Intermittent charging control method and system for lithium battery pack

Abstract

The present invention relates to the field of battery charging control, and in particular to an intermittent charging control method and system for a lithium battery pack. The method comprises the following steps: obtaining battery pack state parameters and real-time grid load data; performing load fluctuation trend quantitative analysis on real-time grid load data to generate grid load fluctuation trend data; obtaining current SOC state data based on battery pack state parameters; obtaining battery pack historical charging logs; predicting charging time for current SOC state data based on battery pack historical charging logs to obtain charging time prediction values; generating segmented charging intermittent window sequences based on grid load fluctuation trend data and charging time prediction values; performing segmented cyclic charging processing based on segmented charging intermittent window sequences, and obtaining multiple groups of battery pack charging monitoring parameters. The present invention improves the charging life and performance of the battery through intermittent charging control.

Description

Technical Field

[0001] The present invention relates to the field of battery charging control, and in particular to an intermittent charging control method and system for a lithium battery pack. Background Art

[0002] With the rapid development of renewable energy and electric vehicles, backup energy storage systems play an increasingly important role. Among them, lithium iron phosphate battery packs have become an ideal choice for backup energy storage systems due to their high safety, long life and good performance. However, in practical applications, there are still some problems in the charging control of lithium iron phosphate battery packs, such as energy loss during charging, limitations on charging efficiency and the impact on battery life. Traditional charging control methods often adopt a continuous charging mode, that is, continuous charging when the battery capacity is insufficient. However, long-term continuous charging can cause problems such as battery overheating and capacity attenuation, thereby reducing the life and performance of the battery pack. Therefore, an intelligent intermittent charging control method is needed. Summary of the invention

[0003] The present invention provides an intermittent charging control method and system for a lithium battery pack to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides an intermittent charging control method for a lithium battery pack, comprising the following steps:

[0005] Step S1: obtaining battery pack state parameters and real-time grid load data; performing load fluctuation trend quantitative analysis on the real-time grid load data to generate grid load fluctuation trend data; obtaining current SOC state data based on the battery pack state parameters;

[0006] Step S2: Obtain the battery pack historical charging log; predict the charging time of the current SOC state data based on the battery pack historical charging log, so as to obtain the charging time prediction value; generate a segmented charging interval window sequence based on the power grid load fluctuation trend data and the charging time prediction value;

[0007] Step S3: performing segmented cyclic charging processing based on the segmented charging intermittent window sequence, and obtaining multiple groups of battery pack charging monitoring parameters; obtaining capacity attenuation amplitude data and battery internal resistance fluctuation data based on the multiple groups of battery pack charging monitoring parameters;

[0008] Step S4: performing charging layer aging analysis based on the capacity attenuation amplitude data and the battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; performing battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data;

[0009] Step S5: performing a deep discharge repair simulation based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack; performing a life minimum loss optimization on the battery charging life loss data based on the deep discharge strategy for the battery pack to generate battery life minimum loss optimization data;

[0010] Step S6: Obtain load trend prediction data based on the grid load fluctuation trend data; adjust the charging interval dynamic parameters of the segmented charging intermittent window sequence based on the battery life minimum loss optimization data, and build an intelligent intermittent charging control model to perform intermittent charging control operations.

[0011] The present invention provides an understanding of the current status of the system by acquiring battery pack status parameters and real-time grid load data, provides basic data for subsequent charging control, performs quantitative analysis of load fluctuation trends on real-time grid load data to reveal the changing trend of grid load, and helps predict future load conditions. Based on the battery pack status parameters, the current SOC status data is obtained to understand the current charge and discharge status of the battery, providing a basis for subsequent charging time prediction and charging control. The battery pack historical charging log is obtained to provide past charging records, providing a basis for predicting the charging time of the current SOC state. By predicting the charging time of the current SOC state data, the time required for battery charging is estimated, providing a reference for charging control. Based on the grid load fluctuation trend The data and charging time prediction value generate a segmented charging interval window sequence to divide the charging process into multiple stages to adapt to the changes in the grid load and improve the charging efficiency and stability. The segmented cyclic charging process performs staged charging operations on the battery pack according to the setting of the segmented charging interval window sequence to reduce the pressure and loss during the charging process and improve the charging efficiency. Multiple groups of battery pack charging monitoring parameters are obtained to monitor the charging status and performance indicators of the battery pack in real time, providing data support for subsequent performance analysis and charging life evaluation. Based on multiple groups of battery pack charging monitoring parameters, the capacity attenuation amplitude data and battery internal resistance fluctuation data are obtained to evaluate the battery's performance attenuation and internal resistance change, providing a basis for subsequent charging layer aging analysis. Electrical layer aging analysis understands the performance degradation of the battery during the charging process, including changes in capacity attenuation and internal resistance increase, provides a basis for battery charging life assessment, generates battery performance degradation trend evolution data to reveal the performance change trend of the battery during the charging process, helps to judge the health of the battery and predict its future life, and performs battery charging life loss assessment on the battery performance degradation trend evolution data to quantify the degree of battery charging life loss, providing a reference for subsequent optimization measures, and performs deep discharge repair to simulate the deep discharge behavior of the battery pack, evaluate its impact on battery performance, and formulate corresponding deep discharge strategies. Based on the deep discharge strategy of the battery pack, the battery charging life loss data is optimized for minimum life loss. By adjusting the charging strategy and discharging behavior, the loss of battery charging life can be minimized, and the battery life and performance can be improved. The load trend forecast data is obtained based on the grid load fluctuation trend data to predict the future changes in grid load, and accurate load forecast information is provided for charging control. The charging interval parameters of the segmented charging intermittent window sequence are adjusted dynamically based on the battery life optimization target. According to the battery life optimization target, the charging interval is dynamically adjusted during the charging process to minimize the loss of battery life. An intelligent intermittent charging control model is constructed. According to the load prediction information and the battery life optimization target, automated charging control is realized, and the charging efficiency and the utilization rate of the battery life are improved, thereby achieving the goal of intermittent charging control.

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: Obtaining battery pack status parameters and real-time grid load data;

[0014] Step S12: performing time series load analysis on the real-time load data of the power grid to obtain a time series load curve;

[0015] Step S13: performing load peak identification on the time series load curve and extracting the load peak point;

[0016] Step S14: performing a quantitative analysis of the load fluctuation trend on the time series load curve according to the load peak point to generate power grid load fluctuation trend data;

[0017] Step S15: Evaluate the current SOC state of the battery pack state parameters to obtain current SOC state data.

[0018] The present invention provides an understanding of the current state of the battery pack by acquiring battery pack state parameters, including information such as battery capacity, voltage, temperature, etc., to provide basic data for subsequent charging control, and acquires real-time load data of the power grid to understand the current load situation of the power grid, including information such as load size and load fluctuation, to provide a reference basis for charging control. By performing time series analysis on the real-time load data of the power grid, a curve of the power grid load changing over time is obtained to understand the periodic changes and trend changes of the load. The time series load curve provides detailed load change information, providing a data basis for subsequent load fluctuation trend analysis and load peak recognition. Load peak recognition determines the peak point in the time series load curve, that is, the peak period of load fluctuation, which is helpful for analyzing the intensity and frequency of load fluctuation. Extracting load peak points concentrates load fluctuations within key time periods in order to more accurately analyze load fluctuation trends and their impact on battery charging control. Quantitative analysis of load fluctuation trends reveals the changing trend of grid loads, including information such as the amplitude, frequency, and period of fluctuations, which helps predict future load conditions. Generating grid load fluctuation trend data provides a quantitative description of the nature of grid load fluctuations, providing a reference for subsequent charging control. The current SOC state evaluation estimates the current charge and discharge state, i.e., the remaining charge capacity of the battery, based on the battery pack state parameters such as voltage, current, and capacity. Obtaining the current SOC state data provides an understanding of the current charge and discharge state of the battery, providing a basis for subsequent charging time prediction and charging control.

[0019] Preferably, the specific steps of step S2 are:

[0020] Step S21: Obtaining the battery pack charging history log;

[0021] Step S22: analyzing the temperature variation of the charging room of the battery pack historical charging log to generate charging temperature variation data;

[0022] Step S23: quantifying the impact of battery charging performance based on the charging temperature change data, and generating multi-temperature charging performance impact data;

[0023] Step S24: predicting the charging time of the current SOC state data based on the multi-temperature charging performance impact data, thereby obtaining a predicted charging time value;

[0024] Step S25: Perform multi-charging interval analysis on the charging duration prediction value based on the grid load fluctuation trend data to generate a segmented charging interval window sequence.

