Lithium ion battery cell capacity online estimation method and related device

Abstract

The invention discloses a lithium ion battery cell capacity online estimation method and a related device, and the method comprises the steps: collecting CC-CV charging data of a lithium ion battery cell, and constructing a first cycle CV current sequence and a target cycle CV current sequence; the first cycle CV current sequence serves as an original curve, the target cycle CV current sequence serves as a fitting curve, and a determination coefficient R < 2 > is calculated; according to the determination coefficient R < 2 >, the capacity of the single battery is determined on the basis of the relation between the determination coefficient and the maximum available capacity Q < max > of the battery, the method and the related device can conduct online estimation on the capacity of the single lithium ion battery, data requirements are small, complex preprocessing is not needed, and robustness is high.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of lithium ion battery state monitoring, and relates to a lithium ion battery monomer capacity online estimation method and a related device. BACKGROUND

[0002] Lithium ion batteries have become the core energy storage units of electric vehicles and energy storage systems due to their high energy density and long cycle life. Battery capacity, as a core representation parameter of SOH, is crucial for ensuring battery safety and avoiding over-maintenance or insufficient maintenance. At present, data-based capacity estimation methods have become the mainstream choice for BMS because they do not require complex electrochemical models. However, existing methods have significant defects: Poor robustness of health indicators (HI): Existing HI (such as capacity increment curve peak value, charging time) has low correlation with battery aging, is easily disturbed by current measurement noise and temperature fluctuations, leading to decreased estimation accuracy; Data requirements are demanding: Most methods require complete constant current (CC) or CV charging curves, but in actual charging of electric vehicles, only partial charging is often completed due to "range anxiety", and estimation fails when data is incomplete; Complex preprocessing: HI extraction relies on curve fitting (such as IC / DV curve smoothing) or parameter identification, and improper initialization or incorrect smoothing window selection can easily lead to HI distortion; Weak anti-interference ability: Small drift of current sensors can significantly affect HI calculation, thereby amplifying capacity estimation errors.

[0003] The above problems make it difficult for existing methods to adapt to dynamic charging scenarios in electric vehicles, leading to capacity estimation deviations and causing overcharging / overdischarging risks or unnecessary maintenance costs. Therefore, there is an urgent need to develop a lithium ion battery monomer capacity online estimation method with less data requirement, no complex preprocessing, and strong robustness. SUMMARY

[0004] The purpose of the present application is to overcome the above-mentioned shortcomings of the prior art, and to provide a lithium ion battery monomer capacity online estimation method and related device, which can estimate the capacity of lithium ion battery monomers online, with less data requirement, no complex preprocessing, and strong robustness.

[0005] To achieve the above-mentioned purpose, the present application discloses a lithium ion battery monomer capacity online estimation method, comprising: Collecting CC-CV charging data of the lithium ion battery monomer, constructing a first cycle CV current sequence and a target cycle CV current sequence; Taking the first cycle CV current sequence as the original curve and the target cycle CV current sequence as the fitting curve, calculating the determination coefficient R 2 ; According to the decision coefficient R 2 , the capacity of the battery cell is determined based on the relationship between the decision coefficient and the maximum available capacity of the battery Q max .

[0006] Further, it also includes: using Thevenin equivalent circuit model to express the battery dynamics characteristics of the lithium ion battery cell in the CV stage.

[0007] Further, the discretized state equation of the Thevenin equivalent circuit model is:

[0008] wherein, U p is the polarization voltage; T s is the sampling interval; k is the time step; I L is the battery current at the last time step; R p is the polarization resistance; C p is the polarization capacitance; U t is the battery terminal voltage; U OCV (SOC) is the battery OCV calculated by the OCV-SOC function relationship; SOC is the state of charge.

[0009] Further, the decision coefficient R 2 is:

[0010] wherein, n is the total number of data points; f is the data point on the original curve; is the data point on the fitted curve; is the average value of all data points on the original curve.

[0011] Further, when the CV data is insufficient, the attenuation coefficient Z is calculated based on the first current point, the subsequent current points are predicted using the attenuation coefficient Z , and the CV data is then completed, and then the decision coefficient R 2 is calculated.

