Method and device for detecting electric quantity of energy storage system

Abstract

The present invention relates to a method and device for detecting the electric quantity of an energy storage system, and the method comprises: obtaining the electric energy data and temperature data of the energy storage system, and performing real-time measurement processing to obtain the corresponding real-time measurement results; performing cumulative electric quantity calculation and correction processing based on the real-time measurement results to obtain the corrected electric quantity data; obtaining the open circuit voltage data of the energy storage system, and performing table lookup estimation on the open circuit voltage data based on a preset voltage SOC correspondence relationship to obtain the corresponding SOC estimation value; performing comprehensive analysis on the corrected electric quantity data and the SOC estimation value to obtain the corresponding electric quantity display information; performing data recording on the electric quantity display information and the real-time measurement results to obtain the corresponding comprehensive recorded data; performing safety comprehensive detection on the comprehensive recorded data based on a preset safety threshold to obtain the corresponding comprehensive detection result, and generating the corresponding system status early warning report according to the comprehensive detection result. The present invention can help to accurately grasp the actual capacity and usage of the energy storage system.

Description

Technical Field

[0001] The present invention relates to the technical field of power detection of energy storage systems, and in particular to a power detection method and device for energy storage systems. Background Art

[0002] As one of the key technologies for the development of new energy and the stable operation of power grids, energy storage systems play an increasingly important role in modern energy systems. With the large-scale access of renewable energy and the continuous growth of electricity demand, how to accurately monitor and manage the power state of energy storage systems to achieve efficient operation and safety and reliability of the system has become one of the research focuses. Existing power detection methods for energy storage systems often only focus on a single parameter, such as voltage or current, while ignoring the comprehensive impact of multiple factors such as temperature and charging and discharging efficiency on the power state. This simplified method may lead to inaccurate power estimation, thereby affecting the performance and service life of the energy storage system. In addition, the lack of real-time safety monitoring and early warning mechanisms also increases the potential risks of energy storage systems during operation. Summary of the invention

[0003] The main purpose of the present invention is to provide a method and device for detecting the power of an energy storage system, which can help to accurately grasp the actual capacity and usage of the energy storage system.

[0004] To achieve the above object, the present invention provides a method for detecting the amount of electricity in an energy storage system, comprising:

[0005] Obtain the power data and temperature data of the energy storage system, perform real-time measurement and processing, and obtain corresponding real-time measurement results;

[0006] Based on the real-time measurement results, cumulative power calculation and correction processing are performed to obtain corrected power data;

[0007] Acquire the open circuit voltage data of the energy storage system, and estimate the open circuit voltage data by looking up a table based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value;

[0008] Comprehensively analyzing the corrected power data and the SOC estimated value to obtain corresponding power display information;

[0009] Recording the power display information and the real-time measurement results to obtain corresponding comprehensive record data;

[0010] Based on a preset safety threshold, a safety comprehensive test is performed on the comprehensive record data to obtain a corresponding comprehensive test result, and a corresponding system status early warning report is generated according to the comprehensive test result.

[0011] Furthermore, the acquisition of electric energy data and temperature data of the energy storage system and real-time measurement processing to obtain corresponding real-time measurement results include:

[0012] Sampling the voltage and current of the energy storage system to obtain voltage sampling data and current sampling data;

[0013] Performing instantaneous power calculation according to the voltage sampling data and the current sampling data to obtain instantaneous power data;

[0014] Performing temperature collection on a plurality of temperature sensors of the energy storage system to obtain a plurality of temperature collection data;

[0015] Performing distribution interpolation calculation on the energy storage system according to the plurality of temperature collection data to obtain temperature distribution data;

[0016] Identify hot spots of the energy storage system according to the temperature distribution data to obtain hot spot location information;

[0017] Performing a power-temperature correlation analysis on the energy storage system according to the power data and the temperature distribution data to obtain a power-temperature correlation coefficient;

[0018] Performing heat dissipation evaluation on the energy storage system according to the power-temperature correlation coefficient and the hotspot location information to obtain a heat dissipation efficiency evaluation result;

[0019] The power data, the temperature distribution data, the hotspot location information and the heat dissipation efficiency evaluation result are integrated to obtain a corresponding real-time measurement result.

[0020] Furthermore, the accumulated power calculation and correction processing based on the real-time measurement results to obtain comprehensive corrected power data includes:

[0021] Integrating the current data in the real-time measurement result to obtain initial accumulated electricity data;

[0022] Matching the real-time measurement result with a preset temperature correction coefficient table to obtain a corresponding temperature correction coefficient;

[0023] Performing a product operation on the initial accumulated power data and the temperature correction coefficient to obtain temperature corrected power data;

[0024] Performing self-discharge compensation calculation on the temperature-corrected electrical quantity data according to a preset self-discharge rate and time interval to obtain self-discharge-corrected electrical quantity data;

[0025] Matching the real-time measurement result with a preset voltage correction coefficient table to obtain a corresponding voltage correction coefficient;

[0026] Perform voltage correction calculation on the self-discharge correction power data and the voltage correction coefficient to obtain voltage correction power data;

[0027] Performing efficiency calculation on the real-time measurement result according to a preset charge and discharge efficiency lookup table to obtain a current charge and discharge efficiency value;

[0028] A correction operation is performed on the voltage-corrected electric quantity data and the current charge-discharge efficiency value to obtain the comprehensive corrected electric quantity data.

[0029] Further, the acquiring of the open circuit voltage data of the energy storage system, and the estimation of the open circuit voltage data by table lookup based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value include:

[0030] Performing open circuit voltage measurement and sampling processing on the energy storage system to obtain multiple groups of real-time open circuit voltage sampling data;

[0031] Calculate the average value according to the multiple groups of real-time open circuit voltage sampling data to obtain corresponding average open circuit voltage data;

[0032] Performing interpolation calculation on the average open circuit voltage data according to a preset voltage SOC correspondence table to obtain a preliminary estimated value;

[0033] Performing exponential weighted processing on historical SOC data of the energy storage system to obtain a historical SOC trend value;

[0034] Performing a fusion process based on the preliminary estimated value and the historical SOC trend value to obtain a fused SOC value;

[0035] The fused SOC value is adaptively corrected according to a preset SOC correction coefficient to obtain a final SOC estimation value.

