Battery circuit monomer short-circuit resistor fault identification method and terminal

Abstract

The invention discloses a battery circuit monomer short-circuit resistance fault identification method and a terminal, and the method comprises the steps: building an equivalent circuit model containing short-circuit equivalent resistance for each battery monomer of a series lithium battery system; according to circuit parameters of the equivalent circuit model, in each detection period, SOC estimation is carried out on each single battery through a Kalman family algorithm to obtain a first SOC data set, and SOC estimation is carried out through an ampere-hour integral method to obtain a second SOC data set; calculating a short-circuit resistance data set according to the first SOC data set and the second SOC data set; according to the short-circuit resistance data set, calculating the deviation between the short-circuit resistance of each single battery and a short-circuit resistance mean value, and judging whether the single short-circuit resistance has a fault or not; and short circuit identification without complex electrochemical principle modeling is realized.

Description

[0001] This application is a divisional application of the invention patent with the application date of April 2, 2025, the application number of 202510407229.5, and the name of "a method and terminal for short circuit identification and prediction of battery circuit", which is the parent application. TECHNICAL FIELD

[0002] The present application relates to the field of lithium battery state early warning, in particular to a battery circuit single short circuit resistance fault identification method and terminal. BACKGROUND

[0003] In the lithium ion battery energy storage system, internal short circuit of the battery is the most representative serious fault, and is one of the important reasons leading to early failure of power and thermal runaway safety accidents of the system. The detection or prediction method of the internal short circuit of the battery has become a research focus in the field. The lithium battery has the possibility of thermal runaway in the whole life cycle of manufacturing, use and recycling. For the use link, such as overcharge, overdischarge and high temperature, etc. Unfavorable use conditions are more likely to cause irreversible impedance reduction originating from internal defects or battery materials, which is reflected as internal short circuit of the battery. Research shows that the initial stage of this process can last for a certain period of time, which is manifested as slow abnormal reduction of short circuit resistance; the middle stage is manifested as prominent unevenness of the battery and obvious abnormal heat generation; and the late stage is manifested as strong temperature rise and action of the fire alarm device.

[0004] Current researches on internal short circuit identification and early warning mainly include the following categories: (1) Electrochemical model method is to establish equations in the reaction processes of solid phase, liquid phase, interface and deintercalation of battery materials, so as to define the simulation of real battery reaction process, replace the commonly used OCV(SOC, T) curve to describe the battery working process, and especially estimate the impedance for equivalent internal short circuit circuit. When the actual working state of the battery deviates from the modeled working state, it is considered that the impedance reduction trend is generated. This kind of method needs to establish complex mathematical model to describe the electrochemical process, which is difficult to realize for lithium battery users other than research institutions. And the accurate use of the model needs to be calibrated with real data to adapt to different material systems, which is a long process and difficult to operate.

[0005] (2) The machine learning method based on data is essentially to analyze other battery data by using the trained fault diagnosis model. This method depends on a large number of test set data, especially the data generated after the internal short circuit until the thermal runaway occurs. This is not easy to obtain for lithium ion batteries, especially for iron lithium phosphate batteries, which emphasize safety, resulting in less data available for training model and reduced accuracy.

[0006] (3) Based on the state quantity collected by the monitoring system, the inconsistent change and abnormal mutation of the battery are analyzed by the parameter characteristic statistical calculation method, which does not need to establish an accurate model of the battery, and only needs to analyze the difference between the battery voltage and the temperature to realize the internal short circuit detection, but the accuracy of the statistical calculation depends on the sampling accuracy, and the consistency of the single battery in the battery pack and the initial state deviation directly affect the detection result.

[0007] Therefore, it is necessary to propose a short circuit identification and early warning method which does not depend on the data of short circuit internal resistance abnormal gradual change or entering failure in historical operation data, and does not make complex electrochemical principle modeling, and only needs to make circuit level modeling of the battery. SUMMARY

[0008] The technical problem to be solved by the present application is to provide a battery circuit single battery short circuit resistance fault identification method and terminal, which realizes short circuit identification without depending on the data of short circuit internal resistance abnormal gradual change or entering failure in historical operation data, and without making complex electrochemical principle modeling.

[0009] In order to solve the above technical problems, the technical scheme adopted by the present application is: A short circuit identification and prediction method of a battery circuit, comprising the steps of: S1, for each battery monomer of a series lithium battery system, an equivalent circuit model containing a short circuit equivalent resistance is established; S2, according to the circuit parameters of the equivalent circuit model, in each detection period, for each battery monomer, the first SOC data set is obtained by SOC estimation through Kalman algorithm, and the second SOC data set is obtained by SOC estimation through ampere-hour integral method; S3, according to the first SOC data set and the second SOC data set, the short circuit resistance data set is calculated; S4, according to the short circuit resistance data set, the deviation between the short circuit resistance of each battery monomer and the average short circuit resistance is calculated, and whether the monomer short circuit resistance exists fault is judged;

