A method and system for online joint estimation of battery remaining capacity, SOC and self-discharge.

Abstract

This invention discloses an online joint estimation method and system for battery remaining capacity, SOC, and self-discharge, relating to the field of electrochemical energy storage power station management technology. This invention uses characteristic points on the voltage curve caused by the lithium-ion / lithium-deintercalation phase transition in lithium batteries as reference points, and estimates battery capacity and SOC online through three steps: feature point identification, calculation of charge / discharge capacity between feature points, and OCV-SOC curve mapping. The feature point identification method extracts features from the voltage-time curve to obtain the voltage value and time corresponding to each feature point; the charge / discharge capacity between each feature point is obtained based on the ampere-hour integration method; the remaining battery capacity is calculated by mapping the capacity between feature points to the OCV-SOC curve and calculating the proportional relationship; further, during operation, using the SOC of the feature point as a reference point, the SOC value at a certain moment is obtained based on the increase in charge / discharge capacity between that moment and the feature point moment.

Description

Technical Field

[0001] This invention relates to the field of electrochemical energy storage power station management technology, and in particular to an online joint estimation method for battery remaining capacity, SOC and self-discharge of lithium battery energy storage systems. Background Technology

[0002] Lithium-ion battery energy storage systems are a crucial component for the effective integration of new energy sources into the power grid and a means of ensuring power quality and grid security, including peak-valley regulation, frequency regulation, and fluctuation mitigation. However, due to the inherent safety risks of lithium-ion batteries, an increasing number of accidents caused by them have raised concerns about their safety. Current lithium-ion battery safety management primarily relies on monitoring data such as voltage, current, and temperature to determine battery safety. However, this threshold-based method struggles to effectively identify and warn of internal short-circuit faults. Alarms are only triggered when the fault evolves to a certain extent, even leading to a thermal runaway chain reaction, at which point effective safety control is difficult. In reality, lithium-ion batteries with internal short circuits generate additional self-discharge during cycling and resting. Effectively identifying abnormal self-discharge characteristics would allow for earlier identification of faulty batteries, preventing thermal runaway.

[0003] The identification of abnormal self-discharge characteristics can be roughly estimated by analyzing the voltage difference or SOC difference between batteries within a certain time window. However, for lithium batteries used in energy storage, most currently use lithium iron phosphate cathode materials, which exhibit minimal voltage variation over a wide SOC range, making it very difficult to determine self-discharge characteristics based on voltage differences. Furthermore, the SOC estimation of lithium iron phosphate batteries is affected by various factors such as material hysteresis, capacity decay, and increased internal resistance. Existing technologies for SOC estimation, such as ampere-hour integration methods, OCV-SOC correction methods, and Kalman filtering methods, all face certain difficulties and are therefore unsuitable for online identification of self-discharge characteristics. Summary of the Invention

[0004] To address the shortcomings of existing technologies in estimating battery capacity and SOC, this invention provides an online joint estimation method for battery remaining capacity, SOC, and self-discharge, which can more accurately identify battery capacity and SOC online and improve the overall safety of the system.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] An online joint estimation method for lithium battery energy storage systems includes the following steps:

[0007] Under the set experimental conditions, charge and discharge tests were performed on the battery under test to obtain the OCV-SOC curve and voltage-time curve of the battery under test.

[0008] Multiple feature points in the OCV-SOC curve generated by the negative electrode delithiation phase transition or the negative electrode lithium insertion phase transition are marked, and the SOC values ​​corresponding to the multiple feature points are obtained.

[0009] The voltage values ​​and times corresponding to the multiple feature points are obtained on the voltage-time curve, and the charge-discharge capacity between each feature point is obtained by the current ampere-hour integration method.

[0010] The charge / discharge capacity between the feature points is mapped to the OCV-SOC curve, and the remaining capacity of the battery under test is obtained according to the proportional relationship.

[0011] The online joint estimation method described above further includes:

[0012] Obtain the charge / discharge capacity increment between a specific time point and a characteristic point time point;

[0013] Based on the remaining capacity of the battery under test and the charge / discharge capacity increment, the estimated SOC value of the battery under test at that moment during a single charge or discharge cycle is obtained.