[0036] Furthermore, the corrected power data and the SOC estimated value are comprehensively analyzed to obtain corresponding power display information, including:

[0037] Performing wavelet transform processing on the corrected electric quantity data to obtain electric quantity characteristic coefficients;

[0038] Adaptively adjusting the electric quantity characteristic coefficient by a threshold value according to the SOC estimation value to obtain an adjusted electric quantity characteristic coefficient;

[0039] Performing inverse wavelet transform on the adjusted power characteristic coefficient to obtain reconstructed power data;

[0040] Constructing a curve according to the reconstructed power data and the SOC estimation value to obtain a corresponding power SOC curve;

[0041] Performing piecewise linear fitting on the power SOC curve to obtain piecewise fitting parameters;

[0042] Correcting the reconstructed electrical quantity data according to the segmented fitting parameters to obtain corrected electrical quantity data;

[0043] The corrected power data is subjected to information generation based on a preset display rule to obtain the power display information.

[0044] Furthermore, the curve is constructed according to the reconstructed power data and the SOC estimation value to obtain a corresponding power SOC curve, including:

[0045] Performing data smoothing on the reconstructed electric quantity data to obtain smoothed reconstructed electric quantity data;

[0046] Establishing a corresponding relationship between the reconstructed power data and the SOC estimation value to obtain an initial power SOC data pair;

[0047] Performing least squares fitting on the initial power SOC data pair to obtain an initial power SOC curve equation;

[0048] According to the initial power SOC curve equation, abnormal points are eliminated to obtain a corrected power SOC data pair;

[0049] Performing spline interpolation on the corrected power SOC data pair to obtain interpolated power SOC data points;

[0050] Performing curve fitting based on the interpolated power SOC data points to obtain a final power SOC curve equation;

[0051] A curve image is generated based on the final power SOC curve equation to obtain the power SOC curve.

[0052] Furthermore, the data recording of the power display information and the real-time measurement result to obtain corresponding comprehensive recorded data includes:

[0053] Performing data fusion processing on the power display information and the real-time measurement result to obtain a fused multi-dimensional data set;

[0054] Perform feature extraction based on the fused multidimensional data set to obtain key data features;

[0055] Performing time series analysis on the key data features to obtain a time series feature pattern;

[0056] Constructing a power variation law matrix according to the time series characteristic pattern to obtain power variation law data;

[0057] Performing nonlinear interpolation processing on the power change law data to obtain a power change curve;

[0058] Performing numerical differentiation operation according to the electric quantity change curve to obtain electric quantity change rate data;

[0059] Performing threshold judgment based on the power change rate data to obtain power abnormality mark data;

[0060] The multidimensional data set, the key data features, the time series feature patterns, the power quantity change law data, the power quantity change rate data and the power quantity abnormality mark data are integrated to obtain the comprehensive record data.

[0061] Further, the comprehensive safety detection is performed on the comprehensive record data based on the preset safety threshold to obtain the corresponding comprehensive detection result, and a corresponding system status early warning report is generated according to the comprehensive detection result, including:

[0062] Performing characteristic time trend analysis on the comprehensive recorded data to obtain a corresponding trend characteristic vector;

[0063] Mapping the trend feature vector according to a preset multi-dimensional safety threshold space to obtain a system state point;

[0064] Performing spatial position analysis on the system state point to obtain a system safety distance index;

[0065] Performing interpolation calculation on a preset risk level curve according to the system safety distance index to obtain a system risk level;

[0066] Perform multi-factor coupling analysis based on the system risk level and the trend characteristic vector to obtain a set of potential risk factors;

[0067] Prioritizing the potential risk factor set to obtain a risk factor sequence;

[0068] A system status warning report is generated according to the system risk level and the risk factor sequence.

[0069] The present invention further provides an electric quantity detection device of an energy storage system, which is applied to any one of the above-mentioned electric quantity detection methods of an energy storage system, comprising:

[0070] An acquisition module, which is used to acquire the electric energy data and temperature data of the energy storage system, and perform real-time measurement processing to obtain corresponding real-time measurement results;

[0071] An analysis module, the analysis module is used to calculate the accumulated power and perform correction processing based on the real-time measurement result to obtain corrected power data;

[0072] An association module, the association module is used to obtain the open circuit voltage data of the energy storage system, and perform a table lookup estimation on the open circuit voltage data based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value;

[0073] A processing module, the processing module is used to comprehensively analyze the corrected power data and the SOC estimated value to obtain corresponding power display information;

[0074] A control module, the control module is used to record the power display information and the real-time measurement results to obtain corresponding comprehensive record data;

[0075] An execution module is used to perform a comprehensive safety test on the comprehensive record data based on a preset safety threshold, obtain a corresponding comprehensive test result, and generate a corresponding system status early warning report based on the comprehensive test result.

[0076] The present invention provides a method and device for detecting the amount of electricity in an energy storage system, which has the following beneficial effects:

[0077] By acquiring power data and temperature data at the same time and performing real-time measurement and processing, the actual state of the energy storage system can be more comprehensively evaluated, the accuracy of power estimation can be improved, and a more reliable basis for system management can be provided. Based on the real-time measurement results, the accumulated power is calculated and corrected, which realizes the refined management of power data, helps to accurately grasp the actual capacity and usage of the energy storage system, and avoids energy waste caused by estimation deviation. Combining the open circuit voltage data and the preset voltage SOC correspondence for table lookup estimation, the state of charge of the energy storage system can be more accurately evaluated under different working conditions, and the reliability of system operation can be improved. By comprehensively analyzing and correcting the power data and SOC estimation values, more accurate power display information can be obtained, which helps system managers and users to understand the actual state of the energy storage system in a timely manner and provide a basis for reasonable scheduling and use. Data recording of power display information and real-time measurement results, and comprehensive detection based on safety thresholds, realizes comprehensive monitoring of the energy storage system and effectively reduces potential risks during system operation. By generating a system status early warning report, possible problems can be discovered and prevented in a timely manner, improving the safety and reliability of the energy storage system and extending the service life of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 The present invention provides a flow chart of a method for detecting electric quantity of an energy storage system;

[0079] Figure 2 The present invention provides a structural diagram of a power detection device for an energy storage system.