[0010] S5, according to the circuit parameters of the equivalent circuit model and the short circuit resistance data set, the average heat generation power of each battery monomer is calculated, the maximum heat dissipation power of the battery monomer is obtained, and the time when the system reaches the heat dissipation capacity limit value of each battery is predicted. A battery circuit single battery short circuit resistance fault identification method, comprising the steps of: S1, for each battery monomer of a series lithium battery system, a first order or second order equivalent circuit model containing a short circuit equivalent resistance is established, and circuit parameters including short circuit current, charging or discharging current and output voltage are defined; S2, according to the circuit parameters of the equivalent circuit model, in each detection period, for each battery monomer, the first SOC data set is obtained by Kalman algorithm, and the second SOC data set is obtained by ampere-hour integral method; S3, according to the first SOC data set and the second SOC data set, a short-circuit resistance data set is calculated; Step S3 is specifically: According to the first SOC data set and the second SOC data set, the short-circuit resistance is calculated: ; ; The short-circuit resistance of the i-th battery monomer in the j-th detection period is recorded as , and a short-circuit resistance data set is constructed; S4, according to the short-circuit resistance data set, the deviation between the short-circuit resistance of each battery monomer and the average short-circuit resistance is calculated, and whether the monomer short-circuit resistance is faulty is judged; Wherein, Indicates the equivalent short-circuit resistance of battery monomer i from the initial time to the t time period in a detection period, Indicates the equivalent average short-circuit current of battery monomer i at t time in the current detection period, And Indicate the sampling voltage of battery monomer i at the initial time and t time in the current detection period respectively, Indicates the rated capacity of battery monomer i, Indicates the SOC value of battery monomer i at t time estimated by ampere-hour integral method in the current detection period, Indicates the SOC value of battery monomer i at t time estimated by Kalman algorithm in the current detection period.

[0011] In order to solve the above technical problems, the technical scheme adopted by the present application is: A terminal for short-circuit identification and prediction of battery circuit, comprising a processor, a memory 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 above-mentioned method for short-circuit identification and prediction of battery circuit.

[0012] A terminal for short-circuit resistance fault identification of battery circuit monomer, comprising a processor, a memory 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 above-mentioned method for short-circuit resistance fault identification of battery circuit monomer.

[0013] The beneficial effects of the present application are that the battery circuit single short-circuit resistance fault identification method and terminal of the present application simplify the calculation complexity by means of circuit level modeling, realize the indirect calculation of the short-circuit resistance by combining the Kalman filtering and the ampere-hour integral method to estimate the SOC difference, do not need to rely on complex electrochemical models or historical fault data, directly reflect the capacity loss of the short-circuit current through the SOC difference, provide a quantitative basis for short-circuit detection, realize the short-circuit identification without relying on the abnormal gradual change of the short-circuit internal resistance in the historical operation data or the data of entering the fault, and do not make complex electrochemical principle modeling. BRIEF DESCRIPTION OF DRAWINGS

[0014] Figure 1 A second-order equivalent circuit example diagram of the battery circuit single short-circuit resistance fault identification method of the embodiment of the present application; Figure 2 A brief flow example diagram of the battery circuit single short-circuit resistance fault identification method of the embodiment of the present application; Figure 3 A battery single average heat generation power curve fitting example diagram of the battery circuit single short-circuit resistance fault identification method of the embodiment of the present application; Figure 4 A structure diagram of the battery circuit single short-circuit resistance fault identification terminal of the embodiment of the present application; KEY 1. A battery circuit single short-circuit resistance fault identification terminal; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0015] To explain the technical content, the purposes and effects of the present application in detail, the following will be explained in combination with the embodiments and the drawings.

[0016] Please refer to Figures 1 to 3 A short-circuit identification and prediction method of a battery circuit, comprising the steps of: S1. For each battery single of a series lithium battery system, an equivalent circuit model containing a short-circuit equivalent resistance is established; S2. According to the circuit parameters of the equivalent circuit model, in each detection period, for each battery single, the first SOC data set is obtained by performing SOC estimation through the Kalman family algorithm, and the second SOC data set is obtained by performing SOC estimation through the ampere-hour integral method; S3. According to the first SOC data set and the second SOC data set, the short-circuit resistance data set is calculated; S4. According to the short-circuit resistance data set, the deviation between the short-circuit resistance of each battery single and the average short-circuit resistance is calculated, and whether the single short-circuit resistance is faulty is judged; S5, calculating the average heat generation power of each battery monomer according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, obtaining the maximum heat dissipation power of the battery monomer, and predicting the time when the heat dissipation capacity limit of the system to each battery cell is reached.

[0017] From the above description, the beneficial effects of the present application are that: the short-circuit identification and prediction method of the battery circuit of the present application simplifies the calculation complexity by using circuit-level modeling, realizes the indirect calculation of the short-circuit resistance by combining Kalman filtering and ampere-hour integral method to estimate the SOC difference, and does not need to rely on complex electrochemical models or historical fault data; the capacity loss of the short-circuit current is directly reflected by the SOC difference, which provides a quantitative basis for short-circuit detection; at the same time, by combining heat generation prediction and heat dissipation capacity analysis, the thermal runaway risk can be warned in advance, and the detection delay problem caused by insufficient data or complex model in the traditional method can be solved.