[0014] The online joint estimation method described above further includes:

[0015] The theoretical SOC value of a single cell of the battery under test after a certain cycle period is obtained by using the current ampere-hour integration method.

[0016] The SOC value corresponding to the feature point is corrected, and the SOC deviation value is obtained by subtracting the theoretical SOC value of the battery cell from the corrected SOC value.

[0017] The self-discharge rate during this cycle is obtained based on the SOC deviation value and the remaining capacity of the battery cell.

[0018] In addition to the online joint estimation method described above, the OCV-SOC curve is obtained from the charge-discharge conditions of the new battery at a rate below 0.02C.

[0019] In addition to the online joint estimation method described above, each feature point in the OCV-SOC curve is obtained and recorded through offline data fitting.

[0020] As described above, online joint estimation methods, and further methods for obtaining multiple feature points, include: data fitting, regression algorithms, particle swarm optimization, or Monte Carlo methods.

[0021] A joint online estimation system includes a battery system, a battery management system (BMS), and an energy management system (EMS). The voltage, current, and temperature data of the battery system are collected in real time by the BMS and uploaded to the EMS. The energy management system (EMS) stores historical battery data. The energy management system (EMS) executes the online joint estimation method described above in real time.

[0022] Compared with the prior art, the advantages of this invention are as follows:

[0023] 1. Existing technologies estimate battery state solely by combining current ampere-hour integral with voltage and OCV curves. However, when applied to lithium iron phosphate (LFP) batteries, issues such as operating plateau voltage and hysteresis mean that even small voltage deviations can lead to significant SOC differences, resulting in substantial estimation errors. This invention establishes a correlation between the battery's internal state and external parameters based on the characteristic point of the lithium intercalation / deintercalation phase transition at the negative electrode of the LFP battery, which is reflected in the voltage curve. Using this characteristic point as a reference, it is unaffected by the hysteresis effect and plateau voltage of the LFP battery, enabling more accurate online identification of battery capacity and SOC.

[0024] 2. Existing technologies for identifying abnormal battery self-discharge typically rely on voltage outlier characteristics. However, after a certain period of aging, batteries exhibit inconsistencies in parameters such as remaining capacity and internal resistance, directly affecting the voltage distribution of each cell and making it difficult to accurately determine the amount of abnormal self-discharge. This invention determines the leakage charge within a battery cycle by measuring the deviation between the State of Charge (SOC) calibrated at feature points and the integral SOC (SOC based on ampere-hours). This method is unaffected by differences in remaining battery capacity and internal resistance, thus more effectively identifying abnormal self-discharge characteristics. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the method for estimating battery remaining capacity, SOC, and self-discharge in an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of the method for mapping characteristic points of online monitoring terminals to the OCV-SOC curve. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, in the embodiments of this invention are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0031] The word “exemplary” as used below means “serving as an example, embodiment, or illustration.” Any embodiment illustrated as an “exemplary” need not be construed as superior to or better than other embodiments.

[0032] To better understand the technical solutions provided in the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions provided in the embodiments of this application are applicable. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided in the embodiments of this application can be flexibly applied according to actual needs.

[0033] The hardware components for implementing the online joint estimation method of the present invention include a lithium battery system, a battery management system (BMS), and an energy management system (EMS). The voltage, current, and temperature data of the battery system are collected in real time by the BMS and uploaded to the EMS. The energy management system (EMS) stores historical battery data. The online joint estimation method is executed in real time by the EMS.

[0034] This invention establishes a correlation between the battery's internal state and external parameters based on the characteristic point of the lithium intercalation / deintercalation phase transition at the negative electrode of a lithium iron phosphate battery, as reflected in the voltage curve. Using this characteristic point as a reference, it is unaffected by the hysteresis effect and plateau voltage of lithium iron phosphate batteries, enabling more accurate online identification of battery capacity and SOC. Furthermore, this invention determines the leakage charge within the battery cycle by calibrating the deviation between the SOC and the ampere-hour integrated SOC using the characteristic point, unaffected by differences in remaining battery capacity and internal resistance, thus more effectively identifying abnormal self-discharge characteristics.

[0035] Example 1:

[0036] See Figure 1 This invention proposes an online joint estimation method for battery capacity, which specifically includes the following steps:

[0037] Step 101: Under the set experimental conditions, perform charge and discharge tests on the battery to be tested, and then obtain the OCV-SOC curve and voltage-time curve of the battery to be tested.

