Short circuit identification and prediction method of battery circuit and terminal

Abstract

According to the short circuit identification and prediction method of the battery circuit and the terminal, an equivalent circuit model containing short circuit equivalent resistance is established 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; according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, the average heat production power of each battery monomer is calculated, the maximum heat dissipation power of the battery monomer is obtained, and the time for reaching the heat dissipation capacity limit value of the system to each battery cell is predicted.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery status early warning, and in particular to a method and a terminal for identifying and predicting short circuits in battery circuits. Background Art

[0002] In lithium-ion battery energy storage systems, internal short circuits are the most representative serious faults and one of the important reasons for early power failure and thermal runaway safety accidents in the system. The detection or prediction methods of internal short circuits in batteries have become a key research direction in the field. Lithium batteries are likely to have thermal runaway throughout their entire life cycle of manufacturing, use, and recycling. For the use process, such as overcharging, overdischarging, and high temperature, it is more likely to cause irreversible impedance reduction caused by internal defects or battery materials, which is reflected as an internal short circuit of the battery. Studies have shown that this process can last for a certain period of time in the early stage, manifested as a slow and abnormal decrease in short-circuit resistance; in the middle stage, it is manifested as prominent battery imbalance problems and obvious abnormal heat generation; in the late stage, it is manifested as a strong temperature rise and the action of the fire alarm device.

[0003] Current research on internal short circuit identification and warning can be divided into the following categories: (1) The electrochemical model method is to establish equations for the solid phase, liquid phase, interface, and deintercalation reaction processes of battery materials, thereby defining a simulation of the real battery reaction process, replacing the commonly used OCV (SOC, T) curve to describe the battery working process, especially estimating the impedance used for the equivalent internal short-circuit circuit. When the actual working state of the battery deviates from the modeled working state, it is considered that an impedance reduction trend has occurred. This type of method requires the establishment of a complex mathematical model to describe the electrochemical process, which is difficult for lithium battery users outside of research institutions to implement. In addition, the accurate use of the model requires corresponding real data to undergo complex calibration to adapt to different material systems, which is a long process and difficult to operate.

[0004] (2) The essence of data-based machine learning methods is to analyze other battery data through a trained fault diagnosis model. This method relies on a large amount of test set data, especially data from the time when an internal short circuit occurs until thermal runaway occurs. This is not easy to obtain for lithium-ion batteries that emphasize safety, especially lithium iron phosphate batteries, resulting in relatively scarce data that can be used to train the model and reduced accuracy.

[0005] (3) Based on the state quantity collected by the monitoring system, the inconsistent changes of the battery and the abnormal mutation fault information are analyzed through the parameter characteristic statistical calculation method. This type of method does not need to establish an accurate battery model. It only needs to analyze the difference in battery voltage and temperature to achieve internal short circuit detection. However, the disadvantage is that the accuracy of statistical calculation depends on the sampling accuracy. The consistency of the cells in the battery pack and the initial state deviation directly affect the detection results.

[0006] Therefore, it is necessary to propose a short-circuit identification and early warning method that does not rely on the data of abnormal gradual change of short-circuit internal resistance or fault in historical operation data, and does not perform complex electrochemical principle modeling, but only requires circuit-level modeling of the battery. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method and terminal for short-circuit identification and prediction of a battery circuit, so as to realize short-circuit identification and early warning which is independent of the data of abnormal gradual change of short-circuit internal resistance or failure in historical operation data and does not require complex electrochemical principle modeling.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for identifying and predicting short circuits in a battery circuit comprises the following steps: S1. For each battery cell in the series-connected lithium battery system, an equivalent circuit model including a short-circuit equivalent resistance is established; S2. According to the circuit parameters of the equivalent circuit model, in each detection cycle, for each battery cell, SOC estimation is performed by using a Kalman family algorithm to obtain a first SOC data set, and SOC estimation is performed by using an ampere-hour integration method to obtain a second SOC data set; S3, calculating a short-circuit resistance data set according to the first SOC data set and the second SOC data set; S4. Calculate the deviation between the short-circuit resistance of each battery cell and the short-circuit resistance mean value according to the short-circuit resistance data set, and determine whether there is a fault in the short-circuit resistance of the cell; S5. Calculate the average heat generation power of each battery cell according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, obtain the maximum heat dissipation power of the battery cell, and predict the time to reach the heat dissipation capacity limit of the system for each battery cell.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is: A terminal for identifying and predicting short circuits of a battery circuit comprises a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the method for identifying and predicting short circuits of a battery circuit described above are implemented.