[0012] Further, the attenuation coefficient Z is:

[0013] wherein, I L , CV , fitted (0) is the initial current point of the target cycle; I L , CV , original (0) is the initial current point of the original curve.

[0014] Further, the method further comprises: calculating an absolute value of a difference between the decision coefficient of the adjacent cycle and the decision coefficient of the current cycle, and when the absolute value is greater than a threshold value, adopting a moving window weighted average to correct the decision coefficient of the current cycle. R 2 Further, the method further comprises: calculating an absolute value of a difference between the decision coefficient of the adjacent cycle and the decision coefficient of the current cycle, and when the absolute value is greater than a threshold value, adopting a moving window weighted average to correct the decision coefficient of the current cycle.

[0015] The application discloses a lithium ion battery monomer capacity online estimation system, comprising: A collection module is configured to collect CC-CV charging data of the lithium ion battery monomer, and construct a first cycle CV current sequence and a target cycle CV current sequence. A calculation module is configured to take the first cycle CV current sequence as an original curve and the target cycle CV current sequence as a fitting curve, and calculate a decision coefficient R 2 ; A determination module is configured to determine the capacity of the battery monomer based on a relationship between the decision coefficient R 2 and a maximum available capacity of the battery. Q max A determination module is configured to determine the capacity of the battery monomer based on a relationship between the decision coefficient

[0016] The application discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize the steps of the lithium ion battery monomer capacity online estimation method.

[0017] The application discloses a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to realize the steps of the lithium ion battery monomer capacity online estimation method.

[0018] The application has the following beneficial effects: The lithium ion battery monomer capacity online estimation method and related device take the first cycle CV current sequence as an original curve and the target cycle CV current sequence as a fitting curve, calculate a decision coefficient R 2， determine the capacity of the battery monomer based on a relationship between the decision coefficient R 2, based on the relationship between the determination coefficient and the maximum available capacity of the battery Q max The application determines the capacity of the battery cell based on the relationship between the determination coefficient and the maximum available capacity of the battery, overcomes the defects of poor robustness, high data requirement, complex preprocessing and weak anti-interference capability in the existing online estimation method of the capacity of the lithium ion battery, and is extremely practical. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the application. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative labor under the premise of the drawings.

[0020] Figure 1 The method flowchart of the application; Figure 2 The capacity estimation result graph of the two batteries in the first group in case one; Figure 3 The current prediction and R 2 The calculation result graph of the two batteries in the first group in case two. Figure 4 The capacity estimation result graph of the two batteries in the first group in case two. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the application will be described clearly and completely in the following with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are some embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without any creative labor are within the protection scope of the application.

[0022] In the description of the application, it should be understood that the terms “include” and “contain” indicate the existence of the described features, whole, steps, operations, elements and / or components, but do not exclude the existence or addition of one or more other features, whole, steps, operations, elements, components and / or sets thereof.

[0023] It should also be understood that the terms used in the specification of the application are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in the specification and the appended claims of the application, unless the context clearly indicates otherwise, the singular forms “a”, “an” and “the” are intended to include the plural forms.

[0024] It should also be further understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term "at least one of" followed by a list of two or more items means any single one of the items in the list, and that the term "one or more of" followed by a list of two or more items means any single one or plurality of the items in the list. In addition, the character " / " is generally used herein as a replacement for the term "or" in expressing one or more of the associated objects before and after the " / ".

[0025] It should be understood that, even though the terms first, second, third, etc. can be used herein to describe various ranges or the like, these ranges are not to be limited to these terms. These terms are only used to distinguish one range from another. For example, a first range could be termed a second range without departing from the scope of the example embodiments.

[0026] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining" or "upon detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]."

[0027] In order to make the purposes, technical solutions, and advantages of the example embodiments of the present application clearer, the following will be a clear and complete description of the technical solutions in the example embodiments of the present application in conjunction with the accompanying drawings of the example embodiments of the present application. Obviously, the described example embodiments are only a part of the example embodiments of the present application, rather than all the example embodiments. The components of the example embodiments of the present application described and shown in the accompanying drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the example embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but merely represents selected example embodiments of the present application. Based on the example embodiments in the present application, all other example embodiments obtained by a person of ordinary skill in the art without creative effort fall within the scope of the present application.

