A power battery micro-short circuit diagnosis method and system based on multi-scenario fusion

Abstract

The present invention discloses a power battery micro-short circuit diagnosis method and system based on multi-scenario fusion, which utilizes the difference in capacitance caused by self-discharge under the dynamic charging and parking static conditions of the vehicle, and combines the confidence coefficient to comprehensively obtain the equivalent self-discharge internal resistance value under the dynamic charging and parking static conditions of the vehicle, so as to accurately identify the internal micro-short circuit degree of the battery and provide early warning. The implementation of the present invention has higher accuracy and stronger robustness, and can improve the safety of the power battery.

Description

Technical Field

[0001] The present invention relates to the technical field of micro-short circuit diagnosis of power batteries, and in particular to a power battery micro-short circuit diagnosis method and system based on multi-scenario fusion. Background Art

[0002] For electric vehicles and other power tools, the safety of power batteries during use has always been a key concern. In some cases, accidents of fire and explosion caused by thermal runaway are an urgent problem to be solved in the electric vehicle field.

[0003] Generally speaking, thermal runaway is mainly caused by two aspects: one is that foreign matter invades the battery pack due to mechanical collision of the vehicle or other battery connection components are displaced after collision, resulting in a short circuit in the battery, which generates a large amount of heat accumulation and leads to thermal runaway; the other is that the battery cell has foreign particles or moisture in the manufacturing process, and the battery cell is not properly controlled, and the battery is charged beyond its capacity during use, resulting in lithium deposition on the negative electrode of the battery cell, and the lithium deposition slowly grows and punctures the diaphragm, causing a short circuit inside the battery cell and thermal runaway.

[0004] For the first type caused by mechanical collision, in the existing technology, the battery and vehicle structure design are mainly relied on to ensure that thermal runaway is avoided; for the second type of thermal runaway, its main cause lies in the manufacturing and use process of the battery cell, and this process is a slowly changing and developing process, and it is currently impossible to evaluate it through direct detection and measurement methods. In the existing technology, the BMS needs to use software algorithms to identify and warn in advance to prevent the battery from entering a state of thermal runaway. However, the thermal runaway identified by the method of extracting feature points before the occurrence of thermal runaway is already a characteristic phenomenon of thermal runaway, and thermal runaway is unavoidable. Passengers can only be warned in advance to escape the vehicle as soon as possible, but accidents cannot be avoided. Summary of the invention

[0005] The technical problem to be solved by the present invention is that the present invention provides a power battery micro-short circuit diagnosis method and system based on multi-scenario fusion, which can accurately identify the degree of internal micro-short circuit of the battery under dynamic charging and static parking conditions, so as to provide early warning and avoid further deterioration of the battery and cause thermal runaway.

[0006] In order to solve the above technical problems, as one aspect of the present invention, a power battery micro-short circuit diagnosis method based on multi-scenario fusion is provided, which comprises the following steps:

[0007] Step S10: when charging the power battery of the vehicle, sampling the voltage of all selected single cells within a preset sampling interval;

[0008] Step S11, according to the sampling result, obtaining the current charging capacity of each single battery in the sampling interval;

[0009] Step S12, comparing the current charging capacity of each single battery with the corresponding historical charging capacity recorded in the previous charging, calculating the charging capacity difference between the two, and calculating the first self-discharge internal resistance of each single battery according to the charging capacity difference;

[0010] Step S13, after the vehicle is stationary and the battery polarization completely disappears, collecting the open circuit voltage data of each single battery at a preset first time point and a second time point;

[0011] Step S14, calculating the static capacitance difference within the interval between the two time points according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtaining the second self-discharge internal resistance of each single cell in combination with the time interval;

[0012] Step S15, calculating and obtaining the final self-discharge internal resistance value of each single cell according to the first self-discharge internal resistance and the second self-discharge internal resistance of each single cell in combination with the respective pre-calibrated confidence coefficients;

[0013] Step S16, determining the current battery micro-short circuit degree according to the final self-discharge internal resistance values ​​of all single cells.