[0038] Specifically, the new battery cell is subjected to a charge-discharge test at a rate below 0.02C in an offline state or in a laboratory to obtain the OCV-SOC curve.

[0039] Step 102: Mark multiple feature points in the OCV-SOC curve caused by the negative electrode delithiation phase transition or the negative electrode lithium insertion phase transition, and obtain the SOC values ​​corresponding to the multiple feature points.

[0040] Specifically, four characteristic points in the OCV-SOC curve caused by the lithium deintercalation / intercalation phase transition at the negative electrode are marked, such as... Figure 2 The feature points are S1, S2, S3, and S4. A fitting method can be used to label the feature points. For example, feature point S1 can be fitted with a bilinear function, and the x-coordinate of the intersection point is the SOC value corresponding to feature point S1. The SOC values ​​of the feature points are denoted as SOC1, SOC2, SOC3, and SOC4, respectively.

[0041] Step 103: Obtain the voltage value and time corresponding to the multiple feature points on the voltage-time curve, and use the current ampere-hour integration method to obtain the charge and discharge capacity between each feature point.

[0042] Specifically, voltage data is collected by the BMS and recorded in the EMS. Using parameter recognition algorithms such as data fitting, regression, particle swarm optimization, and Monte Carlo simulation, features are extracted from the voltage-time curve to obtain the voltage value and time corresponding to the feature points. Figure 2 Time points C1, C2, and C3 correspond to characteristic points S1, S2, and S3, respectively. The charge / discharge capacity between each characteristic point is obtained using ampere-hour integration; for example, the charge / discharge capacity between time points C1 and C3 is denoted as Q. 1-3 .

[0043] Step 104: Map the charge and discharge capacity between the feature points to the OCV-SOC curve, and obtain the remaining capacity of the battery under test according to the ratio.

[0044] Specifically, the capacity between feature points is mapped to the OCV-SOC curve, and the remaining battery capacity is calculated based on the proportional relationship. For example, after mapping time C1 and C3 to the OCV-SOC curve, the corresponding SOCs are SOC1 and SOC3, respectively. Therefore, the remaining battery capacity Q is calculated. 总 =Q 1-3 / (SOC3-SOC1). The remaining capacity estimation method can be performed during each system maintenance, charging or discharging at a rate below 0.5C, with the charge / discharge SOC range encompassing as many feature points as possible. Since some aging paths can cause a slight shift in the S3 feature point, estimating the remaining battery capacity using the capacity between points S1 and S4 can significantly improve the estimation accuracy.

[0045] Example 2

[0046] See Figure 1 The present invention also proposes an online joint estimation method for estimating the SOC value during a single charge or discharge process, which specifically includes the following steps:

[0047] Step 101: Under the set experimental conditions, perform charge and discharge tests on the battery to be tested, and then obtain the OCV-SOC curve and voltage-time curve of the battery to be tested.

[0048] Specifically, the new battery cell is subjected to a charge-discharge test at a rate below 0.02C in an offline state or in a laboratory to obtain the OCV-SOC curve.

[0049] Step 102: Mark multiple feature points in the OCV-SOC curve caused by the negative electrode delithiation phase transition or the negative electrode lithium insertion phase transition, and obtain the SOC values ​​corresponding to the multiple feature points.

[0050] Specifically, four characteristic points in the OCV-SOC curve caused by the lithium deintercalation / intercalation phase transition at the negative electrode are marked, such as... Figure 2The feature points are S1, S2, S3, and S4. A fitting method can be used to label the feature points. For example, feature point S1 can be fitted with a bilinear function, and the x-coordinate of the intersection point is the SOC value corresponding to feature point S1. The SOC values ​​of the feature points are denoted as SOC1, SOC2, SOC3, and SOC4, respectively.

[0051] Step 103: Obtain the voltage value and time corresponding to the multiple feature points on the voltage-time curve, and use the current ampere-hour integration method to obtain the charge and discharge capacity between each feature point.