[0025] The present invention records the past charging behavior and related data of the battery pack through the battery pack historical charging log, including the charging start time, charging end time, charging current, charging voltage and other information. Obtaining the battery pack historical charging log provides an understanding of the battery pack's past charging behavior, and provides a data basis for the subsequent charging room temperature change analysis and charging performance impact quantification. The charging room temperature change analysis counts and analyzes the temperature data of the battery pack historical charging log to understand the temperature change during the charging process, generates charging temperature change data to provide temperature change curves for each charging stage, and provides a reference for the subsequent battery charging performance impact quantification and charging time prediction. The battery charging performance impact quantification analyzes the charging performance of the battery under different temperature conditions based on the charging temperature change data, such as Charging efficiency, capacity attenuation, etc., generate multi-temperature charging performance impact data to provide charging performance indicators at different temperatures, and provide a basis for subsequent charging time prediction and intermittent charging control. The charging time prediction estimates the time required for the battery pack to be fully charged under specific temperature conditions based on the current SOC state data and multi-temperature charging performance impact data. The charging time prediction value provides an estimate of the time required for battery charging, which provides a basis for subsequent charging interval analysis and charging control. The multi-charging interval analysis combines the power grid load fluctuation trend data and the charging time prediction value to determine the appropriate charging interval window to balance the power grid load and battery charging demand, and generates a segmented charging interval window sequence to provide a reasonable charging interval divided according to the power grid load conditions, and provides a specific time arrangement for battery intermittent charging control.

[0026] Preferably, the specific steps of step S3 are:

[0027] Step S31: performing segmented cyclic charging processing on the battery pack based on the segmented charging intermittent window sequence, and obtaining the battery pack charging monitoring parameters;

[0028] Step S32: performing capacity decay analysis on each of the multiple battery pack charging monitoring parameters to construct a capacity decay curve;

[0029] Step S33: identifying the parameter change amplitude of the capacity decay curve to generate capacity decay amplitude data;

[0030] Step S34: calculating the internal resistance fluctuation of the multiple battery pack charging monitoring parameters, thereby obtaining battery internal resistance fluctuation data.

[0031] The present invention uses segmented cyclic charging processing to cyclically charge the battery pack according to the segmented charging intermittent window sequence, according to the predetermined time period and charging strategy, obtains the battery pack charging monitoring parameters to record the relevant data in the charging process, such as charging current, charging voltage, charging time, etc., for subsequent capacity decay analysis and internal resistance fluctuation calculation. The battery capacity decay analysis is performed group by group according to the battery pack charging monitoring parameters, and the battery capacity in each charging cycle is calculated and compared to understand the decay of the battery capacity. The capacity decay curve is constructed to plot the battery capacity decay data in each charging cycle into a curve to show the change trend of the battery capacity over time, which provides a basis for the generation of capacity decay amplitude data. The capacity attenuation amplitude recognition identifies the capacity attenuation amplitude of the battery, i.e. the attenuation rate or attenuation percentage, by analyzing the changing trend of the capacity attenuation curve, and generates capacity attenuation amplitude data to provide a quantitative description of the battery capacity attenuation, which is used to evaluate the battery life and performance degradation, and provide a basis for subsequent charging time prediction and charging control. The internal resistance fluctuation calculation calculates the internal resistance value of the battery by analyzing the current and voltage data in the battery pack charging monitoring parameters, and performs a fluctuation analysis on the internal resistance value to obtain the battery internal resistance fluctuation data, which provides the change of the battery internal resistance, including the fluctuation range, frequency and other information, which is used to evaluate the performance stability and charging efficiency of the battery, and provide a reference for charging control and fault diagnosis.

[0032] Preferably, the specific steps of step S31 are:

[0033] Extracting the first charging intermittent window based on the segmented charging intermittent window sequence;

[0034] Based on the first charging intermittent window, the battery pack is intermittently charged to obtain the first section of battery pack charging monitoring parameters;

[0035] The intermittent charging is specifically as follows: extracting the charging duration value and the charging interval value based on the segmented charging intermittent window sequence; charging the battery pack and recording the real-time charging duration, and when the real-time charging duration is equal to the charging duration value, performing a charging stop process; determining that the current battery pack is in a charging gap; during the charging gap, performing data monitoring on the battery pack to obtain the first segment battery pack charging monitoring parameters; when the charging gap duration is equal to the charging interval value, performing a recharging process;

[0036] Based on the second charging intermittent window, the battery pack is intermittently charged to obtain the second-stage battery pack charging monitoring parameters;

[0037] Repeat the above operation until all charging interval windows are charged, and obtain multiple battery pack charging monitoring parameters;

[0038] The first-stage battery pack charging monitoring parameters, the second-stage battery pack charging monitoring parameters and multiple-stage battery pack charging monitoring parameters are serialized to obtain multiple groups of battery pack charging monitoring parameters.

[0039] The present invention determines the time range of charging the first battery pack by extracting the first charging intermittent window, that is, the charging start time and the charging end time. The intermittent charging charges the battery pack according to the time range of the extracted first charging intermittent window, obtains the first battery pack charging monitoring parameter to record relevant data in the charging process, such as charging current, charging voltage, charging time, etc., for subsequent analysis and serialization processing. The intermittent charging control performs intermittent charging on the battery pack according to the set charging time value and charging interval value to balance the charging demand and the grid load, obtains multiple battery pack charging monitoring parameters to obtain the charging process data of the battery pack in each charging intermittent window, including charging current, charging voltage, charging time, etc., for subsequent analysis and serialization processing. The serialization processing integrates and records the first, second and multiple battery pack charging monitoring parameters according to a certain format to form a data structure of multiple battery pack charging monitoring parameters, which is convenient for subsequent data analysis and processing. The serialization processing of multiple battery pack charging monitoring parameters integrates the monitoring parameters of each charging intermittent window to form complete charging process data, which is convenient for evaluating and analyzing the performance and life of the battery pack.

[0040] Preferably, the specific steps of step S4 are:

[0041] Step S41: Calculate the time series change rate of the battery internal resistance fluctuation data to obtain the internal resistance change rate;

[0042] Step S42: performing a multi-time point internal resistance change analysis based on the internal resistance change rate to obtain an internal resistance change graph;

[0043] Step S43: Perform charging layer aging analysis on the capacity attenuation amplitude data and the internal resistance change spectrum to generate battery charging layer aging data;

[0044] Step S44: performing degradation trend evolution processing on the battery charging layer aging data to obtain battery performance degradation trend evolution data;

[0045] Step S45: Perform battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data.

[0046] The present invention calculates the time series change rate of the internal resistance to understand the change trend and rate of the battery internal resistance. The internal resistance change rate is one of the important indicators for evaluating the battery performance and health status, and can reflect the attenuation degree and internal state change of the battery. The internal resistance change spectrum intuitively displays the change of the battery internal resistance at different time points. By analyzing the internal resistance change spectrum, abnormal fluctuations and trend changes of the internal resistance are found, providing a basis for the subsequent charging layer aging analysis. The charging layer aging analysis comprehensively considers the capacity attenuation and internal resistance change of the battery to evaluate the aging degree of the battery. By analyzing the charging layer aging data, the life loss of the battery during the charging process is understood, and it is judged whether the battery needs to be replaced or repaired. The degradation trend evolution data shows the changing trend of the battery performance over time. By analyzing the degradation trend evolution data, the future performance change of the battery is predicted, providing a basis for the charging life loss evaluation. The battery charging life loss evaluation quantifies the battery life loss degree and predicts the remaining life. By generating the charging life loss data, the battery service life is evaluated, a reasonable charging strategy is formulated to extend the battery life, and more effective battery management and maintenance suggestions are provided.

[0047] Preferably, the specific steps of step S5 are:

[0048] Step S51: performing a deep discharge repair simulation on the battery pack based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack;

[0049] Step S52: Calculating the average aging rate of the battery based on the deep discharge strategy of the battery pack to obtain the average aging rate under multiple schemes;

[0050] Step S53: Optimizing the battery charging life loss data for minimum life loss based on the average aging rate under multiple schemes to generate battery life minimum loss optimization data.

[0051] The present invention simulates the performance of a battery pack under deep discharge conditions through deep discharge repair simulation. The deep discharge strategy of the battery pack guides the deep discharge operation in actual operation, helps to restore the performance of the battery pack and prolong its life. By calculating the average aging rate of the battery pack under different deep discharge strategies, the impact of different strategies on the battery life is evaluated. The average aging rate data under multiple schemes provides a basis for subsequent life minimum loss optimization, helps to select a suitable discharge strategy to minimize the battery life loss. The battery life minimum loss optimization finds the best charging strategy to minimize the battery life loss by comprehensively considering the average aging rate under different discharge strategies. The generated battery life minimum loss optimization data provides a decision-making basis for battery intermittent charging control, helps to formulate the best charging strategy, prolongs the battery pack life and improves performance.