[0010] The beneficial effects of the present invention are as follows: a method and terminal for identifying and predicting short circuits in a battery circuit of the present invention utilizes circuit-level modeling to simplify computational complexity, estimates SOC differences by combining Kalman filtering with the ampere-hour integration method, and 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, providing a quantitative basis for short-circuit detection; at the same time, combined with heat generation prediction and heat dissipation capacity analysis, it can provide early warning of thermal runaway risks, solving the problem of detection delays caused by insufficient data or complex models in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 An example diagram of a second-order equivalent circuit of a method for identifying and predicting short circuits in a battery circuit according to an embodiment of the present invention; Figure 2 A simplified flowchart of a method for identifying and predicting short circuits in a battery circuit according to an embodiment of the present invention; Figure 3 This is an example diagram of the average heat generation power curve fitting of a battery cell in a method for identifying and predicting a short circuit of a battery circuit according to an embodiment of the present invention; Figure 4 A structural diagram of a terminal for short circuit identification and prediction of a battery circuit according to an embodiment of the present invention; Description of labels: 1. A terminal for identifying and predicting short circuits in a battery circuit; 2. A processor; 3. A memory. DETAILED DESCRIPTION

[0012] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0013] Please refer to Figures 1 to 3 , a method for identifying and predicting a short circuit in a battery circuit, comprising the steps of: S1. For each battery cell in the series-connected lithium battery system, an equivalent circuit model including a short-circuit equivalent resistance is established; S2. According to the circuit parameters of the equivalent circuit model, in each detection cycle, for each battery cell, SOC estimation is performed by using a Kalman family algorithm to obtain a first SOC data set, and SOC estimation is performed by using an ampere-hour integration method to obtain a second SOC data set; S3, calculating a short-circuit resistance data set according to the first SOC data set and the second SOC data set; S4. Calculate the deviation between the short-circuit resistance of each battery cell and the short-circuit resistance mean value according to the short-circuit resistance data set, and determine whether there is a fault in the short-circuit resistance of the cell; S5. Calculate the average heat generation power of each battery cell according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, obtain the maximum heat dissipation power of the battery cell, and predict the time to reach the heat dissipation capacity limit of the system for each battery cell.

[0014] From the above description, it can be seen that the beneficial effects of the present invention are: a method for short-circuit identification and prediction of a battery circuit of the present invention uses circuit-level modeling to simplify calculation complexity, estimates SOC differences by combining Kalman filtering and the ampere-hour integration method, and 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, providing a quantitative basis for short-circuit detection; at the same time, combined with heat generation prediction and heat dissipation capacity analysis, it can provide early warning of thermal runaway risks, solving the detection delay problem caused by insufficient data or complex models in traditional methods.

[0015] Further, 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 cycle is obtained, and variance and mean calculation is performed; Calculate the adaptive alarm threshold value based on the calculated variance and mean; It is determined whether the short-circuit resistance of a single cell exceeds the adaptive alarm threshold range in the current detection, and if so, it is determined that the single cell is faulty.

[0016] From the above description, it can be seen that by calculating the mean and variance of the short-circuit resistance to set the adaptive alarm threshold, the interference of individual differences in the system and sampling noise can be effectively filtered out. Compared with the fixed threshold method, this adaptive mechanism can dynamically adapt to changes in battery pack consistency, reduce the probability of false alarms, and improve the accuracy of fault detection. Combined with the sliding window detection strategy, it can capture abnormal fluctuations in short-circuit resistance in real time and enhance the system's ability to respond to gradual faults.

[0017] Further, the adaptive alarm threshold includes a lower threshold; The lower threshold The calculation is as follows: ; in, and They respectively represent the mean and variance of the short-circuit resistance of each battery cell obtained in the current detection cycle.

[0018] From the above description, we can know that the calculation formula for the lower limit of the threshold (mean minus 3 times the standard deviation) is clear, and a scientific fault judgment standard is established based on statistical principles. This threshold not only takes into account the resistance fluctuation range during normal operation, but also retains sufficient safety margin to ensure that the alarm is reliably triggered when the short-circuit resistance drops significantly. Compared with the traditional empirical threshold, it has stronger universality and robustness, and is especially suitable for application scenarios with high discreteness of battery parameters.

