Energy storage system health state evaluation method and system based on interleaved voltage measurement

Abstract

The present disclosure belongs to the technical field of energy storage system state monitoring, and particularly relates to a health state evaluation method and system for an energy storage system based on staggered voltage measurement, which comprises the following steps: obtaining a topology of the energy storage system based on staggered voltage measurement to obtain a circuit expression of the staggered voltage measurement topology; performing differential processing on the obtained circuit expression to obtain a state equation of the energy storage system; constructing a time sequence differential matrix according to the obtained state equation; and calculating eigenvalues of the time sequence differential matrix to evaluate the health state of the energy storage system in real time.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of energy storage system state monitoring, and particularly relates to a health state evaluation method and system for an energy storage system based on staggered voltage measurement. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute the prior art.

[0003] When the health state of the energy storage battery system changes abnormally, the battery management system should be able to detect and warn the abnormality in time, and then take necessary isolation or self-healing control measures to avoid further development of the abnormal health state, which leads to the deterioration of the performance of the battery pack and even the damage of the energy storage system. The existing real-time health state evaluation methods for the energy storage system are mainly divided into two types: model-driven and data-driven. Among them, the model-driven real-time health state evaluation method for the energy storage system mainly relies on the residual signal between the measured system characteristics and the model estimated characteristics, and judges the occurrence of system health state abnormality based on the experience detection threshold. The experience selection of the detection threshold does not consider the quantitative correlation between the detection threshold and the measured signal characteristics, which leads to the inability to detect the initial abnormality of the system health state in time. The data-driven real-time health state evaluation method for the energy storage system is mainly the post-diagnosis of the deterministic abnormal state of the system, that is, the classification and recognition of different characteristics between the significant system abnormality and the normal state. Its effectiveness depends on the level of significance of the system health state abnormality, and it is also unable to detect the initial abnormality of the system health state in time.

[0004] According to the inventors, the health state abnormality of the energy storage system is closely related to the battery management functions such as the state of charge estimation and the health state estimation of the energy storage battery pack. Even the initial health state abnormality will lead to the failure of the state of charge estimation and the health state estimation, which is not conducive to the reliable and stable operation of the energy storage system. The reasonable detection, evaluation and positioning of the initial abnormality of the health state of the energy storage system still face great challenges. SUMMARY

[0005] In order to solve the above problems, the present disclosure provides a health state evaluation method and system for an energy storage system based on staggered voltage measurement, which realizes the whole life cycle health state management of the energy storage system based on multi-time scale coupling, and realizes the real-time comprehensive evaluation of the health state of the energy storage system.

[0006] According to some embodiments, the first aspect of the present disclosure provides a health state evaluation method for an energy storage system based on staggered voltage measurement, which adopts the following technical scheme:

[0007] A health state evaluation method for an energy storage system based on staggered voltage measurement, comprising:

[0008] Obtaining an energy storage system topology based on staggered voltage measurement, obtaining a circuit expression of the staggered voltage measurement topology;

[0009] Differentially processing the obtained circuit expression to obtain a state equation of the energy storage system;

[0010] According to the obtained state equation, a time sequence difference matrix is constructed;

[0011] The eigenvalues of the time sequence difference matrix are calculated to real-time evaluate the health state of the energy storage system.

[0012] As a further technical limitation, the energy storage system topology includes a plurality of series-connected energy storage monomer batteries and voltage sensors; the voltage sensors are connected to the positive and negative electrodes of adjacent energy storage monomer batteries.

[0013] Further, the obtained circuit expression of the staggered voltage measurement topology is:

[0014]

[0015] Wherein, {V i s} is the voltage sensor measurement voltage sequence, {V i c} is the voltage of the series-connected energy storage monomer batteries, I bp is the working current of the series-connected energy storage battery pack, R i,i+1 is the wiring resistance between adjacent energy storage monomer batteries, R b,1 and R n,b are the positive and negative buses of the energy storage system, respectively, wherein i∈[1,n].