[0014] Wherein, the step S12 further comprises:

[0015] Determine the ratio of the charge capacity difference to the time length of the sampling interval as the leakage current value of the single cell;

[0016] The average voltage of the single battery in the sampling interval is calculated, and the first self-discharge internal resistance of the single battery is determined in combination with the leakage current value.

[0017] Wherein, after step S11, the method further includes:

[0018] A two-dimensional curve is formed and stored according to the sampled voltage of each single battery in the sampling interval and the corresponding battery capacity.

[0019] Wherein, the step S14 further comprises:

[0020] According to the collected open circuit voltage data of each single cell at the first time point and the second time point, the OCV-SOC characteristic curve at the current temperature is retrieved to obtain the SOC difference within the interval between the two time points;

[0021] Obtaining a static capacity difference value according to the SOC difference value, and correcting the static capacity difference value by using the current SOH value of the battery;

[0022] The second self-discharge internal resistance of each single cell is obtained according to the corrected static capacitance difference, the time interval, and the average value of the open circuit voltage data at the first time point and the second time point.

[0023] Wherein, the step S15 further comprises:

[0024] The final self-discharge internal resistance Ris of each single cell is calculated using the following formula:

[0025] Ris=αRc+βRs

[0026] Wherein, Rc is the first self-discharge internal resistance of a certain single cell, Rs is the second discharge internal resistance of the single cell; α is the confidence level corresponding to the first self-discharge internal resistance, β is the confidence level corresponding to the second self-discharge internal resistance, and α+β=1.

[0027] Wherein, the step S16 further comprises:

[0028] The final self-discharge internal resistance value of each single battery is compared with a preset resistance threshold to determine the battery cells with micro-short circuits, and the battery micro-short circuit degree of the current power battery is determined according to the number of battery cells with micro-short circuits.

[0029] Accordingly, another aspect of the present invention further provides a power battery micro-short circuit diagnosis system based on multi-scenario fusion, which includes:

[0030] A charging sampling unit is used to sample the voltage of all selected single cells within a preset sampling interval when charging the power battery of the vehicle;

[0031] The current charging capacity calculation unit is used to obtain the current charging capacity charged to each single battery in the sampling interval according to the sampling result;

[0032] A first self-discharge internal resistance obtaining unit is used to compare the current charging capacity of each single cell with the corresponding historical charging capacity recorded in the previous charging, calculate the charging capacity difference between the two, and obtain the first self-discharge internal resistance of each single cell according to the charging capacity difference;

[0033] A static sampling unit, used to collect the open circuit voltage data of each single battery at a preset first time point and a second time point after the vehicle is stationary and the battery polarization completely subsides;

[0034] A second self-discharge internal resistance obtaining unit is used to calculate the static capacitance difference within the interval between the two time points according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtain the second self-discharge internal resistance of each single cell in combination with the time interval;

[0035] A final self-discharge internal resistance value obtaining unit, used to calculate and obtain the final self-discharge internal resistance value of each single cell according to the first self-discharge internal resistance and the second self-discharge internal resistance of each single cell in combination with the respective pre-calibrated confidence coefficients;

[0036] The battery micro-short circuit degree judgment unit is used to determine the current battery micro-short circuit degree according to the final self-discharge internal resistance values ​​of all single batteries.

[0037] Wherein, the first self-discharge internal resistance obtaining unit further comprises:

[0038] a leakage current calculation unit, configured to determine a ratio of the charge capacity difference to the time length of the sampling interval as a leakage current value of the single cell;

[0039] The first self-discharge internal resistance calculation unit is used to calculate the voltage average value of the single battery during the sampling interval, and determine the first self-discharge internal resistance of the single battery in combination with the leakage current value.

[0040] Among them, further include:

[0041] The storage unit is used to form and store a two-dimensional curve according to the sampled voltage of each single battery in the sampling interval and the corresponding battery capacity.

