Electrochemical energy storage lithium iron phosphate cell outlier diagnosis method and system and electronic equipment

Abstract

The invention provides an electrochemical energy storage lithium iron phosphate cell outlier diagnosis method and system and electronic equipment, and the method comprises the steps: collecting the voltage, current, temperature and other key parameters of a cell in real time, and calculating the internal resistance, SOC and SOH of the cell through a forgetting factor least square method, an extended Kalman method and a neural network model algorithm. And calculating a difference value between the voltage, the temperature and the SOC of the target battery cell and the median of the target parameters of the plurality of battery cells by an algorithm, judging whether an outlier exists or not, and if the outlier exists, increasing an outlier judgment value and accumulating the outlier judgment value. And calculating the difference between the internal resistance and the SOH of the target battery cell and the median in the period, and judging whether the battery cell is outlier or not and accumulating the battery cell. And when the slope accumulated by the outlier judgment value exceeds a preset condition, judging that the battery cell is outlier. The stability and reliability of the energy storage system can be improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of battery cell management technology, and more specifically, to a method, system and electronic equipment for outlier diagnosis of an electrochemical energy storage lithium iron phosphate battery cell. Background Art

[0002] Lithium iron phosphate cells play an increasingly important role in the field of electrochemical energy storage, especially in electric vehicles and large-scale energy storage systems, due to their excellent safety, long life and cost-effectiveness. However, the nonlinear characteristics of this cell, performance differences between different batches and manufacturers, and sensitivity to temperature changes make it difficult for traditional battery management systems (BMS) to achieve accurate battery status monitoring and health status assessment. Existing diagnostic methods are often unable to adapt to these characteristics of lithium iron phosphate cells, resulting in deficiencies in battery performance evaluation and maintenance. Especially in battery packs, the presence of outlier cells can seriously affect the stability and reliability of the entire system. Therefore, it becomes particularly important to develop an algorithm that can accurately diagnose and identify outlier cells.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the traditional diagnostic method cannot effectively identify the outlier state of the battery cell, and has high calculation complexity and inconvenient parameter adjustment, which makes it difficult to meet the high requirements of the energy storage system for battery cell health management. Summary of the invention

[0004] The present invention provides an electrochemical energy storage lithium iron phosphate battery cell outlier diagnosis method, system and electronic equipment.

[0005] In a first aspect of the present invention, a method for diagnosing outliers in an electrochemical energy storage lithium iron phosphate battery cell is provided, comprising:

[0006] Data collection steps: Install an AFE module on each battery cell of the energy storage system, and use the AFE module to collect the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The charging and discharging process collection time accuracy is 10s / time, and the static time is 2h. If the static time is less than 2h, the last moment parameter value is taken;

[0007] Battery state evaluation steps: Use the forgetting factor least squares method to calculate the battery internal resistance based on the voltage, current and temperature data during the battery charge and discharge process, use the extended Kalman method to calculate the battery SOC based on the voltage and current data during the battery charge and discharge process, and use the neural network model algorithm to calculate the battery SOH based on the voltage, current and temperature data during the battery charge and discharge process, and then evaluate the battery state;

[0008] Real-time outlier judgment step: Calculate the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, cancel the cumulative calculation of the target battery cell and judge it as an outlier, and accumulate the outlier judgment value in real time;

[0009] Cycle outlier judgment step: Calculate the difference between the internal resistance and SOH of the target cell in each cycle and the median of the corresponding target parameters of multiple cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, cancel the cumulative calculation of the target cell and judge it as an outlier, and accumulate the outlier judgment value by cycle;

[0010] Outlier determination step: Draw a cumulative value curve according to the real-time accumulated values ​​and the accumulated values ​​of each cycle, where the horizontal axis of the accumulated value curve is time or cycle, and the vertical axis is the value size. Calculate the difference between the slope of the cell voltage, temperature and SOC outlier determination value growth at any moment and the slope at the previous moment, and the difference between the slope of the cell internal resistance and SOH growth at any cycle and the slope of the previous cycle. If they exceed the preset threshold, the target cell is determined to be outlier.

[0011] Processing level determination step: judging the priority based on the periodic outlier accumulation value is higher than judging the priority based on the real-time outlier accumulation value;

[0012] Fault reporting steps: After determining that the battery cell is out of group, report the battery cell out of group fault information and the number of the battery cell.

[0013] Furthermore, in the data acquisition step, the AFE module is used to collect voltage, current and temperature information of the battery cell during the charging and discharging process and the static process, and the charging and discharging process acquisition time accuracy is 10s / time, the static time is 2h, and the last moment parameter value is taken when the static time is less than 2h.

[0014] Furthermore, in the battery cell state evaluation step, the forgetting factor least squares method uses the voltage, current and temperature data during the battery cell charging and discharging process to calculate the battery cell internal resistance, the extended Kalman method uses the voltage and current data during the battery cell charging and discharging process to calculate the battery cell SOC, and the neural network model algorithm uses the voltage, current and temperature data during the battery cell charging and discharging process to calculate the battery cell SOH.