[0016] Further, the obtained circuit expression of the staggered voltage measurement topology is differentially processed, i.e. the state equation of the energy storage system is

[0017]

[0018] Wherein, for i,j∈[1,n], there are

[0019] Further, the state equation of the energy storage system includes three abnormal state types of battery short circuit state abnormality, voltage sensor state abnormality and wiring state abnormality; the battery short circuit state abnormality information is contained in the monomer battery voltage sequence {V o c}, the voltage sensor state abnormality is contained in the differential staggered measurement voltage sequence , and the wiring state abnormality is contained in the wiring equivalent resistance sequence {R o,i+1}.

[0020] Further, the rank of the vector coefficient matrix is n-1, and the difference equation of the state equation of the energy storage system is After generalization, the generalization multivariate process expression x=As1+Bs2 is obtained, wherein, is a difference measurement voltage vector; is a voltage coefficient matrix, is n battery voltages satisfying sample independent and identically distributed; is a wiring coefficient matrix, is n+1 wiring resistance voltage drops satisfying sample independent and identically distributed.

[0021] Further, based on the obtained generalization multivariate process expression, the time sequence measurement matrix corresponding to the sliding window width w at time k can be expressed as

[0022]

[0023] that is, X k =S 1,k A T +S 2,k B T wherein,

[0024] According to some embodiments, the second aspect of the present disclosure provides an energy storage system health state evaluation system based on staggered voltage measurement, which adopts the following technical solution:

[0025] An energy storage system health state evaluation system based on staggered voltage measurement, comprising:

[0026] An acquisition module configured to acquire an energy storage system topology based on staggered voltage measurement, to obtain a circuit expression of the staggered voltage measurement topology;

[0027] A processing module configured to perform difference processing on the obtained circuit expression to obtain a state equation of the energy storage system;

[0028] A construction module configured to construct a time sequence difference matrix according to the obtained state equation;

[0029] An evaluation module configured to calculate eigenvalues of the time sequence difference matrix and to perform real-time evaluation on the health state of the energy storage system.

[0030] According to some embodiments, the third aspect of the present disclosure provides a computer readable storage medium, which adopts the following technical solution:

[0031] A computer readable storage medium having a program stored thereon, the program being executed by a processor to implement the steps in the energy storage system health state evaluation method based on staggered voltage measurement according to the first aspect of the present disclosure.

[0032] According to some embodiments, the fourth solution of this disclosure provides an electronic device that adopts the following technical solution:

[0033] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the energy storage system health status assessment method based on interleaved voltage measurements as described in the first aspect of this disclosure.

[0034] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0035] This disclosure enables effective real-time assessment of system health status with the goal of efficiently identifying micro-short circuit anomalies, without affecting the individual battery voltage monitoring and state of charge estimation functions of the energy storage system. It can meet the real-time requirements for micro-short circuit anomaly detection and identification, ensure the detectability of system health status anomalies in the early stages, cut off the chain reaction of system thermal runaway and explosion accidents from the source, enhance system control performance, and improve system operation safety and stability. Attached Figure Description

[0036] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0037] Figure 1 This is a flowchart of the energy storage system health status assessment method based on interleaved voltage measurement in Embodiment 1 of this disclosure;

[0038] Figure 2 This is a schematic diagram of the interleaved voltage measurement topology in Embodiment 1 of this disclosure;

[0039] Figure 3 This is the overall flowchart of real-time health status assessment of the energy storage system in Embodiment 1 of this disclosure;

[0040] Figure 4(a) shows an embodiment of this disclosure. The real-time health status assessment index curve corresponding to curve cluster 1;

[0041] Figure 4(b) shows an embodiment of this disclosure. The real-time health status assessment index curve chart corresponding to curve cluster 2;

[0042] Figure 4(c) is the abnormal single cell location result curve in Embodiment 1 of this disclosure;

[0043] Figure 5(a) shows an embodiment of this disclosure. The real-time health status assessment index curve corresponding to curve cluster 1;

[0044] Figure 5(b) shows an embodiment of this disclosure. The real-time health status assessment index curve chart corresponding to curve cluster 2;

[0045] Figure 6 This is a structural block diagram of the energy storage system health status assessment system based on interleaved voltage measurement in Embodiment 2 of this disclosure. Detailed Implementation

[0046] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, 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 disclosure pertains.