[0042] Wherein, the second self-discharge internal resistance obtaining unit further comprises:

[0043] An SOC difference obtaining unit is used to retrieve the OCV-SOC characteristic curve at the current temperature according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtain the SOC difference within the interval between the two time points;

[0044] a correction unit, configured to obtain a static capacity difference value according to the SOC difference value, and to correct the static capacity difference value by using a current SOH value of the battery;

[0045] The second self-discharge internal resistance calculation unit is used to obtain the second self-discharge internal resistance of each single cell according to the corrected static capacitance difference, the time interval, and the average value of the open circuit voltage data at the first time point and the second time point.

[0046] The final self-discharge internal resistance value obtaining unit is specifically used to calculate the final self-discharge internal resistance value Ris of each single battery using the following formula:

[0047] Ris=αRc+βRs

[0048] Wherein, Rc is the first self-discharge internal resistance of a certain single cell, Rs is the second discharge internal resistance of the single cell; α is the confidence level corresponding to the first self-discharge internal resistance, β is the confidence level corresponding to the second self-discharge internal resistance, and α+β=1.

[0049] Among them, the battery micro-short circuit degree judgment unit is specifically used to compare the final self-discharge internal resistance value of each single battery with a preset resistance threshold, determine the battery cells with micro-short circuits, and determine the battery micro-short circuit degree of the current power battery according to the number of battery cells with micro-short circuits.

[0050] The implementation of the embodiments of the present invention has the following beneficial effects:

[0051] The embodiment of the present invention provides a power battery micro-short circuit diagnosis method and system based on multi-scenario fusion, which utilizes the difference in capacitance caused by self-discharge under the dynamic charging and static parking conditions of the vehicle, and combines the confidence coefficient to comprehensively obtain the equivalent self-discharge internal resistance value under the dynamic charging and static parking conditions of the vehicle, with higher accuracy and stronger robustness. The present invention can be used in the operation management, life management and safety monitoring of electric tools such as electric vehicles, etc. It can accurately identify the internal micro-short circuit degree of the battery and provide early warning, which can avoid the battery from further deteriorating and causing thermal runaway, and improve the safety of the power battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying creative labor, other drawings obtained based on these drawings still belong to the scope of the present invention.

[0053] Figure 1 A schematic diagram of the main process of an embodiment of a power battery micro-short circuit diagnosis method based on multi-scenario fusion provided by the present invention;

[0054] Figure 2 for Figure 1 A schematic diagram of a two-dimensional curve of battery capacity and voltage involved;

[0055] Figure 3 for Figure 1 Schematic diagram of the OCV-SOC characteristic curve involved;

[0056] Figure 4A schematic diagram of the structure of an embodiment of a multi-scenario fusion power battery micro-short circuit diagnosis system provided by the present invention;

[0057] Figure 5 for Figure 4 A schematic structural diagram of the first self-discharge internal resistance obtaining unit;

[0058] Figure 6 for Figure 4 Schematic diagram of the structure of the second self-discharge internal resistance obtaining unit. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings.

[0060] like Figure 1 As shown, a main flow diagram of an embodiment of a power battery micro-short circuit diagnosis method based on multi-scenario fusion provided by the present invention is shown, and combined with Figure 2 , Figure 3 As shown, in this embodiment, the method includes the following steps:

[0061] Step S10: When charging the power battery of the vehicle, the voltage of all selected single cells is sampled within a preset sampling interval; in practical applications, the sampling interval can be set according to the characteristics of the battery cell, and there can be multiple sampling intervals; specifically, in some examples, the sampling interval is set mainly considering the area where charging will often pass, and the charging current in this area needs to be kept stable to eliminate errors caused by current changes. It is understandable that in order to improve accuracy, the resolution of voltage sampling can be increased as much as possible, for example, sampling can be performed once every 1mV change.

