Battery cell self-discharge anomaly detection method and related equipment

Abstract

The invention provides a battery cell self-discharge anomaly detection method and related equipment, and relates to the technical field of energy storage. The method comprises the following steps: acquiring operation data of a single battery in a preset time period; determining a plurality of battery standing intervals and initial voltage values and end voltage values of the battery monomers in each battery standing interval according to the operation data of the battery monomers in the preset time period; according to the initial voltage value, the end voltage value and a preset OCV-SOC relation curve, determining an initial charge state and an end charge state of the battery monomers, and calculating a charge state difference value of the battery monomers in each battery standing interval; calculating the self-discharge coefficient of the single battery; and according to a relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold value, carrying out self-discharge anomaly detection on the single battery.

Description

Background Art

[0002] Battery aging is a natural phenomenon during use. During the aging process, the consistency of the battery cluster gradually deteriorates. The consistency of each battery cell in the battery cluster has a significant impact on the performance of the energy storage system. Battery self-discharge refers to the phenomenon that the battery capacity spontaneously decreases after the battery cell is charged in an open circuit state and left to stand for a period of time. It represents the charge retention ability of the battery cell under certain conditions. Battery cells with high self-discharge rates usually lead to rapid capacity decay, which can cause energy storage system failure.

[0003] In related technologies, the capacity retention method, open circuit voltage decay method, current measurement method, etc. are usually used to detect self-discharge of batteries at the factory stage and in laboratory scenarios. However, the above detection methods have the characteristics of long measurement time and high equipment sensitivity, which cannot be applied to battery self-discharge detection in industrial and commercial scenarios, and the scope of application is limited. Summary of the invention

[0004] The present disclosure provides a battery monomer self-discharge abnormality detection method and related equipment, which at least to a certain extent overcomes the problem that the battery monomer self-discharge detection method in the related art cannot be applied to industrial and commercial scenarios and has a limited scope of application.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a method for detecting abnormal self-discharge of a battery cell is provided, comprising: acquiring operating data of a battery cell within a preset time period; determining a plurality of battery rest intervals according to the operating data of the battery cell within the preset time period, and calculating a starting voltage value and an ending voltage value of the battery cell within each of the battery rest intervals; determining a starting state of charge and an ending state of charge of the battery cell within each of the battery rest intervals according to the starting voltage value, the ending voltage value, and a preset battery open circuit voltage and state of charge relationship curve, and calculating a difference in state of charge of the battery cell within each of the battery rest intervals; calculating a self-discharge coefficient of the battery cell according to the difference in state of charge of the battery cell within each of the battery rest intervals; and performing self-discharge abnormality detection on the battery cell according to a relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold.

[0007] In one embodiment of the present disclosure, the multiple battery rest intervals are determined based on the operating data of the battery cells within the preset time period, including: taking the absolute value of the current magnitude being less than a preset current threshold as the battery rest condition, and obtaining the multiple initial rest intervals of the battery cells within the preset time period by means of a sliding window; and screening the initial rest intervals whose rest time exceeds the preset time threshold to obtain the multiple battery rest intervals.

[0008] In one embodiment of the present disclosure, the calculation of the starting voltage value and the ending voltage value of the battery cell in each of the battery resting intervals includes: obtaining the voltage values ​​from the start of resting to a first preset resting time in the battery resting interval, calculating an average value, and using the average value as the starting voltage value of the battery cell in the battery resting interval; obtaining the voltage values ​​from the second preset resting time to the end of the resting, calculating an average value, and using the average value as the ending voltage value of the battery cell in the battery resting interval, the second preset resting time being greater than the first preset resting time, and the time interval between the second preset resting time and the end of the resting time being the first preset resting time.

[0009] In one embodiment of the present disclosure, before determining the starting state of charge and the ending state of charge of the battery cell in each of the battery static intervals according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, the method further includes: fitting the battery experimental data using a polynomial piecewise fitting method to obtain a fitting piecewise function of the battery open circuit voltage and state of charge relationship curve of the battery cell, wherein the input of the fitting piecewise function is the voltage value of the battery cell, and the output is the state of charge.

[0010] In one embodiment of the present disclosure, the starting state of charge and the ending state of charge of the battery cells in each of the battery resting intervals are determined according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and the state of charge difference of the battery cells in each of the battery resting intervals is calculated, including: inputting the starting voltage value of the battery cell in the battery resting interval into the fitting piecewise function, and outputting the starting state of charge; inputting the ending voltage value of the battery cell in the battery resting interval into the fitting piecewise function, and outputting the ending state of charge; and calculating the difference between the starting state of charge and the ending state of charge to obtain the state of charge difference of the battery cell in each of the battery resting intervals.

[0011] In one embodiment of the present disclosure, the self-discharge coefficient of the battery cell is calculated according to the state of charge difference of the battery cell in each of the battery resting intervals, including: calculating the equivalent state of charge difference of the battery cell according to the state of charge difference of the battery cell in each of the battery resting intervals; in multiple battery resting intervals, screening the equivalent state of charge difference of the battery cell within a preset state of charge difference range to obtain the equivalent state of charge difference of the battery cell after screening; calculating the self-discharge coefficient of the battery cell according to the equivalent state of charge difference of the battery cell after screening.

[0012] In one embodiment of the present disclosure, the equivalent state of charge difference of the battery cells is calculated based on the state of charge difference of the battery cells in each of the battery resting intervals, including: obtaining the equivalent state of charge difference of the battery cells based on the state of charge difference of the battery cells in each of the battery resting intervals, the corresponding resting time and the preset equivalent time.

[0013] In one embodiment of the present disclosure, the self-discharge coefficient of the battery cell is calculated based on the equivalent state of charge difference of the battery cell after screening, including: calculating a first average value of the equivalent state of charge difference of the battery cell according to the equivalent state of charge difference of the battery cell after screening; determining a second average value of the equivalent state of charge difference of all battery cells in the battery group to which the battery cell belongs; and obtaining the self-discharge coefficient of the battery cell according to the first average value and the second average value.

[0014] In one embodiment of the present disclosure, the method further includes: obtaining the preset self-discharge coefficient threshold value according to the second average value and a preset discrimination magnification threshold value.

[0015] In one embodiment of the present disclosure, the self-discharge abnormality detection is performed on the battery cell based on the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold, including: if the self-discharge coefficient of the battery cell is greater than or equal to the preset self-discharge coefficient threshold, then the battery cell is determined to be a self-discharge abnormal battery cell.

[0016] In one embodiment of the present disclosure, after calculating the self-discharge coefficient of the battery cell according to the difference in the state of charge of the battery cell in each of the battery rest intervals, the method further includes: determining a self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs; and determining the self-discharge consistency of the battery cluster according to the self-discharge score.

[0017] In one embodiment of the present disclosure, determining the self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs includes: calculating the range of the self-discharge coefficients of all battery cells in the battery group according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs, and determining the self-discharge score according to the range.

[0018] In one embodiment of the present disclosure, determining the self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs includes: calculating a coefficient of variation according to the self-discharge coefficients of all battery cells in the battery cluster, and determining the self-discharge score according to the coefficient of variation.

[0019] According to another aspect of the present disclosure, a battery cell self-discharge anomaly detection device is provided, comprising: a data acquisition module, used to acquire operating data of the battery cell within a preset time period; a first calculation module, used to determine a plurality of battery rest intervals according to the operating data of the battery cell within the preset time period, and calculate the starting voltage value and the ending voltage value of the battery cell in each of the battery rest intervals; a second calculation module, used to determine the starting state of charge and the ending state of charge of the battery cell in each of the battery rest intervals according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and calculate the difference in state of charge of the battery cell in each of the battery rest intervals; a third calculation module, used to calculate the self-discharge coefficient of the battery cell according to the difference in state of charge of the battery cell in each of the battery rest intervals; and an anomaly identification module, used to perform self-discharge anomaly detection on the battery cell according to the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold.

[0020] According to another aspect of the present disclosure, an electronic device is also provided, which includes: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute any one of the above-mentioned battery cell self-discharge abnormality detection methods by executing the executable instructions.

[0021] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for detecting abnormal self-discharge of a battery cell described above is implemented.

[0022] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the battery monomer self-discharge abnormality detection method described above is implemented.