[0052] Preferably, the specific steps of step S51 are:

[0053] Quantify the battery stress accumulation based on the battery performance degradation trend evolution data to obtain the battery stress accumulation level;

[0054] Setting multiple battery discharge plans according to the battery stress accumulation level; the battery discharge plan specifically includes the battery group discharge depth and the number of discharge cycles;

[0055] Perform deep discharge repair simulation on the battery pack based on multiple battery discharge schemes to obtain multiple groups of deep discharge simulation data;

[0056] Analyze the change trend of internal resistance rate of batteries after discharge for multiple sets of deep discharge simulation data to generate discharge internal resistance change rate;

[0057] Perform battery damage mitigation assessment based on the discharge internal resistance change rate to obtain discharge repair assessment data for each set of solutions;

[0058] The optimal discharge plan decision analysis is performed on multiple battery discharge plans based on the discharge repair evaluation data of each plan to obtain the battery pack deep discharge strategy.

[0059] The present invention analyzes the evolution data of battery performance degradation trend to quantify the stress accumulation of the battery. The battery stress accumulation level reflects the degree of battery degradation and provides a basis for evaluating the battery health status. Different battery discharge schemes are formulated according to the battery stress accumulation level to reduce the degree of battery stress accumulation. The battery discharge scheme includes the setting of discharge depth and discharge cycle number. By controlling the discharge depth and cycle number, the degradation rate of the battery is reduced and the service life of the battery is extended. By performing deep discharge repair simulation on the battery pack and simulating the operation of the battery under different discharge schemes, multiple groups of deep discharge simulation data provide the performance attenuation of the battery under different schemes, which is helpful for subsequent The discharge repair evaluation data of each scheme provides the degree of battery performance damage mitigation under different discharge schemes, and provides a reference for the decision of the optimal discharge scheme. The optimal battery discharge scheme is determined by analyzing the discharge repair evaluation data of each scheme. The deep discharge strategy of the battery pack takes into account the performance attenuation and damage mitigation effects of the battery to reduce the stress accumulation of the battery and extend the battery life.

[0060] Preferably, the specific steps of step S6 are:

[0061] Step S61: performing future period load forecasting on the power grid load fluctuation trend data to obtain load trend forecast data;

[0062] Step S62: fine-tuning the load forecast deviation of the load trend forecast data to generate load forecast deviation optimization data;

[0063] Step S63: adjusting the charging interval dynamic parameter of the segmented charging interval window sequence based on the battery life minimum loss optimization data and the load prediction deviation optimization data, and generating charging interval dynamic parameter adjustment data;

[0064] Step S64: performing intermittent charging decision modeling on the charging interval dynamic parameter adjustment data, and constructing an intelligent intermittent charging control model to perform intermittent charging control operations.

[0065] The present invention predicts the load fluctuation trend data of the power grid to predict the load conditions in the future cycle, including information such as load peak value and load fluctuation range. The load trend prediction data provides a prediction basis for subsequent charging control, helps to reasonably arrange the charging interval and charging strategy to meet the load demand. The load prediction deviation fine-tuning adjusts the load trend prediction data according to the actual situation to reduce the prediction error and improve the prediction accuracy. The generated load prediction deviation optimization data provides a more accurate load prediction result and provides more reliable data support for the subsequent charging control. The charging interval dynamic parameter adjustment dynamically adjusts the charging interval according to the battery life minimum loss optimization data and the load prediction deviation optimization data to minimize the battery life loss and meet the load demand. The generated charging interval dynamic parameter adjustment data provides an adjustment scheme for the subsequent charging control, realizes a flexible charging interval window sequence, and improves the battery life and performance. The intermittent charging decision modeling is based on the charging interval dynamic parameter adjustment data to establish an intelligent intermittent charging control model to realize automatic charging decision. The constructed intelligent intermittent charging control model intelligently adjusts the charging interval and charging strategy according to the real-time load condition and battery status to maximize the battery life and meet the load demand.

[0066] In this specification, an intermittent charging control system of a lithium battery pack is provided, which is used to execute the intermittent charging control method of the lithium battery pack as described above, including:

[0067] The load trend module is used to obtain the battery pack status parameters and the real-time load data of the power grid; perform quantitative analysis on the load fluctuation trend of the real-time load data of the power grid to generate the load fluctuation trend data of the power grid; obtain the current SOC status data based on the battery pack status parameters;

[0068] The intermittent window module is used to obtain the historical charging log of the battery pack; predict the charging time of the current SOC state data based on the historical charging log of the battery pack, so as to obtain the predicted charging time value; generate a segmented charging intermittent window sequence based on the grid load fluctuation trend data and the predicted charging time value;

[0069] A charging monitoring module is used to perform segmented cyclic charging processing based on a segmented charging intermittent window sequence and obtain multiple battery pack charging monitoring parameters; based on multiple battery pack charging monitoring parameters, capacity attenuation amplitude data and battery internal resistance fluctuation data are obtained;

[0070] The life loss assessment module is used to perform charging layer aging analysis based on the capacity attenuation amplitude data and battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; perform battery charging life loss assessment processing on the battery performance degradation trend evolution data to generate battery charging life loss data;

[0071] The deep discharge module is used to simulate deep discharge repair based on the battery performance degradation trend evolution data to obtain the deep discharge strategy of the battery pack; based on the deep discharge strategy of the battery pack, the battery charging life loss data is optimized for minimum life loss to generate battery life minimum loss optimization data;

[0072] The charging interval adjustment module is used to obtain load trend prediction data based on the power grid load fluctuation trend data; based on the battery life minimum loss optimization data, the charging interval dynamic parameters of the segmented charging interval window sequence are adjusted to build an intelligent intermittent charging control model to perform intermittent charging control operations.

[0073] The present invention obtains key input information by acquiring battery pack state parameters and real-time grid load data, provides basic data for subsequent charging control, performs load fluctuation trend analysis on real-time grid load data, quantifies load fluctuation, and provides the ability to understand and predict grid load changes. The current SOC state data is obtained based on the battery pack state parameters, and the power state of the battery pack can be accurately understood, providing an important reference basis for charging control. The battery pack historical charging log is obtained, and the battery pack's past charging records are obtained, providing a historical data basis for charging time prediction and optimization. The current SOC state data is calculated based on the battery pack's historical charging log. Based on the charging time prediction, the charging time of the battery pack can be predicted, which provides a prediction value for the subsequent charging control. Combined with the grid load fluctuation trend data and the charging time prediction value, a segmented charging interval window sequence is generated. According to the grid load condition and charging demand, the charging interval window can be flexibly arranged to optimize the charging control. The segmented cyclic charging processing is performed based on the segmented charging interval window sequence, and the charging process is controlled in segments to improve the charging efficiency and battery life. The charging monitoring parameters of multiple battery packs are obtained, and the charging status and performance indicators of the battery pack can be monitored in real time, which provides data support for subsequent analysis and optimization. Parameters, get capacity attenuation amplitude data and battery internal resistance fluctuation data, which are used to evaluate battery performance and health status, and provide a basis for subsequent charging control and life evaluation. Based on capacity attenuation amplitude data and battery internal resistance fluctuation data, charge stacking aging analysis is performed to analyze the trend and evolution of battery performance degradation, understand the battery life attenuation, and perform battery charging life loss evaluation processing on battery performance degradation trend evolution data. It can evaluate the degree of battery charging life loss and provide a basis for subsequent life optimization and management. Based on battery performance degradation trend evolution data, deep discharge repair simulation is performed to simulate and optimize the deep discharge strategy of the battery pack. In order to extend the life of the battery and improve its performance, the battery charging life loss data is optimized for minimum life loss based on the battery pack deep discharge strategy, and the charging strategy is optimized to minimize the battery life loss and increase the battery service life. The load trend forecast data is obtained based on the grid load fluctuation trend data, which can predict the future change trend of the grid load and provide a more accurate reference for charging control. The charging interval dynamic parameters of the segmented charging interval window sequence are adjusted based on the battery life minimum loss optimization data. According to the requirements of battery life optimization, the charging interval is flexibly adjusted to achieve intelligent intermittent charging control and improve the battery life and performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic flow chart of the steps of an intermittent charging control method for a lithium battery pack of the present invention;

[0075] Figure 2Detailed implementation flow chart of step S1;