[0048] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0049] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0050] Example 1

[0051] Embodiment 1 of this disclosure introduces a method for assessing the health status of an energy storage system based on interleaved voltage measurements.

[0052] like Figure 1 The method for assessing the health status of an energy storage system based on interleaved voltage measurements, as shown, includes:

[0053] Obtain the energy storage system topology based on interleaved voltage measurement, and obtain the circuit expression of the interleaved voltage measurement topology;

[0054] The obtained circuit expression is processed by differential processing to obtain the state equation of the energy storage system;

[0055] Based on the obtained state equations, construct the time difference matrix;

[0056] The eigenvalues ​​of the time-series difference matrix are calculated to assess the health status of the energy storage system in real time.

[0057] The real-time status assessment method for energy storage systems in this embodiment is based on, for example, Figure 2 The alternating voltage measurement topology is shown.

[0058] For an energy storage system with n energy storage cells connected in series, the ith voltage sensor is connected to the positive pole of the ith energy storage cell and the negative pole of the (i+1)th energy storage cell, where i∈[1,n-1]. In particular, for the nth voltage sensor, it is connected to the positive pole of the nth energy storage cell and the negative pole of the 1th energy storage cell.

[0059] Thus, the circuit principle expression corresponding to the interleaved voltage measurement topology is:

[0060]

[0061] where {V i s} is the sensor measurement voltage sequence, {V i c} is the voltage of the energy storage cells connected in series, I bp is the working current of the series energy storage battery pack, R i,i+1 is the connection resistance between the energy storage cell i and the energy storage cell i+1, R b,1 and R n,b are the positive and negative busbars of the energy storage system, respectively. Here, R b,1 and R n,b are considered as a whole, so as shown in equation (6-1), the sampled voltage V i s contains the voltage drop of the connection resistance R i,i+1 , and the sampled voltage V contains the voltage drop of the connection resistances R b,1 and R n,b . The measurement of V i can be realized by a differential measurement circuit to measure the voltage of two non-adjacent cells.

[0062] The measurement topology cannot directly obtain the voltage of each energy storage cell connected in series for battery management, but since the actual connection resistance between the energy storage cells is very small, the voltage drop has negligible effect on the sensor measurement voltage sequence, so under the normal and healthy operating conditions of the system, an effective estimation sequence of the cell voltage can be obtained by inverting equation (1).

[0063] Since the cell voltage {V i c} and the battery pack current I bp vary with the system operating conditions, in order to suppress the influence of the system operating conditions on the health state assessment process, equation (1) is differentiated to obtain equation (2):

[0064]

[0065] Where, for i,j∈[1,n], we have

[0066] Formula (2) includes three typical abnormal state types of energy storage systems: battery short-circuit abnormality, voltage sensor abnormality, and wiring abnormality. As can be seen from Formula (2), the information on the short-circuit abnormality of a single battery is contained in the single battery voltage sequence {V... i c In this context, voltage sensor malfunctions are implied in the differential interleaved voltage measurement sequence. In this context, abnormal wiring conditions are implied in the wiring equivalent resistance sequence {R}. i,i+1 In this context, thanks to the non-unit coefficient matrix structure corresponding to each sequence, the relative positional relationship of the short-circuit state anomaly response channels can directly distinguish the single-cell short-circuit state anomaly from the other two electrical state anomalies. Therefore, as... Figure 2 The energy storage system health status monitoring topology shown can achieve effective real-time assessment of the system health status with the goal of efficiently identifying micro-short circuit anomalies, without affecting the individual battery voltage monitoring and state of charge estimation functions of the energy storage system.

[0067] Since the rank of the vector coefficient matrix in formula (2) is n-1, formula (2) needs to be rearranged into the difference equation form shown in formula (3), that is...

[0068]

[0069] Based on this, a time-series difference matrix can be constructed using formula (3), and a real-time evaluation index for the health status of the energy storage system can be designed based on the eigenvalue analysis of this time-series difference matrix.