[0028] Various structural schematic diagrams according to the disclosed example embodiments of the present application are shown in the accompanying drawings. These diagrams are not drawn to scale, in which some details are exaggerated for the purpose of clarity, and some details can be omitted. The shapes of various regions, layers, and the relative size and positional relationship therebetween shown in the diagrams are only exemplary, and in actuality, there can be deviations due to manufacturing tolerances or technical limitations, and a person of ordinary skill in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0029] Example One refer to Figure 1 The online capacity estimation method for a single lithium-ion battery cell according to the present invention includes the following steps: 1) During the CV charging stage, CC-CV charging data of individual lithium-ion battery cells are collected. The Thevenin equivalent circuit model is used to describe the battery kinetic characteristics of individual lithium-ion battery cells during the CV stage, based on the high SOC region. U OCV The linear relationship with SOC simplifies the model, and the model parameters are determined offline using the first loop CV data; The discretized state equations of the Thevenin equivalent circuit model are:

[0030] in, U p Polarization voltage; T s The sampling interval; k For time step; I L This represents the battery current at the previous time step. R p Polarization resistor; C p Polarizing capacitor; U t This is the battery terminal voltage (constant during the CV phase). U OCV (SOC) is the battery OCV calculated using the OCV-SOC functional relationship; SOC It is in a charged state.

[0031] Based on high SOC region U OCV The simplified model of the linear relationship with SOC is expressed as follows:

[0032] in, k CV The slope of the OCV-SOC function relationship during the CV charging phase. b CV This is the intercept of the functional relationship.

[0033] 2) Use the first cycle CV current sequence as the original curve and the target cycle CV current sequence as the fitted curve; take the curve before the CV stage. n Calculate the coefficient of determination for each of the current data points. R 2 As a health indicator (HI), verification R 2 The correlation coefficient with capacity is ≥0.96; In this embodiment,n =4, the sampling interval for the CV stage is 60~180s. R 2 The calculation formula is:

[0034] in, n This represents the total number of data points. f These are the data points on the original curve; These are the data points on the fitted curve; The average value of all data points on the original curve is calculated. R 2 The correlation coefficient with the measured battery capacity (≥0.96) ensures a strong correlation between HI and aging status.

[0035] 3) When CV data is insufficient, the attenuation coefficient is calculated based on the first current point. Z To predict subsequent current points and complete the data, repeat step 2) to calculate the coefficient of determination. R 2 ; Attenuation coefficient Z for:

[0036] in, I L , CV , fitted (0) represents the target cycle's first current point; I L , CV , original (0) is the first current point of the original curve.

[0037] The formula for predicting subsequent current points is:

[0038] in, k = 1, 2, …, n -1.

[0039] 4) Calculate the coefficient of determination for adjacent cycles. R 2 The absolute value of the difference between the values ​​is used. If the absolute value is greater than a threshold, an anomaly is determined, and a moving window weighted average is used to correct the anomaly. R 2 ; when m ≥ l hour, l To adjust the window size, a weighted average is used, with weights distributed according to an arithmetic sequence; when the number of iterations... m < lIf so, then use the previous loop. R 2 Substitutional anomaly R 2 .

[0040] 5) Linear function fitting based on historical data R 2 With the battery's maximum usable capacity Q max The relationship will be normal / corrected. R 2 Input the model to obtain the capacity estimate.

[0041] The linear function is:

[0042] in, a Q The slope; b Q For the intercept, through at least 12 pairs ( R 2 , Q max The data is fitted and the model parameters are periodically recalibrated to adapt to battery aging.

[0043] This invention is applicable to commercial lithium-ion batteries with NCA, NCM or LFP as the positive electrode material and graphite as the negative electrode material, and the cutoff current during the CV charging stage is 0.01-0.05C.