[0023] In the embodiment of the present disclosure, the operation data of the battery cell in the preset time period is obtained; according to the operation data of the energy storage system in the preset time period, multiple battery static intervals are determined, and the starting voltage value and the ending voltage value of the battery cell in each battery static interval are calculated; according to the starting voltage value, the ending voltage value and the preset battery open circuit voltage and state of charge relationship curve, the starting state of charge and the ending state of charge of the battery cell in each battery static interval are determined, and the difference of the state of charge of the battery cell in each battery static interval is calculated; according to the difference of the state of charge of the battery cell in each battery static interval, the self-discharge coefficient of the battery cell is calculated; according to the relationship between the self-discharge coefficient and the preset self-discharge coefficient threshold, the self-discharge abnormality detection of the battery cell is performed. The present disclosure can realize rapid battery cell self-discharge abnormality detection without affecting the normal operation of the industrial and commercial energy storage system through multiple short-term static working conditions, avoiding dependence on long-term static working conditions, correcting the pressure difference through the battery open circuit voltage and state of charge relationship curve, using the state of charge difference instead of the open circuit voltage difference, and effectively improving the accuracy of self-discharge abnormality detection.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0026] Figure 1 A schematic structural diagram of an energy storage system provided by an embodiment of the present disclosure is shown.

[0027] Figure 2 A flow chart of a method for detecting abnormal self-discharge of a battery cell provided by an embodiment of the present disclosure is shown.

[0028] Figure 3 A flow chart of a method for determining a battery rest interval provided by an embodiment of the present disclosure is shown.

[0029] Figure 4 A flow chart of another method for detecting abnormal self-discharge of a battery cell provided by an embodiment of the present disclosure is shown.

[0030] Figure 5 A curve diagram showing the relationship between the open circuit voltage and the state of charge of a battery provided in an embodiment of the present disclosure is shown.

[0031] Figure 6A flow chart of a method for calculating a self-discharge coefficient of a battery cell provided in an embodiment of the present disclosure is shown.

[0032] Figure 7 A flow chart of another method for calculating the self-discharge coefficient of a battery cell provided in an embodiment of the present disclosure is shown.

[0033] Figure 8 A flow chart of another method for detecting abnormal self-discharge of a battery cell provided in an embodiment of the present disclosure is shown.

[0034] Fig. 9 An example flow chart of a method for detecting abnormal self-discharge of a battery cell provided in an embodiment of the present disclosure is shown.

[0035] Fig.10 A schematic structural diagram of a battery cell self-discharge abnormality detection device provided by an embodiment of the present disclosure is shown.

[0036] Fig.11 A structural block diagram of an electronic device provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0037] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the disclosure will be more comprehensive and complete and to fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0038] In addition, the accompanying drawings are only schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0039] Since the energy people need is highly temporal and spatial, in order to rationally use energy and improve energy utilization, it is necessary to use a medium or device to store one form of energy in the same form or convert it into another form of energy, and then release it in a specific form based on future application needs. At present, the main way to generate green electricity is to develop green energy such as photovoltaics and wind power to replace fossil energy.

[0040] At present, the generation of green electricity generally relies on photovoltaics, wind power, and hydropower, etc. However, wind and solar energy generally have problems of strong intermittency and large volatility, which will cause instability in the power grid, insufficient electricity during peak hours, and too much electricity during valley hours. Unstable voltage will also cause damage to electricity. Therefore, insufficient electricity demand or insufficient grid acceptance capacity may cause the problem of "abandoning wind and solar power". To solve these problems, we must rely on energy storage. That is, converting electrical energy into other forms of energy through physical or chemical means and storing it, and converting energy into electrical energy and releasing it when needed. In simple terms, energy storage is like a large "power bank". When photovoltaic and wind energy are sufficient, electrical energy is stored and the stored electricity is released when needed.

[0041] Taking electrochemical energy storage as an example, the present disclosure provides an energy storage device, which has a group of chemical batteries inside. The chemical elements in the batteries are mainly used as energy storage media. The charging and discharging process is accompanied by chemical reactions or changes in the energy storage media. In simple terms, the electricity generated by wind and solar energy is stored in chemical batteries. When the use of external electricity reaches a peak, the stored electricity is released for use, or transferred to places where electricity is scarce for use.

[0042] At present, energy storage (i.e. energy storage) has a wide range of application scenarios, including power generation side energy storage, grid side energy storage and power consumption side energy storage. The corresponding types of energy storage equipment include:

[0043] (1) Large-scale energy storage power stations applied to wind power and photovoltaic power stations can help renewable energy generation meet grid connection requirements and improve the utilization rate of renewable energy. As a high-quality active / reactive regulating power source on the power supply side, energy storage power stations can achieve load matching of electric energy in time and space, enhance the ability to absorb renewable energy, reduce instantaneous power changes, reduce the impact on the power grid, improve the absorption of new energy generation, and are of great significance in power grid system backup, relieving peak load power supply pressure, and peak and frequency regulation.

[0044] (2) Energy storage containers used on the grid side mainly perform peak load regulation, frequency regulation, and grid congestion relief. They can achieve peak load shaving and valley filling of power loads, that is, charging the energy storage battery when the power load is low and releasing the stored electricity during the peak load period, thereby achieving a balance between power production and consumption, such as energy storage power station systems;

[0045] (3) Small energy storage cabinets used on the power consumption side, mainly for self-generation and self-use of electricity, peak-valley price arbitrage, capacity cost management and improving power supply reliability. According to different application scenarios, energy storage on the power consumption side can be divided into industrial and commercial energy storage cabinets, household energy storage equipment, energy storage charging piles, etc., which are generally used in conjunction with distributed photovoltaics. Industrial and commercial users can use energy storage for valley-peak price arbitrage and capacity cost management. In the power market that implements peak-valley electricity prices, by charging the energy storage system at low electricity prices and discharging the energy storage system at high electricity prices, peak-valley electricity price arbitrage is achieved to reduce electricity costs. In addition, industrial enterprises that are subject to two-part electricity prices can use energy storage systems to store energy during low electricity consumption and discharge at peak loads, thereby reducing peak power and the maximum demand reported, and achieving the purpose of reducing capacity electricity charges. Household photovoltaic storage can improve the level of self-generation and self-use of electricity. Due to high electricity prices and poor power supply stability, the demand for household photovoltaic installations has been driven. Considering that photovoltaic power is generated during the day, and users generally have higher loads at night, energy storage can be configured to better utilize photovoltaic power, improve the level of self-generation and self-use, and reduce electricity costs. In addition, communication base stations, data centers and other fields need to configure energy storage for backup power.

[0046] See also Figure 1 , Figure 1 is a schematic diagram of the structure of an energy storage system according to an embodiment of the present disclosure, and Figure 1 The embodiment is described by taking the energy storage scenario in user-side energy storage as an example. The energy storage system disclosed in the present invention is not limited to its household energy storage scenario, but can also be applied to industrial and commercial energy storage scenarios.

[0047] The present disclosure provides an energy storage system, which includes: an energy storage device 1, an electric energy conversion device 2 (such as a photovoltaic panel), a first user load 3 (such as a street lamp), a second user load 4 (such as an air conditioner and other household appliances), etc. The energy storage device 1 is a small energy storage box that can be mounted on an outdoor wall by wall hanging. Specifically, the photovoltaic panel can convert solar energy into electric energy during the period of low electricity prices, and the energy storage device 1 is used to store the electric energy and supply it to street lamps and household appliances for use during the peak electricity prices, or to supply power when the power grid is off / power outage.

[0048] The number of energy storage devices 1 can be multiple, and multiple energy storage devices 1 are connected in series or in parallel. Multiple energy storage devices 1 are supported and electrically connected by isolation plates (not shown). In this embodiment, "multiple" refers to two or more. An energy storage box can also be provided outside the energy storage device 1 to accommodate the energy storage device 1.

[0049] Optionally, the energy storage device 1 may include but is not limited to a single cell, a battery module, a battery pack, a battery system, etc. The actual application form of the energy storage device 1 provided in the embodiment of the present disclosure may be but is not limited to the listed products, and may also be other application forms. The embodiment of the present disclosure does not strictly limit the application form of the energy storage device 1. The embodiment of the present disclosure is only described by taking the energy storage device 1 as a battery cell as an example. When the energy storage device 1 is a single cell, the energy storage device 1 may be at least one of a cylindrical battery, a square battery, etc.

[0050] The energy storage system also includes a battery management unit (BMU), an energy management unit (EMU), and a power conversion system (PCS). BMU can also be called a battery nanny or a battery butler. The battery management system BMS is mainly composed of a detection module, a control module, a communication module, etc. Its main function is to monitor and control the battery status in real time, including but not limited to monitoring and controlling the battery's voltage, current, temperature, state of charge (SoC), state of health (SOH) and other parameters. At the same time, the battery management system can also perform protection control on the battery, such as overcharging, over-discharging, over-current, etc., to ensure the safety and life of the battery.