[0018] Further, step S4 comprises the steps of: According to the short-circuit resistance data set, the short-circuit resistance of each battery monomer obtained in the current detection period is obtained, and the variance and mean value are calculated; According to the calculated variance and mean value, an adaptive alarm threshold is calculated; It is judged whether the short-circuit resistance of the monomer battery cell in the current detection exists or not, if yes, it is determined that the monomer battery cell has a fault.

[0019] From the above description, by calculating the mean value and variance of the short-circuit resistance to set the adaptive alarm threshold, the interference of system individual difference and sampling noise can be effectively filtered out. Compared with the fixed threshold method, the adaptive mechanism can dynamically adapt to the consistency change of the battery pack, reduce the false alarm probability, and improve the accuracy of fault detection. Combined with the sliding window detection strategy, the abnormal fluctuation of the short-circuit resistance can be captured in real time, and the response ability of the system to the gradual fault can be enhanced.

[0020] Further, the adaptive alarm threshold comprises a threshold lower limit; The threshold lower limit is calculated as follows: ; Wherein, And respectively represent the mean value and variance of the short-circuit resistance of each battery monomer obtained in the current detection period.

[0021] From the above description, it is clear that the lower threshold calculation formula (mean minus 3 times standard deviation) is established based on statistical principles to establish a scientific fault judgment standard. The threshold considers the resistance fluctuation range during normal operation and retains sufficient safety margin to ensure reliable alarm triggering when the short-circuit resistance decreases significantly. Compared with traditional empirical thresholds, it has stronger universality and robustness, especially for applications with high battery parameter dispersion.

[0022] Further, step S5 includes steps of: S51, for each detection period, calculating the average heat generation power of each monomer according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set; S52, calculating the maximum heat dissipation power of each battery monomer according to the total refrigeration power of the cooling system to the battery; S53, combining the average heat generation power calculated in the previous several detection periods to construct an average heat generation power data set for each battery monomer; S54, curve fitting the average heat generation power data set, and judging the number of detection periods required to reach the maximum heat dissipation power according to the fitting result, and predicting the time to reach the heat dissipation capacity limit of the system to each battery cell.

[0023] From the above description, by fitting the heat generation power data to predict the thermal runaway time, the hysteresis of traditional temperature monitoring is broken through. This method calculates the heat generation based on equivalent circuit parameters and short-circuit resistance in real time, and evaluates the cooling system capacity, which can predict the heat accumulation trend in advance. The prediction model considers the evolution law of historical heat generation data, reserves enough fault response time for the system, and significantly improves the safety and operation efficiency of the energy storage system.

[0024] Further, the average heat generation power is calculated as follows: ; wherein, and represent the q-order equivalent circuit parameters of battery monomer i, is the rated current of the battery, is the equivalent average short-circuit current of monomer i at time t in the current detection period, and are the sampling voltages of battery monomer i at the initial time and time t in the current detection period, respectively, represents the equivalent short-circuit resistance of battery monomer i from the initial time to time t in the current detection period.

[0025] As can be known from the above description, the heat generation power formula comprehensively considers the ohmic loss and short-circuit resistance loss of the equivalent circuit, and accurately quantifies the internal energy dissipation of the battery. Compared with the method relying only on external temperature measurement, the formula calculates through multi-parameter coupling, and more truly reflects the abnormal heat generation caused by internal short circuit. Especially in the early stage, when the temperature has not risen significantly, the potential risk can be identified through power analysis, and the early warning of failure is realized.

[0026] Further, the maximum heat dissipation power is calculated as follows: ; wherein, is the total refrigeration power of the cooling system to the battery, and m is the number of battery cells of the battery.

[0027] As can be known from the above description, the total refrigeration power of the system is evenly distributed to the single battery, and a standardized heat dissipation capacity evaluation model is established. This method simplifies the design complexity of the thermal management system, and ensures the safety evaluation of each single battery under the same heat dissipation condition. By directly comparing the heat generation and heat dissipation capacity, the battery with insufficient heat dissipation can be quickly located, and clear priority ranking basis is provided for heat runaway prevention.

[0028] Further, step S3 is specifically: According to the first SOC data set and the second SOC data set, the short-circuit resistance is calculated as follows: ; ; The short-circuit resistance of the i-th battery cell in the j-th detection period is denoted as , and a short-circuit resistance data set is constructed; wherein, represents the equivalent average short-circuit current of the battery cell i at time t in the current detection period, and respectively represent the sampling voltage of the battery cell i at the initial time and at time t in the current detection period, represents the capacity of the battery cell i at time t, represents the SOC value of the battery cell i at time t estimated by the ampere-hour integration method in the current detection period, represents the SOC value of the battery cell i at time t estimated by the Kalman filter algorithm in the current detection period.

