Battery internal short circuit quantitative detection method and system based on multi-multiple discharge difference

Abstract

The application provides a battery internal short circuit quantitative detection method and system based on multi-multiple discharge difference, comprising the following steps: S1, constructing an improved Peukert equation suitable for internal short circuit batteries; S2, constructing a nonlinear least square optimization model for parameter identification of the improved Peukert equation; S3, comprehensively considering battery test time consumption and internal short circuit detection accuracy to obtain an optimal discharge combination; S4, performing online multi-multiple discharge test on a battery to be detected based on the optimal discharge combination to obtain test data; and S5, combining the test data and the improved Peukert equation to quantitatively evaluate the internal short circuit resistance value of the battery. The improved Peukert equation realizes quantitative expression of the relationship between the constant current discharge current and the electric quantity of the battery under the internal short circuit condition by fusing the internal short circuit leakage characteristics and the multi-multiple discharge law of the battery.

Description

Technical Field

[0001] This invention relates to the field of battery internal short circuit detection technology, and more specifically, to a quantitative detection method and system for battery internal short circuits based on differences in discharge rates. Background Technology

[0002] Internal short circuits are a major cause of battery thermal runaway, and accurate detection of internal short circuit conditions can provide important evidence for early warning of battery thermal runaway. However, the electrothermal characteristics of a battery are not significant in the early stages of an internal short circuit, making it difficult to directly determine the internal short circuit condition using physical quantities such as voltage and temperature. Currently, accurate diagnosis of battery internal short circuits faces significant challenges. Existing battery internal short circuit detection methods can be broadly classified into four categories: one is detection methods based on inconsistencies between individual battery cells. This type of method targets battery modules, utilizing the difference in state parameters between healthy batteries and internally short-circuited batteries under the same operating conditions within the same module to qualitatively or quantitatively detect the internal short-circuit state of the battery. Patent document CN113552496A (application number: CN202110727244.X) discloses a method for diagnosing internal short-circuit faults in battery series modules based on voltage cosine similarity. This type of method is only applicable to battery modules, and the inconsistency of individual cells within the module has a significant impact on the results. The second type is a detection method based on battery models. This type of method constructs a battery model, extracts characteristic parameters representing the internal short-circuit state of the battery, and then performs internal short-circuit diagnosis. Patent document CN116520194A (application number: 202310551305.0) discloses a method for diagnosing internal short-circuit faults and capacity loss in lithium-ion batteries. Patent document CNCN1 Patent document 15219905A (application number: 202210675650.0) discloses an online detection method, device, and storage medium for internal short circuits in batteries; patent document CN106154172A (application number: 201610440320.8) discloses a quantitative estimation method for the degree of internal short circuits in lithium-ion power batteries. The accuracy of this type of method depends on the battery model, but high-precision battery models often require complete test data and have high computational complexity, which limits the practical application of this type of method; thirdly, there are detection methods based on self-discharge. Patent document CN113848489A (application number: 202111063248.9) discloses a short circuit identification method, device, and storage medium for batteries. This type of method is based on the characteristic that internal short circuits cause battery self-discharge, detects the changes in the state parameters of the battery after a long period of rest, and then quantitatively detects the internal short circuit state of the battery. This type of method usually requires the battery under test to be left to rest for a long time, and the practical application effect is not ideal. Fourthly, detection methods based on external devices. Patent document CN109877064B (application number: 201910041319.1) discloses a method for rapid screening of self-discharge of parallel batteries; patent document CN108957338B (application number: 201810660711.X) discloses a method for extracting the internal short-circuit current of a power battery. This type of method uses external high-precision meters and other devices to determine the magnitude and direction of the current at different locations inside the battery module, thereby identifying the internally short-circuited battery cells.This type of method requires external high-precision hardware testing equipment. Considering that electric vehicles, energy storage power stations and other scenarios involve a large number of batteries and often use enclosed casings, the testing of external equipment is difficult and its practicality is limited. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for quantitative detection of internal short circuits in batteries based on differences in discharge rates.