[0076] Figure 3 Detailed implementation flow chart of step S2;

[0077] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0078] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0079] The present application example provides an intermittent charging control method and system for a lithium battery pack. The execution subject of the intermittent charging control method and system for a lithium battery pack includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as a general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0080] See also Figures 1 to 4 The present invention provides an intermittent charging control method for a lithium battery pack, comprising the following steps:

[0081] Step S1: obtaining battery pack state parameters and real-time grid load data; performing load fluctuation trend quantitative analysis on the real-time grid load data to generate grid load fluctuation trend data; obtaining current SOC state data based on the battery pack state parameters;

[0082] Step S2: Obtain the battery pack historical charging log; predict the charging time of the current SOC state data based on the battery pack historical charging log, so as to obtain the charging time prediction value; generate a segmented charging interval window sequence based on the power grid load fluctuation trend data and the charging time prediction value;

[0083] Step S3: performing segmented cyclic charging processing based on the segmented charging intermittent window sequence, and obtaining multiple groups of battery pack charging monitoring parameters; obtaining capacity attenuation amplitude data and battery internal resistance fluctuation data based on the multiple groups of battery pack charging monitoring parameters;

[0084] Step S4: performing charging layer aging analysis based on the capacity attenuation amplitude data and the battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; performing battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data;

[0085] Step S5: performing a deep discharge repair simulation based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack; performing a life minimum loss optimization on the battery charging life loss data based on the deep discharge strategy for the battery pack to generate battery life minimum loss optimization data;

[0086] Step S6: Obtain load trend prediction data based on the grid load fluctuation trend data; adjust the charging interval dynamic parameters of the segmented charging intermittent window sequence based on the battery life minimum loss optimization data, and build an intelligent intermittent charging control model to perform intermittent charging control operations.

[0087] The present invention provides an understanding of the current status of the system by acquiring battery pack status parameters and real-time grid load data, provides basic data for subsequent charging control, performs quantitative analysis of load fluctuation trends on real-time grid load data to reveal the changing trend of grid load, and helps predict future load conditions. Based on the battery pack status parameters, the current SOC status data is obtained to understand the current charge and discharge status of the battery, providing a basis for subsequent charging time prediction and charging control. The battery pack historical charging log is obtained to provide past charging records, providing a basis for predicting the charging time of the current SOC state. By predicting the charging time of the current SOC state data, the time required for battery charging is estimated, providing a reference for charging control. Based on the grid load fluctuation trend The data and charging time prediction value generate a segmented charging interval window sequence to divide the charging process into multiple stages to adapt to the changes in the grid load and improve the charging efficiency and stability. The segmented cyclic charging process performs staged charging operations on the battery pack according to the setting of the segmented charging interval window sequence to reduce the pressure and loss during the charging process and improve the charging efficiency. Multiple groups of battery pack charging monitoring parameters are obtained to monitor the charging status and performance indicators of the battery pack in real time, providing data support for subsequent performance analysis and charging life evaluation. Based on multiple groups of battery pack charging monitoring parameters, the capacity attenuation amplitude data and battery internal resistance fluctuation data are obtained to evaluate the battery's performance attenuation and internal resistance change, providing a basis for subsequent charging layer aging analysis. Electrical layer aging analysis understands the performance degradation of the battery during the charging process, including changes in capacity attenuation and internal resistance increase, provides a basis for battery charging life assessment, generates battery performance degradation trend evolution data to reveal the performance change trend of the battery during the charging process, helps to judge the health of the battery and predict its future life, and performs battery charging life loss assessment on the battery performance degradation trend evolution data to quantify the degree of battery charging life loss, providing a reference for subsequent optimization measures, and performs deep discharge repair to simulate the deep discharge behavior of the battery pack, evaluate its impact on battery performance, and formulate corresponding deep discharge strategies. Based on the deep discharge strategy of the battery pack, the battery charging life loss data is optimized for minimum life loss. By adjusting the charging strategy and discharging behavior, the loss of battery charging life can be minimized, and the battery life and performance can be improved. The load trend forecast data is obtained based on the grid load fluctuation trend data to predict the future changes in grid load, and accurate load forecast information is provided for charging control. The charging interval parameters of the segmented charging intermittent window sequence are adjusted dynamically based on the battery life optimization target. According to the battery life optimization target, the charging interval is dynamically adjusted during the charging process to minimize the loss of battery life. An intelligent intermittent charging control model is constructed. According to the load prediction information and the battery life optimization target, automated charging control is realized, and the charging efficiency and the utilization rate of the battery life are improved, thereby achieving the goal of intermittent charging control.

[0088] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of an intermittent charging control method of a lithium battery pack of the present invention. In this example, the steps of the intermittent charging control method of the lithium battery pack include:

[0089] Step S1: obtaining battery pack state parameters and real-time grid load data; performing load fluctuation trend quantitative analysis on the real-time grid load data to generate grid load fluctuation trend data; obtaining current SOC state data based on the battery pack state parameters;

[0090] In this embodiment, the state parameters of the battery pack, such as current, voltage, temperature, etc., are obtained through a battery management system (BMS) or related sensors. These state parameters are used to monitor the operating status and health status of the battery pack. The real-time load data of the power grid is obtained from the power grid monitoring system or related sensors. The real-time load data of the power grid reflects the load changes of the power grid. The real-time load data of the power grid is preprocessed, including data cleaning, denoising and outlier processing, etc., to ensure the accuracy and reliability of the analysis. Based on the preprocessed real-time load data of the power grid, a quantitative analysis of the load fluctuation trend is performed, using statistical methods (such as mean, variance, standard deviation) or time series Analyze the trend and pattern of load fluctuation by using analytical methods (such as moving average, exponential smoothing, and trend decomposition). Generate grid load fluctuation trend data based on the results of load fluctuation trend analysis. These data are in numerical or graphical form and are used to describe the fluctuation of grid load. Calculate the current SOC state by using the current information in the battery pack state parameters, combined with the initial SOC (State of Charge) and charge and discharge efficiency of the battery pack. SOC indicates the current charge state of the battery pack and is an important indicator for evaluating the energy storage of the battery pack. SOC indicates the charging state of the battery and is used to evaluate the available energy and remaining capacity of the battery.

[0091] Step S2: Obtain the battery pack historical charging log; predict the charging time of the current SOC state data based on the battery pack historical charging log, so as to obtain the charging time prediction value; generate a segmented charging interval window sequence based on the power grid load fluctuation trend data and the charging time prediction value;

[0092] In this embodiment, historical charging log data of the battery pack is obtained. These data include information such as charging start time, charging end time, charging duration and charging amount. The historical charging log data is used to analyze the charging mode and behavior of the battery pack. A charging duration prediction model is established using the historical charging log data. A machine learning algorithm (such as a regression algorithm) or a time series analysis method (such as an ARIMA model) is used to predict the charging duration under the current SOC state. The predicted charging duration is a certain value, which represents the expected charging duration. Based on the grid load fluctuation trend data, time periods with lower grid load are identified, and these time periods are divided into multiple segmented charging interval windows. For each window, the charging duration of the window is determined according to the predicted value of the charging duration. In this way, a segmented charging interval window sequence is obtained, in which each window has a predicted charging duration.

[0093] Step S3: performing segmented cyclic charging processing based on the segmented charging intermittent window sequence, and obtaining multiple groups of battery pack charging monitoring parameters; obtaining capacity attenuation amplitude data and battery internal resistance fluctuation data based on the multiple groups of battery pack charging monitoring parameters;

[0094] In this embodiment, the charging behavior of the battery pack is controlled according to the sequence of segmented charging intermittent windows. For each segmented charging intermittent window, the battery pack is connected to the charging device and charged according to the preset charging time. After the charging in each window is completed, the charging device is disconnected and the charging monitoring parameters are recorded. During the charging process of each segmented charging intermittent window, the charging parameters of the battery pack are monitored and recorded. The charging monitoring parameters include charging current, voltage, temperature, charging time, etc. These parameters are obtained by sensors or monitoring devices connected to the battery pack and the charging device. The recorded charging monitoring parameter data are used to analyze the capacity decay amplitude and the battery internal resistance fluctuation. For the calculation of the capacity decay amplitude, the capacity loss amount in each charging cycle is accumulated to obtain the total capacity decay amplitude. For the estimation of the battery internal resistance fluctuation, the voltage and current data are used for calculation, such as evaluating the impedance change by observing the voltage drop rate or the current rise rate. According to the selected algorithm or model, the charging monitoring parameter data is processed, and the capacity decay amplitude and the battery internal resistance fluctuation data are calculated.