[0019] Further, step S5 comprises the steps of: S51, for each detection cycle, 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 cell according to the total cooling power of the cooling system for the battery; S53, constructing an average heat generation power data set for each battery cell based on the average heat generation power calculated in several previous detection cycles; S54, performing curve fitting on the average heat generation power data set, determining the number of detection cycles 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 for each battery cell.

[0020] From the above description, we can see that by fitting the heat generation power data to predict the thermal runaway time, we can break through the lag of traditional temperature monitoring. This method calculates the heat generation in real time based on the equivalent circuit parameters and short-circuit resistance, and combined with the cooling system capacity evaluation, the heat accumulation trend can be predicted in advance. The prediction model takes into account the evolution of historical heat generation data, reserves sufficient fault response time for the system, and significantly improves the safety and operation and maintenance efficiency of the energy storage system.

[0021] Furthermore, the average heat generation power The calculation is as follows: ; in, and represents the q-order equivalent circuit parameters of battery cell i, is the battery rated current, is the equivalent average short-circuit current of monomer i at time t in the current detection cycle, and are the sampled voltages of battery cell i at the initial moment and at moment t in the current detection cycle, It represents the equivalent short-circuit resistance of battery cell i from the initial moment to moment t in the current detection cycle.

[0022] From the above description, it can be seen that the heat generation power formula comprehensively considers the equivalent circuit ohmic loss and short-circuit resistance loss to accurately quantify the internal energy dissipation of the battery. Compared with the method that relies solely on external temperature measurement, this formula uses multi-parameter coupling calculation to more realistically reflect the abnormal heat generation caused by internal short circuits. Especially in the early stages, when the temperature has not risen significantly, potential risks can be identified through power analysis to achieve advance warning of faults.

[0023] Furthermore, the maximum heat dissipation power The calculation is as follows: ; in, is the total cooling power of the cooling system for the battery, and m is the number of battery cells.

[0024] From the above description, it can be seen that the total cooling power of the system is evenly distributed to the single cells to establish a standardized heat dissipation capacity evaluation model. This method simplifies the design complexity of the thermal management system and ensures that each cell is safely evaluated under the same heat dissipation conditions. By directly comparing heat generation with heat dissipation capacity, cells with insufficient heat dissipation can be quickly located, providing a clear priority ranking basis for thermal runaway prevention.

[0025] Furthermore, step S3 is specifically as follows: 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 cell in the j-th detection cycle is recorded as , construct a short-circuit resistance dataset; in, It represents the equivalent average short-circuit current of battery cell i at time t in the current detection cycle, and They represent the sampled voltages of battery cell i at the initial moment and at moment t in the current detection cycle, respectively. represents the capacity of the battery cell at time i, It represents the SOC value of battery cell i at time t estimated by the ampere-hour integration method in the current detection cycle. It represents the SOC value of battery cell i at time t estimated by the Kalman family algorithm in the current detection cycle.

[0026] From the above description, it can be seen that the short-circuit resistance calculation formula converts the short-circuit current that cannot be directly measured into a calculable parameter through the mathematical relationship between the SOC difference and the sampling time. This formula is based on the principle of capacity conservation and uses the SOC difference of ampere-hour integration and Kalman filtering to quantify the short-circuit current, avoiding the technical difficulty of directly measuring high internal resistance short circuits.

[0027] Further, 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, the discretized state equation is constructed: ; in, Q c Represents the monomer capacity, , , as well as 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 Respectively represent the SOC state of the battery cell at time t and time t+1 and the voltage state of the equivalent capacitor C1 and Cq, I t is the monomer current at time t, T s is the calculation step size, W is the process noise; Construct the measurement equation for the monomer: ; in, e t+1 It indicates the voltage measured at the output end of the monomer at time t+1. U OCV is the obtained single cell open circuit voltage, I t+1 is the monomer current at time t+1, R 0_t+1 is the DC internal resistance of the monomer at time t+1, and V is the measurement noise; Use the UKF algorithm or the EKF algorithm to calculate the i-th of all m monomers being evaluated at time t SOC i_t The estimated value of , and form a state of charge set to obtain the first data set : .