[0051] The energy storage converter is mainly composed of inverters, transformers, controllers, etc. It converts the electric energy stored in the battery into AC power to supply the power grid or the user's electrical equipment. The main functions of the energy storage converter include converting DC power into AC power, controlling the input or output of electric energy, and ensuring the safety and stability of the energy storage system. The performance of the energy storage converter directly affects the operating efficiency and service life of the energy storage system.

[0052] The energy management unit is responsible for the control strategy of the energy storage system. The control strategy can affect the attenuation rate and cycle life of the batteries in the system, thereby determining the economy of the energy storage. In addition, the energy management unit can also monitor faults in the operation of the energy storage system, and play a role in timely protecting equipment and ensuring safety. The energy management unit can generate a control strategy for the energy storage system based on the operating parameters of the battery or the real-time operating parameters of energy storage devices such as the battery management system and energy storage inverter, and then control the operating status of the battery device or other equipment.

[0053] Home energy storage and commercial and industrial energy storage are two main types of energy storage systems divided according to application scenarios, scale, demand, etc. Compared with home energy storage, commercial and industrial energy storage is used in scenarios such as large commercial buildings, industrial parks or power companies. It is a large-scale energy storage facility used to support the power supply of the entire enterprise or factory.

[0054] In related technologies, capacity retention method, open circuit voltage decay method, current measurement method and other methods are usually used for battery self-discharge detection at the factory stage and in laboratory scenarios.

[0055] However, the open circuit voltage decay method and capacity retention method require a long measurement time, and the battery needs to be left at rest for several weeks or even months, which is not suitable for daily charging and discharging in industry and commerce. At the same time, since the open circuit voltage is directly related to the battery's state of charge (SoC) or capacity, the open circuit voltage decay rate is inconsistent under different SOCs, making the battery self-discharge detection results easily affected by SOC.

[0056] The current measurement method has high requirements on the sensitivity of sensors and other equipment, and is difficult to promote to industrial and commercial scenarios.

[0057] Therefore, how to design a battery cell self-discharge detection method that can be applied in industrial and commercial scenarios has become a technical problem that needs to be solved urgently.

[0058] In order to solve at least part of the above-mentioned technical problems, the embodiment of the present disclosure provides a method for detecting abnormal self-discharge of a battery cell, which obtains operating data of the battery cell within a preset time period; determines a plurality of battery rest intervals according to the operating data of the battery cell within the preset time period, and calculates the starting voltage value and the ending voltage value of the battery cell within each battery rest interval; determines the starting state of charge and the ending state of charge of the battery cell within each battery rest interval according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and calculates the difference in state of charge of the battery cell within each battery rest interval; calculates the self-discharge coefficient of the battery cell according to the difference in state of charge of the battery cell within each battery rest interval; and performs self-discharge abnormality detection on the battery cell according to the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold. The present invention can realize rapid battery monomer self-discharge anomaly detection through multiple short-term static conditions without affecting the normal operation of industrial and commercial energy storage systems, avoiding dependence on long-term static conditions, correcting the pressure difference through the relationship curve between the battery open circuit voltage and the state of charge, and using the state of charge difference instead of the open circuit voltage difference, effectively improving the accuracy of self-discharge anomaly detection.

[0059] In the present disclosure, it can be applied to the self-discharge abnormality detection of lithium iron phosphate battery cells in industrial and commercial scenarios, and it can also be applied to the self-discharge abnormality detection of nickel-cadmium batteries, nickel-metal hydride batteries, etc. in industrial and commercial scenarios. The present disclosure does not make specific limitations on this.

[0060] Under the above system architecture, a method for detecting abnormal self-discharge of a battery cell is provided in the embodiment of the present disclosure, and the method can be executed by any electronic device with computing and processing capabilities, such as a processor, etc. The processor can be a programmable logic controller (PLC) or a single-chip microcomputer, etc., as long as it can realize the corresponding functions.

[0061] Figure 2 A flow chart of a method for detecting abnormal self-discharge of a battery cell according to an embodiment of the present disclosure is shown as follows: Figure 2 As shown, the battery monomer self-discharge abnormality detection method provided in the embodiment of the present disclosure includes the following steps:

[0062] S202: Obtaining battery cell operation data within a preset period of time.

[0063] A battery cell is one of the batteries in a battery pack. The battery pack may be a battery cluster in an energy storage system, a battery pack, a battery module, etc., or multiple battery cells in a battery pack, etc. The present disclosure does not make specific limitations on this.

[0064] In one embodiment, the preset period can be a time period during which the energy storage system is continuously used in an industrial or commercial scenario. During the preset period, the energy storage system can be in a normal working state (e.g., charging and discharging) or in a static state. The duration of different states can be determined according to actual needs. The preset period can be a few days for the energy storage system to operate, or a few weeks for the energy storage system to operate.

[0065] During the operation of the energy storage system, the battery management unit can upload the collected operating data of the energy storage system to the cloud platform and store the operating data through IoT or other big data technologies. The processor can pull the operating data of the energy storage system within a preset period from the cloud platform according to the need for abnormal self-discharge detection of battery cells, and extract the operating data of the battery cells to be detected from the operating data of the energy storage system.

[0066] The operating data of the battery cell may include but is not limited to the voltage value, current value, and corresponding timestamp of the battery cell.

[0067] The energy storage system may include one or more battery clusters, each battery cluster includes multiple battery packs, and each battery pack includes multiple battery cells connected in series and parallel. It should be noted that the number of battery clusters, the number of battery packs, and the number of battery cells can be determined according to actual needs.

[0068] S204 , determining a plurality of battery rest intervals according to the operating data of the battery cells in a preset period of time, and calculating a starting voltage value and an ending voltage value of the battery cells in each battery rest interval.

[0069] The battery rest interval refers to the time interval corresponding to when the battery cell is in a rest state. The left endpoint of the battery rest interval indicates the time corresponding to the start of the battery cell rest state, and the right endpoint indicates the time corresponding to the end of the battery cell rest state. For example, on January 1, 2024, the battery rest intervals of a battery cell are [2024 / 1 / 18:00:05, 2024 / 1 / 1 10:15:05] and [2024 / 1 / 1 16:30:00, 2024 / 1 / 1 22:35:55].

[0070] In one embodiment, the static interval of the energy storage system can be determined from the operation data of the energy storage system, and then the operation data corresponding to the battery cells to be detected for self-discharge abnormality in each battery static interval can be screened.

[0071] In one embodiment, the charge and discharge status of the battery cell can be determined based on the current of the battery cluster. For example, the absolute value of the current is less than a preset current threshold (such as 5A) as the battery static condition. When the absolute value of the current is less than 5A, the battery cell is in a static state. When the absolute value of the current is greater than or equal to 5A, the battery cell is in a non-static state.

[0072] The voltage value corresponding to the left endpoint of the battery rest interval is the starting voltage value of the battery cell in the battery rest interval, and the voltage value corresponding to the right endpoint is the ending voltage value of the battery cell in the battery rest interval.

[0073] In one embodiment, the operating data of the battery cells within a preset period of time are screened according to the battery rest conditions to obtain multiple battery rest intervals, and then the operating data is queried according to the left endpoint and the right endpoint of the battery rest interval to obtain the starting voltage value and the ending voltage value of the battery cells within the battery rest interval.

[0074] In one embodiment, the average value of the voltage value 10 minutes after the start of the static state can be obtained as the starting voltage value of the battery cell, and the average value of the voltage value 10 minutes before the end of the static state can be obtained as the ending voltage value of the battery cell, thereby solving the problem that the detection accuracy of the voltage sensor of the industrial and commercial energy storage cabinet is 1mV and there is discontinuous voltage change, effectively reducing the impact of the discontinuous voltage change, and improving the accuracy of abnormal self-discharge detection of the battery cell.

[0075] It should be noted that calculations are performed separately for multiple battery cells in multiple battery rest intervals, and self-discharge abnormality detection of the battery cells is performed separately.

[0076] S206, determining the starting state of charge and ending state of charge of the battery cells in each battery resting interval according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and calculating the state of charge difference of the battery cells in each battery resting interval.

[0077] The above-mentioned battery open circuit voltage and state of charge relationship curve can be determined based on battery cell experimental data. It should be noted that the battery open circuit voltage and state of charge relationship curve can be determined in any way, such as based on an adaptive proportional integral method, and the present disclosure does not make any specific limitations on this.