[0004] A method for quantitative detection of internal short circuits in a battery based on differences in discharge rates, according to the present invention, includes:

[0005] Step S1: Construct the improved Peukert equation suitable for internally short-circuited batteries;

[0006] Step S2: Construct a nonlinear least squares optimization model for improving parameter identification of the Peukert equation;

[0007] Step S3: Taking into account the battery testing time and the accuracy of internal short circuit detection, obtain the optimal discharge combination;

[0008] Step S4: Based on the preferred discharge combination, perform online multi-rate discharge tests on the battery under test to obtain test data;

[0009] Step S5: Quantitatively evaluate the internal short-circuit resistance of the battery by combining test data and the improved Peukert equation;

[0010] The improved Peukert equation achieves a quantitative expression of the relationship between the constant current discharge current and the charge of the battery under internal short-circuit conditions by integrating the internal short-circuit leakage characteristics and the battery's multi-rate discharge law.

[0011] Preferably, step S1 employs:

[0012]

[0013] Where n is the number of data points sampled; a, b, and the internal short-circuit resistance R isc These are the parameters of the improved Peukert equation; I load U is the load current; t This is the battery terminal voltage.

[0014] Preferably, step S2 employs:

[0015] Objective function:

[0016]

[0017] Constraints:

[0018]

[0019] a, b, R isc >0

[0020] Where n is the number of data points sampled; m is the cumulative number of discharges in the multi-rate discharge test; a, b, and the internal short-circuit resistance R isc These are the parameters of the improved Peukert equation; I load U is the load current; t This is the battery terminal voltage.

[0021] Preferably, step S3 employs the following methods:

[0022] Step S3.1: Construct a complete set of discharge currents for multi-rate discharge tests by considering the upper and lower bounds of the battery discharge current. min , ..., I max Based on this, a series of current sets containing different discharge currents are generated through permutations and combinations; considering the battery charge and discharge voltage limitations, a full charge and discharge voltage range (V) is constructed. min V max Based on this, a series of voltage windows containing different discharge voltage ranges are generated with preset values ​​varying at intervals; by combining the current set and the voltage windows, discharge combinations corresponding to different discharge current and voltage ranges are constructed.

[0023] Step S3.2: Substitute the discharge data corresponding to the generated discharge combinations into the improved Peukert equation, and obtain the estimated internal short-circuit resistance R through parameter identification. isc_estimated The actual value of the internal short-circuit resistance R isc_real Compare and calculate the estimation error θ of the internal short-circuit resistance for each discharge combination:

[0024]

[0025] Step S3.3: Calculate the overall charge / discharge test time t and error θ for different discharge combinations, and perform maximum-minimum normalization on θ and t:

[0026]

[0027]

[0028] calculate and 2-norm

[0029]

[0030] L2 norm As a comprehensive performance evaluation index of the discharge assembly;

[0031] Step S3.4: Compare the comprehensive performance evaluation indicators of each discharge combination. The smaller the index value, the better the test time and detection accuracy of the discharge combination. The discharge combination with the minimum index value is selected as the final preferred discharge combination.

[0032] Preferably, step S4 involves: first charging the battery under test at a constant current and constant voltage rate of 1C to the cutoff voltage; then, performing multi-rate discharge tests on the battery under test using the preferred discharge combinations to obtain multi-rate discharge data for the corresponding voltage window; throughout the process, the battery terminal voltage U... t and load current I load All data are collected synchronously at a preset sampling frequency.

[0033] Preferably, step S5 involves: based on the acquired end-point voltage U t and load current I load The parameters of the improved Peukert equation were identified, and the identified parameters R were obtained. isc This is the estimated result of the short-circuit resistance inside the battery.