[0028] From the above description, it can be seen that the Kalman filter algorithm is used to deal with the state estimation problem of nonlinear systems, and the SOC estimation value is dynamically corrected through the state equation and 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 accuracy when the battery parameters vary (such as temperature and aging). Experiments show that the SOC estimation error can be controlled within 0.05%, laying a reliable foundation for the subsequent short-circuit resistance calculation.

[0029] Further, the estimation of the second SOC data set includes the steps of: The state of charge of all battery cells is estimated using the ampere-hour integration method: ; in, and are the state of charge of battery cell i at time t-1 in the detection cycle and the state of charge at the initial moment of the detection cycle, is the charging or discharging efficiency of battery cell i at time t, is the average charge or discharge current of battery cell i from time t-1 to t, is the duration from t-1 to time t, is the rated capacity of battery cell i; According to the state of charge of m battery cells, the second SOC data set is obtained : ; Among them, at the initial moment of the current detection cycle, the SOC calculated by the ampere-hour integration method and the SOC calculated by the Kalman family algorithm have the same initial value.

[0030] From the above description, it can be seen that the ampere-hour integration method synchronizes the Kalman filter results through initial state alignment to eliminate the initial deviation of SOC estimation. This design ensures that the SOC difference between the two methods truly reflects the capacity loss of short-circuit current and avoids misjudgment caused by initial error. Combined with dynamic charge and discharge efficiency compensation, the ampere-hour integration results are closer to actual operating conditions and the accuracy of short-circuit resistance calculation is enhanced.

[0031] Please refer to Figure 4 A terminal for identifying and predicting short circuits of battery circuits comprises a processor, a memory and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the method for identifying and predicting short circuits of battery circuits described above are implemented.

[0032] From the above description, it can be seen that the beneficial effects of the present invention are: a terminal for short-circuit identification and prediction of a battery circuit of the present invention uses circuit-level modeling to simplify calculation complexity, estimates SOC differences by combining Kalman filtering and the ampere-hour integration method, and 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, providing a quantitative basis for short-circuit detection; at the same time, combined with heat generation prediction and heat dissipation capacity analysis, it can provide early warning of thermal runaway risks, solving the detection delay problem caused by insufficient data or complex models in traditional methods.

[0033] A method and terminal for identifying and predicting short circuits in a battery circuit of the present invention are suitable for status detection and early warning of lithium batteries.

[0034] Please refer to Figures 1 to 3 , Embodiment 1 of the present invention is: A method for identifying and predicting short circuits in a battery circuit comprises the following steps: S1. For each battery cell in the series lithium battery system, an equivalent circuit model including a short-circuit equivalent resistance is established.

[0035] In this embodiment, for a lithium battery system containing multiple battery cells connected in series, a power supply network consisting of m batteries connected in series is established, a first-order or second-order equivalent circuit model containing a short-circuit equivalent resistance is established for each battery cell, and the short-circuit current Is, the charging or discharging current Io, and the output voltage U are defined. In this embodiment, a second-order equivalent circuit is used as an example, which can be referred to Figure 1 .

[0036] S2. According to the circuit parameters of the equivalent circuit model, in each detection cycle, for each battery cell, SOC estimation is performed by using a Kalman family algorithm to obtain a first SOC data set, and SOC estimation is performed by using an ampere-hour integration method to obtain a second SOC data set; The estimation of the first SOC dataset includes the following steps: According to the circuit parameters of the q-order equivalent circuit of the battery cell, the discretized state equation is constructed: ; in, Q c Represents the monomer capacity, , , as well as Represents the equivalent circuit resistance and capacitance at temperature T and state of charge SOC, SOC t 、U1 t , Uq t and SOCt+1 、U1 t+1 , Uq t+1 Respectively represent the SOC state of the battery cell at time t and time t+1 and the voltage state of the equivalent capacitor C1 and Cq, I t is the monomer current at time t, T s is the calculation step size, W is the process noise; Construct the measurement equation for the monomer: ; in, e t+1 It indicates the voltage measured at the output end of the monomer at time t+1. U OCV is the single cell open circuit voltage obtained, I t+1 is the monomer current at time t+1, R 0_t+1 is the DC internal resistance of the monomer at time t+1, and V is the measurement noise; Use the UKF algorithm or the EKF algorithm to calculate the i-th of all m monomers being evaluated at time t SOC i_t The estimated value of , and form a state of charge set to obtain the first data set : .