[0078] The relationship curve between the battery open circuit voltage and charge can be described as: SOC = f(OCV).

[0079] The initial state of charge of the battery cell is the state of charge of the battery cell when the battery cell starts to be stationary, and the final state of charge of the battery cell is the state of charge of the battery cell when the battery cell ends being stationary.

[0080] In one embodiment, after the starting voltage value and the ending voltage value are determined, the starting voltage value and the ending voltage value are respectively substituted into the preset battery open circuit voltage and state of charge relationship curve to determine the starting state of charge and the ending state of charge of the battery cell. For example, if the starting voltage value is OCV1 and the ending voltage value is OCV2, the starting state of charge SOC1 = f(OCV1) and the ending state of charge SOC2 = f(OCV2).

[0081] The state of charge difference ΔSOC of the battery cell is the difference between the initial state of charge and the final state of charge, which is expressed as ΔSOC=SOC1-SOC2=f(OCV1)-f(OCV2).

[0082] S208 , calculating the self-discharge coefficient of the battery cell according to the charge state difference of the battery cell in each battery rest interval.

[0083] The self-discharge coefficient of a battery cell is used to characterize the charge retention capacity of the battery cell, which refers to the ability of the battery cell to retain stored electricity under certain conditions when in an open circuit state. The self-discharge coefficient may be affected by factors such as battery manufacturing process, materials, and storage conditions. Different types of battery cells have different self-discharge coefficients.

[0084] The self-discharge coefficient can be evaluated by the change of open circuit voltage. However, the change of open circuit voltage is small, resulting in poor accuracy of self-discharge detection of battery cells.

[0085] In the present disclosure, the state of charge difference of the battery cells is used instead of the open circuit voltage difference to calculate the self-discharge coefficient of the battery cells, thereby improving the accuracy of the self-discharge detection of the battery cells.

[0086] S210 , performing self-discharge abnormality detection on the battery cell according to the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold.

[0087] The preset self-discharge coefficient threshold is used as a criterion for determining whether the self-discharge of the battery cell is abnormal. The self-discharge coefficient threshold can be pre-stored in the processor, and the value of the self-discharge coefficient threshold can be determined based on experiments.

[0088] The preset self-discharge coefficient threshold value may also be determined according to the performance parameters of the battery pack to which the battery cell belongs. In this case, the self-discharge coefficient threshold value serves as a variable and changes dynamically with the performance parameters of the battery pack.

[0089] In one embodiment, the above S210 performs self-discharge abnormality detection on the battery cell according to the relationship between the self-discharge coefficient and the preset self-discharge coefficient threshold, including: if the self-discharge coefficient of the battery cell is greater than or equal to the preset self-discharge coefficient threshold, then the battery cell is determined to be a self-discharge abnormal battery cell.

[0090] When there is a battery cell with abnormal self-discharge, an abnormal reminder message can be generated and pushed to the cloud platform to promptly remind the user to handle the battery cell with abnormal self-discharge. The abnormal reminder message may include but is not limited to the abnormal type, the location identifier of the battery cell with abnormal self-discharge, etc. The abnormal type may be a self-discharge abnormality, and the location identifier may include the battery pack number and battery cell number of the battery cell with abnormal self-discharge.

[0091] If the self-discharge coefficient of the battery cell is less than the preset self-discharge coefficient threshold, it is determined that there is no self-discharge abnormality in the battery cell and only needs to be continuously monitored.

[0092] In the embodiment of the present disclosure, the operation data of the battery cell in the preset time period is obtained; according to the operation data of the battery cell in the preset time period, multiple battery static intervals are determined, and the starting voltage value and the ending voltage value of the battery cell in each battery static interval are calculated; according to the starting voltage value, the ending voltage value and the preset battery open circuit voltage and state of charge relationship curve, the starting state of charge and the ending state of charge of the battery cell in each battery static interval are determined, and the difference of the state of charge of the battery cell in each battery static interval is calculated; according to the difference of the state of charge of the battery cell in each battery static interval, the self-discharge coefficient of the battery cell is calculated; according to the relationship between the self-discharge coefficient and the preset self-discharge coefficient threshold, the self-discharge abnormality detection of the battery cell is performed. The present disclosure can realize rapid battery cell self-discharge abnormality detection without affecting the normal operation of the industrial and commercial energy storage system through multiple short-term static working conditions, avoiding dependence on long-term static working conditions, correcting the pressure difference through the battery open circuit voltage and state of charge relationship curve, using the state of charge difference instead of the open circuit voltage difference, and effectively improving the accuracy of self-discharge abnormality detection.

[0093] Figure 3 FIG. 2 shows a flow chart of a method for determining a battery rest interval provided by an embodiment of the present disclosure. Figure 3 As shown, in one embodiment, the above S204 determines a plurality of battery rest intervals according to the operating data of the battery cells within a preset period of time, including:

[0094] S302, taking the absolute value of the current magnitude being less than a preset current threshold as a battery rest condition, and obtaining a plurality of initial rest intervals of the battery cell within a preset period of time by means of a sliding window;

[0095] S304: Filter initial rest intervals whose rest duration exceeds a preset duration threshold to obtain multiple battery rest intervals.

[0096] The preset current threshold can be pre-configured in the processor, and the value of the preset current threshold can be determined according to actual needs. For example, the preset current threshold of the battery cluster is set to 5A, that is, the absolute value of the current is less than 5A as the battery static condition.

[0097] If the current value in the sliding window satisfies that the absolute value of the previous current value in the sliding window is greater than or equal to 5A and the absolute value of the first current value in the sliding window is less than 5A, the timestamp corresponding to the first current value is determined as the left endpoint of the initial static interval; if the absolute value of the last current value in the sliding window is less than 5A and the absolute value of the next current value in the sliding window is greater than or equal to 5A, the timestamp corresponding to the last current value is determined as the right endpoint of the initial static interval. Multiple initial static intervals within a preset period can be determined by the above sliding window method.

[0098] Since the open circuit voltage corresponding to the unit state of charge change of the battery cell voltage in the non-plateau period (SOC < 5% or SOC > 95%) is relatively large, it is necessary to filter out the static segment data of the initial static interval in the non-plateau period. Through experiments, it is found that when the static time of the battery cell exceeds 4 hours, the voltage change caused by the self-discharge of the battery cell can be detected by the voltage sensor of the battery cell. Therefore, in the present disclosure, the initial static interval whose static time exceeds the preset time threshold is filtered to obtain multiple battery static intervals.

[0099] In one embodiment, the preset duration threshold may be pre-configured in the processor, and the preset duration threshold may be 4 hours, or any value greater than 4, and the present disclosure does not make any specific limitation on this.

[0100] In the embodiment of the present disclosure, by setting the battery rest condition, a plurality of initial rest intervals satisfying the battery rest condition are screened from the operating data of a preset time period, and intervals whose rest time exceeds a preset time threshold are further screened from the plurality of initial rest intervals to obtain a plurality of battery rest intervals, thereby screening out the voltage values ​​of the battery cells in the non-plateau period, avoiding the influence of the excessive change in the unit state of charge corresponding to the excessive change in the open circuit voltage on the self-discharge detection result of the battery cells, and improving the accuracy of the detection result.

[0101] In one embodiment, the calculation of the starting voltage value and the ending voltage value of each battery cell in each battery rest interval in S204 includes:

[0102] Obtaining voltage values ​​from the start of the battery resting period to the first preset resting period in the battery resting period, and calculating an average value as a starting voltage value of the battery cell in the battery resting period;

[0103] The voltage value of the battery static interval from the second preset static time to the end of the static interval is obtained, and the average value is calculated as the end voltage value of the battery cell in the battery static interval. The second preset time is greater than the first preset time, and the time interval between the second preset time and the end of the static interval is the first preset time.

[0104] The first preset time length mentioned above may be pre-configured in the processor, and the value of the first preset time length may be determined according to actual needs. For example, the first preset time length may be 10 minutes.

[0105] For a battery rest interval, the voltage value corresponding to the left endpoint of the battery rest interval can be selected as the starting point, and the voltage values ​​of the battery cells collected within 10 consecutive minutes are calculated, and the average value of the voltage values ​​of the battery cells collected within 10 consecutive minutes is calculated as the starting voltage value of the battery cells in the battery rest interval; the voltage value corresponding to the right endpoint of the battery rest interval can be selected as the ending point, and the voltage values ​​of the battery cells collected within 10 consecutive minutes before the right endpoint are calculated, and the average value of the voltage values ​​is calculated as the ending voltage value of the battery cells in the battery rest interval.