[0070] Without loss of generality, combining formula (2), formula (3) can be generalized to:

[0071] x=As1+Bs2 (4)

[0072] in, The voltage vector is measured as a differential component. This is the voltage coefficient matrix. To satisfy the requirement that the voltages of the n individual cells are independently and identically distributed; This is the wiring coefficient matrix. To satisfy the voltage drop of n+1 independent and identically distributed wiring resistors.

[0073] For the generalized multivariate process shown in formula (4), the time series measurement matrix with a sliding window width of w at time k can be expressed as follows:

[0074]

[0075] Thus, the system health state can be obtained

[0076] X k = S 1,k A T + S 2,k B T (6)

[0077] wherein,

[0078] Further, the formula (6) can be normalized as,

[0079]

[0080] wherein, is the mean vector of the reference samples, is the standard deviation diagonal matrix of the reference samples, which can be obtained based on sufficient normal operation state historical samples;

[0081] The covariance matrix of the can be expressed as,

[0082]

[0083] wherein, Λ k = diag{λ 1,k ,λ 2,k ,…,λ n-1,k} and V k are the descending order eigenvalue diagonal matrix and the corresponding eigenvector matrix of the covariance matrix C k , respectively.

[0084] Since the high-order statistical characteristics of the system abnormal state and the normal state are usually orthogonal to each other, different system covariance matrix eigenvalues contain different system state high-order characteristics. Therefore, the system health state anomaly can be evaluated by the eigenvalue to eliminate the influence of the system normal state. In view of this, the energy storage system health state real-time evaluation index D k can be designed as the infinity norm of the normalized covariance matrix maximum eigenvalue to enhance the detection performance of the initial health state anomaly of the system, that is,

[0085]

[0086] wherein, λ 1,k is the maximum eigenvalue of the covariance matrix C k , and and are the corresponding normal state reference eigenvalue mean and standard deviation, respectively.

[0087] The real-time health state assessment method of the energy storage system in this embodiment is mainly aimed at the additive anomaly of single battery micro-short circuit state, and assumes that the anomaly only occurs in a single battery cell, so as to effectively assess the health state anomaly in the initial stage.

[0088] For the micro-short circuit state anomaly in the initial stage, let the additive anomaly f occur in the lth channel s of the single battery voltage vector 1,l Then, formula (4) can be arranged as

[0089] x = A(s1+ ξf) + Bs2 (10)

[0090] wherein, represents the weighted vector of the state anomaly, which is only nonzero in the abnormal state channel, and ‖ξ‖2 = 1.

[0091] Thus, the time series measurement matrix X k can be normalized as

[0092]

[0093] wherein, represents the system measurement matrix component under the normal health state, and the normalized matrix is denoted as

[0094] The corresponding covariance matrix can be represented as

[0095]

[0096] wherein,

[0097]

[0098] Let δ sc represent the micro-short circuit state anomaly assessment threshold, and it is necessary to ensure that Thus, for i ∈ [1, n-1], there is According to the additivity of the matrix trace, there is

[0099]

[0100] Note that C 1,k is the normalized covariance matrix under the health state, and the eigenvalue expectation satisfies Therefore, In addition, for C 2,k , there is Therefore, For C 3,k , wherein, is the lth column of .

[0101] According to formula (2), there is

[0102]

[0103] Considering the influence of the working condition cycle, combined with formula (14) and formula (15), the micro-short circuit state abnormality evaluation threshold should satisfy

[0104]

[0105] Since the energy storage system health state real-time evaluation index D k shown in formula (9) is not related to the Gaussian distribution, the evaluation threshold δ sc in formula (16) can be obtained by empirical method according to the historical data set under the health state. The significance level of the empirical method process can be set to α = 0.01. For the two time series difference measurement matrix as shown in formula (3), the corresponding detectable micro-short circuit state evaluation thresholds are δ sc, and δ sc, respectively. Thus, the final energy storage system real-time health state evaluation threshold δ HS should satisfy

[0106] δ HS = max{δ sc,1 , δ sc,2} (17)

[0107] For the normalized abnormal state amplitude f, the corresponding energy storage system real-time health state evaluation threshold is δ HS . Specifically to the single battery micro-short circuit fault studied in this chapter, based on the zero-order equivalent circuit analysis, the corresponding instantaneous detectable single battery equivalent short circuit resistance R sc,id can be approximately quantified as

[0108]

[0109] where E m is the open circuit voltage of the abnormal single battery under the health state, I sc = f / R sc,id is the approximate short circuit current obtained based on the abnormal state amplitude f, I d is the discharge current, and R 0,SOC is the equivalent series internal resistance of the abnormal single battery under the health state at 25℃. Here, I d = 2I nom , where I nom is the 1C rated current of the energy storage system.