[0029] As can be known from the above description, the short-circuit resistance calculation formula converts the non-directly measurable short-circuit current into a calculable parameter through the mathematical relationship between the SOC difference and the sampling time. Based on the capacity conservation principle, the formula quantifies the short-circuit current by using the SOC difference between the ampere-hour integration and the Kalman filter, avoiding the technical difficulty of directly measuring the high internal resistance short circuit.

[0030] Further, the estimation of the first SOC dataset comprises the steps of: constructing a discretized state equation according to the circuit parameters of the qth-order equivalent circuit of the battery cell: ; wherein, Q c represents the cell capacity, , , and represents the equivalent circuit resistance and capacitance at temperature T and state of charge SOC, SOC t 、U1 t , Uq t and SOC t+1 、U1 t+1 , Uq t+1 represents the SOC state and the equivalent capacitances C1 and Cq voltage state of the battery cell at time t and t+1 respectively, I t is the cell current at time t, T s is the calculation step, and W is the process noise; constructing a measurement equation for the cell: ; wherein, e t+1 represents the measured voltage at the output end of the cell at time t+1, U OCV is the obtained open circuit voltage of the cell, I t+1 is the cell current at time t+1, R 0_t+1 is the DC internal resistance of the cell at time t+1, and V is the measurement noise; using the UKF algorithm or the EKF algorithm to calculate the estimation value of at time t of the i-th of all m cells being evaluated, denoted as SOC i_t , and constituting a state of charge set to obtain the first dataset : .

[0031] ​From the above description, it can be seen that the Kalman filtering algorithm is used to process the state estimation problem of the nonlinear system, and the SOC estimation value is dynamically corrected through the state equation and the measurement equation. Compared with the traditional OCV curve method, this method can effectively suppress the influence of measurement noise and model error, and still maintain high precision under the condition of time-varying battery parameters (such as temperature, aging). Experiments show that the SOC estimation error can be controlled within 0.05%, which lays a reliable foundation for subsequent short-circuit resistance calculation.

[0032] Further, the estimation of the second SOC dataset comprises the steps of: The state of charge of all battery cells is estimated using the ampere-hour integration method: ; wherein, and are the state of charge of the battery cell i at time t-1 and the state of charge at the beginning of the detection period, respectively, is the charging or discharging efficiency of the battery cell i at time t, is the average charging or discharging current of the battery cell i from time t-1 to t, is the duration from time t-1 to t, is the rated capacity of the battery cell i; from the state of charge of the m battery cells, a second SOC dataset is obtained : ; wherein, at the beginning of the current detection period, the SOC calculated by the ampere-hour integration method and the SOC calculated using the Kalman family algorithm have the same initial value.

[0033] From the above description, it can be seen that the ampere-hour integration method synchronizes with the Kalman filtering result through the initial state alignment, eliminating the initial deviation of the SOC estimation. This design ensures that the SOC difference of the two methods truly reflects the capacity loss of the short-circuit current, avoiding misjudgment caused by initial error. Combined with dynamic charging and discharging efficiency compensation, the ampere-hour integration result is closer to the actual operating condition, enhancing the accuracy of short-circuit resistance calculation.

[0034] Please refer to Figure 4 , a terminal for short-circuit identification and prediction of a battery circuit, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for short-circuit identification and prediction of a battery circuit when executing the computer program.

[0035] From the above description, the beneficial effects of the present application are that: the terminal for short circuit identification and prediction of a battery circuit of the present application simplifies the calculation complexity by using circuit level modeling, estimates the SOC difference by combining Kalman filtering and ampere-hour integration method, realizes indirect calculation of short circuit resistance, and does not need to rely on complex electrochemical model or historical fault data; the capacity loss of short circuit current is directly reflected by the SOC difference, which provides quantitative basis for short circuit detection; at the same time, in combination with heat production prediction and heat dissipation capacity analysis, the thermal runaway risk can be early warned, and the detection delay problem caused by insufficient data or complex model of the traditional method is solved.

[0036] The method and terminal for short circuit identification and prediction of a battery circuit of the present application are suitable for state detection and early warning of lithium batteries.

[0037] Please refer to Figures 1 to 3 , the first embodiment of the present application is: A method for short circuit identification and prediction of a battery circuit, comprising the steps of: S1, for each battery monomer of a series lithium battery system, an equivalent circuit model containing a short circuit equivalent resistance is established.

[0038] In this embodiment, for a lithium battery system containing multiple battery monomers in series, a power supply network composed of m monomers in series is established, a first-order or second-order equivalent circuit model containing a short circuit equivalent resistance is established for each monomer, and a short circuit current Is, a charging or discharging current Io, and an output voltage U are defined, and in this embodiment, a second-order equivalent circuit is taken as an example, which can be referred to in Figure 1 .