[0037] In this embodiment, the Kalman family (UKF or EKF) method is used to estimate the charge state set of all monomers , as shown below: The circuit parameters of each monomer are estimated offline at different temperatures. For the first-order equivalent circuit, they include R0, R1, and C1, and for the second-order equivalent circuit, they include R0, R1, C1, R2, and C2. The specific estimation method can be found in "First-order and Second-order Thevenin Equivalent Circuit Parameter Identification Method".

[0038] Obtain the corresponding relationship function between the open circuit voltage and the state of charge SOC of the monomer at different temperatures U OCV =f T (SOC) Curve, for the acquisition method, see Section 2 of "Battery SOC Estimation Method and Energy Storage System".

[0039] Establish the state equation and measurement equation of the battery cell: Establish 1st, 2nd or other order equivalent circuits of battery cells; according to the parameters of the cells 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 voltage across U2 is C 2_(T,SOC) Voltage across the two ends; Parameters include footnotes (T,SOC) It means that its value is related to the monomer temperature T and SOC.

[0040] Taking the second-order equivalent circuit as an example, the discretized state equation is as follows: ; in Q c is the monomer capacity, R 1_(T,SOC) 、C 1_(T,SOC) , R 2_(T,SOC) 、C 2_(T,SOC) are the equivalent circuit resistance and capacitance at a certain temperature T and SOC; SOC t 、U1 t , U2 t and SOC t+1 、U1 t+1 、U2 t+1 They are the single cell SOC state and the voltage state of equivalent capacitors C1 and C2 at time t and time 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.

[0041] The measurable state quantities of the monomer are voltage, current, and temperature. The measurement equation for the monomer is constructed as follows:

[0042] in e t+1 It indicates the voltage measured at the output end of the monomer at time t+1. U OCV The single cell open circuit voltage ，I t+1 is the monomer current at time t+1, R 0_t+1is the DC internal resistance of the monomer at time t+1, and V is the measurement noise.

[0043] Use the UKF algorithm or the EKF algorithm to calculate the i-th of all m monomers being evaluated at time t SOC i_t The estimated value of , and constitute the charge state set : .

[0044] The estimation of the second SOC data set comprises the steps of: The state of charge of all battery cells is estimated using the ampere-hour integration method: ; in, and are the state of charge of battery cell i at time t-1 in the detection cycle and the state of charge at the initial moment of the detection cycle, is the charging or discharging efficiency of battery cell i at time t, is the average charge or discharge current of battery cell i from time t-1 to t, is the duration from t-1 to time t, is the rated capacity of battery cell i; According to the state of charge of m battery cells, the second SOC data set is obtained : ; Among them, at the initial moment of the current detection cycle, the SOC calculated by the ampere-hour integration method and the SOC calculated by the Kalman family algorithm have the same initial value.

[0045] In this embodiment, for the lithium battery system in use, the ampere-hour integration method is used to estimate the charge state set of all cells. : The value of monomer i at time t in a detection cycle is calculated as follows : ; in and They are respectively the charge state of monomer i estimated by the ampere-hour integration method at time t-1 and the charge state at the initial time of the detection cycle; is the charging or discharging efficiency of monomer i at time t; is the average charge or discharge current of monomer i from time t-1 to t (charging is positive and discharging is negative); is the duration from time t-1 to time t; is the rated capacity of monomer i.

[0046] .

[0047] At the beginning of the detection cycle, and The values ​​of each monomer in are aligned, that is, , so that the SOC calculated by the ampere-hour integration method and the SOC calculated by the KF family method have the same initial value.

[0048] in, is the SOC of monomer i at the initial moment of the detection cycle using the ampere-hour integration method, is the SOC of cell i at the beginning of the detection cycle using the Kalman method.

[0049] S3, calculating a short-circuit resistance data set according to the first SOC data set and the second SOC data set; Step S3 is specifically as follows: 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 cell in the j-th detection cycle is recorded as , construct a short-circuit resistance dataset; in, It represents the equivalent average short-circuit current of battery cell i at time t in the current detection cycle, and They represent the sampled voltages of battery cell i at the initial moment and at moment t in the current detection cycle, respectively. Indicates the capacity of the battery cell at time i.