[0106] It should be noted that the second preset time length is counted from the rest start time as the zeroth time. Since the rest time lengths of multiple battery rest intervals are different, different battery rest intervals correspond to different second preset time lengths.

[0107] In the embodiment of the present disclosure, since the accuracy of the voltage sensor of the industrial and commercial energy storage system is usually 1mV, there is a situation where the voltage changes are discontinuous. By obtaining the average voltage value of the first preset time threshold at the beginning and end of the static state as the voltage value of the battery cell before and after the static state, the impact of the discontinuous voltage change can be reduced and the accuracy of the battery cell self-discharge detection can be improved.

[0108] Figure 4 Another method for detecting abnormal self-discharge of a battery cell provided by an embodiment of the present disclosure is shown in the flowchart. Figure 2 On the basis of the embodiment, S205 is added between S204 and S206 to limit the acquisition method of the relationship curve between the battery open circuit voltage and the state of charge. Figure 4 As shown, in one embodiment, before determining the starting state of charge and the ending state of charge of each battery cell in each battery resting interval according to the starting voltage value, the ending voltage value and the preset battery open circuit voltage and state of charge relationship curve in S206, the method further includes:

[0109] S205, fitting the battery experimental data by adopting a polynomial piecewise fitting method to obtain a fitting piecewise function of the relationship curve between the battery open circuit voltage and the state of charge of the battery cell, wherein the input of the fitting piecewise function is the voltage value of the battery cell, and the output is the state of charge.

[0110] In one embodiment, the polynomial segmented fitting method refers to a data fitting method, which divides the experimental data of the battery cell into multiple intervals, and uses a polynomial function to fit the time delay data in each interval. The fitting segmented curve is used to characterize the corresponding relationship between the battery open circuit voltage and the state of charge.

[0111] Figure 5 The figure shows the relationship between the open circuit voltage and the state of charge of the battery provided by the embodiment of the present disclosure. Figure 5 It can be seen that the horizontal axis of the battery open circuit voltage and state of charge relationship curve is the state of charge SOC, and the vertical axis is the open circuit voltage OCV. When the SOC is in the range of (0, 15%), the open circuit voltage OCV rises rapidly. When the SOC is in the range of (15%, 35%), the rising trend of the open circuit voltage OCV gradually slows down. When the SOC is in the range of (35%, 95%), the open circuit voltage OCV is stable. When it is in the range of (95%, 100%), the open circuit voltage OCV rises rapidly. Therefore, the open circuit voltage and state of charge relationship curve can be divided into 4 sections, and each section is fitted with a polynomial to obtain the corresponding fitting segmented curve.

[0112] In one embodiment, the above S206 determines the starting state of charge and the ending state of charge of the battery cells in each battery resting interval according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and calculates the difference in state of charge of the battery cells in each battery resting interval, including: inputting the starting voltage value of the battery cell in the battery resting interval into a fitting piecewise function, and outputting the starting state of charge; inputting the ending voltage value of the battery cell in the battery resting interval into the fitting piecewise function, and outputting the ending state of charge; and calculating the difference between the starting state of charge and the ending state of charge, to obtain the difference in state of charge of the battery cells in each battery resting interval.

[0113] The input of the fitting piecewise function is the open circuit voltage, and the output is the state of charge. The starting state of charge of the battery cell can be determined by inputting the starting voltage value of the battery cell into the fitting piecewise function, and the ending state of charge of the battery cell can be determined by inputting the ending voltage value of the battery cell into the fitting piecewise function.

[0114] The difference between the initial state of charge and the final state of charge is calculated to obtain the state of charge difference of the battery cell.

[0115] In the disclosed embodiment, a fitting piecewise function of the battery open circuit voltage and state of charge relationship curve is obtained by polynomial piecewise fitting, so that the starting state of charge and the ending state of charge of the battery cell in the battery static interval are determined according to the fitting piecewise function, which greatly improves the accuracy of battery cell self-discharge detection.

[0116] Figure 6 FIG. 1 is a flow chart of a method for calculating the self-discharge coefficient of a battery cell provided in an embodiment of the present disclosure. Figure 6 As shown, in one embodiment, the above S208 calculates the self-discharge coefficient of the battery cell according to the charge state difference of the battery cell in each battery static interval, including:

[0117] S602, calculating the equivalent state of charge difference of the battery cells according to the state of charge difference of the battery cells in each battery rest interval;

[0118] S604, in a plurality of battery rest intervals, screening equivalent state of charge differences of battery cells within a preset state of charge difference range, and obtaining equivalent state of charge differences of the screened battery cells;

[0119] S606: Calculate the self-discharge coefficient of each battery cell according to the difference in equivalent state of charge of the screened battery cells.

[0120] In one embodiment, in S602, the equivalent state of charge difference of each battery cell may be obtained according to the state of charge difference of the battery cells in each battery resting interval, the corresponding resting time and the preset equivalent time.

[0121] The rest time of the battery rest interval is the difference between the right endpoint and the left endpoint, and the rest time can be expressed in hours.

[0122] The preset equivalent time can be pre-configured in the processor, and the preset equivalent time can be 24 hours. By calculating the difference in equivalent state of charge, the data of battery resting intervals with different resting times can be equivalent to resting intervals with the same resting time, that is, a short battery resting interval is equivalent to a long battery resting interval, thereby ensuring the validity of the data.

[0123] If the state of charge difference of the battery cells is ΔSOC, the static time is ΔT, and the preset equivalent time is T0, then the equivalent state of charge difference of the battery cells is ΔSOC 等效 It is expressed as:

[0124] ΔSOC 等效 =ΔSOC×T0 / ΔT (Formula 1).

[0125] It should be noted that the value of the preset equivalent time length can be determined according to actual needs, and the present disclosure does not make any specific limitation on this.

[0126] In S604, the equivalent state of charge difference of the battery cells within the preset state of charge difference range is screened to avoid the influence of abnormal data on the battery cells during operation. The preset state of charge difference range can be pre-configured in the processor, and the value of the preset state of charge difference range can be determined according to actual needs, for example, 5% to 95%.

[0127] In one embodiment, in S606, after obtaining multiple battery rest intervals and screening data with equivalent state of charge differences within a preset state of charge difference range, the number of equivalent state of charge differences of each battery cell is less than or equal to the number of battery rest intervals, the average value of the equivalent state of charge differences of the battery cells can be used as the self-discharge coefficient of the battery cells.

[0128] In the disclosed embodiments, the equivalent state of charge difference of the battery cells is calculated based on the state of charge difference of the battery cells in each battery resting interval; in multiple battery resting intervals, the equivalent state of charge difference of the battery cells within a preset state of charge difference range is screened to obtain the equivalent state of charge difference of the screened battery cells; based on the equivalent state of charge difference of the screened battery cells, the self-discharge coefficient of the battery cells is calculated, thereby improving the accuracy and reliability of battery cell self-discharge detection by unifying the resting time of each battery resting interval and eliminating abnormal data.

[0129] Figure 7 FIG. 2 is a flow chart showing another method for calculating the self-discharge coefficient of a battery cell provided in an embodiment of the present disclosure. Figure 7 As shown, in one embodiment, the above S606 calculates the self-discharge coefficient of the battery cell according to the difference in equivalent state of charge of the battery cell after screening, including:

[0130] S6062, calculating a first average value of the equivalent state of charge differences of the battery cells according to the equivalent state of charge differences of the battery cells after screening;

[0131] S6064, determining a second average value of the equivalent state of charge differences of all battery cells in the battery group to which the battery cell belongs;

[0132] S6066: Obtain a self-discharge coefficient of the battery cell according to the first average value and the second average value.

[0133] In S6062, after obtaining multiple battery rest intervals and filtering data with equivalent state of charge differences within a preset state of charge difference range, the number of equivalent state of charge differences of each battery cell is less than or equal to the number of battery rest intervals, and a first average value of the equivalent state of charge differences of the battery cells can be calculated.

[0134] If the number of the equivalent state of charge differences of the battery cells is m, the first average value ΔSOC of the equivalent state of charge differences of the battery cells is 均 It is expressed as:

[0135]

[0136] Among them, ΔSOC i is the ith equivalent state of charge difference of the battery cell.

[0137] In S6064, the battery pack to which the battery cell belongs includes n battery cells, and the second average value ΔSOC of the equivalent state of charge differences of all battery cells in the battery pack is 组 :

[0138]

[0139] In S6066, the self-discharge coefficient K of battery cell i i It is expressed as:

[0140] K i =ΔSOC 均i / ΔSOC 组 (Formula 4)

[0141] In the disclosed embodiment, the self-discharge coefficient of the battery cell is determined by the average value of the equivalent state of charge difference of the battery cell and the average value of the equivalent state of charge difference of all battery cells in the battery group to which the battery cell belongs, thereby improving the calculation accuracy of the self-discharge coefficient of the battery cell and increasing the accuracy of self-discharge anomaly detection.

