11.

15. A computer program product, comprising a computer program, characterized in that, The computer program is executed by a processor to implement the method according to any one of claims 1-11.

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