[0095] Step S4: performing charging layer aging analysis based on the capacity attenuation amplitude data and the battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; performing battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data;

[0096] In this embodiment, the capacity decay amplitude data is statistically analyzed and analyzed to understand the reduction of the battery pack capacity with the number of charge and discharge cycles, and the battery internal resistance fluctuation data is analyzed to understand the change of the internal impedance of the battery pack with the charge and discharge cycles. Various data analysis techniques and algorithms, such as regression analysis and time series analysis, are used to evaluate the degradation trend of battery performance. Based on the analysis results of the capacity decay amplitude data and the battery internal resistance fluctuation data, battery performance degradation trend evolution data are generated. These data are capacity decay rate, internal resistance change curve, capacity loss curve, etc., which are used to describe the evolution of battery performance over time. The battery performance degradation trend is visualized by using charts, curves or other forms to facilitate further analysis and evaluation of the battery life status. The battery performance degradation trend evolution data is used to evaluate the battery charging life loss. According to a pre-set evaluation index or model, the battery performance degradation trend is evaluated to estimate the battery life loss degree, including calculating the expected life loss percentage of the battery, predicting the remaining life, etc.

[0097] Step S5: performing a deep discharge repair simulation based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack; performing a life minimum loss optimization on the battery charging life loss data based on the deep discharge strategy for the battery pack to generate battery life minimum loss optimization data;

[0098] In this embodiment, a deep discharge repair simulation is performed to determine the deep discharge strategy of the battery pack. The deep discharge repair simulation is based on an existing battery performance degradation model and is combined with the actual working conditions of the battery pack to simulate the effects of different depths of discharge on battery performance recovery. The optimal deep discharge strategy is determined by simulating the influence of different depths of discharge on battery capacity recovery and the influence on battery internal resistance. The battery charging life loss data is optimized for minimum life loss. An optimization algorithm, such as a genetic algorithm, a particle swarm algorithm, or a simulated annealing algorithm, is used to optimize the minimum life loss. The optimization objective function is defined to minimize the sum or average of the battery charging life loss or maximize the expected value of the battery life. Various factors in the battery charging process, such as the charging rate, the charging cut-off voltage, the charging temperature, etc., are taken into consideration and adjusted as optimization variables. During the optimization process, the influence of each charging strategy on the battery life is evaluated through simulation or experimental data, and adjustments and optimizations are performed according to the evaluation results. Through an iterative optimization process, the optimal charging strategy for minimizing the battery charging life loss is finally obtained.

[0099] Step S6: Obtain load trend prediction data based on the grid load fluctuation trend data; adjust the charging interval dynamic parameters of the segmented charging intermittent window sequence based on the battery life minimum loss optimization data, and build an intelligent intermittent charging control model to perform intermittent charging control operations.

[0100] In this embodiment, power grid load fluctuation trend data, including historical records of load power and related environmental data, are collected, and the collected load fluctuation trend data are analyzed and processed. Statistical methods, time series analysis methods, or machine learning methods are used to predict load trends based on the analysis results to obtain load change trend data for a period of time in the future. The load trend prediction data is used to adjust the charging control strategy to adapt to future load changes, and the dynamic parameters of the charging interval are adjusted for the segmented charging intermittent window sequence. The segmented charging intermittent window sequence refers to dividing the charging process into multiple intermittent windows and performing charging operations in each window. According to the battery life minimum loss optimization data, the dynamic parameters of the charging interval, such as the interval time, the charging rate, etc., are adjusted to minimize the battery life loss during the charging process. The dynamic parameters of the charging interval are adjusted according to the real-time battery status and load prediction data, and a feedback control algorithm or an optimization algorithm is used for adaptive adjustment.

[0101] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0102] Step S11: Obtaining battery pack status parameters and real-time grid load data;

[0103] Step S12: performing time series load analysis on the real-time load data of the power grid to obtain a time series load curve;

[0104] Step S13: performing load peak identification on the time series load curve and extracting the load peak point;

[0105] Step S14: performing a quantitative analysis of the load fluctuation trend on the time series load curve according to the load peak point to generate power grid load fluctuation trend data;

[0106] Step S15: Evaluate the current SOC state of the battery pack state parameters to obtain current SOC state data.

[0107] In this embodiment, the state parameters of the battery pack, including voltage, current, temperature and other information, are obtained from a battery management system (BMS) or other monitoring equipment. The real-time load data of the power grid, including the total load information of the power grid, such as load power, frequency, voltage, etc., are obtained from a power grid monitoring system or other data sources. Time series analysis is performed on the real-time load data of the power grid. The real-time load data is smoothed using statistical methods or signal processing techniques, such as sliding average or low-pass filtering, to remove noise and rapid changes. The load data is sampled or resampled according to the required time resolution to obtain smooth time series load data. The time series load data is visualized as a curve using a drawing tool for subsequent analysis and processing. The time series load curve is analyzed to identify the peak point in the curve. The peak point indicates a high load period of the load. Peak detection algorithms, such as threshold method, differential method, peak and trough method, etc., are used to extract peak points in the load curve. The load peak points are used to divide the time series load curve into multiple load cycles. The fluctuation trend analysis is performed on each load cycle, and the standard deviation, mean, peak and other indicators of the load are calculated to quantify the degree of load fluctuation. Statistical methods or time series analysis methods, such as sliding window statistics and Fourier transform, are used to analyze the load fluctuation trend and generate grid load fluctuation trend data. Battery pack state parameters, such as voltage, current and other information, are used to evaluate the SOC (State of Charge) state. According to the characteristics of the battery pack and the battery model, the current SOC state data is calculated using an estimation algorithm or a filtering method. SOC represents the charging state of the battery and is used to evaluate the available energy and remaining capacity of the battery.

[0108] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0109] Step S21: Obtaining the battery pack charging history log;

[0110] Step S22: analyzing the temperature variation of the charging room of the battery pack historical charging log to generate charging temperature variation data;

[0111] Step S23: quantifying the impact of battery charging performance based on the charging temperature change data, and generating multi-temperature charging performance impact data;

[0112] Step S24: predicting the charging time of the current SOC state data based on the multi-temperature charging performance impact data, thereby obtaining a predicted charging time value;

[0113] Step S25: Perform multi-charging interval analysis on the charging duration prediction value based on the grid load fluctuation trend data to generate a segmented charging interval window sequence.

[0114] In this embodiment, according to the sequence of segmented charging intermittent windows and a predetermined charging strategy, the battery pack is subjected to segmented cyclic charging processing. In each charging intermittent window, the battery pack is connected to a charging device to perform a charging operation. In each charging intermittent window, the charging monitoring parameters of the battery pack, such as charging current, voltage, temperature, etc., are recorded. The acquired charging monitoring parameters of the battery pack are grouped according to different battery packs. The capacity decay analysis is performed on the charging monitoring parameters of each group of battery packs. A battery capacity decay algorithm or model, such as the Coulomb counting method, the Peukert model, etc., is used to calculate the capacity decay of the battery pack. According to the charging monitoring parameters and the capacity decay calculation results, a capacity decay analysis of each group of battery packs is constructed. The capacity attenuation curve of each battery pack is obtained, the capacity attenuation curve of each battery pack is analyzed, the parameter change amplitude in the curve is identified, and the change amplitude of the capacity attenuation curve is calculated by using statistical methods or signal processing techniques, such as differentiation and filtering, etc. The capacity attenuation amplitude data is generated by identifying the change amplitude in the curve to indicate the capacity attenuation degree of each battery pack. The acquired battery pack charging monitoring parameters are grouped according to different battery packs, and the internal resistance fluctuation of the charging monitoring parameters of each battery pack is calculated. The internal resistance fluctuation of the battery pack is calculated by using a battery internal resistance calculation method, such as a voltage-current method, a polarization method, etc. According to the calculation results, the internal resistance fluctuation data of each battery pack is obtained to indicate the change of the battery internal resistance.

[0115] In this embodiment, reference Figure 4 The above is a schematic flow chart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0116] Step S31: performing segmented cyclic charging processing on the battery pack based on the segmented charging intermittent window sequence, and obtaining the battery pack charging monitoring parameters;

[0117] Step S32: performing capacity decay analysis on each of the multiple battery pack charging monitoring parameters to construct a capacity decay curve;

[0118] Step S33: identifying the parameter change amplitude of the capacity decay curve to generate capacity decay amplitude data;

[0119] Step S34: calculating the internal resistance fluctuation of the multiple battery pack charging monitoring parameters, thereby obtaining battery internal resistance fluctuation data.