[0015] Furthermore, in the real-time outlier judgment step, the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells is calculated, and the outlier judgment value is added accordingly according to the different proportions in which the absolute value of the difference exceeds the median, and real-time accumulation is performed at the same time.

[0016] Furthermore, in the periodic outlier judgment step, the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells is calculated, and the outlier judgment value is added accordingly according to the different proportions in which the absolute value of the difference exceeds the median, and accumulated by cycle.

[0017] Furthermore, in the outlier determination step, a cumulative value curve is drawn according to the real-time and periodically accumulated values, a corresponding slope difference is calculated, and the slope difference is compared with a preset threshold value to determine whether the battery cell is an outlier.

[0018] Furthermore, in the processing level determination step, the periodic outlier cumulative value is used as a basis for priority judgment.

[0019] Furthermore, in the fault reporting step, when the battery cell is determined to be outliers, the outlier fault information of the battery cell and the battery cell number are reported.

[0020] In a second aspect of the present invention, a system for diagnosing outliers of an electrochemical energy storage lithium iron phosphate battery cell is provided, comprising:

[0021] The data acquisition module is used to install an AFE module on each battery cell of the energy storage system. The AFE module collects the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The charging and discharging process collection time accuracy is 10s / time, and the static time is 2h. If the static time is less than 2h, the last parameter value is taken;

[0022] The battery cell status evaluation module is used to calculate the battery cell internal resistance based on the voltage, current and temperature data during the battery cell charging and discharging process using the forgetting factor least squares method, calculate the battery cell SOC based on the voltage and current data during the battery cell charging and discharging process using the extended Kalman method, and calculate the battery cell SOH based on the voltage, current and temperature data during the battery cell charging and discharging process using the neural network model algorithm, thereby evaluating the battery cell status;

[0023] A real-time outlier judgment module is used to calculate the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is canceled and the battery cell is judged to be outlier, and the outlier judgment value is accumulated in real time;

[0024] The cycle outlier judgment module is used to calculate the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is cancelled and the battery cell is judged to be outlier, and the outlier judgment value is accumulated by cycle at the same time;

[0025] The outlier judgment module is used to draw a cumulative value curve according to the real-time accumulated values ​​and the accumulated values ​​of each cycle, where the horizontal axis of the accumulated value curve is time or cycle, and the vertical axis is the value size. The difference between the slope of the increase of the cell voltage, temperature and SOC outlier judgment value at any moment and the slope at the previous moment is calculated, and the difference between the slope of the increase of the cell internal resistance and SOH at any cycle and the slope of the previous cycle is calculated. If it exceeds the preset threshold, the target cell is judged to be outlier;

[0026] A processing level determination module, for determining a priority based on a periodic outlier cumulative value over a real-time outlier cumulative value;

[0027] The fault reporting module is used to report the fault information of the battery cell and the number of the battery cell after determining that the battery cell is out of the group.

[0028] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory and an input-output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0029] According to the above-mentioned embodiment of the present invention, at least the following beneficial effects are achieved: the electrochemical energy storage lithium iron phosphate battery cell outlier diagnosis algorithm of the present invention can collect key parameters such as voltage, current and temperature of the battery cell in real time, and calculate the internal resistance, SOC and SOH of the battery cell by using the forgetting factor least squares method, the extended Kalman method and the neural network model algorithm, so as to evaluate the battery cell state online. This method can quickly and accurately identify batteries with inconsistent states, and sort the processing levels of various problems by calculating the deviations of different target parameters of the battery cell as the outlier coefficient, thereby improving the stability and reliability of the energy storage system. In addition, the algorithm has high recognition accuracy and low computational complexity, is easy to adjust parameters, can effectively reduce the probability of outlier misjudgment and missed judgment, and improve the robustness of the core functions of the battery management system (BMS). This technical means is not only applicable to energy storage systems of various sizes, but can also be widely used in fields such as electric vehicles, providing effective technical support for health management and fault diagnosis of batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0031] Figure 1 A schematic diagram of a process flow of an outlier diagnosis method for an electrochemical energy storage lithium iron phosphate battery cell provided in one embodiment of the present invention;

[0032] Figure 2 A schematic diagram of the structure of an electrochemical energy storage lithium iron phosphate battery outlier diagnosis system provided by an embodiment of the present invention;

[0033] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0035] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention may be implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0036] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0037] Reference below Figure 1 , Figure 1 A schematic diagram of a process flow of an electrochemical energy storage lithium iron phosphate battery outlier diagnosis method provided by an embodiment of the present invention. Figure 1 As shown, a method for diagnosing outliers in an electrochemical energy storage lithium iron phosphate battery cell includes:

[0038] Data collection steps: Install an AFE module on each battery cell of the energy storage system, and use the AFE module to collect the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The charging and discharging process collection time accuracy is 10s / time, and the static time is 2h. If the static time is less than 2h, the last moment parameter value is taken;

[0039] Battery state evaluation steps: Use the forgetting factor least squares method to calculate the battery internal resistance based on the voltage, current and temperature data during the battery charge and discharge process, use the extended Kalman method to calculate the battery SOC based on the voltage and current data during the battery charge and discharge process, and use the neural network model algorithm to calculate the battery SOH based on the voltage, current and temperature data during the battery charge and discharge process, and then evaluate the battery state;

[0040] Real-time outlier judgment step: Calculate the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, cancel the cumulative calculation of the target battery cell and judge it as an outlier, and accumulate the outlier judgment value in real time;

[0041] Cycle outlier judgment step: Calculate the difference between the internal resistance and SOH of the target cell in each cycle and the median of the corresponding target parameters of multiple cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, cancel the cumulative calculation of the target cell and judge it as an outlier, and accumulate the outlier judgment value by cycle;

[0042] Outlier determination step: Draw a cumulative value curve according to the real-time accumulated values ​​and the accumulated values ​​of each cycle, where the horizontal axis of the accumulated value curve is time or cycle, and the vertical axis is the value size. Calculate the difference between the slope of the cell voltage, temperature and SOC outlier determination value growth at any moment and the slope at the previous moment, and the difference between the slope of the cell internal resistance and SOH growth at any cycle and the slope of the previous cycle. If they exceed the preset threshold, the target cell is determined to be outlier.

[0043] Processing level determination step: judging the priority based on the periodic outlier accumulation value is higher than judging the priority based on the real-time outlier accumulation value;

[0044] Fault reporting steps: After determining that the battery cell is out of group, report the battery cell out of group fault information and the number of the battery cell.

[0045] It should be noted that the present invention relates to an outlier diagnosis algorithm for an electrochemical energy storage lithium iron phosphate battery cell, which collects key parameters such as voltage, current and temperature of the battery cell in real time, and uses the forgetting factor least squares method, the extended Kalman method and the neural network model algorithm to calculate the internal resistance, SOC and SOH of the battery cell, thereby performing an online evaluation of the battery cell state. Among them, voltage refers to the potential difference across the battery cell, current is the flow rate of charge in the battery cell, and temperature is the temperature change caused by the heat generated by the battery cell during the charging and discharging process. Real-time monitoring of these parameters is crucial for accurately evaluating the health status of the battery cell.

[0046] Specifically, the algorithm first installs an AFE module on each battery cell of the energy storage system. The AFE module is used to collect voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The acquisition time accuracy of the charging and discharging process is set to 10 seconds / time to ensure that the subtle changes of the battery cell during the charging and discharging process can be captured. The static time is set to 2 hours. If the static time is less than 2 hours, the parameter value at the last moment is taken. Through the collection of these parameters, accurate data support can be provided for the subsequent battery cell status evaluation.

[0047] More specifically, in the process of battery cell status evaluation, the forgetting factor least squares method is used to calculate the internal resistance of the battery cell. This method can better reflect the current state of the battery cell by considering the timeliness of the data; the extended Kalman method is used to calculate the SOC of the battery cell. This method can effectively handle the nonlinear characteristics of the battery cell state; the neural network model algorithm is used to calculate the SOH of the battery cell. This algorithm can accurately predict the health status of the battery cell by learning a large amount of historical data.

[0048] Preferably, in order to further improve the accuracy of diagnosis, a specific threshold value can be set when calculating the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of multiple battery cell target parameters. For example, when the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five.

[0049] Furthermore, the parameters in the algorithm can be adjusted according to the needs of actual applications to adapt to different battery cell types and usage environments. For example, for batteries of different capacities or different manufacturers, the parameter settings in the algorithm can be adjusted to ensure the accuracy and reliability of the diagnosis results.

[0050] In some embodiments, in the data acquisition step, the AFE module is used to collect the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process, and the charging and discharging process collection time accuracy is 10s / time, the static time is 2h, and the last moment parameter value is taken when the static time is less than 2h.

[0051] It should be noted that the data acquisition step mentioned in the present invention involves installing an AFE module on each battery cell of the energy storage system. The AFE module is used to collect the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The AFE module is an analog front-end module, which is responsible for converting the analog signal of the battery cell into a digital signal for subsequent data processing and analysis. The acquisition time accuracy of the charging and discharging process is set to 10 seconds / time, which means that the voltage, current and temperature data of the battery cell are collected every 10 seconds to ensure that the dynamic changes of the battery cell during the charging and discharging process can be captured. The static time is set to 2 hours. If the static time is less than 2 hours, the parameter value at the last moment is taken to ensure that stable parameter data can be obtained during the static process of the battery cell.