[0110] Since formula (18) is about R sc,idThe implicit expression of the state of health of the system is that the minimum equivalent short-circuit impedance of the state of health of the system is the minimum equivalent short-circuit impedance of the state of health of the system. Therefore, the approximate minimum equivalent short-circuit impedance of the state of health of the system can be obtained by iterative search by bisection method, so that the minimum equivalent short-circuit current to which the health state evaluation method can respond is obtained.

[0111] Once the system micro-short-circuit abnormal state is detected and identified, it is necessary to further determine the specific location of the abnormal state.

[0112] For the normalized time series measurement matrix The empirical eigenvalues {λ k} and the corresponding eigenvectors {v m,k} of the covariance matrix C m,k satisfy

[0113] C k v m,k = λ m,k v m,k , m = 1, …, n-1 (19)

[0114] Where v m,k is the mth column of the eigenvector matrix.

[0115] Therefore, the contribution of the i-th row of the covariance matrix to the eigenvalue λ m,k can be quantified by the i-th element of the corresponding eigenvector v m,k . Further, the location of the initial micro-short-circuit abnormal monomer battery causing D k > δ HS can be evaluated by the normalized contribution η 1,k of the extreme eigenvalue λ i , that is,

[0116]

[0117] The normalized contribution η i satisfies η i ∈(0, 1) and ∑ i η i = 1. Therefore, the micro-short-circuit state abnormal contribution degree of the following two time series differential voltage measurement matrices is determined,

[0118]

[0119] On this basis, if the non-adjacent two elements {η i , η j} satisfying the relationship shown in formula (22) take the maximum value, it can be determined that the initial micro-short-circuit state abnormality of the system occurs in the jth monomer battery. Note that for

[0120] ​

[0121] The overall flow of the real-time evaluation of the health state of the energy storage system for micro-short-circuit anomaly detection and positioning is shown in FIG. 1. The process is mainly divided into the following three steps: initialization step, anomaly state detection and identification step, and anomaly state evaluation and positioning step. In the initialization step, the baseline parameters and the normalized evaluation threshold of the system health state are calculated; based on the estimated detectable micro-short-circuit state anomaly equivalent short-circuit impedance and the detectable equivalent short-circuit current. In the anomaly state detection and identification step, the health state real-time evaluation index can be calculated to determine whether a micro-short-circuit state anomaly occurs in the system. Once the system anomaly state evaluation and positioning step is triggered, the anomaly contribution degree is calculated and the system cell where the state anomaly occurs is further determined. Figure 3

[0122] In this embodiment, the 12S energy storage battery pack is selected to constitute the energy storage system health state operation and simulate the single cell short-circuit anomaly state operation test data; the rated capacity of each series single cell is 32 Ah at 1C discharge rate, and the rated voltage is 3.25 V. Three groups of experiments are performed on the energy storage battery pack, respectively, which are one group of health state operation experiment and two groups of simulated single cell short-circuit anomaly state operation experiment. The experimental baseline condition is the WLTC condition under the 2C charge-discharge rate. Among them, the numbers of the two groups of short-circuit anomaly state single cells are 1 and 9, respectively, to evaluate the performance of the method for different positions of state anomalies.

[0123] The complete discharge-charge cycle single cell measurement voltage curve cluster of the 12S energy storage battery pack is obtained by testing the energy storage system, which is used as the system health state historical sample set; based on the health state historical sample data, the corresponding two clusters of time series differential measurement curve clusters are obtained; although the simple differential operation can significantly reduce the influence of the working condition change on the time series measurement voltage smoothness, so that the system differential voltage sequence fluctuation under the health state is kept within a small fluctuation range, due to the inherent nonlinear characteristics and state of charge correlation of the energy storage system, the influence of the working condition change cannot be completely eliminated. Therefore, the initialization characteristic parameters under the health state of the system need to be calculated to ensure the effectiveness of the real-time health evaluation process of the system.