[0120] In this embodiment, according to the segmented charging intermittent window sequence, the battery pack is connected to the charging device, and the segmented cyclic charging process is performed according to the predetermined charging strategy. In each charging intermittent window, the charging monitoring parameters of the battery pack are recorded, including the charging current, voltage, temperature, etc., and suitable charging equipment and measuring instruments are used to ensure that various parameters in the charging process of the battery pack are accurately recorded. The acquired battery pack charging monitoring parameters are grouped according to different battery groups, and the battery capacity attenuation analysis is performed group by group on the charging monitoring parameters of each battery pack. The battery capacity attenuation algorithm or model, such as the Coulomb counting method, the Peukert model, etc., is used to calculate the capacity attenuation of the battery pack. According to the charging monitoring parameters and the capacity attenuation calculation results, a capacity attenuation curve of each battery pack is constructed, and the horizontal axis of the curve is the charging time or the number of cycles. , the ordinate represents the percentage of capacity attenuation, analyze the capacity attenuation curve of each battery pack, identify the parameter change amplitude in the curve, use statistical methods or signal processing techniques, such as differentiation, filtering, etc., to calculate the change amplitude of the capacity attenuation curve, and generate capacity attenuation amplitude data by identifying the change amplitude in the curve to represent the capacity attenuation degree of each battery pack, using percentage or other suitable units to represent, group the acquired battery pack charging monitoring parameters according to different battery packs, calculate the internal resistance fluctuation of each battery pack charging monitoring parameter, and use battery internal resistance calculation methods, such as voltage-current method, polarization method, etc., to calculate the internal resistance fluctuation of the battery pack. According to the calculation results, the internal resistance fluctuation data of each battery pack is obtained to represent the change of the battery internal resistance, and the internal resistance value is represented by ohm or other suitable units.

[0121] In this embodiment, the specific steps of step S31 are:

[0122] Extracting the first charging intermittent window based on the segmented charging intermittent window sequence;

[0123] Based on the first charging intermittent window, the battery pack is intermittently charged to obtain the first section of battery pack charging monitoring parameters;

[0124] The intermittent charging is specifically as follows: extracting the charging duration value and the charging interval value based on the segmented charging intermittent window sequence; charging the battery pack and recording the real-time charging duration, and when the real-time charging duration is equal to the charging duration value, performing a charging stop process; determining that the current battery pack is in a charging gap; during the charging gap, performing data monitoring on the battery pack to obtain the first segment battery pack charging monitoring parameters; when the charging gap duration is equal to the charging interval value, performing a recharging process;

[0125] Based on the second charging intermittent window, the battery pack is intermittently charged to obtain the second-stage battery pack charging monitoring parameters;

[0126] Repeat the above operation until all charging interval windows are charged, and obtain multiple battery pack charging monitoring parameters;

[0127] The first-stage battery pack charging monitoring parameters, the second-stage battery pack charging monitoring parameters and multiple-stage battery pack charging monitoring parameters are serialized to obtain multiple groups of battery pack charging monitoring parameters.

[0128] In this embodiment, according to the segmented charging intermittent window sequence, the charging duration value and the charging interval value of the first charging intermittent window are extracted, the battery pack is connected to the charging device, and charging starts, the real-time charging duration is recorded and compared with the charging duration value, if the real-time charging duration is equal to the charging duration value, the charging stop processing is performed, and the charging process is suspended. During the charging stop period, the battery pack is monitored for data, and the first segment battery pack charging monitoring parameters, including charging current, voltage, temperature, etc., are obtained. The charging interval duration timer is set and starts timing. According to the segmented charging intermittent window sequence, the charging duration value and the charging interval value of the second charging intermittent window are extracted, and the battery pack continues to be intermittently charged, and the charging process described is operated. During the charging stop period, the second-segment battery pack charging monitoring parameters, including charging current, voltage, temperature, etc., are obtained. According to the segmented charging intermittent window sequence, the charging duration value and charging interval value of the charging intermittent window are extracted one by one, and the battery pack is intermittently charged in turn, and the charging monitoring parameters of each segment of the battery pack are obtained. The above operation is repeated until all charging intermittent windows are charged, and multiple segments of battery pack charging monitoring parameters are obtained. The first segment of battery pack charging monitoring parameters, the second segment of battery pack charging monitoring parameters and multiple segments of battery pack charging monitoring parameters are serialized according to a certain data structure, such as stored as an array, a matrix or other data format. The serialization process integrates multiple groups of battery pack charging monitoring parameters together to facilitate subsequent analysis and processing.

[0129] In this embodiment, the specific steps of step S4 are:

[0130] Step S41: Calculate the time series change rate of the battery internal resistance fluctuation data to obtain the internal resistance change rate;

[0131] Step S42: performing a multi-time point internal resistance change analysis based on the internal resistance change rate to obtain an internal resistance change graph;

[0132] Step S43: Perform charging layer aging analysis on the capacity attenuation amplitude data and the internal resistance change spectrum to generate battery charging layer aging data;

[0133] Step S44: performing degradation trend evolution processing on the battery charging layer aging data to obtain battery performance degradation trend evolution data;

[0134] Step S45: Perform battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data.

[0135] In this embodiment, the internal resistance fluctuation data of the battery is obtained. This data is obtained by a battery internal resistance tester or other monitoring equipment. The internal resistance fluctuation data is time-series processed to convert it into a time-series data sequence. The internal resistance change rate is calculated by calculating the difference between adjacent data points or using more advanced mathematical methods, such as differentials and derivatives. The internal resistance change rate represents the change of the internal resistance in unit time. Based on the time-series data sequence of the internal resistance change rate, a multi-time point internal resistance change analysis is performed, and the internal resistance change rate is associated with the corresponding time point. The change of the internal resistance is displayed by drawing a line graph or other visual methods. The internal resistance change map shows the change trend and amplitude of the battery internal resistance at different time points, which is used to analyze the evolution of the battery's internal resistance and obtain the battery's capacity attenuation amplitude data. These data are obtained by charge and discharge cycle tests or other methods, combined with the internal resistance change map , analyze the capacity decay amplitude and internal resistance change to evaluate the battery's charging stacking aging. According to the analysis results, generate the battery's charging stacking aging data, including charging capacity decay, internal resistance change, etc., perform degradation trend evolution processing on the battery charging stacking aging data, use statistical analysis, mathematical modeling and other methods to analyze the changing trend of the battery charging stacking aging data, such as capacity decay rate, internal resistance growth rate, etc., according to the analysis results, obtain the battery performance degradation trend evolution data to describe the battery performance changes over time, based on the battery performance degradation trend evolution data, perform battery charging life loss evaluation processing, predict the battery's charging life loss according to the trend and rate of battery performance degradation, generate battery charging life loss data, including remaining life estimation, decay rate and other indicators, for evaluating the battery's life state and remaining available life.

[0136] In this embodiment, the specific steps of step S5 are:

[0137] Step S51: performing a deep discharge repair simulation on the battery pack based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack;

[0138] Step S52: Calculating the average aging rate of the battery based on the deep discharge strategy of the battery pack to obtain the average aging rate under multiple schemes;

[0139] Step S53: Optimizing the battery charging life loss data for minimum life loss based on the average aging rate under multiple schemes to generate battery life minimum loss optimization data.

[0140] In this embodiment, the battery performance degradation trend evolution data is used to simulate the deep discharge repair of the battery pack. According to the changes in the battery performance degradation trend evolution data, a suitable deep discharge strategy is designed to restore the performance of the battery pack. The deep discharge strategy includes the setting of parameters such as discharge time, discharge current, and discharge termination conditions, aiming to achieve the best battery pack repair effect. Based on the deep discharge strategy of the battery pack, the average aging rate of the battery is calculated. The deep discharge strategy of the battery pack is used to perform discharge operations, and the monitoring parameters during the discharge process, such as capacity decay and internal resistance growth, are recorded. According to the monitoring parameters recorded during the discharge process, the average aging rate of the battery is calculated, and the calculation is performed using mathematical statistical methods or other models. The above steps are repeated to obtain the average aging rate under multiple schemes according to different discharge strategies. The average aging rate under multiple discharge strategies and the battery charging life loss data are combined to perform optimization calculations to find a scheme that minimizes the battery life loss. Different discharge strategies are compared and evaluated using optimization algorithms, mathematical modeling, and other methods to obtain optimized data for minimum battery life loss.