[0052] Specifically, during the data acquisition process, the AFE module measures the voltage, current and temperature of the battery cell through high-precision sensors. The voltage sensor is used to measure the potential difference between the two ends of the battery cell, the current sensor is used to measure the flow rate of charge in the battery cell, and the temperature sensor is used to measure the temperature change caused by the heat generated by the battery cell during the charging and discharging process. These sensors transmit the measured analog signals to the AFE module, and the AFE module converts these analog signals into digital signals for subsequent data processing and analysis. During the charging and discharging process, the AFE module collects the voltage, current and temperature data of the battery cell every 10 seconds, and stores these data in the system for subsequent battery cell status evaluation and outlier judgment. During the static process, the AFE module collects the parameter values ​​of the battery cell at the end of the static time. If the static time is less than 2 hours, the parameter value at the last moment is taken at the end of the static time.

[0053] Preferably, in order to improve the accuracy and reliability of data acquisition, a higher precision sensor can be used in the AFE module, and the sensor can be calibrated regularly to ensure the accuracy of its measurement results. In addition, the AFE module can be optimized to improve its data processing capability and anti-interference ability, so that data can be collected stably in a complex working environment. For example, a filtering algorithm can be used to filter the collected data to eliminate noise and interference and improve the accuracy of the data. At the same time, the parameter settings of the AFE module can also be adjusted according to the needs of actual applications to adapt to different battery cell types and usage environments.

[0054] In some embodiments, in the battery cell state evaluation step, the forgetting factor least squares method uses the voltage, current and temperature data of the battery cell during the charging and discharging process to calculate the battery cell internal resistance, the extended Kalman method uses the voltage and current data of the battery cell during the charging and discharging process to calculate the battery cell SOC, and the neural network model algorithm uses the voltage, current and temperature data of the battery cell during the charging and discharging process to calculate the battery cell SOH.

[0055] It should be noted that the cell state assessment step involves calculating the internal resistance, SOC and SOH of the cell using the forgetting factor least squares method, the extended Kalman method and the neural network model algorithm. The forgetting factor least squares method is an algorithm for parameter estimation. It introduces a forgetting factor to reduce the impact of old data on the current estimate, thereby better reflecting the current state of the cell. The extended Kalman method is an algorithm for state estimation of nonlinear systems. It can effectively handle the nonlinear characteristics of the cell state and is suitable for calculating the SOC of the cell. The neural network model algorithm is an algorithm based on a large amount of data training. It can learn the complex characteristics of the cell and is used to calculate the SOH of the cell, thereby evaluating the health status of the cell.

[0056] Specifically, when calculating the internal resistance of a battery cell, the forgetting factor least squares method collects the voltage, current and temperature data during the charging and discharging process of the battery cell, and uses the forgetting factor to weight the data to reduce the impact of old data on the current internal resistance estimation. When calculating the SOC of a battery cell, the extended Kalman method establishes a nonlinear state model of the battery cell and uses the voltage and current data during the charging and discharging process of the battery cell for state estimation, which can accurately reflect the charging and discharging state of the battery cell. When calculating the SOH of a battery cell, the neural network model algorithm trains a large amount of historical data to learn the relationship between the voltage, current, temperature and other parameters of the battery cell and the SOH, thereby achieving accurate prediction of the health status of the battery cell.

[0057] Preferably, in order to improve the accuracy of the cell state assessment, a suitable forgetting factor value can be selected in the forgetting factor least squares method to balance the influence of new and old data on the internal resistance estimation. For example, the forgetting factor can be selected as 0.9 so that the new data has a greater influence on the internal resistance estimation, thereby better reflecting the current state of the cell. In the extended Kalman method, the accuracy of SOC estimation can be improved by adjusting the parameters of the state model and the noise covariance matrix.

[0058] Furthermore, more complex neural network structures, such as deep neural networks, can be used to improve the accuracy of SOH prediction. At the same time, other algorithms or data processing technologies, such as data fusion or filtering algorithms, can be combined to further improve the accuracy and reliability of cell status assessment.

[0059] In some embodiments, in the real-time outlier judgment step, the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells is calculated, and the outlier judgment value is added accordingly according to the different proportions in which the absolute value of the difference exceeds the median, and real-time accumulation is performed at the same time.

[0060] It should be noted that the real-time outlier judgment step involves calculating the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells, and performing a corresponding addition operation on the outlier judgment value according to the absolute value of the difference. The outlier judgment value is an indicator used to evaluate whether the battery cell deviates from the normal state. By calculating the difference between the battery cell parameter and the median, it can be determined whether the battery cell is abnormal. The median is a statistical concept that represents the middle value of a set of data. It can effectively reflect the central trend of the data and avoid the influence of extreme values.

[0061] Specifically, in the real-time outlier judgment step, the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells is first calculated. For example, if the voltage of the target battery cell is 3.5V, and the median of the voltages of multiple battery cells is 3.4V, the difference is 0.1V. Then, the outlier judgment value is added according to the absolute value of the difference: when the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is added by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is added by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is added by five. If the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is canceled and the battery cell is judged to be outlier. At the same time, the outlier judgment value is accumulated in real time for subsequent outlier judgment and processing.