[0052] Specifically, voltage data is collected by the BMS and recorded in the EMS. Using parameter recognition algorithms such as data fitting, regression, particle swarm optimization, and Monte Carlo simulation, features are extracted from the voltage-time curve to obtain the voltage value and time corresponding to the feature points. Figure 2 Time points C1, C2, and C3 correspond to characteristic points S1, S2, and S3, respectively. The charge / discharge capacity between each characteristic point is obtained using ampere-hour integration; for example, the charge / discharge capacity between time points C1 and C3 is denoted as Q. 1-3 .

[0053] Step 104: Map the charge and discharge capacity between the feature points to the OCV-SOC curve, and obtain the remaining capacity of the battery under test according to the ratio.

[0054] Specifically, the capacity between feature points is mapped to the OCV-SOC curve, and the remaining battery capacity is calculated based on the proportional relationship. For example, after mapping time C1 and C3 to the OCV-SOC curve, the corresponding SOCs are SOC1 and SOC3, respectively. Therefore, the remaining battery capacity Q is calculated. 总 =Q 1-3 / (SOC3-SOC1). The remaining capacity estimation method can be performed during each system maintenance, charging or discharging at a rate below 0.5C, with the charge / discharge SOC range encompassing as many feature points as possible. Since some aging paths can cause a slight shift in the S3 feature point, estimating the remaining battery capacity using the capacity between points S1 and S4 can significantly improve the estimation accuracy.

[0055] Step 105: Obtain the charge / discharge capacity increment between a specific time and a feature point time; based on the remaining capacity of the battery under test and the charge / discharge capacity increment, obtain the estimated SOC value of the battery under test at that time during a single charge or discharge cycle.

[0056] Specifically, for the SOC estimation value during a single charge or discharge process, a certain characteristic point can be selected as a reference, and the SOC value at that moment can be obtained based on the increase in charge / discharge capacity between a certain moment and the characteristic point. For example... Figure 2 At time A, its SOC A =SOC1+Q1-3 / Q 总 .

[0057] Example 3

[0058] See Figure 1 This invention proposes an online joint estimation method for estimating battery self-discharge, which specifically includes the following steps:

[0059] Step 101: Under the set experimental conditions, perform charge and discharge tests on the battery to be tested, and then obtain the OCV-SOC curve and voltage-time curve of the battery to be tested.

[0060] Specifically, corresponding Figure 1 For example, in the case of Zhong 101, a charge-discharge test is performed on the new battery cell at a rate below 0.02C in an offline state or in a laboratory to obtain the OCV-SOC curve.

[0061] Step 102: Mark multiple feature points in the OCV-SOC curve caused by the negative electrode delithiation phase transition or the negative electrode lithium insertion phase transition, and obtain the SOC values ​​corresponding to the multiple feature points.

[0062] Specifically, corresponding Figure 1 In section 102, four characteristic points in the OCV-SOC curve caused by the lithium deintercalation / intercalation phase transition at the negative electrode were marked, such as... Figure 2 The feature points are S1, S2, S3, and S4. A fitting method can be used to label the feature points. For example, feature point S1 can be fitted with a bilinear function, and the x-coordinate of the intersection point is the SOC value corresponding to feature point S1. The SOC values ​​of the feature points are denoted as SOC1, SOC2, SOC3, and SOC4, respectively.

[0063] Step 103: Obtain the voltage value and time corresponding to the multiple feature points on the voltage-time curve, and use the current ampere-hour integration method to obtain the charge and discharge capacity between each feature point.

[0064] Specifically, corresponding Figure 1 In section 103, voltage data was collected via BMS and recorded in EMS. Using parameter recognition algorithms such as data fitting, regression, particle swarm optimization, and Monte Carlo simulation, features were extracted from the voltage-time curve to obtain the voltage value and time corresponding to each feature point. Figure 2 Time points C1, C2, and C3 correspond to characteristic points S1, S2, and S3, respectively. The charge / discharge capacity between each characteristic point is obtained using ampere-hour integration; for example, the charge / discharge capacity between time points C1 and C3 is denoted as Q. 1-3 .

[0065] Step 104: Corresponding Figure 1In step 104, the charge / discharge capacity between the feature points is mapped to the OCV-SOC curve, and the remaining capacity of the battery under test is obtained according to the proportional relationship.