[0142] In one embodiment, the method further includes: obtaining a preset self-discharge coefficient threshold value according to the second average value and a preset discrimination magnification threshold value.

[0143] The discrimination rate threshold can be pre-configured in the processor. The discrimination rate threshold can be obtained by performing a battery cluster charge and discharge experiment, connecting a resistor in parallel to a battery cell, simulating self-discharge anomaly, and collecting experimental data, thereby obtaining the discrimination rate threshold of self-discharge anomaly based on the experimental data.

[0144] The product of the second average value of the equivalent state of charge difference of all battery cells in the battery cluster and the discrimination rate threshold can be used as the self-discharge coefficient threshold, thereby avoiding the influence of the battery group on the self-discharge detection under different operating conditions and improving the detection accuracy.

[0145] Figure 8 A flow chart of another method for detecting abnormal self-discharge of a battery cell provided in an embodiment of the present disclosure is shown. Figure 2 On the basis of the embodiment, S212 is added after S210 to determine the self-discharge consistency of the battery pack by the self-discharge coefficient of the battery cell. Figure 8 As shown, in one embodiment, after calculating the self-discharge coefficient of the battery cell according to the charge state difference of the battery cell in each battery rest interval at S210, the method further includes:

[0146] S212: Determine a self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs, and determine the self-discharge consistency of the battery group according to the self-discharge score.

[0147] In one embodiment, determining a self-discharge score according to the self-discharge coefficients of all battery cells in the battery pack to which the battery cell belongs includes: calculating the range of the self-discharge coefficients of all battery cells in the battery pack according to the self-discharge coefficients of all battery cells in the battery pack, and determining the self-discharge score according to the range.

[0148] After obtaining the self-discharge coefficients of all battery cells in the battery pack, they can be sorted in order from large to small or from small to large to determine the maximum and minimum values ​​of the self-discharge coefficients of the battery cells, and the difference between the maximum and minimum values ​​of the self-discharge coefficients is used as the range of the self-discharge coefficients of all battery cells in the battery pack.

[0149] Based on the correspondence between the range and the self-discharge score, the range in which the range is located can be determined, and according to the above correspondence, the self-discharge score corresponding to the range can be determined.

[0150] The self-discharge consistency can be divided into different levels according to the self-discharge score. For example, the self-discharge consistency can be divided into three levels: high, medium, and low.

[0151] It should be noted that the larger the range, the higher the self-discharge score and the worse the self-discharge consistency of the battery pack.

[0152] In one embodiment, determining the self-discharge score according to the self-discharge coefficients of all battery cells in the battery pack to which the battery cell belongs includes: calculating the coefficient of variation according to the self-discharge coefficients of all battery cells in the battery pack, and determining the self-discharge score according to the coefficient of variation.

[0153] The coefficient of variation is the ratio between the standard deviation of the self-discharge coefficients of all cells in the battery pack and the average value of the self-discharge coefficients of all cells in the battery pack. The coefficient of variation is expressed as a percentage. The standard deviation is an indicator of the dispersion of the self-discharge coefficients of all cells in the battery pack. The average value is the sum of the self-discharge coefficients of all cells divided by the number of cells in the battery pack.

[0154] The self-discharge score corresponding to the coefficient of variation may also be determined through a preset correspondence between the coefficient of variation and the self-discharge score.

[0155] It should be noted that the implementation method of determining the self-discharge score by the coefficient of variation is similar to the implementation method of determining the self-discharge score by the range, which will not be repeated here.

[0156] In one embodiment, a self-discharge score determined by the range and a self-discharge score determined by the coefficient of variation may be weightedly summed to obtain a self-discharge score of the battery pack, and then the self-discharge consistency of the battery pack may be determined based on the self-discharge score of the battery pack.

[0157] Self-discharge rating of battery pack F 总 It is expressed as:

[0158] F 总 =αF1+βF2 (Formula 1)

[0159] Among them, α is the weight coefficient of the self-discharge score F1 determined by the range, and β is the weight coefficient of the self-discharge score F2 determined by the coefficient of variation. It should be noted that the weights of the two self-discharge scores can be determined according to actual needs, for example, α=0.6, β=0.4.

[0160] In the embodiments of the present disclosure, the self-discharge score is determined by the range and / or coefficient of variation, and the self-discharge consistency of the battery pack is determined based on the self-discharge score, which can further screen out abnormal self-discharge battery cells and realize self-discharge detection of battery cells in a small number of static working conditions.

[0161] In order to deepen the understanding of the battery monomer self-discharge abnormality detection method disclosed in the present invention, the following is combined with the attached Fig. 9 A specific example is described in detail, wherein a battery cluster to which a battery cell belongs is taken as an example as a battery pack.

[0162] like Fig. 9 As shown, the battery monomer self-discharge abnormality detection method disclosed in the present invention comprises the following steps:

[0163] S901, obtaining a plurality of initial static intervals within a preset period of time through current;

[0164] S902, selecting an initial resting interval with a resting time greater than 4 hours, and obtaining a battery resting interval and corresponding operating data;

[0165] S903, taking the average voltage value of 10 minutes after the start of the static state and the average voltage value of 10 minutes before the end of the static state as the starting voltage value and the ending voltage value of the battery cell respectively;

[0166] S904, obtaining non-plateau voltage data, and obtaining a starting SOC corresponding to the starting voltage value and an ending SOC corresponding to the ending voltage value through a SOC-OCV curve;

[0167] S905, calculating the equivalent battery SOC difference of the battery cells in each battery resting interval; calculating the difference between the starting SOC and the ending SOC, multiplying the difference by the equivalent time (e.g., 24 hours) and then dividing by the resting time to obtain the equivalent battery SOC difference of the battery cells in each battery resting interval;

[0168] S906, based on the SOC differences of the battery cells in each battery rest interval, selecting the equivalent battery SOC differences of the battery cells within a preset state of charge difference range, and calculating the average of the equivalent battery SOC differences corresponding to the multiple battery rest intervals to obtain the self-discharge coefficient of the battery cell;

[0169] S907, performing self-discharge abnormality detection on the battery cells based on a preset self-discharge coefficient threshold;

[0170] S908. Calculate the range and coefficient of variation, obtain a consistency score, and perform battery cluster consistency detection.

[0171] It should be noted that in S906, the average value of the equivalent battery SOC difference of all battery cells in the battery cluster can also be calculated based on the equivalent battery SOC difference of multiple battery cells and the number of battery cells, and the ratio of the equivalent battery SOC difference of each battery cell to the average value of the equivalent battery SOC difference of all battery cells in the battery cluster can be determined as the self-discharge coefficient of the battery cell.

[0172] The preset self-discharge coefficient threshold may be the product of an average value of the equivalent battery SOC differences of all battery cells in the battery cluster and a discrimination rate threshold.

[0173] In S908, a weighted sum of the consistency score obtained by the range and the consistency score obtained by the coefficient of variation may be performed to obtain a final consistency score, and then the self-discharge consistency degree of the battery cluster may be obtained according to the final consistency score.

[0174] In the present disclosure, the self-discharge size of battery cells and excessive self-discharge abnormalities are quickly detected without affecting the normal operation of industrial and commercial energy storage systems through the operation data of multiple short-term battery static intervals, thereby avoiding dependence on long-term static conditions, and performing SOC-OCV curve correction on the open circuit voltage difference, calculating the SOC difference instead of the open circuit voltage difference, thereby improving the accuracy of the detection result; using the product of the SOC difference of the equivalent battery of the entire battery pack and the discrimination rate threshold as the self-discharge coefficient threshold, and by comparing the self-discharge coefficient of the battery cell with the self-discharge coefficient threshold, the influence of the battery cluster on the self-discharge detection under different operating conditions is avoided; based on the cloud platform big data technology, all historical operation data of the energy storage system are analyzed, and the operation data of multiple effective static conditions are obtained through large data volume, thereby reducing the error of the calculation process.

[0175] Based on the same inventive concept, the present disclosure also provides a battery monomer self-discharge abnormality detection device, as described in the following embodiments. Since the principle of solving the problem in the device embodiment is similar to that in the above method embodiment, the implementation of the device embodiment can refer to the implementation of the above method embodiment, and the repeated parts will not be repeated.