[0062] Preferably, in order to improve the accuracy and reliability of real-time outlier judgment, the addition rule of the outlier judgment value can be further refined or optimized. For example, the addition threshold of the outlier judgment value can be adjusted according to the actual use and performance characteristics of the battery cell to adapt to different types of battery cells and application scenarios.

[0063] Furthermore, other parameters or indicators, such as the charge and discharge current and cycle number of the battery cell, can be introduced to make a comprehensive judgment together with the voltage, temperature and SOC, so as to more comprehensively evaluate the outlier status of the battery cell. At the same time, data smoothing or filtering algorithms can be used to process the real-time collected data to reduce the impact of noise and interference on outlier judgment and improve the accuracy of the judgment results.

[0064] In some embodiments, in the periodic outlier judgment step, the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells is calculated, and the outlier judgment value is added accordingly according to the different proportions in which the absolute value of the difference exceeds the median, and accumulated by cycle.

[0065] It should be noted that the periodic outlier judgment step involves calculating the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells, and performing a corresponding addition operation on the outlier judgment value according to the absolute value of the difference. Periodic outlier judgment is to evaluate the state changes of the battery cell within a certain time period to determine whether it has a long-term outlier trend. Internal resistance refers to the size of the internal resistance of the battery cell during the charging and discharging process, while SOH (State of Health) indicates the health status of the battery cell, which is usually used to evaluate the life and performance of the battery cell.

[0066] Specifically, in the cycle outlier judgment step, first calculate the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells. For example, if the internal resistance of the target battery cell in a charge and discharge cycle is 0.1Ω, and the median of the internal resistance of multiple battery cells is 0.08Ω, the difference is 0.02Ω. Then, the outlier judgment value is added according to the absolute value of the difference: when the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is added by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is added by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is added by five. If the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is canceled and it is judged as an outlier. At the same time, the outlier judgment value is accumulated by cycle for subsequent outlier judgment and processing.

[0067] Preferably, in order to improve the accuracy and reliability of periodic outlier judgment, the addition rule of the outlier judgment value can be further refined or optimized. For example, the addition threshold of the outlier judgment value can be adjusted according to the actual use and performance characteristics of the battery cell to adapt to different types of battery cells and application scenarios.

[0068] Furthermore, other parameters or indicators, such as the cycle life of the battery cell, capacity retention rate, etc., can be introduced to make a comprehensive judgment together with the internal resistance and SOH, so as to more comprehensively evaluate the outlier status of the battery cell. At the same time, data smoothing or filtering algorithms can be used to process the periodic data to reduce the impact of noise and interference on outlier judgment and improve the accuracy of the judgment results.

[0069] In some embodiments, in the outlier determination step, a cumulative value curve is drawn according to the real-time and periodically accumulated values, the corresponding slope difference is calculated, and the slope difference is compared with a preset threshold to determine whether the battery cell is an outlier.

[0070] It should be noted that the outlier determination step involves drawing a cumulative value curve graph based on the real-time accumulated values ​​and the accumulated values ​​of each cycle, and determining whether the battery cell is outlier by calculating the slope difference. The cumulative value curve graph is a graph used to visually display the changes in the outlier judgment value of the battery cell over time or period. The horizontal axis can be time or period, and the vertical axis is the size of the outlier judgment value. The slope difference refers to the difference between the slope of the outlier judgment value growth at a certain moment or period and the slope of the previous moment or period. By comparing with a pre-set threshold, it can be determined whether the battery cell has an abnormality.

[0071] Specifically, in the outlier determination step, first, a cumulative value curve graph is drawn based on the real-time accumulated outlier judgment value and the accumulated value of each cycle. For example, if the real-time accumulated outlier judgment value is 10, and the accumulated value of a certain cycle is 5, the corresponding numerical point is drawn at the corresponding position on the curve graph. Then calculate the difference between the slope of the growth of the outlier judgment value of the battery cell voltage, temperature and SOC at any moment and the slope at the previous moment, as well as the difference between the slope of the growth of the internal resistance and SOH of the battery cell in any cycle and the slope of the previous cycle. For example, if the slope at a certain moment is 0.5, and the slope at the previous moment is 0.3, the slope difference is 0.2. If the slope difference exceeds a preset threshold, such as 0.3, the target battery cell is judged to be outlier. The preset threshold can be adjusted according to the actual use and performance characteristics of the battery cell to improve the accuracy of the judgment.

[0072] Preferably, in order to improve the accuracy and reliability of outlier determination, the cumulative value curve graph may be further analyzed and processed. For example, a curve fitting algorithm may be used to smooth the cumulative value curve to eliminate noise and fluctuations in the data and make the curve smoother and more stable.