[0124] According to the corresponding two clusters of time series differential measurement curve clusters, the system health state initialization characteristic parameters can be obtained as shown in Table 1.

[0125] Table 1 Energy storage system health state initialization characteristic parameters

[0126]

[0127]

[0128] Although​ The health state real-time evaluation index D corresponding to the curve cluster 1 k1 Exceeds its corresponding evaluation threshold δ in the evaluation period sc,1 But because The health state real-time evaluation index D corresponding to the curve cluster 2 k2 Does not exceed its corresponding evaluation threshold δ in the evaluation period sc,2 Therefore, D k > δ HS The condition is not established, indicating that the system is running in a healthy state at this time.

[0129] According to the detectable short-circuit state abnormality evaluation method in the embodiment, the calculated detectable equivalent single battery short-circuit impedance is 0.1886Ω, and its corresponding equivalent short-circuit current is about 16.21A (about 0.5C), which meets the system initial short-circuit abnormality state definition (not greater than 1C).

[0130] Accordingly, the power resistors with a parallel resistance of about 0.2Ω of the two groups of single batteries numbered 1 and 5 are simulated to simulate the system initial short-circuit abnormality state, and the input time interval of the equivalent parallel short-circuit resistance is [800s, 1000s].

[0131] (1) 1# single battery short-circuit abnormality state operation evaluation

[0132] For the 1# single battery short-circuit abnormality, the system short-circuit abnormality state operation experiment verification result under the single WLTC working condition cycle based on the health state real-time evaluation method proposed in this chapter is shown in FIG. 4; wherein, FIG. 4(a) and FIG. 4(b) respectively show the health state real-time evaluation index curves of different differential measurement voltage curve clusters, and the health state real-time evaluation indexes of the two curve clusters trigger the evaluation threshold limit at 1.5s and 6.4s respectively, indicating that the system has a short-circuit abnormality state.

[0133] It should be noted that for the 1# single battery short-circuit abnormality, because its abnormal contribution component is 2 in the curve cluster 1 and only 1 in the curve cluster 2, combined with the comprehensive influence of the difference of standard deviations of different differential channels as shown in Table 6.1, the peak difference of FIG. 4(a) and FIG. 4(b) is caused. In addition, the short-circuit state abnormality belongs to a cumulative state abnormality, that is, as the duration of the short-circuit state increases, the measurement voltage difference caused by the short-circuit state will continue to increase. Therefore, on the one hand, when the short-circuit state abnormality is eliminated, the inherent abnormal accumulation will continue to trigger abnormal warning, as shown in FIG. 4(c), if the battery equalization management function configured by the system is considered, the continuous alarm after the short-circuit state abnormality is cleared can be reduced; on the other hand, even if the actual equivalent parallel short-circuit impedance is less than the detectable estimated value of 0.1886Ω, the short-circuit state abnormality will be detected and identified within a limited time, but the lag of the evaluation threshold trigger will be more obvious.

[0134] (2) Operational assessment of No. 5 single battery under short circuit abnormality

[0135] Figure 5 shows the experimental verification results of the system short-circuit anomaly under a single WLTC operating cycle, obtained based on the real-time health status assessment method proposed in this embodiment. Figures 5(a) and 5(b) show the real-time health status assessment index curves for different differential voltage measurement curve clusters. The corresponding real-time health status assessment indices for the two curve clusters trigger the assessment threshold limit at 2.5s and 2.8s, respectively, indicating the presence of a short-circuit anomaly in the system. Unlike the aforementioned experiment on the short-circuit anomaly of single battery 1, for the short-circuit anomaly of single battery 5, the number of anomaly contribution components is the same across different curve clusters, resulting in smaller differences in the response amplitude of the health status assessment index. However, similarly, due to the cumulative effect of the short-circuit anomaly, even after the short-circuit anomaly is eliminated, its inherent anomaly accumulation will continue to trigger anomaly warnings.