[0062] Step S11, according to the sampling result, obtaining the current charging capacity of each single battery in the sampling interval; it can be understood that the charging capacity can be obtained by the sampled voltage value in a variety of ways. For example, in one example, the current value in the charging curve can be queried by the sampled voltage value, and then the charging capacity can be obtained by integrating the current value with time;

[0063] Wherein, after step S11, the method further includes:

[0064] According to the sampling voltage of each single battery in the sampling interval and the corresponding battery capacity, a two-dimensional curve is formed and stored. Figure 2 As shown, a schematic diagram of a two-dimensional curve of battery capacity and voltage is shown, wherein Ah is ampere-hour, and the interval from V0 to V1 is the sampling interval.

[0065] Step S12, comparing the current charging capacity of each single battery with the corresponding historical charging capacity recorded in the previous charging, calculating the charging capacity difference between the two, and calculating the first self-discharge internal resistance of each single battery according to the charging capacity difference;

[0066] Wherein, the step S12 further comprises:

[0067] Determine the ratio of the charge capacity difference to the time length of the sampling interval as the leakage current value of the single cell;

[0068] The voltage average value of the single battery in the sampling interval is calculated, and the first self-discharge internal resistance of the single battery is determined in combination with the leakage current value. For example, the first self-discharge internal resistance is obtained by dividing the voltage average value by the leakage current value.

[0069] Step S13, after the vehicle is stationary and the battery polarization completely disappears, collecting the open circuit voltage data of each single battery at a preset first time point and a second time point;

[0070] Step S14, calculating the static capacitance difference within the interval between the two time points according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtaining the second self-discharge internal resistance of each single cell in combination with the time interval;

[0071] Wherein, the step S14 further comprises:

[0072] According to the collected open circuit voltage data of each single cell at the first time point and the second time point, the open circuit voltage-state of charge (OCV-SOC) characteristic curve at the current temperature is retrieved to obtain the SOC difference within the interval of the two time points; wherein the OCV-SOC characteristic curve is pre-established, and the corresponding SOC value can be queried on the curve through the OCV at the first time point and the OCV at the second time point, and the SOC difference can be obtained by subtracting the two; Figure 3 That is, a schematic diagram of an OCV-SOC characteristic curve is shown; the curve between the two dotted lines is the OCV and SOC values ​​corresponding to the first time point and the second time point;

[0073] A static capacity difference is obtained according to the SOC difference, and the static capacity difference is corrected by using the current state of health (SOH) of the battery. The SOH value may be a percentage value. For example, the current SOH value may be multiplied by the SOC difference to obtain a corrected static capacity difference. It is understood that, through the above correction, the final capacity difference may be more in line with the actual situation.

[0074] The second self-discharge internal resistance of each single cell is obtained according to the corrected static capacitance difference, the time interval, and the average value of the open circuit voltage data at the first time point and the second time point. It can be understood that a comprehensive analysis and evaluation result can be obtained by performing dynamic and static calculations based on the confidence coefficient.

[0075] Step S15, calculating and obtaining the final self-discharge internal resistance value of each single cell according to the first self-discharge internal resistance and the second self-discharge internal resistance of each single cell in combination with the respective pre-calibrated confidence coefficients;

[0076] Wherein, the step S15 further comprises:

[0077] The final self-discharge internal resistance Ris of each single cell is calculated using the following formula:

[0078] Ris=αRc+βRs

[0079] Wherein, Rc is the first self-discharge internal resistance of a certain single cell, Rs is the second discharge internal resistance of the single cell; α is the confidence level corresponding to the first self-discharge internal resistance, β is the confidence level corresponding to the second self-discharge internal resistance, and α+β=1.

[0080] Step S16, determining the current battery micro-short circuit degree according to the final self-discharge internal resistance values ​​of all single cells.

[0081] Wherein, the step S16 further comprises:

[0082] The final self-discharge internal resistance value of each single battery is compared with a preset resistance threshold to determine the battery cells with micro-short circuits, and the battery micro-short circuit degree of the current power battery is determined according to the number of battery cells with micro-short circuits.