[0073] Furthermore, other statistical methods or machine learning algorithms, such as cluster analysis and anomaly detection algorithms, can be introduced to conduct a comprehensive analysis of the cumulative value curve, so as to more accurately determine the outlier state of the battery cell. At the same time, the pre-set threshold can be dynamically adjusted according to the needs of actual applications to adapt to different battery cell types and usage environments, thereby improving the adaptability and flexibility of outlier determination.

[0074] In some embodiments, in the processing level determination step, the periodic outlier cumulative value is used as a basis for priority judgment.

[0075] It should be noted that the processing level determination step refers to determining the processing priority of the battery cell problem based on the periodic outlier cumulative value and the real-time outlier cumulative value after judging the battery cell outliers. The periodic outlier cumulative value refers to the cumulative result of the outlier judgment value of the battery cell within a certain period of time, while the real-time outlier cumulative value refers to the cumulative result of the outlier judgment value of the battery cell during the real-time monitoring process. Generally, the periodic outlier cumulative value can more comprehensively reflect the state changes of the battery cell over a longer period of time. Therefore, when determining the processing level, the periodic outlier cumulative value is given priority to ensure that the battery cell problems with long-term outlier trends can be handled in a timely manner.

[0076] Specifically, in the processing level determination step, the cycle outlier cumulative value and the real-time outlier cumulative value are first calculated. For example, if the outlier judgment value of a certain battery cell in the most recent charge and discharge cycle is accumulated to 10, and the cumulative value in the real-time monitoring process is 5, then the cycle outlier cumulative value is 10, and the real-time outlier cumulative value is 5. Then the processing priority is determined based on the size relationship between the cycle outlier cumulative value and the real-time outlier cumulative value. If the cycle outlier cumulative value is greater than the real-time outlier cumulative value, the problem of the battery cell is processed first; if the two are equal, a comprehensive judgment is made based on the actual situation to determine the final processing priority. In addition, a comprehensive evaluation can be made based on other parameters or indicators of the battery cell, such as capacity, life, etc., to more accurately determine the processing level.

[0077] Preferably, in order to improve the accuracy and reliability of the processing level determination, more evaluation indicators and analysis methods can be introduced. For example, the battery cell's usage history data, maintenance records and other information can be combined to conduct a more comprehensive analysis of the battery cell's status, thereby more accurately determining the processing priority.

[0078] Furthermore, machine learning algorithms, such as decision trees and support vector machines, can be used to classify and predict the outlier status of the battery cells to achieve automated and intelligent processing level determination. At the same time, the processing level division criteria can be dynamically adjusted according to the needs of actual applications to adapt to different battery cell types and usage environments, thereby improving processing efficiency and effects.

[0079] In some embodiments, in the fault reporting step, when a battery cell is determined to be outliers, outlier fault information of the battery cell and the battery cell number are reported.

[0080] It should be noted that the fault reporting step refers to reporting the fault information of the cell out of group and the number of the cell to the system or relevant personnel after determining that the cell is out of group. The fault information includes detailed information such as the specific reason for the cell out of group and the degree of out of group, while the cell number is used to uniquely identify each cell to facilitate tracking and handling of cell faults. The purpose of reporting fault information is to promptly notify relevant personnel or systems so that appropriate measures can be taken for fault handling and maintenance, thereby ensuring the normal operation, safety and stability of the energy storage system.

[0081] Specifically, in the fault reporting step, the outlier fault information of the battery cell is first determined based on the outlier judgment result. For example, if the outlier judgment value of a certain battery cell exceeds the preset threshold value, it is judged as an outlier fault, then the outlier reason of the battery cell is recorded, such as abnormal voltage, excessive temperature, excessive internal resistance, etc., and the degree of outlier, such as the size of the outlier judgment value, is calculated. Then the number of the battery cell is obtained. Usually the battery cell number is pre-set and stored in the system, and can be obtained by querying the system database. Finally, the outlier fault information and number of the battery cell are reported to the system monitoring center or relevant personnel through the communication module. The communication module can use wired or wireless communication methods, such as Ethernet, wireless LAN, mobile communication, etc., to ensure timely transmission and reception of information.

[0082] Preferably, in order to improve the efficiency and accuracy of fault reporting, the format and content of fault information can be standardized and optimized. For example, a unified fault information template can be designed, including fields such as cell number, outlier reason, outlier degree, and occurrence time, so as to quickly identify and handle faults. At the same time, an automated reporting mechanism can be adopted. When the cell outlier judgment result meets the reporting conditions, the system automatically triggers the reporting process without manual intervention, thereby improving the timeliness and efficiency of fault handling.

[0083] Furthermore, the reported fault information can be further analyzed and processed in combination with fault diagnosis and prediction algorithms to prevent and resolve potential battery cell problems in advance and improve the reliability and safety of the energy storage system.