[0039] S2, according to the circuit parameters of the equivalent circuit model, in each detection period, for each battery monomer, the first SOC data set is obtained by Kalman algorithm for SOC estimation, and the second SOC data set is obtained by ampere-hour integration method for SOC estimation; The estimation of the first SOC data set comprises the steps of: According to the circuit parameters of the q-order equivalent circuit of the battery monomer, a discretized state equation is constructed: ; Among them, Q c represents the monomer capacity, , , and represents the equivalent circuit resistance and capacitance under the temperature T and the state of charge SOC, SOC t 、U1 t , Uq t and SOCt+1 、U1 t+1 , Uq t+1 respectively represent the SOC state and the equivalent capacitance C1 and Cq voltage state of the battery cell at time t and t+1 respectively, I t is the cell current at time t, T s is the calculation step, and W is the process noise; Construct the measurement equation of the cell: ; wherein, e t+1 represents the measured voltage at the output end of the cell at time t+1, U OCV is the open-circuit voltage of the cell obtained, I t+1 is the cell current at time t+1, R 0_t+1 is the DC internal resistance of the cell at time t+1, and V is the measurement noise; Use the UKF algorithm or the EKF algorithm to calculate the estimate of the i-th of all m cells at time t, SOC i_t is recorded as and constitutes a set of state of charge, obtaining a first data set : .

[0040] In this embodiment, the Kalman family (UKF or EKF) method is used to estimate the state of charge of all cells , as shown below: The circuit parameters of each cell are estimated offline at different temperatures. For a first-order equivalent circuit, R0, R1, and C1 are included. For a second-order equivalent circuit, R0, R1, C1, R2, and C2 are included. The specific estimation method can be found in the article “1st and 2nd order Thevenin equivalent circuit parameter identification method”.

[0041] The open-circuit voltage and state of charge SOC corresponding relationship function of the cell is obtained at different temperatures U OCV =f T (SOC) curve. The acquisition method is described in Section 2 of the article “Battery SOC estimation method and energy storage system”.

[0042] Establish the state equation and measurement equation of the battery cell: Establish the 1st, 2nd or other order equivalent circuit of the battery cell; according to the parameters of the cell R1_(T,SOC) 、C 1_(T,SOC)、 R 2_(T,SOC) 、C 2_(T,SOC ) Initial value constructs the discretized state equation; U1 is C 1_(T,SOC) The two-terminal voltage, U2 is C 2_(T,SOC) The two-terminal voltage; the parameter contains the subscript (T,SOC) Its value is related to the monomer temperature T and SOC.

[0043] Taking a second-order equivalent circuit as an example, the discretized state equation is as follows: ; Wherein Q c The monomer capacity, R 1_(T,SOC) 、C 1_(T,SOC) , R 2_(T,SOC) 、C 2_(T,SOC) The resistance and capacitance of the equivalent circuit at temperature T and SOC; SOC t 、U1 t , U2 t And SOC t+1 、U1 t+1 、U2 t+1 SOC state and equivalent capacitance C1 and C2 voltage state of the monomer at time t and t+1 respectively, I t The monomer current at t, T s The calculation step, W is the process noise.

[0044] The measurable state quantity of the monomer is voltage, current and temperature, and the measurement equation of the monomer is constructed:

[0045] Wherein e t+1 The voltage measured at the output end of the monomer at t+1, U OCV The open-circuit voltage of the monomer obtained ，I t+1 The monomer current at t+1, R 0_t+1is the DC internal resistance of the i-th battery cell at time t+1, and V is the measurement noise.

[0046] The estimation value of the i-th battery cell at time t is calculated using the UKF algorithm or the EKF algorithm, and is denoted as SOC i_t The estimation value of the i-th battery cell at time t is calculated using the UKF algorithm or the EKF algorithm, and is denoted as The estimation value of the i-th battery cell at time t is calculated using the UKF algorithm or the EKF algorithm, and is denoted as : .

[0047] The estimation of the second SOC data set includes the following steps: The state of charge of all battery cells is estimated using the ampere-hour integration method: ; wherein and are the state of charge of the i-th battery cell at time t-1 and the state of charge at the beginning of the detection period, respectively, is the charging or discharging efficiency of the i-th battery cell at time t, is the average charging or discharging current of the i-th battery cell from time t-1 to t, is the duration from time t-1 to t, and is the rated capacity of the i-th battery cell. According to the state of charge of the m battery cells, the second SOC data set is obtained: ; wherein the SOC calculated by the ampere-hour integration method and the SOC calculated using the Kalman algorithm have the same initial value at the beginning of the current detection period.

[0048] In this embodiment, the state of charge of all battery cells in the lithium battery system in use is estimated using the ampere-hour integration method, and the state of charge set is obtained: The state of charge of the i-th battery cell at time t in a detection period is calculated according to the following method: ; wherein and are the state of charge of the i-th battery cell at time t-1 and the state of charge at the beginning of the detection period, respectively; is the charging or discharging efficiency of the i-th battery cell at time t, is the average charging or discharging current of the i-th battery cell from time t-1 to t (positive for charging and negative for discharging), is the duration from time t-1 to t, and is the rated capacity of the i-th battery cell. ​

[0049] .

[0050] At the initial moment of the detection cycle, the values of each monomer are aligned with and , that is, let , so that the SOC calculated by the ampere-hour integral method and the SOC calculated by the KF family method have the same initial value.