[0083] It is understandable that in the embodiments of the present invention, for the power battery charging scenario, during two consecutive charging times, in addition to the capacity loss caused by the battery's own equalization, the remaining capacity loss can be considered to be caused by the battery's self-discharge. Therefore, the difference in the charged capacity between two consecutive charges in the sampling interval is measured and calculated, that is, the capacity loss caused by self-discharge and the equivalent internal short-circuit resistance during this period of time can be obtained. Under the static condition of the vehicle, the vehicle is not on high voltage, and the battery will also be equalized. Therefore, the capacity loss during this period of time can be considered to be caused by self-discharge, and the self-discharge internal resistance value during this period of time can also be calculated based on the capacity.

[0084] In an embodiment of the present invention, by comprehensively calculating the difference in charging capacity during two charging processes and the difference in capacity over a period of time under parking static conditions, the comprehensive equivalent internal short-circuit results under vehicle charging dynamics and parking static conditions are obtained. The calculation results under vehicle charging dynamics and parking static conditions are combined in multiple vehicle operation scenarios to obtain higher robustness and reliability, and early warning of thermal runaway in various operating scenarios of electric vehicles can be achieved based on the diagnosis results.

[0085] like Figure 4 FIG. 1 is a schematic diagram showing a structure of an embodiment of a power battery micro-short circuit diagnosis system based on multi-scenario fusion provided by the present invention. Figure 5 and Figure 6 As shown, in this embodiment, the power battery micro-short circuit diagnosis system 1 based on multi-scenario fusion includes:

[0086] The charging sampling unit 10 is used to sample the voltage of all selected single cells within a preset sampling interval when charging the power battery of the vehicle;

[0087] The current charging capacity calculation unit 11 is used to obtain the current charging capacity charged to each single battery in the sampling interval according to the sampling result;

[0088] A first self-discharge internal resistance obtaining unit 12 is used to compare the current charging capacity of each single cell with the corresponding historical charging capacity recorded in the previous charging, calculate the charging capacity difference between the two, and obtain the first self-discharge internal resistance of each single cell according to the charging capacity difference;

[0089] The static sampling unit 13 is used to collect the open circuit voltage data of each single battery at a preset first time point and a preset second time point after the vehicle is stationary and the battery polarization completely disappears;

[0090] The second self-discharge internal resistance obtaining unit 14 is used to calculate the static capacitance difference within the interval between the two time points according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtain the second self-discharge internal resistance of each single cell in combination with the time interval;

[0091] The final self-discharge internal resistance value obtaining unit 15 is used to calculate and obtain the final self-discharge internal resistance value of each single cell according to the first self-discharge internal resistance and the second self-discharge internal resistance of each single cell in combination with the respective pre-calibrated confidence coefficients;

[0092] The battery micro-short circuit degree judgment unit 16 is used to determine the current battery micro-short circuit degree according to the final self-discharge internal resistance values ​​of all single cells;

[0093] The storage unit 17 is used to form and store a two-dimensional curve according to the sampled voltage of each single battery in the sampling interval and the corresponding battery capacity.

[0094] Specifically, Figure 5 As shown, the first self-discharge internal resistance obtaining unit 12 further includes:

[0095] A leakage current calculation unit 120, configured to determine a ratio of the charge capacity difference to the time length of the sampling interval as a leakage current value of the single cell;

[0096] The first self-discharge internal resistance calculation unit 121 is used to calculate the average voltage of the single battery during the sampling interval, and determine the first self-discharge internal resistance of the single battery in combination with the leakage current value.

[0097] Specifically, Figure 6 As shown, the second self-discharge internal resistance obtaining unit 14 further includes:

[0098] The SOC difference obtaining unit 140 is used to retrieve the OCV-SOC characteristic curve at the current temperature according to the collected open circuit voltage data of each single battery at the first time point and the second time point, and obtain the SOC difference within the interval between the two time points;

[0099] A correction unit 141, configured to obtain a static capacity difference value according to the SOC difference value, and to correct the static capacity difference value by using a current SOH value of the battery;

[0100] The second self-discharge internal resistance calculation unit 142 is used to obtain the second self-discharge internal resistance of each single cell according to the corrected static capacitance difference, the time interval, and the average value of the open circuit voltage data at the first time point and the second time point.