[0136] This embodiment proposes a real-time health status assessment method for energy storage systems based on an interleaved voltage measurement topology. This method enables effective real-time assessment of system health status, with the goal of efficiently identifying micro-short-circuit anomalies, without affecting the single-cell voltage monitoring and state of charge estimation functions of the energy storage system. Theoretical and verification analysis results show that the proposed real-time health status assessment method can achieve rapid and accurate assessment of micro-short-circuit anomalies, a major type of health status anomaly. For micro-short-circuit anomalies in single cells at both ends of the system, accurate detection and location of micro-short-circuit states under a 0.5C equivalent short-circuit current can be completed within 6.4 seconds. For micro-short-circuit anomalies in single cells at other ends of the system, accurate detection and location of micro-short-circuit states under a 0.5C equivalent short-circuit current can be completed within 2.8 seconds.

[0137] In summary, the energy storage system health status assessment method proposed in this embodiment can meet the real-time requirements for detecting and identifying micro-short circuit anomalies, ensure the detectability of system health status anomalies in the early stages, cut off the chain reaction of system thermal runaway and explosion accidents from the source, enhance system control performance, and improve system operation safety and stability.

[0138] Example 2

[0139] Embodiment 2 of this disclosure introduces a health status assessment system for energy storage systems based on interleaved voltage measurement.

[0140] like Figure 6 The illustrated energy storage system health status assessment system based on interleaved voltage measurement includes:

[0141] an acquisition module configured to acquire an energy storage system topology based on interleaved voltage measurement, to obtain a circuit expression of the interleaved voltage measurement topology;

[0142] a processing module configured to perform differential processing on the obtained circuit expression, to obtain a state equation of the energy storage system;

[0143] a construction module configured to construct a time-difference matrix according to the obtained state equation;

[0144] an evaluation module configured to calculate eigenvalues of the time-difference matrix, to perform real-time evaluation on a health state of the energy storage system.

[0145] The detailed steps are the same as those in the energy storage system health state evaluation method based on interleaved voltage measurement provided in Embodiment One, and are not described here again.

[0146] Embodiment Three

[0147] The disclosure provides an electronic device.

[0148] The disclosure provides an electronic device.

[0149] The detailed steps are the same as those in the energy storage system health state evaluation method based on interleaved voltage measurement provided in Embodiment One, and are not described here again.

[0150] Embodiment Four

[0151] The disclosure provides an electronic device.

[0152] The disclosure provides an electronic device.

[0153] The detailed steps are the same as those in the energy storage system health state evaluation method based on interleaved voltage measurement provided in Embodiment One, and are not described here again.

[0154] The above merely provides preferred embodiments of the disclosure but not for limiting the disclosure. For those skilled in the art, the disclosure can have various modifications and changes. Any modified, equivalent replacement, improvement, etc. within the spirit and principle of the disclosure shall be included in the protection scope of the disclosure.

[0155] The specific embodiments of the present disclosure are described above with reference to the accompanying drawings, but are not intended to limit the protection scope of the present disclosure, and those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present disclosure without creative labor are still within the protection scope of the present disclosure.

Claims

1. A method for assessing the health status of an energy storage system based on interleaved voltage measurement, characterized in that, include: Obtain the energy storage system topology based on interleaved voltage measurement, and derive its circuit expression. The energy storage system topology includes several energy storage cells connected in series and voltage sensors, with the voltage sensors connected to the positive and negative terminals of adjacent energy storage cells. The circuit expression for the interleaved voltage measurement topology is as follows: in, For measuring voltage sequences using voltage sensors, The voltage of the individual energy storage cells connected in series. This refers to the operating current of the series-connected energy storage battery pack. This refers to the wiring resistance between adjacent individual energy storage cells. and These are the positive and negative buses of the energy storage system, respectively. ; The obtained circuit expression is processed by differential processing to obtain the state equation of the energy storage system; Based on the obtained state equations, construct the time difference matrix; The eigenvalues ​​of the time-series difference matrix are calculated to assess the health status of the energy storage system in real time.

2. The method for assessing the health status of an energy storage system based on interleaved voltage measurement as described in claim 1, characterized in that, By performing differential processing on the circuit expression of the obtained interleaved voltage measurement topology, the state equation of the energy storage system is obtained as follows: Among them, for ,have .