[0050] According to the above formula, the equivalent short-circuit resistance of battery cell i from the initial moment to time t in a certain detection cycle is calculated. For the jth detection cycle, j∈(1,n), n is the number of detection cycles, and the equivalent short-circuit resistance is recorded as .

[0051] The meaning of the formula is that in a certain detection cycle from 0 to t sampling period, and The difference in measurement represents the accumulated capacity loss due to the short-circuit current during that period. The result is the average value of the equivalent short-circuit current.

[0052] In this embodiment, the time t calculated in each cycle of the consecutive n detection cycles is , which constitutes the short-circuit resistance dataset of the battery system : .

[0053] S4. Calculate the deviation between the short-circuit resistance of each battery cell and the short-circuit resistance mean value according to the short-circuit resistance data set, and determine whether there is a fault in the short-circuit resistance of the cell; 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 cycle is obtained, and variance and mean calculation is performed; Calculate the adaptive alarm threshold value based on the calculated variance and mean; The adaptive alarm threshold includes a lower threshold; The lower threshold The calculation is as follows: ; ; ; in, and Respectively represent the mean and variance of the short-circuit resistance of each battery cell obtained in the current detection cycle; It is determined whether the short-circuit resistance of a single cell exceeds the adaptive alarm threshold range in the current detection, and if so, it is determined that the single cell is faulty.

[0054] In this embodiment, for the lithium battery system being evaluated, when the cell i < Issue an alarm for abnormal reduction of short-circuit internal resistance: (1) It represents the fluctuation degree of short-circuit resistance deviation. When the jth detection cycle exceeds the limit, an alarm is issued at the end of the cycle.

[0055] (2) The detection cycle is t, but the interval between the starting points of two adjacent detection cycles can be less than t, that is, the sliding window method is used for detection.

[0056] S5. Calculate the average heat generation power of each battery cell according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, obtain the maximum heat dissipation power of the battery cell, and predict the time when the heat dissipation capacity limit of the system for each battery cell is reached; Step S5 comprises the steps of: S51, for each detection cycle, 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; The average heat generation power The calculation is as follows: ; in, and represents the q-order equivalent circuit parameters of battery cell i, is the battery rated current, is the equivalent average short-circuit current of monomer i at time t in the current detection cycle, and are the sampled voltages of battery cell i at the initial moment and at moment t in the current detection cycle respectively; S52, calculating the maximum heat dissipation power of each battery cell according to the total cooling power of the cooling system for the battery; The maximum heat dissipation power The calculation is as follows: ; in, is the total cooling power of the cooling system for the battery, and m is the number of battery cells in the battery; S53, constructing an average heat generation power data set for each battery cell based on the average heat generation power calculated in several previous detection cycles; S54, performing curve fitting on the average heat generation power data set, determining the number of detection cycles 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 for each battery cell.

[0057] In this embodiment, the heat generated by each cell is calculated to evaluate whether the heat generation limit caused by the increase in the short-circuit resistance of the cell exceeds the thermal management capability of the battery system; and based on the estimated short-circuit resistance values ​​obtained within several detection cycles, the time when the system's heat dissipation capacity limit for each battery cell is reached is predicted, and a long-period short-circuit fault time prediction and alarm are given.

[0058] Specifically, according to the obtained resistance R0, R1 parameters (first-order equivalent circuit), or R0, R1, R2 parameters (second-order equivalent circuit), calculate the average heat generation power of each monomer during the detection period. .

[0059] Taking the second-order equivalent circuit as an example, ; in, is the second-order equivalent circuit parameter of monomer i, is the rated current of the battery.

[0060] Calculate the maximum heat dissipation power of each cell based on the capacity of the battery thermal management system . Assuming that the heat dissipation power of each monomer is equal, then: ; is the total cooling power of the battery by the cooling system.

[0061] Calculate for consecutive n detection cycles to obtain the average heat generation power evaluation data set of monomer i .

[0062] ; For The data sequence formed is fitted, and reference can be made to Figure 3 and obtain the number of detection cycles x required to reach ; when x*y < z, trigger the long-cycle short-circuit fault time prediction alarm. Where y is the starting point interval time between two adjacent detection cycles, and z is the early warning time limit.