[0141] In this embodiment, the specific steps of step S51 are:

[0142] Quantify the battery stress accumulation based on the battery performance degradation trend evolution data to obtain the battery stress accumulation level;

[0143] Setting multiple battery discharge plans according to the battery stress accumulation level; the battery discharge plan specifically includes the battery group discharge depth and the number of discharge cycles;

[0144] Perform deep discharge repair simulation on the battery pack based on multiple battery discharge schemes to obtain multiple groups of deep discharge simulation data;

[0145] Analyze the change trend of internal resistance rate of batteries after discharge for multiple sets of deep discharge simulation data to generate discharge internal resistance change rate;

[0146] Perform battery damage mitigation assessment based on the discharge internal resistance change rate to obtain discharge repair assessment data for each set of solutions;

[0147] The optimal discharge plan decision analysis is performed on multiple battery discharge plans based on the discharge repair evaluation data of each plan to obtain the battery pack deep discharge strategy.

[0148] In this embodiment, the battery performance degradation trend evolution data is used to analyze the battery performance degradation, and the battery performance degradation is converted into the concept of stress accumulation, which is used to quantify the stress degree of the battery. According to the battery performance degradation and the evaluation standard of stress accumulation, the battery is divided into different stress accumulation levels for the discharge scheme design in the subsequent steps. According to the battery stress accumulation level obtained in the previous step, multiple groups of different battery discharge schemes are set. Each group of battery discharge schemes includes the setting of discharge depth and discharge cycle number, which is intended to repair the performance of the battery pack through deep discharge and cyclic discharge. Each group of battery discharge schemes is used to perform deep discharge repair simulation on the battery pack. According to the setting of the discharge scheme, the corresponding discharge operation is performed, and the monitoring during the discharge process is recorded. Measure parameters such as voltage, current, and capacity attenuation, analyze the change trend of the internal resistance rate of the battery after discharge in each group of deep discharge simulation data, use statistical analysis or other related methods to calculate the internal resistance change rate of each group after discharge to evaluate the degree of battery damage, and conduct battery damage mitigation evaluation for each group of discharge schemes based on the discharge internal resistance change rate. According to the degree of the discharge internal resistance change rate, evaluate the repair effect of each group of discharge schemes on the battery, generate discharge repair evaluation data for each group of schemes, and use it for the optimal discharge scheme decision analysis in the subsequent steps. Consider the discharge repair evaluation data of each group of schemes comprehensively, conduct decision analysis for the optimal discharge scheme, compare the discharge repair evaluation data of different schemes, and select the discharge scheme with the best repair effect as the deep discharge strategy of the battery pack.

[0149] In this embodiment, the specific steps of step S6 are:

[0150] Step S61: performing future period load forecasting on the power grid load fluctuation trend data to obtain load trend forecast data;

[0151] Step S62: fine-tuning the load forecast deviation of the load trend forecast data to generate load forecast deviation optimization data;

[0152] Step S63: adjusting the charging interval dynamic parameter of the segmented charging interval window sequence based on the battery life minimum loss optimization data and the load prediction deviation optimization data, and generating charging interval dynamic parameter adjustment data;

[0153] Step S64: performing intermittent charging decision modeling on the charging interval dynamic parameter adjustment data, and constructing an intelligent intermittent charging control model to perform intermittent charging control operations.

[0154] In this embodiment, historical grid load data is collected, including load changes in the past period of time, and time series analysis, regression analysis or other related methods are used to perform prediction modeling on the grid load data. According to the established prediction model, the load in the future period is predicted to generate load trend prediction data, and an error analysis is performed on the load trend prediction data. The difference between the prediction result and the actual load data is compared. According to the error analysis result, the load trend prediction data is fine-tuned to reduce the prediction deviation, and load prediction deviation optimization data is generated as more accurate load prediction data, which is used in subsequent steps. The battery life minimum loss optimization data and the load prediction deviation optimization data are used as As input, the segmented charging intermittent window sequence is dynamically adjusted to adapt to load changes and minimize battery life loss. The parameters of the charging interval include charging time, interval time, etc., which are adjusted according to actual needs to generate dynamic parameter adjustment data for charging intervals, which are used for charging decision modeling in subsequent steps. Based on the dynamic parameter adjustment data for charging intervals, an intermittent charging decision model is established. Considering factors such as the state of the battery pack, load conditions, and grid characteristics, an intelligent intermittent charging control strategy is formulated. According to the control strategy, the charging interval is adjusted to achieve efficient charging of the battery pack and extend the battery life. The intelligent intermittent charging control operation is executed, and charging control is performed according to the established model and strategy.

[0155] In this embodiment, an intermittent charging control system of a lithium battery pack is provided, which is used to execute the intermittent charging control method of the lithium battery pack as described above, including:

[0156] The load trend module is used to obtain the battery pack status parameters and the real-time load data of the power grid; perform quantitative analysis on the load fluctuation trend of the real-time load data of the power grid to generate the load fluctuation trend data of the power grid; obtain the current SOC status data based on the battery pack status parameters;

[0157] The intermittent window module is used to obtain the historical charging log of the battery pack; predict the charging time of the current SOC state data based on the historical charging log of the battery pack, so as to obtain the predicted charging time value; generate a segmented charging intermittent window sequence based on the grid load fluctuation trend data and the predicted charging time value;

[0158] A charging monitoring module is used to perform segmented cyclic charging processing based on a segmented charging intermittent window sequence and obtain multiple battery pack charging monitoring parameters; based on multiple battery pack charging monitoring parameters, capacity attenuation amplitude data and battery internal resistance fluctuation data are obtained;

[0159] The life loss assessment module is used to perform charging layer aging analysis based on the capacity attenuation amplitude data and battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; perform battery charging life loss assessment processing on the battery performance degradation trend evolution data to generate battery charging life loss data;

[0160] The deep discharge module is used to simulate deep discharge repair based on the battery performance degradation trend evolution data to obtain the deep discharge strategy of the battery pack; based on the deep discharge strategy of the battery pack, the battery charging life loss data is optimized for minimum life loss to generate battery life minimum loss optimization data;

[0161] The charging interval adjustment module is used to obtain load trend prediction data based on the power grid load fluctuation trend data; based on the battery life minimum loss optimization data, the charging interval dynamic parameters of the segmented charging interval window sequence are adjusted to build an intelligent intermittent charging control model to perform intermittent charging control operations.

[0162] The present invention obtains key input information by acquiring battery pack state parameters and real-time grid load data, provides basic data for subsequent charging control, performs load fluctuation trend analysis on real-time grid load data, quantifies load fluctuation, and provides the ability to understand and predict grid load changes. The current SOC state data is obtained based on the battery pack state parameters, and the power state of the battery pack can be accurately understood, providing an important reference basis for charging control. The battery pack historical charging log is obtained, and the battery pack's past charging records are obtained, providing a historical data basis for charging time prediction and optimization. The current SOC state data is calculated based on the battery pack's historical charging log. Based on the charging time prediction, the charging time of the battery pack can be predicted, which provides a prediction value for the subsequent charging control. Combined with the grid load fluctuation trend data and the charging time prediction value, a segmented charging interval window sequence is generated. According to the grid load condition and charging demand, the charging interval window can be flexibly arranged to optimize the charging control. The segmented cyclic charging processing is performed based on the segmented charging interval window sequence, and the charging process is controlled in segments to improve the charging efficiency and battery life. The charging monitoring parameters of multiple battery packs are obtained, and the charging status and performance indicators of the battery pack can be monitored in real time, which provides data support for subsequent analysis and optimization. Parameters, get capacity attenuation amplitude data and battery internal resistance fluctuation data, which are used to evaluate battery performance and health status, and provide a basis for subsequent charging control and life evaluation. Based on capacity attenuation amplitude data and battery internal resistance fluctuation data, charge stacking aging analysis is performed to analyze the trend and evolution of battery performance degradation, understand the battery life attenuation, and perform battery charging life loss evaluation processing on battery performance degradation trend evolution data. It can evaluate the degree of battery charging life loss and provide a basis for subsequent life optimization and management. Based on battery performance degradation trend evolution data, deep discharge repair simulation is performed to simulate and optimize the deep discharge strategy of the battery pack. In order to extend the life of the battery and improve its performance, the battery charging life loss data is optimized for minimum life loss based on the battery pack deep discharge strategy, and the charging strategy is optimized to minimize the battery life loss and increase the battery service life. The load trend forecast data is obtained based on the grid load fluctuation trend data, which can predict the future change trend of the grid load and provide a more accurate reference for charging control. The charging interval dynamic parameters of the segmented charging interval window sequence are adjusted based on the battery life minimum loss optimization data. According to the requirements of battery life optimization, the charging interval is flexibly adjusted to achieve intelligent intermittent charging control and improve the battery life and performance.