[0080] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0083] Reference Figure 1 As shown, the present invention provides a method for detecting the amount of electricity in an energy storage system, comprising:

[0084] Step S1: Acquire the electric energy data and temperature data of the energy storage system, and perform real-time measurement and processing to obtain corresponding real-time measurement results;

[0085] Step S2: Calculate the accumulated power based on the real-time measurement results and perform correction processing to obtain corrected power data;

[0086] Step S3: acquiring the open circuit voltage data of the energy storage system, and performing a table lookup estimation on the open circuit voltage data based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value;

[0087] Step S4: Comprehensively analyzing the corrected power data and the SOC estimated value to obtain corresponding power display information;

[0088] Step S5: Record the power display information and real-time measurement results to obtain corresponding comprehensive record data;

[0089] Step S6: Perform a comprehensive safety test on the comprehensive record data based on a preset safety threshold, obtain a corresponding comprehensive test result, and generate a corresponding system status early warning report based on the comprehensive test result.

[0090] Based on the above steps, the detailed process is as follows:

[0091] Step S1: Use current sensors and voltage sensors to collect the charging and discharging current and voltage signals of the energy storage system in real time. Use multi-point temperature sensors to measure the temperature of different positions of the battery pack, including the temperature of the battery cells, modules and the overall temperature of the battery pack. It is recommended to use a high-frequency sampling method with a sampling frequency of not less than 1Hz to ensure the real-time and accuracy of the data.

[0092] Use digital filtering algorithms (such as low-pass filtering and Kalman filtering) to remove high-frequency noise in current and voltage signals. Perform zero-point calibration and range calibration on the sensor to eliminate measurement deviations. Set reasonable data thresholds to eliminate abnormal measurement values ​​that exceed the range.

[0093] The battery charge and discharge capacity is calculated by integrating the real-time current data. The current measurement results are temperature corrected according to the real-time temperature data. The instantaneous charge and discharge power of the energy storage system is calculated by combining the voltage and current data to generate a real-time measurement data packet: including multi-dimensional information such as current, voltage, temperature, and power.

[0094] Create data timestamps: record the exact time of each measurement to facilitate subsequent data analysis.

[0095] Output format standardization: ensure that the measurement results comply with the system internal data interface specifications.

[0096] Step S2: Calculate the accumulated power based on the real-time measurement results and perform correction processing. In the stage of accumulated power calculation, the system adopts the improved ampere-hour integration method and improves the accuracy of power calculation through the trapezoidal integration algorithm. The charging and discharging efficiency is considered in the calculation process, and the efficiency factor is introduced to correct the theoretical power. Due to factors such as sensor error, integration error and battery aging, the system has designed multiple power correction strategies. During the static period of the battery, the coulomb meter is used for benchmark calibration; the power correction is performed based on the corresponding relationship between the open circuit voltage and the battery state of charge (SOC); and temperature compensation is performed according to the battery temperature change. The system continuously monitors the accumulated error of the power calculation, sets an adaptive correction threshold, and adjusts the correction intensity. Finally, the accurate power data after multiple corrections is output to ensure the long-term accuracy of the power calculation.

[0097] Step S3: Obtain the open circuit voltage data of the energy storage system, and perform a table lookup estimate on the open circuit voltage data based on the preset voltage-SOC correspondence relationship. In the open circuit voltage SOC estimation stage, the open circuit voltage is measured in a stable constant temperature environment when the battery is stationary for a specific time (usually more than 1 hour). Through sophisticated charge and discharge experiments, the accurate open circuit voltage values ​​under different SOC states are obtained, and the voltage-SOC nonlinear mapping relationship is constructed using polynomial fitting or spline interpolation methods. For ease of calculation, the continuous mapping relationship is discretized and a table lookup algorithm is established. When the real-time open circuit voltage is obtained, the linear interpolation method is used for SOC estimation, and a strict voltage range and table lookup accuracy threshold are set. In order to improve the accuracy of the estimation, a multi-dimensional correction mechanism is introduced to correct the SOC estimation value in combination with factors such as temperature and current. The advanced self-learning algorithm can continuously optimize the voltage-SOC mapping model and add a confidence index to each SOC estimation value to enhance the reliability of the estimation result.

[0098] Step S4: Comprehensively analyze the corrected power data and SOC estimated values ​​to obtain corresponding power display information. In the comprehensive power analysis stage, the corrected power data and SOC estimated values ​​obtained in the first three steps are integrated, and comprehensive and accurate power display information is constructed through multi-dimensional data fusion technology.

[0099] Perform consistency check on the corrected power data and SOC estimation value to identify possible differences and anomalies. Through the weighted fusion algorithm, the credibility of various estimation methods such as current integration method, open circuit voltage method and temperature correction is comprehensively considered to calculate the final power display information. The fusion algorithm not only considers the accuracy of the numerical value, but also pays attention to the confidence interval and error range of various estimation methods. For different types of energy storage systems, the algorithm can adaptively adjust the weight ratio to ensure high-precision power display under different working conditions. The final generated power display information includes multi-dimensional indicators such as current power percentage, available power, and estimated remaining usage time.

[0100] Step S5: Record the power display information and real-time measurement results to obtain the corresponding comprehensive record data. Data recording is a key link in the power detection method of the energy storage system. The system associates and stores the power display information generated in step S4 with the original real-time measurement results to form structured comprehensive record data. The recording process adopts efficient data compression and encoding technology to ensure data integrity while reducing storage space occupancy. Each record contains metadata such as accurate timestamp, power display information, original measurement data, data source and processing method. To meet the needs of different application scenarios, the system supports a variety of data storage formats, such as relational databases, time series databases and distributed storage systems. The recording mechanism has significant scalability and can adapt to the diverse needs from small portable energy storage devices to large-scale power grid energy storage systems. At the same time, the system has built-in data integrity and consistency verification mechanisms to ensure the high reliability of recorded data, providing a reliable basis for subsequent data analysis, system diagnosis and performance evaluation.