[0063] Effect verification: For the calculation in the method Perform simulation verification: Connect a parallel resistor at the output position in the single-cell battery model to simulate the occurrence of a short circuit. Set the resistor to 10 ohms and the battery capacity to 280 Ah. Simulate the difference between the estimated_SOC and the ampere-hour integrated SOC. During the detection cycle with a total duration of 200,000 s (t = 200,000 s), is 0.0525.

[0064] 0.0525 * 280 / 200000 * 3600 = 0.265 A; ; The error is 22.6%.

[0065] Adjust the short-circuit resistance to 5 ohms, 0.103 * 280 / 200000 * 3600 = 0.5192 A; 6.26 ohms, the error is 25%.

[0066] Adjust the short-circuit resistance to 20 ohms, 0.0295 * 280 / 200000 * 3600 = 0.149 A; 21.82 ohms, the error is 9%.

[0067] The actual additional heat generations of the single cell under the three resistor conditions are 1.05 W, 2.11 W, and 0.52 W respectively, all of which are relatively small and cannot be quickly detected by temperature detection. The method plays an early detection role.

[0068] The error generated comes from the acquisition accuracy, the estimation error of the Kalman filter algorithm and the ampere-hour integration algorithm. The method can effectively detect the value of the short-circuit resistance in the order of a few ohms to tens of ohms.

[0069] Please refer to Figure 4 , Embodiment 2 of the present invention is: A terminal 1 for identifying and predicting short circuits in a battery circuit comprises a processor 2, a memory 3, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps in the method for identifying and predicting short circuits in a battery circuit described in the first embodiment above are implemented.

[0070] In summary, the present invention provides a method and terminal for short circuit identification and prediction of a battery circuit, which utilizes circuit-level modeling to simplify calculation complexity, estimates SOC differences by combining Kalman filtering with the ampere-hour integration method, and 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, providing a quantitative basis for short-circuit detection; at the same time, combined with heat generation prediction and heat dissipation capacity analysis, it can provide early warning of thermal runaway risks, solving the detection delay problem caused by insufficient data or complex models in traditional methods.

[0071] 1. The present invention adopts the Kalman filter method for dealing with nonlinear problems, and identifies the battery cell 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.

[0072] 2. The SOC estimation method based on ampere-hour integration cannot identify the current consumed in the internal circuit of the battery cell. When a short circuit occurs, the SOC value is higher than the identification result of the UKF method. When the initial state of each evaluation cycle is aligned, the evolution of the deviation can reflect the capacity consumption of the short-circuit current, thereby reflecting the magnitude of the short-circuit current.

[0073] 3. The present invention provides the calculation result of the short-circuit resistance, which is convenient for accumulating the data of the short-circuit resistance during the operation of the system and lays the foundation for the data-based evaluation method.

[0074] 4. The present invention combines the gradual change of the system's own operating state to provide an adaptive short-circuit resistance determination threshold, which has an adaptive filtering effect and can filter out errors introduced by individual systems.

[0075] 5. According to the thermal runaway prevention mechanism, the internal short circuit of the battery is a gradual irreversible process. When the heat generated by 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 generation to achieve the prediction function, providing a new evaluation basis and direction for the early state judgment of the internal short circuit.

[0076] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying and predicting short circuits in a battery circuit, characterized in that: Includes steps: S1. For each battery cell in the series-connected lithium battery system, an equivalent circuit model including a short-circuit equivalent resistance is established; S2. According to the circuit parameters of the equivalent circuit model, in each detection cycle, for each battery cell, SOC estimation is performed by using a Kalman family algorithm to obtain a first SOC data set, and SOC estimation is performed by using an ampere-hour integration method to obtain a second SOC data set; S3, calculating a short-circuit resistance data set according to the first SOC data set and the second SOC data set; S4. Calculate the deviation between the short-circuit resistance of each battery cell and the short-circuit resistance mean value according to the short-circuit resistance data set, and determine whether there is a fault in the short-circuit resistance of the cell; S5. Calculate the average heat generation power of each battery cell according to the circuit parameters of the equivalent circuit model and the short-circuit resistance data set, obtain the maximum heat dissipation power of the battery cell, and predict the time to reach the heat dissipation capacity limit of the system for each battery cell.