[0034] A quantitative detection system for internal short circuits in a battery based on differences in discharge rates, according to the present invention, includes:

[0035] Module M1: Constructing an improved Peukert equation suitable for internally short-circuited batteries;

[0036] Module M2: Constructs a nonlinear least squares optimization model for improving parameter identification of the Peukert equation;

[0037] Module M3: Optimal discharge combination is obtained by comprehensively considering battery testing time and internal short-circuit detection accuracy;

[0038] Module M4: Based on the selected discharge combination, online multi-rate discharge tests are performed on the battery under test to obtain test data;

[0039] Module M5: Quantitatively evaluates the internal short-circuit resistance of a battery by combining test data and the improved Peukert equation;

[0040] The improved Peukert equation achieves a quantitative expression of the relationship between the constant current discharge current and the charge of the battery under internal short-circuit conditions by integrating the internal short-circuit leakage characteristics and the battery's multi-rate discharge law.

[0041] Preferably, module M1 adopts:

[0042]

[0043] Where n is the number of data points sampled; a, b, and the internal short-circuit resistance R iscThese are the parameters of the improved Peukert equation; I load U is the load current; t This refers to the battery terminal voltage.

[0044] The module M2 adopts:

[0045] Objective function:

[0046]

[0047] Constraints:

[0048]

[0049] a, b, R isc >0

[0050] Where n is the number of data points sampled; m is the cumulative number of discharges in the multi-rate discharge test; a, b, and the internal short-circuit resistance R isc These are the parameters of the improved Peukert equation; I load U is the load current; t This is the battery terminal voltage.

[0051] Preferably, the module M3 adopts:

[0052] Module M3.1: Constructing a complete set of discharge currents for multi-rate discharge tests by considering the upper and lower bounds of the battery discharge current. min , ..., I max Based on this, a series of current sets containing different discharge currents are generated through permutations and combinations; considering the battery charge and discharge voltage limitations, a full charge and discharge voltage range {V} is constructed. min V max Based on this, a series of voltage windows containing different discharge voltage ranges are generated with preset values ​​varying at intervals; by combining the current set and the voltage windows, discharge combinations corresponding to different discharge current and voltage ranges are constructed.

[0053] Module M3.2: Substitute the discharge data corresponding to the generated discharge combinations into the improved Peukert equation, and obtain the estimated internal short-circuit resistance R through parameter identification. isc_estimated The actual value of the internal short-circuit resistance R isc_real Compare and calculate the estimation error θ of the internal short-circuit resistance for each discharge combination:

[0054]

[0055] Module M3.3: Calculates the overall charge / discharge test time t and error θ for different discharge combinations, and performs maximum-minimum normalization on θ and t.

[0056]

[0057]

[0058] calculate and 2-norm

[0059]

[0060] L2 norm As a comprehensive performance evaluation index of the discharge assembly;

[0061] Module M3.4: Compare the comprehensive performance evaluation indicators of various discharge combinations The smaller the index value, the better the test time and detection accuracy of the discharge combination. The discharge combination with the minimum index value is selected as the final preferred discharge combination.

[0062] Preferably, module M4 employs the following method: the battery under test is first charged at a constant current and constant voltage rate of 1C to the cutoff voltage; then, using the preferred discharge combinations, the battery under test is subjected to multi-rate discharge tests to obtain multi-rate discharge data corresponding to the voltage window; throughout the process, the battery terminal voltage U... t and load current I load All data are collected synchronously at a preset sampling frequency;

[0063] The module M5 adopts: based on the acquired road-end voltage U t and load current I load The parameters of the improved Peukert equation were identified, and the identified parameters R were obtained. isc This is the estimated result of the short-circuit resistance inside the battery.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. This invention constructs an improved Peukert equation for coupled internal short-circuit resistance, which simplifies the empirical formula to quantitatively describe the relationship between the discharge current and discharge capacity of an internal short-circuit battery under constant current discharge conditions. This eliminates the need to construct a complex battery model, thereby achieving accurate quantitative detection of internal short-circuit resistance with lower computational complexity.