[0044] Example 2 To illustrate the estimation process and results of this invention in detail, two typical application examples are presented below. The data in these examples are all from the battery aging dataset of Underwriters Laboratories-Purdue University (UL-PUR), and the test subjects are commercial 18650 lithium-ion batteries.

[0045] Case 1: Capacity estimation in scenarios with sufficient data: This case study aims to verify the estimation accuracy of the present invention when complete early-stage CV charging data is available. Batteries 3 and 4 (Group 1), whose initial capacities are approximately the same as the reference battery (Battery 1), were selected as test subjects. A capacity estimation model based on the data from Battery 1 was pre-stored in the BMS. Q max = 0.4895 R 2 +2.7671. Throughout the aging process of batteries 3 and 4, the BMS was able to completely collect the first four current data points each time the CV charging stage began (Case A). The system directly calculates... R 2 The data, after anomaly correction, is input into the capacity estimation model. (See attached image.)Figure 2 As shown in (b) and (d), the capacity estimation curves (solid lines) of batteries 3 and 4 are in high agreement with the reference capacity curve (dashed line, measured by the ampere-hour integration method). The estimation errors over their entire lifespan are calculated. The mean absolute error (MAE) of the capacity estimation for battery 3 is 0.90%, and the root mean square error (RMSE) is 1.11%; the MAE of the capacity estimation for battery 4 is 0.75%, and the RMSE is 0.91%. These results verify that, under conditions of sufficient data, the method of this invention can achieve high-precision online capacity monitoring with an MAE of less than 1%.

[0046] Case 2: Capacity estimation in scenarios with insufficient data: This case study aims to verify that even under extreme data loss conditions, the present invention can maintain reliable estimation capabilities after data completion through prediction. The test subjects are the same as above (batteries 3 and 4). In the BMS, a data insufficiency scenario is simulated: during each CV charging phase, the system only records the first current data point and forces the subsequent current prediction algorithm (case B) to complete the first four data points, thereby calculating... R 2 As attached Figure 3 (a)- Figure 3 As shown in (d), the error range between the predicted and measured current values ​​based on the first measured point is controlled within -0.08A to 0.01A. Based on this, the calculated... R 2 Compared with calculations based on complete data R 2 The error shall not exceed ±0.06. (See attached document) Figure 4 (b) and Figure 4 As shown in (d), even in the case of insufficient data, the capacity estimation curves of batteries 3 and 4 can still follow the reference curve well. The statistical results of the estimation error over their entire life cycle are as follows: the MAE of battery 3 is 1.33%, and the RMSE is 1.56%; the MAE of battery 4 is 0.93%, and the RMSE is 1.10%. This result proves that the present invention can still control the MAE and RMSE of capacity estimation to approximately below 1.5% in the case of insufficient data, maintaining the effectiveness and practicality of the method.

[0047] As demonstrated by the above examples, this invention can achieve high-precision and robust online estimation of the capacity of a single lithium-ion battery cell in both typical application scenarios of "sufficient data" and "insufficient data," effectively improving the state monitoring capability of the BMS in dynamic charging scenarios.

[0048] Example 3 The online capacity estimation system for a single lithium-ion battery cell according to the present invention includes: The acquisition module is used to acquire CC-CV charging data of lithium-ion battery cells and construct the first cycle CV current sequence and the target cycle CV current sequence. The calculation module is used to calculate the coefficient of determination by taking the first cycle CV current sequence as the original curve and the target cycle CV current sequence as the fitted curve. R 2 ; The determining module is used to determine the coefficients. R 2 Based on the coefficient of determination and the maximum usable capacity of the battery Q max The relationship between the battery cells determines the capacity of each individual cell.