3. The method for assessing the health status of an energy storage system based on interleaved voltage measurement as described in claim 2, characterized in that, The state equation of the energy storage system includes three abnormal state types: battery short-circuit abnormality, voltage sensor abnormality, and wiring abnormality; the battery short-circuit abnormality information is contained in the voltage sequence of individual cells. In this context, the voltage sensor malfunction is implied in the differential interleaved voltage measurement sequence. In this context, the abnormal wiring condition is implied in the wiring equivalent resistance sequence. middle.

4. The method for assessing the health status of an energy storage system based on interleaved voltage measurement as described in claim 2, characterized in that, The rank of the vector coefficient matrix is The difference equation of the state equation of the energy storage system is: After generalization, the generalized multivariate process expression is obtained. ;in, The differential component measures the voltage vector; This is the voltage coefficient matrix. To satisfy the condition that the samples are independent and identically distributed Individual cell voltage; This is the wiring coefficient matrix. To satisfy the condition that the samples are independent and identically distributed Voltage drop across the wiring resistor.

5. The method for assessing the health status of an energy storage system based on interleaved voltage measurement as described in claim 4, characterized in that, Based on the obtained generalized multivariate process expression, its corresponding The width of the sliding window at any given time is The time series measurement matrix can be represented as ， Right now in, , .

6. A health status assessment system for an energy storage system based on interleaved voltage measurement, characterized in that, include: The acquisition module is configured to acquire the energy storage system topology based on interleaved voltage measurement and obtain the circuit expression of the interleaved voltage measurement topology. The energy storage system topology includes several energy storage cells connected in series and voltage sensors, with the voltage sensors connected to the positive and negative terminals of adjacent energy storage cells. The circuit expression of the interleaved voltage measurement topology is as follows: in, For measuring voltage sequences using voltage sensors, The voltage of the individual energy storage cells connected in series. This refers to the operating current of the series-connected energy storage battery pack. This refers to the wiring resistance between adjacent individual energy storage cells. and These are the positive and negative buses of the energy storage system, respectively. ; The processing module is configured to perform differential processing on the obtained circuit expression to obtain the state equation of the energy storage system. The construction module is configured to construct a time-difference matrix based on the obtained state equations; An evaluation module is configured to calculate the eigenvalues ​​of the time-series difference matrix to perform a real-time assessment of the health status of the energy storage system.

7. The energy storage system health status assessment system based on interleaved voltage measurement as described in claim 6, characterized in that, By performing differential processing on the circuit expression of the obtained interleaved voltage measurement topology, the state equation of the energy storage system is obtained as follows: Among them, for ,have .

8. The energy storage system health status assessment system based on interleaved voltage measurement as described in claim 7, characterized in that, The state equation of the energy storage system includes three abnormal state types: battery short-circuit abnormality, voltage sensor abnormality, and wiring abnormality; the battery short-circuit abnormality information is contained in the voltage sequence of individual cells. In this context, the voltage sensor malfunction is implied in the differential interleaved voltage measurement sequence. In this context, the abnormal wiring condition is implied in the wiring equivalent resistance sequence. middle.

9. The energy storage system health status assessment system based on interleaved voltage measurement as described in claim 7, characterized in that, The rank of the vector coefficient matrix is The difference equation of the state equation of the energy storage system is: After generalization, the generalized multivariate process expression is obtained. ;in, The differential component measures the voltage vector; This is the voltage coefficient matrix. To satisfy the condition that the samples are independent and identically distributed Individual cell voltage; This is the wiring coefficient matrix. To satisfy the condition that the samples are independent and identically distributed Voltage drop across the wiring resistor.

10. The energy storage system health status assessment system based on interleaved voltage measurement as described in claim 9, characterized in that, Based on the obtained generalized multivariate process expression, its corresponding The width of the sliding window at any given time is The time series measurement matrix can be represented as ， Right now in, , .

11. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the energy storage system health status assessment method based on interleaved voltage measurement as described in any one of claims 1-5.

12. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the energy storage system health status assessment method based on interleaved voltage measurement as described in any one of claims 1-5.

Images