[0066] 2. This invention reduces the time required for internal short circuit detection and improves the accuracy of internal short circuit detection by constructing a comprehensive evaluation index that integrates test time and detection accuracy, optimizing the discharge current set and discharge voltage window, and reducing the number of multi-rate discharges.

[0067] 3. This invention performs quantitative detection of internal short circuits by analyzing the difference in charge during multiple rate discharges of the battery, without relying on external high-precision battery state detection equipment, thus achieving flexible and low-cost internal short circuit detection.

[0068] 4. This invention performs internal short circuit detection by comparing the differences in discharge rates of individual cells. It does not rely on its own historical operating data or healthy battery data as a reference. It is applicable to multiple scenarios such as single cells and modules, and can also be extended to batteries with different chemical systems that obey the Peukert equation, such as lead-acid batteries and sodium-ion batteries, thereby achieving a more universal quantitative detection of internal short circuits. Attached Figure Description

[0069] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0070] Figure 1 This is a flowchart of the internal short-circuit detection method.

[0071] Figure 2 This is a schematic diagram of the equivalent circuit model of an internally short-circuited battery.

[0072] Figure 3 A schematic diagram of the discharge combination optimization algorithm. Detailed Implementation

[0073] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0074] Example 1

[0075] The present invention provides a method and system for quantitative detection of internal short circuit in a battery based on the difference in discharge rates, comprising: constructing an improved Peukert equation coupled with the internal short circuit resistance to quantitatively describe the relationship between the discharge current and discharge capacity of the battery under constant current discharge conditions in a simplified empirical formula form, and then combining parameter identification methods to quantitatively evaluate the internal short circuit resistance of the battery.

[0076] The method for quantitative detection of internal short circuits in batteries based on differences in discharge rates, such as... Figure 1-3 As shown, it includes:

[0077] Step 1: Combining the internal short-circuit leakage characteristics and the multi-rate discharge law of the battery, construct an improved Peukert equation suitable for internal short-circuit batteries;

[0078] Step 2: Construct a nonlinear least squares optimization model to improve parameter identification for the Peukert equation;

[0079] Step 3: Taking into account the battery testing time and the accuracy of internal short circuit detection, implement the optimal discharge combination;

[0080] Step 4: Based on the optimized discharge combination, perform online multi-rate discharge tests on the battery under test;

[0081] Step 5: Combine test data and the improved Peukert equation to quantitatively evaluate the short-circuit resistance inside the battery.

[0082] Specifically, step 1 involves: constructing an improved Peukert equation;

[0083] Under constant current discharge or near-constant current discharge conditions, the discharge capacity and discharge current of a lithium-ion battery can be quantitatively described by the Peukert equation, namely:

[0084] C I =a*I b

[0085] Where a and b are constants, related to the battery electrochemical system, ambient temperature, aging state, etc.; I represents the discharge current; C I Indicates discharge capacity;

[0086] For batteries experiencing internal short circuits, an internal electronic circuit forms, leading to self-discharge. During self-discharge, both the discharge current and discharge capacity obtained from external circuit testing are affected by the internal short-circuit leakage current, requiring correction based on the internal short-circuit resistance. Therefore, this invention proposes an improved Peukert equation, which can be used to describe the quantitative relationship between the discharge current and discharge capacity of an internally short-circuited battery under constant current discharge conditions. Batteries experiencing internal short circuits can typically be... Figure 3 The equivalent circuit model shown is used to describe it. Where R... isc R represents the short-circuit resistance inside the battery. load Indicates an external load connected to the battery, U t This represents the battery terminal voltage and the actual discharge current I. discharge Including load current I load and internal short-circuit current I isc Actual discharge capacity Q dischar ge Including load power consumption Q load and the leakage current Q due to internal short circuit isc , combined Figure 3 The circuit model shown represents the actual battery discharge current I under internal short-circuit conditions. discharge and actual discharge capacity Q discharge The following corrections can be made:

[0087]

[0088]

[0089] Furthermore, based on the corrected discharge current and corrected discharge charge, the Peukert equation is improved as follows:

[0090]

[0091] Where n is the number of data points sampled; a, b, and the internal short-circuit resistance R isc These are the parameters of the improved Peukert equation, and their specific values ​​can be obtained through parameter identification.