[0101] The final self-discharge internal resistance value obtaining unit 15 is specifically used to calculate the final self-discharge internal resistance value Ris of each single battery using the following formula:

[0102] Ris=αRc+βRs

[0103] Wherein, Rc is the first self-discharge internal resistance of a certain single cell, Rs is the second discharge internal resistance of the single cell; α is the confidence level corresponding to the first self-discharge internal resistance, β is the confidence level corresponding to the second self-discharge internal resistance, and α+β=1.

[0104] The battery micro-short circuit degree judgment unit 16 is specifically used to compare the final self-discharge internal resistance value of each single battery with a preset resistance threshold, determine the battery cells with micro-short circuits, and determine the battery micro-short circuit degree of the current power battery according to the number of battery cells with micro-short circuits.

[0105] For more details, please refer to the above Figures 1 to 3 The description is not elaborated here.

[0106] The implementation of the embodiments of the present invention has the following beneficial effects:

[0107] The embodiment of the present invention provides a power battery micro-short circuit diagnosis method and system based on multi-scenario fusion, which utilizes the difference in capacitance caused by self-discharge under the dynamic charging and static parking conditions of the vehicle, and combines the confidence coefficient to comprehensively obtain the equivalent self-discharge internal resistance value under the dynamic charging and static parking conditions of the vehicle, with higher accuracy and stronger robustness. The present invention can be used in the operation management, life management and safety monitoring of electric tools such as electric vehicles, etc. It can accurately identify the internal micro-short circuit degree of the battery and provide early warning, which can avoid the battery from further deteriorating and causing thermal runaway, and improve the safety of the power battery.

[0108] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0110] The above disclosure is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A power battery micro-short circuit diagnosis method based on multi-scenario fusion, characterized in that: The steps include: Step S10: when charging the power battery of the vehicle, sampling the voltage of all selected single cells within a preset sampling interval; Step S11, according to the sampling result, obtaining the current charging capacity of each single battery in the sampling interval; Step S12, comparing the current charging capacity of each single battery with the corresponding historical charging capacity recorded in the previous charging, calculating the charging capacity difference between the two, and calculating the first self-discharge internal resistance of each single battery according to the charging capacity difference; Step S13, after the vehicle is stationary and the battery polarization completely disappears, collecting the open circuit voltage data of each single battery at a preset first time point and a second time point; Step S14, calculating the static capacitance difference within the interval between the two time points according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtaining the second self-discharge internal resistance of each single cell in combination with the time interval; Step S15, calculating and obtaining the final self-discharge internal resistance value of each single cell according to the first self-discharge internal resistance and the second self-discharge internal resistance of each single cell in combination with the respective pre-calibrated confidence coefficients; Step S16, determining the current battery micro-short circuit degree according to the final self-discharge internal resistance values ​​of all single cells.

2. The method according to claim 1, characterized in that The step S12 further comprises: Determine the ratio of the charge capacity difference to the time length of the sampling interval as the leakage current value of the single cell; The average voltage of the single battery in the sampling interval is calculated, and the first self-discharge internal resistance of the single battery is determined in combination with the leakage current value.

3. The method according to claim 1, characterized in that After step S11, the method further comprises: A two-dimensional curve is formed and stored according to the sampled voltage of each single battery in the sampling interval and the corresponding battery capacity.

4. The method according to any one of claims 1 to 3, characterized in that The step S14 further comprises: According to the collected open circuit voltage data of each single cell at the first time point and the second time point, the OCV-SOC characteristic curve at the current temperature is retrieved to obtain the SOC difference within the interval between the two time points; Obtaining a static capacity difference value according to the SOC difference value, and correcting the static capacity difference value by using the current SOH value of the battery; The second self-discharge internal resistance of each single cell is obtained according to the corrected static capacitance difference, the time interval, and the average value of the open circuit voltage data at the first time point and the second time point.