[0066] Specifically, the capacity between feature points is mapped to the OCV-SOC curve, and the remaining battery capacity is calculated based on the proportional relationship. For example, after mapping time C1 and C3 to the OCV-SOC curve, the corresponding SOCs are SOC1 and SOC3, respectively. Therefore, the remaining battery capacity Q is calculated. 总 =Q 1-3 / (SOC3-SOC1). The remaining capacity estimation method can be performed during each system maintenance, charging or discharging at a rate below 0.5C, with the charge / discharge SOC range encompassing as many feature points as possible. Since some aging paths can cause a slight shift in the S3 feature point, estimating the remaining battery capacity using the capacity between points S1 and S4 can significantly improve the estimation accuracy.

[0067] Step 105: Obtain the charge / discharge capacity increment between a specific time and a feature point time; based on the remaining capacity of the battery under test and the charge / discharge capacity increment, obtain the estimated SOC value of the battery under test at that time during a single charge or discharge cycle.

[0068] Specifically, corresponding to Figure 1 In section 105, for estimating the State of Charge (SOC) during a single charge or discharge cycle, a specific characteristic point can be selected as a reference. The SOC value at that moment is obtained based on the increase in charge / discharge capacity between that moment and the characteristic point. For example... Figure 2 At time A, its SOC A =SOC1+Q 1-3 / Q 总 .

[0069] Step 106: Use the current ampere-hour integration method to obtain the theoretical SOC value of a certain battery cell of the battery under test after a certain cycle period; correct the SOC value corresponding to the feature point, and use the difference between the theoretical SOC value of the battery cell and the corrected SOC value to obtain the SOC deviation value; obtain the self-discharge amount in this cycle period based on the SOC deviation value and the remaining capacity of the battery cell.

[0070] Specifically, corresponding to Figure 1 In step 106, after a certain cycle period, the theoretical SOC value of battery cell i is calculated based on the current-ampere-hour integration, and then the difference between this theoretical value and the SOC value after characteristic point correction is obtained to obtain the deviation value ΔSOC. i Multiply by the remaining capacity Q of battery i i Obtain the self-discharge amount within this cycle.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0072] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. An online federated estimation method, characterized in that, The method comprises the following steps: Under the set experimental conditions, the battery to be tested is subjected to charge-discharge test, so as to obtain the OCV-SOC curve and the voltage-time curve of the battery to be tested; The plurality of characteristic points in the OCV-SOC curve due to the negative electrode delithiation phase transition or the negative electrode lithium intercalation phase transition are marked, and the SOC values corresponding to the plurality of characteristic points are obtained; The voltage values and time points corresponding to the plurality of characteristic points on the voltage-time curve are obtained, and the charge-discharge capacity between each characteristic point is obtained by using the current ampere-hour integration method; The charge-discharge capacity between the characteristic points is mapped to the OCV-SOC curve, and the residual capacity of the battery to be tested is obtained according to the proportional relationship; The charge-discharge capacity increment between the set time and the characteristic point time is obtained; According to the residual capacity of the battery to be tested and the charge-discharge capacity increment, the SOC estimation value of the battery to be tested at the time in the single charging or discharging process is obtained; The SOC theoretical value of a certain battery monomer after a certain cycle period of the battery to be tested is obtained by using the current ampere-hour integration method; The SOC value corresponding to the characteristic point is corrected, and the SOC deviation value is obtained by subtracting the SOC theoretical value of the battery monomer from the corrected SOC value; according to the SOC deviation value and the residual capacity of the battery monomer, the self-discharge amount in the cycle period is obtained.

2. The online federated estimation method of claim 1, wherein, The OCV-SOC curve is obtained by the charge-discharge working condition of a new battery below 0.02C rate.

3. The online federated estimation method of claim 1, wherein, Each characteristic point in the OCV-SOC curve is obtained by offline data fitting and recorded.

4. The online federated estimation method of claim 1, wherein, The method for obtaining a plurality of characteristic points comprises any one of data fitting, regression algorithm, particle swarm, Monte Carlo.

5. A line joint estimation system characterized by, It comprises a battery system, a battery management system BMS, and an energy management system EMS, wherein the voltage, current, and temperature data of the battery system are collected by the battery management system BMS in real time and uploaded to the energy management system EMS, the energy management system EMS stores the historical data of the battery, and the energy management system EMS executes the online joint estimation method according to claims 1-4 in real time.

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