[0092] Specifically, step 2 employs: improved Peukert equation parameter identification. The parameter identification of the improved Peukert equation can be transformed into the following nonlinear least squares optimization model, where m is the cumulative number of discharges in the multi-rate discharge test.

[0093] Objective function:

[0094]

[0095] Constraints:

[0096]

[0097] a, b, R isc >0

[0098] By solving the above optimization problem, the parameter identification of the Peukert equation can be improved.

[0099] Specifically, step 3 employs: optimal discharge combination. The improved Peukert equation is used for parameter identification based on multi-rate discharge data. The discharge current and voltage window have a significant impact on the overall test time and parameter identification accuracy. Therefore, this invention aims to reduce test time and improve detection accuracy, and proposes a method such as... Figure 3 The algorithm for optimizing the discharge current set and voltage window shown is as follows, where I min The lower limit of the discharge current is set as the applicable lower current limit for the Peukert equation; I max The upper limit of the discharge current is set as the upper limit of the safe operating current of the battery system; V min This is the lower limit of the discharge voltage, set as the battery discharge cutoff voltage; V max The upper limit of the discharge voltage is set as the battery charging cutoff voltage. Step 3 includes the following steps:

[0100] Step 3.1: Discharge Combination Generation. Considering the upper and lower bounds of the battery discharge current, a complete set of discharge currents for multi-rate discharge tests {I} is constructed. min , ..., I max Based on this, a series of current sets containing different discharge currents are generated through permutations and combinations. Taking into account the battery charge and discharge voltage limitations, a full charge and discharge voltage range {V} is constructed. min V max Based on this, a series of voltage windows containing different discharge voltage ranges are generated with a variation interval of 0.01V. Combining the generated current sets and voltage windows, discharge combinations corresponding to different discharge current and voltage ranges are constructed.

[0101] Step 3.2: Error Analysis of Internal Short-Circuit Resistance Estimation. The discharge data corresponding to the series of discharge combinations generated in Step 1 are substituted into the improved Peukert equation, and the estimated internal short-circuit resistance R is obtained through parameter identification. isc_estimated And compared with the true value of the internal short-circuit resistance R isc_real Compare and calculate the estimation error θ of the internal short-circuit resistance corresponding to each discharge combination. The formula for calculating θ is as follows:

[0102]

[0103] Step 3.3: Comprehensive Performance Evaluation of Discharge Combinations. The overall charge / discharge test time t and parameter identification error θ for different discharge combinations are calculated, and θ and t are normalized to their maximum and minimum values ​​using the following formula:

[0104]

[0105]

[0106] The normalization result is denoted as and Then, calculate and 2-norm The calculation formula is as follows:

[0107]

[0108] L2 norm As a comprehensive performance evaluation index of the discharge assembly.

[0109] Step 3.4: Optimal Selection of Discharge Combinations. Compare the comprehensive performance evaluation indicators of each discharge combination. The smaller the index value, the better the test time and detection accuracy of the discharge combination. The discharge combination (discharge current set {I}) corresponding to the minimum index value is... selected_1 I selected_2 , ...} and discharge voltage window {Vselected_begin V selected_end}) as the final preferred discharge combination.

[0110] Specifically, step 4 employs an online multi-rate discharge test. The battery under test is first charged at a constant current and constant voltage of 1C to the cutoff voltage; then, multi-rate discharge tests are performed on the battery under test using the preferred discharge combination obtained in step 3, and multi-rate discharge data for the corresponding voltage window is obtained. Throughout the process, the battery terminal voltage and load current are synchronously acquired at a sampling frequency of 1Hz.