[0101] Step S6: Perform a comprehensive safety test on the comprehensive recorded data based on the preset safety threshold, obtain the corresponding comprehensive test results, and generate the corresponding system status early warning report based on the comprehensive test results. Safety detection is the last line of defense of the energy storage system power detection method. The system pre-sets a series of multi-dimensional safety thresholds according to the battery type, usage environment and application scenario, covering key parameters such as power, voltage, temperature, and charge and discharge current. Perform a comprehensive and real-time safety test on the comprehensive recorded data. The detection algorithm not only performs static threshold comparison, but also introduces a dynamic risk assessment model to identify potential safety hazards. When any indicator beyond the safety range is detected, a detailed system status early warning report is immediately generated. The early warning report adopts a hierarchical risk grading mechanism to provide corresponding processing suggestions according to different risk levels, including reducing the charge and discharge power, starting the cooling system, limiting the charging current, and even emergency shut-off of the system. In order to improve the practicality of the early warning, the report not only contains numerical risk indicators, but also incorporates natural language descriptions, so that non-professional users can quickly understand the system status.

[0102] The present invention provides a method and device for detecting the amount of electricity in an energy storage system, which has the following beneficial effects:

[0103] By acquiring power data and temperature data at the same time and performing real-time measurement and processing, the actual state of the energy storage system can be more comprehensively evaluated, the accuracy of power estimation can be improved, and a more reliable basis for system management can be provided. Based on the real-time measurement results, the accumulated power is calculated and corrected, which realizes the refined management of power data, helps to accurately grasp the actual capacity and usage of the energy storage system, and avoids energy waste caused by estimation deviation. Combining the open circuit voltage data and the preset voltage SOC correspondence for table lookup estimation, the state of charge of the energy storage system can be more accurately evaluated under different working conditions, and the reliability of system operation can be improved. By comprehensively analyzing and correcting the power data and SOC estimation values, more accurate power display information can be obtained, which helps system managers and users to understand the actual state of the energy storage system in a timely manner and provide a basis for reasonable scheduling and use. Data recording of power display information and real-time measurement results, and comprehensive detection based on safety thresholds, realizes comprehensive monitoring of the energy storage system and effectively reduces potential risks during system operation. By generating a system status early warning report, possible problems can be discovered and prevented in a timely manner, improving the safety and reliability of the energy storage system and extending the service life of the system.

[0104] In one embodiment, the electric energy data and temperature data of the energy storage system are acquired, and real-time measurement processing is performed to obtain corresponding real-time measurement results, including:

[0105] Voltage and current sampling are the basic links of data acquisition. A high-precision analog-to-digital converter (ADC) is used to sample the voltage and current of the energy storage system at the millisecond level to ensure the time resolution and accuracy of the data. The voltage sampling points are selected at the main nodes of the battery pack, and the current sampling covers the key positions of the charge and discharge circuit. The voltage sampling data and current sampling data are obtained by sampling at a frequency of not less than 1 kHz.

[0106] Instantaneous power calculation is based on real-time operation of voltage sampling data and current sampling data. The instantaneous power calculation formula P=U*I, where U is the voltage value and I is the current value, is used to accurately calculate the instantaneous power of the energy storage system at each sampling moment. Millisecond-level power calculation is achieved through a high-speed digital signal processor (DSP) to generate a continuous instantaneous power data sequence.

[0107] Temperature data collection uses a multi-point distributed temperature sensor array. Sensors are placed in key locations of the energy storage system, such as battery cells, battery clusters, connection terminals, and heat dissipation modules. The measurement accuracy of each temperature sensor is no less than ±0.5°C, and the sampling interval is 1 second. By acquiring multiple temperature collection data, the foundation is laid for subsequent temperature distribution calculations.

[0108] The temperature distribution interpolation calculation uses a high-precision spatial interpolation algorithm. Based on the Kriging interpolation method, the spatial position and measurement data of each temperature sensor are comprehensively considered to build a continuous and smooth temperature field distribution model. The interpolation calculation can not only obtain the temperature distribution between sensors, but also predict the temperature state of areas that are not directly measured and generate detailed temperature distribution data.

[0109] Hotspot identification is the detection of abnormal areas based on temperature distribution data. A temperature threshold standard is set, and areas that exceed the average temperature of the surrounding environment by 15°C are usually defined as hotspots. The identification algorithm combines spatial gradient analysis and local extreme value detection to accurately locate the area with the highest temperature anomaly in the energy storage system and output hotspot location information.

[0110] The power-temperature correlation analysis uses statistical methods. The Pearson correlation coefficient of the instantaneous power data and the temperature distribution data is calculated to quantify the degree of correlation between the power change and the temperature distribution. The correlation coefficient ranges from -1 to 1. A positive value indicates that the power increase is positively correlated with the temperature increase, a negative value indicates an inverse correlation, and a zero value means no linear correlation.

[0111] The heat dissipation efficiency evaluation integrates the power-temperature correlation coefficient and hotspot location information. By establishing a heat dissipation model, the temperature gradient, heat conduction characteristics and cooling capacity of the hotspot area are comprehensively analyzed. The evaluation indicators include the hotspot temperature decay rate, local heat diffusion efficiency and the overall thermal balance state of the system, and finally a quantitative heat dissipation efficiency evaluation result is given.

[0112] The integration of real-time measurement results is the final step in data processing. Instantaneous power data, temperature distribution data, hotspot location information, and heat dissipation efficiency evaluation results are integrated according to predefined data formats and communication protocols.

[0113] This embodiment significantly improves the accuracy and comprehensiveness of energy storage system performance detection through multi-dimensional data collection and precision analysis technology. High-frequency sampling and multi-point sensor deployment are used to achieve millisecond-level real-time capture of power and temperature data of the energy storage system. The innovative temperature distribution interpolation algorithm and hotspot identification technology make it possible to accurately locate and evaluate temperature anomalies in the energy storage system. The introduction of power-temperature correlation analysis and heat dissipation efficiency evaluation technology enables dynamic and comprehensive diagnosis of energy storage system performance. By quantitatively analyzing the correlation between power changes and temperature distribution, and comprehensively evaluating heat conduction and diffusion characteristics, it provides strong technical support for the safe operation and performance optimization of the energy storage system, and significantly enhances the reliability and operation efficiency of the system.