[0084] The above-mentioned embodiments of the present invention have the following beneficial effects: the present invention collects key parameters such as voltage, current and temperature of the battery cell in real time, and uses the forgetting factor least squares method, the extended Kalman method and the neural network model algorithm to calculate the internal resistance, SOC and SOH of the battery cell, so as to realize online evaluation and accurate monitoring of the battery cell state. This method can effectively identify the abnormal state of the battery cell during the charging and discharging process, and timely discover the outlier battery cell, thereby improving the stability and reliability of the energy storage system. In addition, by setting specific collection time and accuracy requirements, such as the collection time accuracy of the charging and discharging process is 10 seconds / time, and the standing time is 2 hours, the accuracy and consistency of data collection can be ensured, providing reliable data support for the subsequent battery cell state evaluation.

[0085] Furthermore, by calculating the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of multiple battery cell target parameters, and adding the outlier judgment value according to the absolute value of the difference, the outlier state of the battery cell can be quickly judged and accumulated in real time. This method can effectively reduce the probability of misjudgment and missed judgment of outliers and improve the accuracy of diagnosis. At the same time, by drawing a cumulative value curve and analyzing its slope, it is possible to further determine whether the battery cell has an abnormality, thereby providing a scientific basis for fault diagnosis and maintenance of the battery cell. In addition, by sorting the processing level according to the periodic outlier cumulative value, more serious battery cell problems can be prioritized to improve maintenance efficiency and system safety.

[0086] like Figure 2 As shown, in some embodiments, an electrochemical energy storage lithium iron phosphate battery cell outlier diagnosis system includes:

[0087] The data acquisition module is used to install an AFE module on each battery cell of the energy storage system. The AFE module collects the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The charging and discharging process collection time accuracy is 10s / time, and the static time is 2h. If the static time is less than 2h, the last parameter value is taken;

[0088] The battery cell status evaluation module is used to calculate the battery cell internal resistance based on the voltage, current and temperature data during the battery cell charging and discharging process using the forgetting factor least squares method, calculate the battery cell SOC based on the voltage and current data during the battery cell charging and discharging process using the extended Kalman method, and calculate the battery cell SOH based on the voltage, current and temperature data during the battery cell charging and discharging process using the neural network model algorithm, thereby evaluating the battery cell status;

[0089] A real-time outlier judgment module is used to calculate the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is canceled and the battery cell is judged to be outlier, and the outlier judgment value is accumulated in real time;

[0090] The cycle outlier judgment module is used to calculate the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is cancelled and the battery cell is judged to be outlier, and the outlier judgment value is accumulated by cycle at the same time;

[0091] The outlier judgment module is used to draw a cumulative value curve according to the real-time accumulated values ​​and the accumulated values ​​of each cycle, where the horizontal axis of the accumulated value curve is time or cycle, and the vertical axis is the value size. The difference between the slope of the increase of the cell voltage, temperature and SOC outlier judgment value at any moment and the slope at the previous moment is calculated, and the difference between the slope of the increase of the cell internal resistance and SOH at any cycle and the slope of the previous cycle is calculated. If it exceeds the preset threshold, the target cell is judged to be outlier;

[0092] A processing level determination module, for determining a priority based on a periodic outlier cumulative value over a real-time outlier cumulative value;

[0093] The fault reporting module is used to report the fault information of the battery cell and the number of the battery cell after determining that the battery cell is out of the group.

[0094] It is understandable that the modules described in the electrochemical energy storage lithium iron phosphate battery outlier diagnosis system 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the electrochemical energy storage lithium iron phosphate battery outlier diagnosis method are also applicable to the electrochemical energy storage lithium iron phosphate battery outlier diagnosis system 200 and the modules contained therein, and will not be repeated here.

[0095] Reference below Figure 3, which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include but are not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0096] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0097] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0098] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0099] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A method for outlier diagnosis of an electrochemical energy storage lithium iron phosphate battery cell, characterized in that: The following steps are involved: Data collection steps: Install an AFE module on each battery cell of the energy storage system, and use the AFE module to collect the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The charging and discharging process collection time accuracy is 10s / time, and the static time is 2h. If the static time is less than 2h, the last moment parameter value is taken; Battery state evaluation steps: Use the forgetting factor least squares method to calculate the battery internal resistance based on the voltage, current and temperature data during the battery charge and discharge process, use the extended Kalman method to calculate the battery SOC based on the voltage and current data during the battery charge and discharge process, and use the neural network model algorithm to calculate the battery SOH based on the voltage, current and temperature data during the battery charge and discharge process, and then evaluate the battery state; Real-time outlier judgment step: Calculate the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, cancel the cumulative calculation of the target battery cell and judge it as an outlier, and accumulate the outlier judgment value in real time; Cycle outlier judgment step: Calculate the difference between the internal resistance and SOH of the target cell in each cycle and the median of the corresponding target parameters of multiple cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, cancel the cumulative calculation of the target cell and judge it as an outlier, and accumulate the outlier judgment value by cycle; Outlier determination step: Draw a cumulative value curve according to the real-time accumulated values ​​and the accumulated values ​​of each cycle, where the horizontal axis of the accumulated value curve is time or cycle, and the vertical axis is the value size. Calculate the difference between the slope of the cell voltage, temperature and SOC outlier determination value growth at any moment and the slope at the previous moment, and the difference between the slope of the cell internal resistance and SOH growth at any cycle and the slope of the previous cycle. If they exceed the preset threshold, the target cell is determined to be outlier. Processing level determination step: judging the priority based on the periodic outlier accumulation value is higher than judging the priority based on the real-time outlier accumulation value; Fault reporting steps: After determining that the battery cell is out of group, report the battery cell out of group fault information and the number of the battery cell.

2. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the data acquisition step, the AFE module is used to collect the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process, and the charging and discharging process acquisition time accuracy is 10s / time, the static time is 2h, and the last moment parameter value is taken when the static time is less than 2h.

3. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the battery cell state evaluation step, the forgetting factor least squares method uses the voltage, current and temperature data during the battery cell charging and discharging process to calculate the battery cell internal resistance, the extended Kalman method uses the voltage and current data during the battery cell charging and discharging process to calculate the battery cell SOC, and the neural network model algorithm uses the voltage, current and temperature data during the battery cell charging and discharging process to calculate the battery cell SOH.

4. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the real-time outlier judgment step, the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells is calculated, and the outlier judgment value is added accordingly according to the different proportions in which the absolute value of the difference exceeds the median, and real-time accumulation is performed at the same time.

5. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the periodic outlier judgment step, the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells is calculated, and the outlier judgment value is added accordingly according to the different proportions in which the absolute value of the difference exceeds the median, and accumulated by cycle.

6. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the outlier determination step, a cumulative value curve is drawn according to the real-time and periodically accumulated values, the corresponding slope difference is calculated, and the slope difference is compared with a preset threshold value to determine whether the battery cell is an outlier.

7. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the processing level determination step, the periodic outlier cumulative value is used as a basis for priority judgment.

8. The electrochemical energy storage lithium iron phosphate battery outlier diagnosis method according to claim 1, characterized in that: In the fault reporting step, when the battery cell is determined to be out of group, the out of group fault information of the battery cell and the battery cell number are reported.

9. An electrochemical energy storage lithium iron phosphate battery cell outlier diagnosis system, characterized in that: include: The data acquisition module is used to install an AFE module on each battery cell of the energy storage system. The AFE module collects the voltage, current and temperature information of the battery cell during the charging and discharging process and the static process in real time. The charging and discharging process collection time accuracy is 10s / time, and the static time is 2h. If the static time is less than 2h, the last parameter value is taken; The battery cell status evaluation module is used to calculate the battery cell internal resistance based on the voltage, current and temperature data during the battery cell charging and discharging process using the forgetting factor least squares method, calculate the battery cell SOC based on the voltage and current data during the battery cell charging and discharging process using the extended Kalman method, and calculate the battery cell SOH based on the voltage, current and temperature data during the battery cell charging and discharging process using the neural network model algorithm, thereby evaluating the battery cell status; A real-time outlier judgment module is used to calculate the difference between the voltage, temperature and SOC of the real-time target battery cell and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is canceled and the battery cell is judged to be outlier, and the outlier judgment value is accumulated in real time; The cycle outlier judgment module is used to calculate the difference between the internal resistance and SOH of the target battery cell in each cycle and the median of the corresponding target parameters of multiple battery cells. When the absolute value of the difference exceeds 0.5% of the median, the outlier judgment value is increased by one; when the absolute value of the difference exceeds 1% of the median, the outlier judgment value is increased by two; when the absolute value of the difference exceeds 3% of the median, the outlier judgment value is increased by five; when the absolute value of the difference exceeds 5% of the median, the cumulative calculation of the target battery cell is cancelled and the battery cell is judged to be outlier, and the outlier judgment value is accumulated by cycle at the same time; The outlier judgment module is used to draw a cumulative value curve according to the real-time accumulated values ​​and the accumulated values ​​of each cycle, where the horizontal axis of the accumulated value curve is time or cycle, and the vertical axis is the value size. The difference between the slope of the increase of the cell voltage, temperature and SOC outlier judgment value at any moment and the slope at the previous moment is calculated, and the difference between the slope of the increase of the cell internal resistance and SOH at any cycle and the slope of the previous cycle is calculated. If it exceeds the preset threshold, the target cell is judged to be outlier; A processing level determination module, for determining a priority based on a periodic outlier cumulative value over a real-time outlier cumulative value; The fault reporting module is used to report the fault information of the battery cell and the number of the battery cell after determining that the battery cell is out of the group.

10. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores at least one instruction; the processor implements the electrochemical energy storage lithium iron phosphate battery cell outlier diagnosis method as claimed in any one of claims 1 to 8 by loading and executing the at least one instruction.

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