5. The method according to claim 4, characterized in that The step S15 further comprises: The final self-discharge internal resistance Ris of each single cell is calculated using the following formula: Ris=αRc+βRs Wherein, Rc is the first self-discharge internal resistance of a certain single cell, Rs is the second discharge internal resistance of the single cell; α is the confidence level corresponding to the first self-discharge internal resistance, β is the confidence level corresponding to the second self-discharge internal resistance, and α+β=1.

6. The method according to claim 5, characterized in that The step S16 further comprises: The final self-discharge internal resistance value of each single battery is compared with a preset resistance threshold to determine the battery cells with micro-short circuits, and the battery micro-short circuit degree of the current power battery is determined according to the number of battery cells with micro-short circuits.

7. A power battery micro-short circuit diagnosis system based on multi-scenario fusion, characterized in that: include: A charging sampling unit is used to sample the voltage of all selected single cells within a preset sampling interval when charging the power battery of the vehicle; The current charging capacity calculation unit is used to obtain the current charging capacity charged to each single battery in the sampling interval according to the sampling result; A first self-discharge internal resistance obtaining unit is used to compare the current charging capacity of each single cell with the corresponding historical charging capacity recorded in the previous charging, calculate the charging capacity difference between the two, and obtain the first self-discharge internal resistance of each single cell according to the charging capacity difference; A static sampling unit, used to collect the open circuit voltage data of each single battery at a preset first time point and a preset second time point after the vehicle is stationary and the battery polarization completely subsides; A second self-discharge internal resistance obtaining unit is used to calculate the static capacitance difference within the interval between the two time points according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtain the second self-discharge internal resistance of each single cell in combination with the time interval; A final self-discharge internal resistance value obtaining unit, used to calculate and obtain the final self-discharge internal resistance value of each single cell according to the first self-discharge internal resistance and the second self-discharge internal resistance of each single cell in combination with the respective pre-calibrated confidence coefficients; The battery micro-short circuit degree judgment unit is used to determine the current battery micro-short circuit degree according to the final self-discharge internal resistance values ​​of all single batteries.

8. The system according to claim 7, characterized in that The first self-discharge internal resistance obtaining unit further comprises: a leakage current calculation unit, configured to determine a ratio of the charge capacity difference to the time length of the sampling interval as a leakage current value of the single cell; The first self-discharge internal resistance calculation unit is used to calculate the voltage average value of the single battery during the sampling interval, and determine the first self-discharge internal resistance of the single battery in combination with the leakage current value.

9. The system according to claim 7, characterized in that Further including: The storage unit is used to form and store a two-dimensional curve according to the sampled voltage of each single battery in the sampling interval and the corresponding battery capacity.

10. The system according to any one of claims 7 to 9, characterized in that The second self-discharge internal resistance obtaining unit further comprises: An SOC difference obtaining unit is used to retrieve the OCV-SOC characteristic curve at the current temperature according to the collected open circuit voltage data of each single cell at the first time point and the second time point, and obtain the SOC difference within the interval between the two time points; a correction unit, configured to obtain a static capacity difference value according to the SOC difference value, and to correct the static capacity difference value by using a current SOH value of the battery; The second self-discharge internal resistance calculation unit is used to obtain the second self-discharge internal resistance of each single cell according to the corrected static capacitance difference, the time interval, and the average value of the open circuit voltage data at the first time point and the second time point.

11. The system according to claim 10, characterized in that The final self-discharge internal resistance value obtaining unit is specifically used to calculate and obtain the final self-discharge internal resistance value Ris of each single battery using the following formula: Ris=αRc+βRs Wherein, Rc is the first self-discharge internal resistance of a certain single cell, Rs is the second discharge internal resistance of the single cell; α is the confidence level corresponding to the first self-discharge internal resistance, β is the confidence level corresponding to the second self-discharge internal resistance, and α+β=1.

12. The system according to claim 11, characterized in that The battery micro-short circuit degree judgment unit is specifically used to compare the final self-discharge internal resistance value of each single battery with a preset resistance threshold, determine the battery cells with micro-short circuits, and determine the battery micro-short circuit degree of the current power battery according to the number of battery cells with micro-short circuits.

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