[0114] In one embodiment, the accumulated power is calculated and corrected based on the real-time measurement results to obtain comprehensive corrected power data, including:

[0115] Accurate power calculation and correction are performed based on real-time measurement results. The current sensor collects the current signal of the energy storage system in real time and obtains the initial accumulated power data through current integration. During this process, the system has a built-in temperature correction coefficient table to select a matching temperature correction coefficient based on the current ambient temperature and battery characteristics. The temperature correction coefficient is preset based on the changes in the electrochemical performance of the battery under different temperature environments, and is established through a large amount of experimental data and theoretical models to ensure the accuracy of the correction.

[0116] The temperature correction coefficient is multiplied by the initial accumulated power data to obtain the temperature corrected power data. This step corrects the effect of temperature on battery capacity and performance, because the electrochemical reaction activity of the battery will change significantly in low or high temperature environments. The temperature correction coefficient table usually contains correction factors for different temperature ranges, which are accurately interpolated according to the real-time temperature.

[0117] Self-discharge is an inherent performance characteristic of batteries, which will lead to a continuous loss of stored power. The energy storage system presets a detailed self-discharge rate lookup table, which records the self-discharge characteristics under different battery types, temperatures and aging levels. According to the preset self-discharge rate and measurement time interval, the temperature-corrected power data is self-discharge compensated and the self-discharge corrected power data is obtained. The self-discharge rate lookup table is constructed through long-term testing and electrochemical models, which can accurately simulate the self-discharge behavior of the battery.

[0118] Voltage is a key parameter that reflects the battery status. The system is equipped with a voltage correction coefficient table, which records the battery performance correction factor under different voltage conditions. The real-time measured voltage value is matched with the preset table to obtain the corresponding voltage correction coefficient. The self-discharge correction power data is multiplied by the voltage correction coefficient, and the voltage correction calculation is performed to obtain the voltage correction power data. The voltage correction coefficient table comprehensively considers complex factors such as the battery's charge and discharge curve and internal resistance changes.

[0119] The charge and discharge efficiency is an important indicator to measure the energy conversion performance of the energy storage system. The system has a built-in charge and discharge efficiency lookup table, which records the charge and discharge conversion efficiency of the battery under different working conditions. According to the real-time measurement results, the current charge and discharge efficiency value is found and calculated. The efficiency lookup table is based on a large amount of experimental data, covering multiple dimensional factors such as current size, temperature, and charge and discharge depth.

[0120] The voltage-corrected power data is corrected with the current charge-discharge efficiency value to obtain the comprehensive corrected power data. This step integrates multiple factors such as temperature, self-discharge, voltage, and charge-discharge efficiency.

[0121] This embodiment significantly improves the accuracy and reliability of power detection through a multi-dimensional and multi-level precise correction algorithm. The use of a temperature correction coefficient table can accurately compensate for changes in battery performance under different temperature environments, overcoming the defect that traditional power detection methods do not fully consider temperature factors. The introduction of a self-discharge rate lookup table and a voltage correction coefficient table comprehensively considers the self-discharge characteristics and voltage state of the battery, making the power estimation more comprehensive and accurate. Through the built-in charge and discharge efficiency lookup table, the energy conversion performance of the battery under different working conditions is analyzed in multiple dimensions, breaking through the limitations of traditional single-factor correction. Ultimately, the collaborative correction algorithm of multiple factors not only improves the accuracy of power detection, but also provides a more reliable data basis for performance evaluation and health status monitoring of energy storage systems.

[0122] In one embodiment, the open circuit voltage data of the energy storage system is obtained, and the open circuit voltage data is estimated by looking up a table based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value, including:

[0123] By measuring the open-circuit voltage of the energy storage system, collecting the corresponding voltage data, and estimating it based on the preset corresponding relationship, the accurate power status can be obtained.

[0124] The energy storage system uses a high-precision voltage sampling device to measure the open circuit voltage. The sampling frequency is set to 10Hz, and the continuous sampling is performed for 30 seconds to obtain 300 sets of open circuit voltage sampling data. The sampling frequency ensures the timeliness of the data, and the sampling duration ensures the adequacy of the data.

[0125] The arithmetic mean of the 300 sets of open circuit voltage sampling data collected was calculated to eliminate the random errors and interference factors in the sampling process and obtain a stable average open circuit voltage value. The average value was calculated by accumulating and then dividing by the total number.

[0126] Interpolation calculation is performed based on a pre-established voltage-SOC correspondence table, which is obtained through experimental testing and records the corresponding standard open circuit voltage values ​​under different SOC states. The interpolation calculation uses a linear interpolation method to perform numerical estimation between known voltage points to obtain a preliminary estimate of the current SOC.

[0127] The energy storage system records the most recent 100 SOC data and processes them using an exponentially weighted average method. The weight coefficient decays step by step in chronological order, with the weight coefficient of the latest data being 0.1 and decreasing by 0.001 in sequence to obtain a historical trend value that reflects the SOC change trend.

[0128] The preliminary estimated value of SOC and the historical trend value are weightedly fused. The fusion coefficient is determined according to the credibility of the two. The weight of the preliminary estimated value is 0.7, and the weight of the historical trend value is 0.3. The fused SOC value is obtained through weighted calculation.

[0129] The SOC correction factor is determined based on the operating temperature, charge and discharge times and other parameters of the energy storage system. The temperature influence coefficient varies in the range of -20°C to 60°C, and the correction factor is 0.95 when the charge and discharge cycles reach 3,000 times. The fused SOC value is corrected according to the real-time operating parameters to obtain the final SOC estimation result.

[0130] This embodiment overcomes the limitations of a single voltage measurement method through multi-frequency, high-precision open-circuit voltage sampling, significantly improving the data accuracy of SOC estimation. Exponentially weighted historical data processing technology is used to introduce long-term system operating trends, making SOC estimation more dynamic and accurate. The multi-dimensional fusion calculation method comprehensively considers voltage data, historical trends, and system operating parameters to effectively reduce the estimation deviation caused by a single data source. The adaptive correction mechanism for factors such as temperature and charge and discharge cycles enhances the adaptability and reliability of energy storage system power detection, providing a more intelligent and accurate power state assessment solution for different types of energy storage systems.