2. The method for short circuit identification and prediction of a battery circuit according to claim 1, characterized in that: 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 cycle is obtained, and variance and mean calculation is performed; Calculate the adaptive alarm threshold value based on the calculated variance and mean; It is determined whether the short-circuit resistance of a single cell exceeds the adaptive alarm threshold range in the current detection, and if so, it is determined that the single cell is faulty.

3. The method for short circuit identification and prediction of a battery circuit according to claim 2, characterized in that: The adaptive alarm threshold includes a lower threshold; The lower threshold The calculation is as follows: ； in, and They respectively represent the mean and variance of the short-circuit resistance of each battery cell obtained in the current detection cycle.

4. The method for short circuit identification and prediction of a battery circuit according to claim 1, characterized in that: Step S5 comprises the steps of: S51, for each detection cycle, 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 cell according to the total cooling power of the cooling system for the battery; S53, constructing an average heat generation power data set for each battery cell based on the average heat generation power calculated in several previous detection cycles; S54, performing curve fitting on the average heat generation power data set, determining the number of detection cycles 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 for each battery cell.

5. The method for short circuit identification and prediction of a battery circuit according to claim 4, characterized in that: The average heat generation power The calculation is as follows: ； in, and represents the q-order equivalent circuit parameters of battery cell i, is the battery rated current, is the equivalent average short-circuit current of monomer i at time t in the current detection cycle, and are the sampled voltages of battery cell i at the initial moment and at moment t in the current detection cycle, It represents the equivalent short-circuit resistance of battery cell i from the initial moment to moment t in the current detection cycle.

6. A method for short circuit identification and prediction of a battery circuit according to claim 4, characterized in that: The maximum heat dissipation power The calculation is as follows: ； in, is the total cooling power of the cooling system for the battery, and m is the number of battery cells.

7. The method for short circuit identification and prediction of a battery circuit according to claim 1, characterized in that: Step S3 is specifically as follows: 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 cell in the j-th detection cycle is recorded as , construct a short-circuit resistance dataset; in, It represents the equivalent average short-circuit current of battery cell i at time t in the current detection cycle, and They represent the sampled voltages of battery cell i at the initial moment and at moment t in the current detection cycle, respectively. represents the capacity of the battery cell at time i, It represents the SOC value of battery cell i at time t estimated by the ampere-hour integration method in the current detection cycle. It represents the SOC value of battery cell i at time t estimated by the Kalman family algorithm in the current detection cycle.

8. The method for short circuit identification and prediction of a battery circuit according to claim 1, characterized in that: 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, the discretized state equation is constructed: ； in, Q c Represents the monomer capacity, , , as well as 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 Respectively represent the SOC state of the battery cell at time t and time t+1 and the voltage state of the equivalent capacitor C1 and Cq, I t is the monomer current at time t, T s is the calculation step size, W is the process noise; Construct the measurement equation for the monomer: ； in, e t+1 It indicates the voltage measured at the output end of the monomer at time t+1. U OCV is the single cell open circuit voltage obtained, I t+1 is the monomer current at time t+1, R 0_t+1 is the DC internal resistance of the monomer at time t+1, and V is the measurement noise; Use the UKF algorithm or the EKF algorithm to calculate the i-th of all m monomers being evaluated at time t SOC i_t The estimated value of , and form a state of charge set to obtain the first data set : 。 9. The method for short circuit identification and prediction of a battery circuit according to claim 1, characterized in that: The estimation of the second SOC data set comprises the steps of: The state of charge of all battery cells is estimated using the ampere-hour integration method: ； in, and are the state of charge of battery cell i at time t-1 in the detection cycle and the state of charge at the initial moment of the detection cycle, is the charging or discharging efficiency of battery cell i at time t, is the average charge or discharge current of battery cell i from time t-1 to t, is the duration from t-1 to time t, is the rated capacity of battery cell i; According to the state of charge of m battery cells, the second SOC data set is obtained : ； Among them, at the initial moment of the current detection cycle, the SOC calculated by the ampere-hour integration method and the SOC calculated by the Kalman family algorithm have the same initial value.

10. A terminal for identifying and predicting short circuits in a battery circuit, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps in the method for short circuit identification and prediction of a battery circuit as described in any one of claims 1 to 9 are implemented.

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