[0051] wherein, is the SOC of monomer i at the initial moment of the detection cycle calculated by the ampere-hour integral method, is the SOC of monomer i at the initial moment of the detection cycle calculated by the Kalman method.

[0052] S3, according to the first SOC data set and the second SOC data set, a short-circuit resistance data set is calculated; Step S3 is specifically: According to the first SOC data set and the second SOC data set, the short-circuit resistance is calculated: ; ; The short-circuit resistance of the i-th battery monomer in the j-th detection cycle is recorded as , and a short-circuit resistance data set is constructed; wherein, represents the equivalent average short-circuit current of battery monomer i at time t in the current detection cycle, and respectively represent the sampling voltage of battery monomer i at the initial moment and at time t in the current detection cycle, represents the capacity of battery monomer i at time t.

[0053] According to the above formula, the equivalent short-circuit resistance of battery monomer i from the initial moment to time t in a certain detection cycle is calculated, and for the j-th detection cycle, j∈(1,n), n is the number of detection cycles, the equivalent short-circuit resistance is recorded as .

[0054] The meaning of the formula is that, in a certain detection cycle from 0~t sampling period, and the difference between the measurements represents the capacity loss accumulated by the short-circuit current in this period of time, and the result is the average value of the equivalent short-circuit current.

[0055] In this embodiment, the calculated at time t in each cycle of the continuous n detection cycles constitutes the short-circuit resistance data set of the battery system : .

[0056] S4, according to the short-circuit resistance data set, calculate the deviation between each battery monomer short-circuit resistance and the average short-circuit resistance, judge whether the monomer short-circuit resistance is faulty or not; Step S4 includes the steps of: According to the short-circuit resistance data set, the short-circuit resistance of each battery monomer obtained in the current detection period is obtained, and the variance and mean value are calculated; According to the calculated variance and mean value, an adaptive alarm threshold is calculated; The adaptive alarm threshold includes a threshold lower limit; The threshold lower limit The calculation is specifically: ; ; ; Among them, And The mean and variance of the short-circuit resistance of each battery monomer obtained in the current detection period are represented; Judge whether the short-circuit resistance of the monomer battery core in the current detection exists or not, if yes, it is determined that the monomer battery core exists fault.

[0057] In this embodiment, for the running lithium battery system to be evaluated, when the short-circuit resistance of monomer i Send short-circuit resistance abnormality reduction alarm: (1) Represent the fluctuation degree of short-circuit resistance deviation, when the jth detection period is out of limit, that is, the alarm is sent at the end of the period.

[0058] (2) The detection period is t, but the start point interval of adjacent two detection periods can be less than t, that is, the detection is carried out in a sliding window mode.

[0059] S5, according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, calculate the average heat power of each battery monomer, obtain the maximum heat dissipation power of the battery monomer, and predict the time when the system reaches the heat dissipation capacity limit of each battery core; Step S5 includes the steps of: S51, for each detection period, according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, calculate the average heat power of each monomer; The calculation of the average heat power The calculation is specifically: ;​ wherein, and denote the q-th order equivalent circuit parameters of the battery cell i, is the battery rated current, is the equivalent average short-circuit current of the cell i at time t in the current detection cycle, and are the sampled voltages of the battery cell i at the initial time and at time t in the current detection cycle, respectively; S52, according to the total refrigeration power of the cooling system on the battery, the maximum heat dissipation power of each battery cell is calculated; The maximum heat dissipation power is calculated as follows: ; wherein, is the total refrigeration power of the cooling system on the battery, and m is the number of battery cells of the battery; S53, combining the average heat generation power calculated in the previous several detection cycles, an average heat generation power dataset of each battery cell is constructed; S54, curve fitting is performed on the average heat generation power dataset, the number of detection cycles required to reach the maximum heat dissipation power is judged according to the fitting result, and the time to reach the heat dissipation capacity limit of the system on each battery cell is predicted.

[0060] In this embodiment, the heat generation of each cell is calculated, and it is evaluated whether the heat generation limit caused by the increase of the short-circuit resistance of the cell exceeds the heat management capacity of the battery system; and according to the short-circuit resistance estimation value in the obtained several detection cycles, the time to reach the heat dissipation capacity limit of the system on each battery cell is predicted, and long-period short-circuit fault time prediction and alarm are given.

[0061] Specifically, according to the obtained resistance R0, R1 parameters (first-order equivalent circuit), or R0, R1, R2 parameters (second-order equivalent circuit), the average heat generation power of each cell in the detection cycle is calculated .

[0062] Taking the second-order equivalent circuit as an example, ; wherein, is the second-order equivalent circuit parameter of the cell i, is the battery rated current.

[0063] According to the capacity of the battery thermal management system, the maximum heat dissipation power of each cell is calculated. Assuming that the heat dissipation power of each cell is equal, then: ; The total refrigeration power of the cooling system to the battery.

[0064] The average heat generation power of the single battery i is calculated for the continuous n detection cycles , to obtain the single battery i average heat generation power evaluation data set .