[0111] Specifically, step 5 involves: quantitative evaluation of the internal short-circuit resistance. This is based on the end-point voltage U obtained in step 4. t and load current I load The parameters of the improved Peukert equation were identified, and the identified parameters R were obtained. isc This is the estimated result of the short-circuit resistance inside the battery.

[0112] The present invention also provides a quantitative detection system for internal short circuits in batteries based on differences in discharge rates. The quantitative detection system for internal short circuits in batteries based on differences in discharge rates can be implemented by executing the process steps of the quantitative detection method for internal short circuits in batteries based on differences in discharge rates. That is, those skilled in the art can understand the quantitative detection method for internal short circuits in batteries based on differences in discharge rates as a preferred embodiment of the quantitative detection system for internal short circuits in batteries based on differences in discharge rates.

[0113] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0114] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for quantitative detection of internal short circuits in batteries based on differences in discharge rates, characterized in that, include: Step S1: Construct the improved Peukert equation suitable for internally short-circuited batteries; Step S2: Construct a nonlinear least squares optimization model for improving parameter identification of the Peukert equation; Step S3: Taking into account the battery testing time and the accuracy of internal short circuit detection, obtain the optimal discharge combination; Step S4: Based on the preferred discharge combination, perform online multi-rate discharge tests on the battery under test to obtain test data; Step S5: Quantitatively evaluate the internal short-circuit resistance of the battery by combining test data and the improved Peukert equation; The improved Peukert equation achieves a quantitative expression of the relationship between the constant current discharge current and the charge of the battery under internal short-circuit conditions by integrating the internal short-circuit leakage characteristics and the battery's multi-rate discharge law. Step S1 adopts the following: in, The number of data points sampled; , and internal short-circuit resistance These are the parameters of the improved Peukert equation; This is the load current; This is the battery terminal voltage.

2. The method for quantitative detection of internal short circuits in batteries based on differences in discharge rates according to claim 1, characterized in that, Step S2 employs the following: Objective function: Constraints: in, The number of data points sampled; This represents the cumulative number of discharges in the multi-rate discharge test. , and internal short-circuit resistance These are the parameters of the improved Peukert equation; This is the load current; This is the battery terminal voltage.

3. The method for quantitative detection of internal short circuits in batteries based on differences in discharge rates according to claim 1, characterized in that, Step S3 employs the following: Step S3.1: Construct a complete set of discharge currents for multi-rate discharge tests by considering the upper and lower bounds of the battery discharge current. Based on this, a series of current sets containing different discharge currents are generated through permutations and combinations; taking into account the battery charge and discharge voltage limitations, a full charge and discharge voltage range is constructed. Based on this, a series of voltage windows containing different discharge voltage ranges are generated with preset values ​​as the variation interval; by combining the current set and the voltage windows, discharge combinations corresponding to different discharge current and voltage ranges are constructed. Step S3.2: Substitute the discharge data corresponding to the generated discharge combinations into the improved Peukert equation, and obtain the estimated internal short-circuit resistance value through parameter identification. The actual value of the internal short-circuit resistance Compare and calculate the estimation error of the internal short-circuit resistance corresponding to each discharge combination. : ； Step S3.3: Calculate the overall charge-discharge test time for different discharge combinations. and error and to and Perform max-min normalization: calculate and L2 norm : L2 norm As a comprehensive performance evaluation index of the discharge assembly; Step S3.4: Compare the comprehensive performance evaluation indicators of each discharge combination. The smaller the index value, the better the test time and detection accuracy of the discharge combination. The discharge combination with the minimum index value is selected as the final preferred discharge combination.

4. The method for quantitative detection of internal short circuits in a battery based on differences in discharge rates according to claim 1, characterized in that, Step S4 involves: first charging the battery under test at a constant current and constant voltage of 1C to the cutoff voltage; then, using the preferred discharge combination, performing multi-rate discharge tests on the battery under test to obtain multi-rate discharge data for the corresponding voltage window. Throughout the process, the battery terminal voltage and load current All data are collected synchronously at a preset sampling frequency.