[0049] The module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0050] Example 4 A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the online capacity estimation method for a single lithium-ion battery cell, for example including: acquiring CC-CV charging data of a single lithium-ion battery cell; constructing a first-cycle CV current sequence and a target-cycle CV current sequence; using the first-cycle CV current sequence as the original curve and the target-cycle CV current sequence as the fitted curve; and calculating the coefficient of determination. R 2 According to the determination coefficient R 2 Based on the coefficient of determination and the maximum usable capacity of the battery Q max The relationship between the memory and the processor determines the capacity of each battery cell. The memory may include main memory, such as high-speed random access memory, or non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which can be an industry-standard architecture bus, a peripheral component interconnection standard bus, or an extended industry-standard architecture bus. The bus can be categorized as an address bus, data bus, or control bus. The memory stores programs; specifically, the programs may include program code, which includes computer operation instructions. The memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0051] Example 5 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the online capacity estimation method for a single lithium-ion battery cell, for example including: acquiring CC-CV charging data of a single lithium-ion battery cell, constructing a first-cycle CV current sequence and a target-cycle CV current sequence; using the first-cycle CV current sequence as the original curve and the target-cycle CV current sequence as the fitted curve, and calculating the coefficient of determination. R 2 According to the determination coefficient R 2 Based on the coefficient of determination and the maximum usable capacity of the battery Q max The relationship between the battery cells is used to determine their capacity. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a processFigure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0057] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

[0058] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for online estimation of lithium-ion battery cell capacity, characterized in that, The method comprises the following steps: The CC-CV charging data of the lithium ion battery cell is collected to construct a first cycle CV current sequence and a target cycle CV current sequence; The determination coefficient is calculated by taking the first cycle CV current sequence as the original curve and the target cycle CV current sequence as the fitting curve R 2 ; According to the decision coefficient R 2 , the capacity of the battery cell is determined based on the relationship between the decision coefficient and the maximum available capacity of the battery Q max .

2. The method of online estimation of lithium-ion battery cell capacity according to claim 1, wherein, The method further comprises the following steps: The battery dynamics characteristics of the lithium ion battery cell in the CV stage are expressed by using a Thevenin equivalent circuit model.

3. The method of online capacity estimation of a lithium-ion battery cell according to claim 2, wherein, The discrete state equation of the Thevenin equivalent circuit model is as follows: wherein, U p is the polarization voltage; T s is the sampling interval; k is the time step; I L is the previous time step battery current; R p is the polarization resistance; C p is the polarization capacitance; U t is the battery terminal voltage; U OCV (SOC) is the battery OCV calculated through the OCV-SOC function relationship; SOC is the state of charge.

4. The method of online capacity estimation of Li-ion battery cells according to claim 1, characterized in that, The decision coefficient R 2 is: wherein, n is the total number of data points; f is the data point on the original curve; is the data point on the fitted curve; is the average of all data points on the original curve.

5. The method of online capacity estimation of Li-ion battery cells according to claim 1, characterized in that, When CV data is insufficient, decay coefficient is calculated based on the first current point ζ , the subsequent current point is predicted by using the decay coefficient ζ , and the CV data is completed, and then the determination coefficient is calculated R 2 .

6. The method of online capacity estimation of a lithium-ion battery cell according to claim 5, wherein, The attenuation coefficient ζ is: wherein I L , CV , fitted (0) is the first current point of the target cycle; I L , CV , original (0) is the first current point of the original curve.

7. The method of online capacity estimation of Li-ion battery cells according to claim 1, characterized in that, The method further comprises the following steps: calculating a decision coefficient for a current cycle R 2 an absolute value of a difference between the decision coefficient of the adjacent cycle and the decision coefficient of the current cycle, and when the absolute value is greater than a threshold value, correcting the decision coefficient of the current cycle by using a moving window weighted average.

8. A lithium-ion battery cell capacity on-line estimation system, characterized by, The method comprises the following steps: The CC-CV charging data of the lithium ion battery cell is collected to construct a first cycle CV current sequence and a target cycle CV current sequence; A computing module is configured to calculate a determination coefficient by taking the first cycle CV current sequence as a raw curve and the target cycle CV current sequence as a fitting curve. R 2 ; determining module configured to determine the capacity of the battery cell based on a relationship between the decision coefficient and a maximum available capacity of the battery cell R 2 Q max ​​ 9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the lithium ion battery cell capacity online estimation method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The processor executes the computer program to realize the steps of the lithium ion battery cell capacity online estimation method according to any one of claims 1-7.

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