[0065] ; The data sequence is fitted, which can refer to , and the number of detection cycles x required to reach Figure 3 is obtained; when x*y<z, a long-cycle short-circuit fault time prediction alarm is triggered. Wherein y is the starting point interval time between adjacent two detection cycles, and z is the early warning advance time limit value.

[0066] Effect verification: The calculation of in the method is simulated and verified: The parallel resistance is output in the single battery model to simulate the short circuit, the resistance is set to 10 ohms, and the battery capacity is 280 Ah. The difference between the estimated SOC and the ampere-hour integral SOC is simulated in the detection cycle of the total time length of 200000s (t=200000s), 0.0525.

[0067] 0.0525*280 / 200000*3600 = 0.265A; ; Error 22.6%.

[0068] Adjust the short-circuit resistance to 5 ohms, 0.103*280 / 200000*3600 = 0.5192A; 6.26 ohms, error 25%.

[0069] Adjust the short-circuit resistance to 20 ohms, 0.0295*280 / 200000*3600 = 0.149A; 21.82 ohms, error 9%.

[0070] The actual additional heat of the single battery under the three resistance conditions is 1.05W, 2.11W and 0.52W respectively, which is small and cannot be quickly detected by temperature detection. The method plays a role in early detection.

[0071] ​The error generated is derived from the collection accuracy, the estimation error of Kalman filtering algorithm and ampere-hour integration algorithm, and the value of the short-circuit resistance can be effectively detected in the range of several ohms to tens of ohms.

[0072] Please refer to Figure 4 Embodiment two of the present application is: A terminal 1 for short-circuit identification and prediction of a battery circuit, comprising a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2, wherein the processor 2 implements the steps in the method for short-circuit identification and prediction of a battery circuit according to the above embodiment one when executing the computer program.

[0073] In summary, the present application provides a method and terminal for short-circuit identification and prediction of a battery circuit, which simplifies the calculation complexity by using circuit-level modeling, estimates the SOC difference by combining Kalman filtering and ampere-hour integration method, realizes indirect calculation of short-circuit resistance without relying on complex electrochemical models or historical fault data, directly reflects the capacity loss of short-circuit current through the SOC difference, provides a quantitative basis for short-circuit detection, and at the same time, combines heat production prediction and heat dissipation capacity analysis to provide early warning of thermal runaway risk, solving the detection delay problem caused by insufficient data or complex model in traditional methods.

[0074] 1. In the present application, the Kalman filtering method for handling nonlinear problems is used to identify the battery monomer equivalent circuit containing short-circuit resistance as battery capacity loss, and the SOC estimation accuracy is not affected by the change of short-circuit resistance.

[0075] 2. The SOC estimation method of ampere-hour integration cannot identify the current consumed in the internal loop of the battery cell, and the SOC value is higher than the identification result of the UKF method when there is a short circuit; in the case of alignment of the initial state at each evaluation period, the evolution of the deviation can reflect the capacity consumption of the short-circuit current, thereby reflecting the size of the short-circuit current.

[0076] 3. The present application gives the calculation result of the short-circuit resistance, which is convenient for accumulating the data of the short-circuit resistance in the system operation, and lays a foundation for the evaluation method based on data.

[0077] 4. The present application gives an adaptive short-circuit resistance determination threshold combined with the gradual change of the system's own running state, has an adaptive filtering effect, and can filter out the errors introduced by the system individual.

[0078] 5. According to the thermal runaway prevention mechanism, the internal short-circuit of the battery is a gradual and irreversible process. When the heat production of the monomer is greater than the heat dissipation, the temperature of the battery cell will continue to rise until thermal runaway occurs. This method does not rely on temperature collection and analysis, but calculates the heat production to realize the prediction function, providing a new evaluation basis and direction for early state judgment of internal short-circuit.

[0079] The above merely illustrates the embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent transformation or direct or indirect application in the related technical field based on the content of the present application specification and drawings is also included in the patent protection scope of the present application.

Claims

1. A battery circuit cell short resistance fault recognition method characterized by, The method comprises the steps of: S1, for each battery cell of the series lithium battery system, a first-order or second-order equivalent circuit model containing a short-circuit equivalent resistance is established, and circuit parameters including short-circuit current, charging or discharging current and output voltage are defined; S2, according to the circuit parameters of the equivalent circuit model, in each detection period, for each battery cell, the SOC is estimated by Kalman algorithm to obtain a first SOC data set, and the SOC is estimated by ampere-hour integral method to obtain a second SOC data set; S3, according to the first SOC data set and the second SOC data set, the short-circuit resistance data set is calculated; Step S3 is specifically: According to the first SOC data set and the second SOC data set, the short-circuit resistance is calculated: ； ； Let the short-circuit resistance of the i-th battery monomer in the j-th detection cycle be denoted as , and a short-circuit resistance data set is constructed; S4, according to the short-circuit resistance data set, the deviation between the short-circuit resistance of each battery cell and the average short-circuit resistance is calculated, and whether the single cell short-circuit resistance is faulty is judged; wherein, represents the equivalent short-circuit resistance of the battery cell i from the initial time to the time t in a certain detection period, represents the equivalent average short-circuit current of the battery cell i at the time t in the current detection period, and respectively represent the sampling voltages of the battery cell i at the initial time and the time t in the current detection period, represents the rated capacity of the battery cell i, represents the SOC value of the battery cell i at the time t estimated by the ampere-hour integral method in the current detection period, represents the SOC value of the battery cell i at the time t estimated by the Kalman family algorithm in the current detection period.