5. The method for quantitative detection of internal short circuits in a battery based on differences in discharge rates according to claim 1, characterized in that, Step S5 employs: based on the acquired end-point voltage and load current Parameter identification was performed on the improved Peukert equation, and the identified parameters were... This is the estimated result of the short-circuit resistance inside the battery.

6. A quantitative detection system for internal short circuits in batteries based on differences in discharge rates, characterized in that, include: Module M1: Constructing an improved Peukert equation suitable for internally short-circuited batteries; Module M2: Constructs a nonlinear least squares optimization model for improving parameter identification of the Peukert equation; Module M3: Optimal discharge combination is obtained by comprehensively considering battery testing time and internal short-circuit detection accuracy; Module M4: Based on the selected discharge combination, online multi-rate discharge tests are performed on the battery under test to obtain test data; Module M5: Quantitatively evaluates the internal short-circuit resistance of a battery by combining test data and the improved Peukert equation; The improved Peukert equation achieves a quantitative expression of the relationship between the constant current discharge current and the charge of the battery under internal short-circuit conditions by integrating the internal short-circuit leakage characteristics and the battery's multi-rate discharge law. The module M1 adopts: in, The number of data points sampled; , and internal short-circuit resistance These are the parameters of the improved Peukert equation; This is the load current; This is the battery terminal voltage.

7. The battery internal short circuit quantitative detection system based on multi-rate discharge difference according to claim 6, characterized in that, The module M2 adopts: Objective function: Constraints: in, The number of data points sampled; This represents the cumulative number of discharges in the multi-rate discharge test. , and internal short-circuit resistance These are the parameters of the improved Peukert equation; This is the load current; This is the battery terminal voltage.

8. The battery internal short circuit quantitative detection system based on multi-rate discharge difference according to claim 6, characterized in that, The module M3 adopts: Module M3.1: Constructs a complete set of discharge currents for multi-rate discharge tests by comprehensively considering the upper and lower bounds of battery discharge current. Based on this, a series of current sets containing different discharge currents are generated through permutations and combinations; taking into account the battery charge and discharge voltage limitations, a full charge and discharge voltage range is constructed. Based on this, a series of voltage windows containing different discharge voltage ranges are generated with preset values ​​as the variation interval; by combining the current set and the voltage windows, discharge combinations corresponding to different discharge current and voltage ranges are constructed. Module M3.2: Substitute the discharge data corresponding to the generated discharge combinations into the improved Peukert equation, and obtain the estimated internal short-circuit resistance through parameter identification. The actual value of the internal short-circuit resistance Compare and calculate the estimation error of the internal short-circuit resistance corresponding to each discharge combination. : ； Module M3.3: Calculates the overall charge / discharge test time for different discharge combinations. and error and to and Perform max-min normalization: calculate and L2 norm : L2 norm As a comprehensive performance evaluation index of the discharge assembly; Module M3.4: Compare the comprehensive performance evaluation indicators of various discharge combinations The smaller the index value, the better the test time and detection accuracy of the discharge combination. The discharge combination with the minimum index value is selected as the final preferred discharge combination.

9. The battery internal short circuit quantitative detection system based on multi-rate discharge difference according to claim 6, characterized in that, The module M4 employs the following method: the battery under test is first charged at a constant current and constant voltage of 1C to the cutoff voltage; then, the battery under test is subjected to multi-rate discharge tests using the preferred discharge combinations to obtain multi-rate discharge data for the corresponding voltage window. Throughout the process, the battery terminal voltage and load current All data are collected synchronously at a preset sampling frequency; The module M5 adopts: based on the acquired road-end voltage and load current Parameter identification was performed on the improved Peukert equation, and the identified parameters were... This is the estimated result of the short-circuit resistance inside the battery.

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