[0131] In one embodiment, the corrected power data and the SOC estimation value are comprehensively analyzed to obtain corresponding power display information, including:

[0132] The first stage of signal processing is the extraction of characteristic coefficients of electric quantity. By implementing wavelet transform on the original corrected electric quantity data, the system can obtain the local characteristics of the signal in both time and frequency domains. Wavelet transform selects appropriate basis functions (such as Haar wavelet or Daubechies wavelet) to decompose complex electric quantity data into multi-scale characteristic coefficients. This step helps to separate the noise components and key information in the electric quantity data, laying the foundation for subsequent processing.

[0133] Adaptive threshold adjustment of characteristic coefficients is a key part of the method. The system dynamically adjusts the threshold of wavelet coefficients in combination with the real-time SOC estimate. The adjustment mechanism is based on the current charge and discharge state of the battery, the temperature environment, and historical operating data. When the SOC is in different ranges, the threshold adjustment strategy will change accordingly to ensure that effective information can be accurately extracted under different battery power states. For example, in the low SOC range, the threshold may be more sensitive to capture small changes in power; while in the high SOC range, the threshold adjustment is smoother.

[0134] The adaptively adjusted characteristic coefficients are reconstructed into power data through wavelet transform. The reconstruction process needs to ensure the integrity and continuity of the signal, and the key features of the original data are retained to the greatest extent by carefully selecting the reconstruction algorithm. The reconstructed data and the SOC estimation value jointly construct the power SOC curve, which reflects the change of battery power over time and state.

[0135] The next step of curve analysis is piecewise linear fitting. The algorithm divides the SOC curve into several linear intervals and fits the best linear model in each interval. The fitting parameters include slope and intercept, which incorporate the characteristics of power changes in different SOC stages. Piecewise linear fitting can capture the overall trend of battery performance and reveal subtle differences in local power changes.

[0136] Based on the segmented fitting parameters, the system corrects the reconstructed power data. The correction process takes into account factors such as battery aging and temperature effects, and eliminates measurement errors and system deviations through parameter adjustment. The corrected power data can more accurately reflect the actual status of the battery.

[0137] Generate power display information according to preset display rules. Display rules comprehensively consider multiple dimensions such as power accuracy, update frequency, user friendliness, etc. Display information may include remaining power percentage, estimated available time, charge and discharge power, etc., and may be presented in an intuitive way such as color coding or progress bar.

[0138] This embodiment uses wavelet transform technology, which realizes multi-scale feature extraction of power data, effectively separates noise and key information, and improves the accuracy of power detection. The introduction of an adaptive threshold adjustment mechanism for the SOC estimation value enables power detection to dynamically adapt to different charging and discharging states of the battery, overcoming the limitations of traditional detection methods in complex working environments. The piecewise linear fitting algorithm can accurately capture the characteristics of power changes and significantly improve the accuracy of the characterization of the power state. By fine-tuning the reconstructed power data, the system effectively eliminates measurement errors and system deviations, and enhances the reliability of battery management. Ultimately, the power information generated based on preset display rules is both intuitive and comprehensive, greatly improving the user's understanding of the battery status and usage experience.

[0139] In one embodiment, a curve is constructed according to the reconstructed power data and the SOC estimation value to obtain a corresponding power SOC curve, including:

[0140] In the data preprocessing stage, the reconstructed power data is smoothed. The moving average filtering algorithm is used, and the window length is selected as 5 data points to smooth the original reconstructed power data, eliminate high-frequency noise and fluctuations in the data, and retain the basic trends and characteristics of the data. Smoothing helps reduce measurement errors and random interference, and improves the accuracy of subsequent data analysis.

[0141] In the power and state association phase, the initial data correspondence is established based on the smoothed reconstructed power data and the corresponding SOC estimation value. By matching the reconstructed power value and the corresponding SOC estimation value at each time point, an initial power SOC data pair set is generated. The construction of the data pair needs to ensure timestamp consistency and data integrity to provide a basis for subsequent analysis.

[0142] The curve fitting stage uses the least squares method to fit the initial power SOC data pair to obtain the initial power SOC curve equation. The least squares method finds the best fitting curve by minimizing the sum of squared errors between the data points and the fitting curve. The selection of the curve equation considers polynomial models, such as quadratic or cubic polynomials, to capture the nonlinear relationship between power and SOC.

[0143] Identification and elimination of abnormal points are key steps to ensure data quality. Based on the initial power SOC curve equation, statistical methods are used to identify and eliminate abnormal data points. Specifically, the residual between each data point and the fitting curve is calculated, and a threshold is set to determine the abnormal point. Data points exceeding ±2 standard deviations are considered abnormal and will be eliminated to obtain a corrected power SOC data pair, which improves the representativeness and reliability of the data.

[0144] The spline interpolation method is selected in the interpolation processing stage to perform fine processing on the corrected power SOC data. Spline interpolation can generate smooth and continuous curves between data points while maintaining the overall trend of the data. During the interpolation process, the cubic spline interpolation algorithm is selected to construct a piecewise cubic polynomial to generate denser and more accurate interpolated power SOC data points while maintaining the continuity and smoothness of the function.

[0145] The final curve is constructed by curve fitting the interpolated power SOC data points to obtain the final power SOC curve equation. The fitting algorithm selects a method that comprehensively considers the minimum fitting error and model complexity, such as the improved least squares method or regularized fitting technology. The curve equation should not only accurately reflect the relationship between power and SOC, but also have good generalization ability and be able to describe the power characteristics of the energy storage system under different working conditions.

[0146] Based on the final power SOC curve equation, an intuitive curve image is generated. The image drawing takes into account visual elements such as coordinate axis scale, color, line type, etc. to ensure the readability and aesthetics of the curve. The curve image clearly shows the changing relationship between the power and SOC of the energy storage system, providing an intuitive visualization tool for system status evaluation and performance analysis.

[0147] This embodiment significantly improves the accuracy and reliability of power detection of the energy storage system by adopting a multi-step data processing and curve construction method. The application of the moving average filtering algorithm effectively eliminates data noise and fluctuation interference, ensuring the basic quality of data analysis. The least squares fitting combined with the outlier removal mechanism not only optimizes the data quality, but also improves the accuracy of curve fitting, making the description of the relationship between power and SOC more accurate. The introduction of spline interpolation technology realizes the refined processing of data, while maintaining the continuity and smoothness of the data, it enhances the expressive power of the curve. This systematic processing method not only overcomes the problems of large data fluctuations and low precision in traditional power detection, but also intuitively displays the working status of the energy storage system through visual curves, providing a powerful tool for system performance evaluation and fault diagnosis, and greatly improving the efficiency and reliability of energy storage system operation and maintenance.