2. The method of claim 1, wherein the method further comprises: Step S4 comprises the steps of: According to the short-circuit resistance data set, the short-circuit resistance of each battery cell obtained in the current detection period is obtained, and the variance and mean value are calculated; According to the calculated variance and mean value, an adaptive alarm threshold is calculated; It is judged whether the short-circuit resistance of the single cell exceeds the adaptive alarm threshold range in the current detection, if yes, it is determined that the single cell exists fault.

3. The method of claim 2, wherein the method further comprises: The adaptive alarm threshold includes a threshold lower limit; The threshold lower bound The calculation is specifically: ； wherein, and respectively represent the mean and variance of the short-circuit resistance of each battery cell obtained in the current detection period.

4. The method of claim 1, wherein the method further comprises: Further comprising the steps of: S5, according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, the average heat generation power of each battery cell is calculated, the maximum heat dissipation power of the battery cell is obtained, and the time when the system reaches the heat dissipation capacity limit of each cell is predicted.

5. The method of claim 4, wherein the method further comprises: Step S5 comprises the steps of: S51, for each detection period, according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, the average heat generation power of each single cell is calculated; S52, according to the total refrigeration power of the cooling system to the battery, the maximum heat dissipation power of each battery cell is calculated; S53, combining the average heat generation power calculated in the previous several detection periods, the average heat generation power data set of each battery cell is constructed; S54, curve fitting is performed on the average heat generation power data set, the number of detection periods required to reach the maximum heat dissipation power is judged according to the fitting result, and the time when the system reaches the heat dissipation capacity limit of each cell is predicted.

6. The method of claim 5, wherein the method further comprises: The average heat production power The calculation of the average heat production power is specifically: ； wherein, and denote the qth order equivalent circuit parameters of the battery cell i, is the battery rated current, is the equivalent average short circuit current of the cell i at time t in the current detection cycle, and are the sampled voltages of the battery cell i at the initial time and at time t in the current detection cycle, respectively, denotes the equivalent short circuit resistance of the battery cell i from the initial time to the time period t in the current detection cycle.

7. The method of claim 5, wherein the method further comprises: the maximum heat dissipation power The calculation of the maximum heat dissipation power is as follows: ； wherein, Ptot is the total power of the cooling system to the battery, m is the number of cells of the battery.

8. The method of claim 1, wherein the method further comprises: The estimation of the first SOC data set comprises the steps of: According to the circuit parameters of the q-order equivalent circuit of the battery cell, a discretized state equation is constructed: ； wherein, Q c denotes the monomer capacity, , , and denotes the equivalent circuit resistance and capacitance at temperature T and state of charge SOC, SOC t 、U1 t , Uq t and SOC t+1 、U1 t+1 , Uq t+1 denote the SOC state and equivalent capacitances Ci and Cq voltage state of the battery monomer at time t and t+1, respectively, I t is the monomer current at time t, T s is the calculation step size, and W is the process noise; The measurement equation of the single cell is constructed: ； wherein, e t+1 Vt+1is the voltage measured at the output of the cell at time t+1, U OCV Vocis the open circuit voltage of the cell obtained, I t+1 It+1is the current of the cell at time t+1, R 0_t+1 Rt+1is the direct current resistance of the cell at time t+1, and V is the measurement noise. The estimation value of the i-th of all the m monomers being evaluated at the t-th time point is calculated using the UKF algorithm or the EKF algorithm, denoted as SOC i_t The estimation value of the i-th of all the m monomers being evaluated at the t-th time point is calculated using the UKF algorithm or the EKF algorithm, denoted as , and a set of state of charge is obtained, to obtain a first data set : 。 9. The method of claim 1, wherein, The estimation of the second SOC data set comprises the steps of: The state of charge of all battery cells is estimated by ampere-hour integral method: ； wherein, and SoCi(t-1) and SoCi(0) are the state of charge of the battery cell i at time t-1 and at the beginning of the detection cycle, respectively, SoCi(t) is the state of charge of the battery cell i at time t, Ii(t-1,t) is the average charging or discharging current of the battery cell i from time t-1 to t, is the duration from time t-1 to t, is the rated capacity of the battery cell i; According to the state of charge of the m battery cells, a second SOC data set is obtained : ； Wherein, at the initial moment of the current detection period, the SOC calculated by ampere-hour integral method and the SOC calculated by Kalman algorithm have the same initial value.

10. A battery circuit monomer short-circuit resistance fault identification terminal, comprising a processor, a memory, 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 in the battery circuit cell short-circuit resistance fault identification method of any one of claims 1-9.

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