[0163] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0164] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for controlling intermittent charging of a lithium battery pack, characterized in that: The intermittent charging specifically comprises: extracting a charging duration value and a charging interval value based on a segmented charging intermittent window sequence; charging the battery pack and recording the real-time charging duration, and when the real-time charging duration is equal to the charging duration value, stopping the charging process; Determine that the current battery pack is in a charging gap; During the charging interval, the battery pack is monitored to obtain the first section of battery pack charging monitoring parameters; When the charging gap length is equal to the charging interval value, recharging is performed; the process includes the following steps: Step S1: obtaining battery pack state parameters and real-time grid load data; performing load fluctuation trend quantitative analysis on the real-time grid load data to generate grid load fluctuation trend data; obtaining current SOC state data based on the battery pack state parameters; Step S2: Obtain the battery pack historical charging log; predict the charging time of the current SOC state data based on the battery pack historical charging log, so as to obtain the charging time prediction value; generate a segmented charging interval window sequence based on the power grid load fluctuation trend data and the charging time prediction value; Step S3: performing segmented cyclic charging processing based on the segmented charging intermittent window sequence, and obtaining multiple groups of battery pack charging monitoring parameters; obtaining capacity attenuation amplitude data and battery internal resistance fluctuation data based on the multiple groups of battery pack charging monitoring parameters; Step S4: performing charging layer aging analysis based on the capacity attenuation amplitude data and the battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; performing battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data; Step S5: performing a deep discharge repair simulation based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack; performing a life minimum loss optimization on the battery charging life loss data based on the deep discharge strategy for the battery pack to generate battery life minimum loss optimization data; Step S6: Obtain load trend prediction data based on the grid load fluctuation trend data; adjust the charging interval dynamic parameters of the segmented charging intermittent window sequence based on the battery life minimum loss optimization data, and build an intelligent intermittent charging control model to perform intermittent charging control operations.

2. The intermittent charging control method of a lithium battery pack according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Obtaining battery pack status parameters and real-time grid load data; Step S12: performing time series load analysis on the real-time load data of the power grid to obtain a time series load curve; Step S13: performing load peak identification on the time series load curve and extracting the load peak point; Step S14: performing a quantitative analysis of the load fluctuation trend on the time series load curve according to the load peak point to generate power grid load fluctuation trend data; Step S15: Evaluate the current SOC state of the battery pack state parameters to obtain current SOC state data.

3. The intermittent charging control method of a lithium battery pack according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Obtaining the battery pack charging history log; Step S22: analyzing the temperature variation of the charging room of the battery pack historical charging log to generate charging temperature variation data; Step S23: quantifying the impact of battery charging performance based on the charging temperature change data, and generating multi-temperature charging performance impact data; Step S24: predicting the charging time of the current SOC state data based on the multi-temperature charging performance impact data, thereby obtaining a predicted charging time value; Step S25: Perform multi-charging interval analysis on the charging duration prediction value based on the grid load fluctuation trend data to generate a segmented charging interval window sequence.

4. The intermittent charging control method of a lithium battery pack according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing segmented cyclic charging processing on the battery pack based on the segmented charging intermittent window sequence, and obtaining the battery pack charging monitoring parameters; Step S32: performing capacity decay analysis on each of the multiple battery pack charging monitoring parameters to construct a capacity decay curve; Step S33: identifying the parameter change amplitude of the capacity decay curve to generate capacity decay amplitude data; Step S34: calculating the internal resistance fluctuation of the multiple battery pack charging monitoring parameters, thereby obtaining battery internal resistance fluctuation data.

5. The intermittent charging control method of a lithium battery pack according to claim 4, characterized in that: The specific steps of step S31 are: Extracting the first charging intermittent window based on the segmented charging intermittent window sequence; Based on the first charging intermittent window, the battery pack is intermittently charged to obtain the first section of battery pack charging monitoring parameters; Based on the second charging intermittent window, the battery pack is intermittently charged to obtain the second-stage battery pack charging monitoring parameters; Repeat the above operation until all charging interval windows are charged, and obtain multiple battery pack charging monitoring parameters; The first-stage battery pack charging monitoring parameters, the second-stage battery pack charging monitoring parameters and multiple-stage battery pack charging monitoring parameters are serialized to obtain multiple groups of battery pack charging monitoring parameters.

6. The intermittent charging control method of a lithium battery pack according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Calculate the time series change rate of the battery internal resistance fluctuation data to obtain the internal resistance change rate; Step S42: performing a multi-time point internal resistance change analysis based on the internal resistance change rate to obtain an internal resistance change graph; Step S43: Perform charging layer aging analysis on the capacity attenuation amplitude data and the internal resistance change spectrum to generate battery charging layer aging data; Step S44: performing degradation trend evolution processing on the battery charging layer aging data to obtain battery performance degradation trend evolution data; Step S45: Perform battery charging life loss evaluation processing on the battery performance degradation trend evolution data to generate battery charging life loss data.

7. The intermittent charging control method of a lithium battery pack according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing a deep discharge repair simulation on the battery pack based on the battery performance degradation trend evolution data to obtain a deep discharge strategy for the battery pack; Step S52: Calculating the average aging rate of the battery based on the deep discharge strategy of the battery pack to obtain the average aging rate under multiple schemes; Step S53: Optimizing the battery charging life loss data for minimum life loss based on the average aging rate under multiple schemes to generate battery life minimum loss optimization data.

8. The intermittent charging control method of a lithium battery pack according to claim 7, characterized in that: The specific steps of step S51 are: Quantify the battery stress accumulation based on the battery performance degradation trend evolution data to obtain the battery stress accumulation level; Setting multiple battery discharge plans according to the battery stress accumulation level; the battery discharge plan specifically includes the battery group discharge depth and the number of discharge cycles; Perform deep discharge repair simulation on the battery pack based on multiple battery discharge schemes to obtain multiple groups of deep discharge simulation data; Analyze the change trend of internal resistance rate of batteries after discharge for multiple sets of deep discharge simulation data to generate discharge internal resistance change rate; Perform battery damage mitigation assessment based on the discharge internal resistance change rate to obtain discharge repair assessment data for each set of solutions; The optimal discharge plan decision analysis is performed on multiple battery discharge plans through the discharge repair evaluation data of each plan to obtain the battery pack deep discharge strategy.

9. The intermittent charging control method of a lithium battery pack according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: performing future period load forecasting on the power grid load fluctuation trend data to obtain load trend forecasting data; Step S62: fine-tuning the load forecast deviation of the load trend forecast data to generate load forecast deviation optimization data; Step S63: adjusting the charging interval dynamic parameter of the segmented charging interval window sequence based on the battery life minimum loss optimization data and the load prediction deviation optimization data, and generating charging interval dynamic parameter adjustment data; Step S64: performing intermittent charging decision modeling on the charging interval dynamic parameter adjustment data, and constructing an intelligent intermittent charging control model to perform intermittent charging control operations.

10. An intermittent charging control system for a lithium battery pack, characterized in that: The intermittent charging control method for executing the lithium battery pack as claimed in claim 1 comprises: The load trend module is used to obtain the battery pack status parameters and the real-time load data of the power grid; perform quantitative analysis on the load fluctuation trend of the real-time load data of the power grid to generate the load fluctuation trend data of the power grid; obtain the current SOC status data based on the battery pack status parameters; The intermittent window module is used to obtain the historical charging log of the battery pack; predict the charging time of the current SOC state data based on the historical charging log of the battery pack, so as to obtain the predicted charging time value; generate a segmented charging intermittent window sequence based on the grid load fluctuation trend data and the predicted charging time value; A charging monitoring module is used to perform segmented cyclic charging processing based on a segmented charging intermittent window sequence and obtain multiple battery pack charging monitoring parameters; based on multiple battery pack charging monitoring parameters, capacity attenuation amplitude data and battery internal resistance fluctuation data are obtained; The life loss assessment module is used to perform charging layer aging analysis based on the capacity attenuation amplitude data and battery internal resistance fluctuation data to generate battery performance degradation trend evolution data; perform battery charging life loss assessment processing on the battery performance degradation trend evolution data to generate battery charging life loss data; The deep discharge module is used to simulate deep discharge repair based on the battery performance degradation trend evolution data to obtain the deep discharge strategy of the battery pack; based on the deep discharge strategy of the battery pack, the battery charging life loss data is optimized for minimum life loss to generate battery life minimum loss optimization data; The charging interval adjustment module is used to obtain load trend prediction data based on the power grid load fluctuation trend data; based on the battery life minimum loss optimization data, the charging interval dynamic parameters of the segmented charging interval window sequence are adjusted to build an intelligent intermittent charging control model to perform intermittent charging control operations.

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