[0176] Fig.10 A schematic diagram of a battery cell self-discharge abnormality detection device according to an embodiment of the present disclosure is shown. Fig.10 As shown, the device includes a data acquisition module 1010 , a first calculation module 1020 , a second calculation module 1030 , a third calculation module 1040 and an abnormality identification module 1050 .

[0177] The data acquisition module 1010 is used to acquire the operating data of the battery cells within a preset period of time;

[0178] A first calculation module 1020, configured to determine a plurality of battery rest intervals according to the operating data of the battery cells in a preset period of time, and calculate a starting voltage value and an ending voltage value of the battery cells in each battery rest interval;

[0179] The second calculation module 1030 is used to determine the starting state of charge and the ending state of charge of the battery cells in each battery resting interval according to the starting voltage value, the ending voltage value and the preset battery open circuit voltage and state of charge relationship curve, and calculate the difference of the state of charge of the battery cells in each battery resting interval;

[0180] A third calculation module 1040, configured to calculate a self-discharge coefficient of a battery cell according to a difference in the state of charge of the battery cell in each battery rest interval;

[0181] The abnormality identification module 1050 is used to perform self-discharge abnormality detection on the battery cell according to the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold.

[0182] It should be noted that the data acquisition module 1010, the first calculation module 1020, the second calculation module 1030, the third calculation module 1040 and the abnormality identification module 1050 correspond to S202 to S210 in the method embodiment, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above method embodiment. It should be noted that the above modules as part of the device can be executed in a computer system such as a set of computer executable instructions.

[0183] In one embodiment, the first calculation module 1020 is used to use the absolute value of the current magnitude being less than a preset current threshold as a battery rest condition, and to obtain multiple initial rest intervals of the battery cell within a preset time period by means of a sliding window; and to screen the initial rest intervals whose rest time exceeds the preset time threshold to obtain multiple battery rest intervals.

[0184] In one embodiment, the first calculation module 1020 is used to obtain the voltage value from the beginning of the battery resting interval to the first preset resting time, calculate the average value, and use it as the starting voltage value of the battery cell in the battery resting interval; obtain the voltage value from the second preset resting time to the end of the battery resting interval, calculate the average value, and use it as the ending voltage value of the battery cell in the battery resting interval, the second preset time is greater than the first preset time, and the time interval between the second preset time and the end of the resting is the first preset time.

[0185] In one embodiment, the device also includes a curve fitting module not shown in the accompanying drawings. The curve fitting module is used to fit the battery experimental data using a polynomial piecewise fitting method before determining the starting state of charge and ending state of charge of the battery cells in each battery static interval based on the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, so as to obtain a fitting piecewise function of the battery open circuit voltage and state of charge relationship curve of the battery cell, wherein the input of the fitting piecewise function is the voltage value of the battery cell, and the output is the state of charge.

[0186] In one embodiment, the second calculation module 1030 is used to input the starting voltage value of the battery cell in the battery rest interval into the fitting piecewise function and output the starting state of charge; input the ending voltage value of the battery cell in the battery rest interval into the fitting piecewise function and output the ending state of charge; calculate the difference between the starting state of charge and the ending state of charge to obtain the difference in the state of charge of the battery cells in each battery rest interval.

[0187] In one embodiment, the third calculation module 1040 is used to calculate the equivalent state of charge difference of the battery cells according to the state of charge difference of the battery cells in each battery resting interval; in multiple battery resting intervals, the equivalent state of charge difference of the battery cells within a preset state of charge difference range is screened to obtain the equivalent state of charge difference of the screened battery cells; and according to the equivalent state of charge difference of the screened battery cells, the self-discharge coefficient of the battery cells is calculated.

[0188] In one embodiment, the third calculation module 1040 is used to obtain the equivalent state of charge difference of the battery cells according to the state of charge difference of the battery cells in each battery resting interval, the corresponding resting time and the preset equivalent time.

[0189] In one embodiment, the third calculation module 1040 is used to calculate a first average value of the equivalent state of charge difference of the battery cell based on the equivalent state of charge difference of the battery cell after screening; determine a second average value of the equivalent state of charge difference of all battery cells in the battery group to which the battery cell belongs; and obtain the self-discharge coefficient of the battery cell based on the first average value and the second average value.

[0190] In one embodiment, the device further includes a threshold determination module not shown in the accompanying drawings, which is used to obtain a preset self-discharge coefficient threshold according to the second average value and a preset discrimination magnification threshold.

[0191] In one embodiment, the abnormality identification module 1050 is used to determine that a battery cell is a battery cell with abnormal self-discharge if the self-discharge coefficient of the battery cell is greater than or equal to a preset self-discharge coefficient threshold.

[0192] In one embodiment, the device also includes a consistency determination module not shown in the accompanying drawings. The consistency determination module is used to calculate the self-discharge coefficient of the battery cell according to the difference in the state of charge of the battery cells in each battery static interval, and then determine the self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs, and determine the self-discharge consistency of the battery cluster according to the self-discharge score.

[0193] In one embodiment, the consistency determination module is used to calculate the range of self-discharge coefficients of all battery cells in the battery pack to which the battery cell belongs according to the self-discharge coefficients of all battery cells in the battery pack, and determine the self-discharge score according to the range.

[0194] In one embodiment, the consistency determination module is used to calculate the coefficient of variation according to the self-discharge coefficients of all battery cells in the battery pack, and determine the self-discharge score according to the coefficient of variation.

[0195] In the embodiment of the present disclosure, the operation data of the battery cell in the preset time period is obtained; according to the operation data of the battery cell in the preset time period, multiple battery static intervals are determined, and the starting voltage value and the ending voltage value of the battery cell in each battery static interval are calculated; according to the starting voltage value, the ending voltage value and the preset battery open circuit voltage and state of charge relationship curve, the starting state of charge and the ending state of charge of the battery cell in each battery static interval are determined, and the difference of the state of charge of the battery cell in each battery static interval is calculated; according to the difference of the state of charge of the battery cell in each battery static interval, the self-discharge coefficient of the battery cell is calculated; according to the relationship between the self-discharge coefficient and the preset self-discharge coefficient threshold, the self-discharge abnormality detection of the battery cell is performed. The present disclosure can realize rapid battery cell self-discharge abnormality detection without affecting the normal operation of the industrial and commercial energy storage system through multiple short-term static working conditions, avoiding dependence on long-term static working conditions, correcting the pressure difference through the battery open circuit voltage and state of charge relationship curve, using the state of charge difference instead of the open circuit voltage difference, and effectively improving the accuracy of self-discharge abnormality detection.

[0196] Those skilled in the art will appreciate that various aspects of the present disclosure may be implemented as systems, methods or program products. Therefore, various aspects of the present disclosure may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as "circuits", "modules" or "systems".

[0197] Refer to the following Fig.11 1100 according to this embodiment of the present disclosure is described. Fig.11 The electronic device 1100 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0198] In one embodiment, the electronic device 1100 includes a processor and a memory, wherein the memory is used to store executable instructions of the processor, wherein the processor is configured to perform the above-mentioned battery cell self-discharge abnormality detection method by executing the executable instructions.

[0199] like Fig.11 As shown, the electronic device 1100 is in the form of a general computing device. The components of the electronic device 1100 may include but are not limited to: at least one processing unit 1110, at least one storage unit 1120, and a bus 1130 connecting different system components (including the storage unit 1120 and the processing unit 1110).

[0200] The storage unit stores a program code, and the program code can be executed by the processing unit 1110, so that the processing unit 1110 executes the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of this specification. For example, the processing unit 1110 can execute the following steps of the above method embodiment: obtaining the operating data of the battery cell in a preset time period; determining multiple battery rest intervals according to the operating data of the battery cell in the preset time period, and calculating the starting voltage value and the ending voltage value of the battery cell in each battery rest interval; determining the starting state of charge and the ending state of charge of the battery cell in each battery rest interval according to the starting voltage value, the ending voltage value and the preset battery open circuit voltage and state of charge relationship curve, and calculating the difference in state of charge of the battery cell in each battery rest interval; calculating the self-discharge coefficient of the battery cell according to the difference in state of charge of the battery cell in each battery rest interval; and performing self-discharge abnormality detection on the battery cell according to the relationship between the self-discharge coefficient and the preset self-discharge coefficient threshold.

[0201] The storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 11201 and / or a cache storage unit 11202 , and may further include a read-only storage unit (ROM) 11203 .