[0148] In one embodiment, data is recorded for the power display information and the real-time measurement results to obtain corresponding comprehensive recorded data, including:

[0149] Data fusion processing is performed on the power display information and real-time measurement results. In the data fusion process, multidimensional data integration technology is introduced to eliminate the possible deviations and limitations of a single data source by establishing a cross-dimensional data association model. The fused multidimensional data set not only contains the power value, but also comprehensively considers the influencing factors such as time and environment, laying a solid foundation for subsequent analysis.

[0150] Based on data fusion, feature extraction algorithms are used to obtain key data features from multidimensional data sets. The feature extraction process is based on information entropy theory and machine learning dimensionality reduction technology to screen out the most representative and information-dense data dimensions. Through this step, complex raw data is converted into more concise and meaningful feature vectors.

[0151] Time series analysis is performed on the extracted key data features to construct the time series characteristic pattern of power changes. Time series analysis uses non-stationary sequence processing methods to identify the periodicity, trend and mutation characteristics of power changes. Based on the time series characteristic pattern, a power change law matrix is ​​established to provide a quantitative description of the dynamic behavior of power.

[0152] The data on the law of power change is processed by nonlinear interpolation to generate a detailed power change curve. The selection of the nonlinear interpolation algorithm takes into account the distribution characteristics of the sample points and the requirements of data continuity to ensure that the curve can truly reflect the inherent law of power change. In the process of curve generation, technologies such as polynomial fitting and spline interpolation are introduced to enhance the smoothness and accuracy of data expression.

[0153] In order to capture the subtle features of the power change, the method performs numerical differentiation on the power change curve to obtain the power change rate data. The numerical differentiation operation uses numerical calculation methods such as central difference to calculate the instantaneous rate and acceleration of the power change. Through the change rate data, the microscopic dynamics of power fluctuations can be sensitively captured.

[0154] Based on the power change rate data, this method designs a multi-dimensional threshold judgment mechanism to realize power anomaly marking. The threshold judgment process comprehensively considers indicators such as the amplitude, slope and duration of the change rate to establish hierarchical and multi-dimensional anomaly identification rules. When the power change exceeds the preset safety threshold range, the anomaly mark is triggered to provide data support for risk warning of the energy storage system.

[0155] The multidimensional data set, key data features, time series feature patterns, power change law data, power change rate data and power abnormality mark data are systematically integrated to form comprehensive record data. The integration process follows the principles of data relevance and hierarchy, and builds a multi-dimensional and multi-level power monitoring information system to provide comprehensive data support for the status evaluation and performance optimization of the energy storage system.

[0156] This embodiment overcomes the limitations of a single data source through multi-dimensional data fusion processing, establishes a cross-dimensional electricity information association model, and significantly improves the accuracy and comprehensiveness of electricity monitoring. The use of feature extraction and time series analysis technology can sensitively capture the micro-dynamics and potential laws of electricity changes, providing more refined and in-depth data support for the status assessment of energy storage systems. The innovative nonlinear interpolation and numerical differentiation methods make the electricity change curve smoother and more continuous, and can accurately characterize the instantaneous characteristics of electricity changes. The introduction of a multi-dimensional threshold judgment mechanism realizes the timely identification of electricity anomalies and risk warnings, providing effective technical guarantees for the safe operation of energy storage systems.

[0157] In one embodiment, a security comprehensive test is performed on the comprehensive recorded data based on a preset security threshold to obtain a corresponding comprehensive test result, and a corresponding system status early warning report is generated according to the comprehensive test result, including:

[0158] In the data feature time trend analysis phase, the system selects multi-dimensional key parameters such as voltage, temperature, current, insulation resistance, etc. of the energy storage equipment to construct a time series data set. Statistical analysis is performed on these data to extract the data's change rate, fluctuation amplitude, periodic characteristics, etc., to form a multi-dimensional trend feature vector. This vector not only reflects the change trend of a single parameter, but also presents the correlation between parameters.

[0159] In the multi-dimensional safety threshold space mapping phase, a high-dimensional safety threshold space is pre-set, which includes voltage safety range, temperature control range, current load limit, insulation resistance critical value, etc. The trend feature vector will be accurately located in this complex spatial coordinate system and mapped to form a specific system state point. The position of this state point will directly reflect the current safety status of the energy storage system.

[0160] The calculation of the system safety distance index is based on the specific position of the state point in the safety threshold space. By calculating the Euclidean distance from the state point to the boundary of the safety area, the degree of deviation of the system from the safety area is quantified. The larger the distance, the closer the system is to the danger boundary; the smaller the distance, the system is in a relatively safe central area.

[0161] The risk level is determined by nonlinear interpolation method, and the preset risk level curve is accurately mapped according to the system safety distance index. The risk level curve is pre-constructed through a large amount of experimental data and theoretical models, forming a continuous risk assessment model from safety level, slight risk, medium risk to high risk.

[0162] In the multi-factor coupling analysis phase, the system comprehensively considers the risk level and trend feature vector to identify a set of potential risk factors. This process uses complex network analysis and machine learning algorithms to mine hidden associations between parameters and identify key factors that may cause system instability. For example, abnormal temperature rise may have a potential correlation with current overload, and this correlation will be accurately captured through coupling analysis.

[0163] The priority ranking of potential risk factors is based on the impact of each factor on system security. The ranking algorithm considers the urgency, development trend, potential cascading effect, etc. of the factors to generate a risk factor sequence. Each factor in the sequence is assigned a weight and priority label.

[0164] Integrate risk levels and risk factor sequences to generate detailed system status warning reports. The report not only contains the current risk assessment results, but also provides detailed risk source analysis, development trend forecasts, and recommended preventive maintenance measures. This intelligent early warning mechanism can help operation and maintenance personnel identify potential risks in advance and achieve preventive maintenance of energy storage systems.