[0202] The storage unit 1120 may also include a program / utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0203] Bus 1130 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0204] The electronic device 1100 may also communicate with one or more external devices 1140 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 1100, and / or any device that enables the electronic device 1100 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed through an input / output (I / O) interface 1150. Furthermore, the electronic device 1100 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 1160. Fig.11As shown, the network adapter 1160 communicates with other modules of the electronic device 1100 via the bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0205] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0206] In particular, according to an embodiment of the present disclosure, the process described in the above reference flowchart can be implemented as a computer program product, which includes: a computer program, which, when executed by a processor, implements the steps described in the above "Exemplary Method" section according to various exemplary embodiments of the present disclosure.

[0207] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section are implemented. The computer-readable storage medium may be a readable signal medium or a readable storage medium. In some possible embodiments, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a computer program, and when the computer program is executed by a processor, the battery cell self-discharge abnormality detection method in any of the above-mentioned embodiments is implemented. In a feasible embodiment, the program product includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present disclosure described in the above-mentioned "Exemplary Method" section of this specification.

[0208] More specific examples of computer-readable storage media in the present disclosure may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0209] In the present disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0210] Alternatively, the program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0211] In a specific implementation, the program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0212] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0213] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.

[0214] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0215] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A method for detecting abnormal self-discharge of a battery cell, characterized in that: include: Obtaining the operating data of the battery cells within a preset period of time; Determine a plurality of battery rest intervals according to the operating data of the battery cells in the preset time period, and calculate the starting voltage value and the ending voltage value of the battery cells in each of the battery rest intervals; Determine the starting state of charge and the ending state of charge of the battery cell in each of the battery rest intervals according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and calculate the difference in state of charge of the battery cell in each of the battery rest intervals; Calculating the self-discharge coefficient of the battery cell according to the difference in the state of charge of the battery cell in each of the battery rest intervals; According to the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold, the battery cell is subjected to self-discharge abnormality detection.

2. The method for detecting abnormal self-discharge of a battery cell according to claim 1, characterized in that: The step of determining a plurality of battery rest intervals according to the operating data of the battery cells within the preset time period includes: Taking the absolute value of the current being less than a preset current threshold as a battery rest condition, obtaining a plurality of initial rest intervals of the battery cell within the preset period of time by means of a sliding window; Initial rest intervals whose rest duration exceeds a preset duration threshold are screened to obtain the plurality of battery rest intervals.

3. The method for detecting abnormal self-discharge of a battery cell according to claim 2, characterized in that: The calculating the starting voltage value and the ending voltage value of the battery cell in each of the battery rest intervals includes: Obtaining voltage values ​​from the start of the battery resting period to the first preset resting period in the battery resting period, and calculating an average value as a starting voltage value of the battery cell in the battery resting period; The voltage value of the battery rest interval from the second preset rest time to the end of the rest is obtained, and the average value is calculated as the end voltage value of the battery cell in the battery rest interval, the second preset time is greater than the first preset time, and the time interval between the second preset time and the end of the rest is the first preset time.

4. The method for detecting abnormal self-discharge of a battery cell according to claim 1, characterized in that: Before determining the starting state of charge and the ending state of charge of the battery cells in each of the battery rest intervals according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, the method further includes: The battery experimental data is fitted by adopting a polynomial piecewise fitting method to obtain a fitting piecewise function of the battery open circuit voltage and charge state relationship curve of the battery cell, wherein the input of the fitting piecewise function is the voltage value of the battery cell, and the output is the charge state.

5. The method for detecting abnormal self-discharge of a battery cell according to claim 4, characterized in that: The step of determining the starting state of charge and the ending state of charge of the battery cell in each of the battery rest intervals according to the starting voltage value, the ending voltage value, and a preset battery open circuit voltage and state of charge relationship curve, and calculating the state of charge difference of the battery cell in each of the battery rest intervals, comprises: Inputting the initial voltage value of the battery cell in the battery rest interval into the fitting piecewise function, and outputting the initial state of charge; Inputting the ending voltage value of the battery cell in the battery rest interval into the fitting piecewise function, and outputting the ending state of charge; The difference between the initial state of charge and the final state of charge is calculated to obtain the difference in state of charge of the battery cells in each of the battery rest intervals.

6. The method for detecting abnormal self-discharge of a battery cell according to claim 1, characterized in that: The step of calculating the self-discharge coefficient of the battery cell according to the charge state difference of the battery cell in each of the battery rest intervals includes: Calculating the equivalent state of charge difference of the battery monomer according to the state of charge difference of the battery monomer in each of the battery rest intervals; In the plurality of battery rest intervals, the equivalent state of charge differences of the battery cells within a preset state of charge difference range are screened to obtain the equivalent state of charge differences of the battery cells after screening; The self-discharge coefficient of the battery cell is calculated according to the difference in equivalent state of charge of the battery cell after screening.

7. The method for detecting abnormal self-discharge of a battery cell according to claim 6, characterized in that: The calculating the equivalent state of charge difference of the battery cells according to the state of charge difference of the battery cells in each of the battery rest intervals includes: The equivalent state of charge difference of the battery monomer is obtained according to the state of charge difference of the battery monomer in each of the battery resting intervals, the corresponding resting time and the preset equivalent time.

8. The method for detecting abnormal self-discharge of a battery cell according to claim 6, characterized in that: The step of calculating the self-discharge coefficient of the battery cell according to the difference in equivalent state of charge of the battery cell after screening includes: Calculating a first average value of the equivalent state of charge differences of the battery cells according to the equivalent state of charge differences of the battery cells after screening; Determine a second average value of the equivalent state of charge differences of all battery cells in the battery group to which the battery cell belongs; The self-discharge coefficient of the battery cell is obtained according to the first average value and the second average value.

9. The method for detecting abnormal self-discharge of a battery cell according to claim 8, characterized in that: The method further comprises: The preset self-discharge coefficient threshold is obtained according to the second average value and the preset discrimination magnification threshold.

10. The method for detecting abnormal self-discharge of a battery cell according to claim 1, characterized in that: The self-discharge abnormality detection of the battery cell according to the relationship between the self-discharge coefficient and the preset self-discharge coefficient threshold comprises: If the self-discharge coefficient of the battery cell is greater than or equal to the preset self-discharge coefficient threshold, the battery cell is determined to be a self-discharging abnormal battery cell.

11. The method for detecting abnormal self-discharge of a battery cell according to any one of claims 1 to 10, characterized in that: After calculating the self-discharge coefficient of the battery cell according to the charge state difference of the battery cell in each of the battery rest intervals, the method further includes: A self-discharge score is determined according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs, and a self-discharge consistency of the battery group is determined according to the self-discharge score.

12. The method for detecting abnormal self-discharge of a battery cell according to claim 11, characterized in that: The step of determining the self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs includes: According to the self-discharge coefficients of all the battery cells in the battery group to which the battery cell belongs, the range of the self-discharge coefficients of all the battery cells in the battery group is calculated, and the self-discharge score is determined according to the range.

13. The method for detecting abnormal self-discharge of a battery cell according to claim 11, characterized in that: The step of determining the self-discharge score according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs includes: The coefficient of variation is calculated according to the self-discharge coefficients of all battery cells in the battery group to which the battery cell belongs, and the self-discharge score is determined according to the coefficient of variation.

14. A battery cell self-discharge abnormality detection device, characterized in that: include: A data acquisition module, used to acquire the operating data of the battery cells within a preset period of time; A first calculation module, used to determine a plurality of battery rest intervals according to the operation data of the battery cells in the preset time period, and calculate the starting voltage value and the ending voltage value of the battery cells in each of the battery rest intervals; a second calculation module, configured to determine the starting state of charge and the ending state of charge of the battery cell in each of the battery rest intervals according to the starting voltage value, the ending voltage value and a preset battery open circuit voltage and state of charge relationship curve, and calculate the state of charge difference of the battery cell in each of the battery rest intervals; A third calculation module, configured to calculate a self-discharge coefficient of the battery cell according to a difference in the state of charge of the battery cell in each of the battery rest intervals; The abnormality identification module is used to perform self-discharge abnormality detection on the battery cell according to the relationship between the self-discharge coefficient and a preset self-discharge coefficient threshold.

15. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; The processor is configured to execute the battery monomer self-discharge abnormality detection method according to any one of claims 1 to 13 by executing the executable instructions.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting abnormal self-discharge of a battery cell according to any one of claims 1 to 13 is implemented.

17. A computer program product, characterized in that The invention comprises a computer program, wherein when the computer program is executed by a processor, the method for detecting abnormal self-discharge of a battery cell according to any one of claims 1 to 13 is implemented.

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