[0165] This embodiment realizes comprehensive intelligent monitoring of the system operation status by constructing a multi-dimensional, high-precision energy storage system safety assessment model. This method breaks through the limitations of traditional single parameter monitoring. Through multi-parameter coupling analysis, it can deeply explore the complex correlations inherent in the system, significantly improving the accuracy and foresight of risk prediction. The innovative introduction of system safety distance index and multi-dimensional safety threshold space mapping technology makes risk assessment more accurate and dynamic, and can capture small abnormal changes in time, providing strong technical support for the preventive maintenance of energy storage systems. In addition, the multi-factor coupling analysis and risk factor priority sorting mechanism can not only comprehensively identify potential risk sources, but also intelligently classify and warn risks, greatly improving the operational safety and reliability of the energy storage system.

[0166] The present invention further provides an electric quantity detection device of an energy storage system, which is applied to any one of the above-mentioned electric quantity detection methods of an energy storage system, comprising:

[0167] The acquisition module is used to obtain the electric energy data and temperature data of the energy storage system, and perform real-time measurement and processing to obtain corresponding real-time measurement results;

[0168] An analysis module, which is used to calculate the accumulated power and perform correction processing based on the real-time measurement results to obtain corrected power data;

[0169] The association module is used to obtain the open circuit voltage data of the energy storage system, and perform a table lookup and estimation on the open circuit voltage data based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value;

[0170] A processing module, the processing module is used to comprehensively analyze the corrected power data and the SOC estimated value to obtain corresponding power display information;

[0171] The control module is used to record the power display information and real-time measurement results to obtain corresponding comprehensive record data;

[0172] The execution module is used to perform comprehensive security detection on the comprehensive recorded data based on a preset security threshold, obtain the corresponding comprehensive detection result, and generate a corresponding system status early warning report based on the comprehensive detection result.

[0173] The present invention provides a power detection device for an energy storage system. By simultaneously acquiring power data and temperature data and performing real-time measurement and processing, the actual state of the energy storage system can be more comprehensively evaluated, the accuracy of power estimation can be improved, and a more reliable basis for system management can be provided. Based on the real-time measurement results, the accumulated power is calculated and corrected, and the refined management of power data is realized, which helps to accurately grasp the actual capacity and usage of the energy storage system and avoid energy waste caused by estimation deviation. By combining the open circuit voltage data and the preset voltage SOC correspondence relationship for table lookup estimation, the state of charge of the energy storage system can be more accurately evaluated under different working conditions, and the reliability of system operation is improved. By comprehensively analyzing and correcting the power data and the SOC estimation value, more accurate power display information is obtained, which helps system managers and users to understand the actual state of the energy storage system in a timely manner and provide a basis for reasonable scheduling and use. Data recording of the power display information and the real-time measurement results, and comprehensive detection based on the safety threshold, realizes comprehensive monitoring of the energy storage system and effectively reduces the potential risks in the system operation process. By generating a system status early warning report, possible problems can be discovered and prevented in a timely manner, the safety and reliability of the energy storage system are improved, and the service life of the system is extended.

[0174] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0175] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for detecting the amount of electricity in an energy storage system, characterized in that: include: Obtain the power data and temperature data of the energy storage system, perform real-time measurement and processing, and obtain corresponding real-time measurement results; Based on the real-time measurement results, cumulative power calculation and correction processing are performed to obtain corrected power data; Acquire the open circuit voltage data of the energy storage system, and estimate the open circuit voltage data by looking up a table based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value; Comprehensively analyzing the corrected power data and the SOC estimated value to obtain corresponding power display information; Recording the power display information and the real-time measurement results to obtain corresponding comprehensive record data; Performing a comprehensive safety test on the comprehensive recorded data based on a preset safety threshold to obtain a corresponding comprehensive test result, and generating a corresponding system status early warning report based on the comprehensive test result; The step of comprehensively analyzing the corrected power data and the SOC estimated value to obtain corresponding power display information includes: The corrected electric quantity data is subjected to wavelet transform processing to obtain electric quantity characteristic coefficients, including: the first stage of signal processing is electric quantity characteristic coefficient extraction; by performing wavelet transform on the corrected electric quantity data, local characteristics of the signal can be obtained in the time-frequency domain; the wavelet transform selects appropriate basis functions to decompose complex electric quantity data into multi-scale characteristic coefficients; Adaptively adjusting the threshold of the power characteristic coefficient according to the SOC estimation value to obtain the adjusted power characteristic coefficient, including: the adaptive threshold adjustment of the characteristic coefficient is the key link of the method, and the threshold of the wavelet coefficient is dynamically adjusted in combination with the SOC estimation value obtained in real time; the adjustment mechanism is based on the current charge and discharge state, temperature environment and historical operation data of the battery; when the SOC is in different intervals, the threshold adjustment strategy will change accordingly to ensure that effective information can be accurately extracted under different states of battery power; Performing inverse wavelet transform on the adjusted power characteristic coefficient to obtain reconstructed power data; Constructing a curve according to the reconstructed power data and the SOC estimation value to obtain a corresponding power SOC curve; Performing piecewise linear fitting on the power SOC curve to obtain piecewise fitting parameters; Correcting the reconstructed electrical quantity data according to the segmented fitting parameters to obtain corrected electrical quantity data; The corrected power data is subjected to information generation based on a preset display rule to obtain the power display information.

2. The method for detecting the electric quantity of the energy storage system according to claim 1, characterized in that: The acquiring of electric energy data and temperature data of the energy storage system and performing real-time measurement processing to obtain corresponding real-time measurement results includes: Sampling the voltage and current of the energy storage system to obtain voltage sampling data and current sampling data; Performing instantaneous power calculation according to the voltage sampling data and the current sampling data to obtain instantaneous power data; Performing temperature collection on a plurality of temperature sensors of the energy storage system to obtain a plurality of temperature collection data; Performing distribution interpolation calculation on the energy storage system according to the plurality of temperature collection data to obtain temperature distribution data; Identify hot spots of the energy storage system according to the temperature distribution data to obtain hot spot location information; Performing a power-temperature correlation analysis on the energy storage system according to the power data and the temperature distribution data to obtain a power-temperature correlation coefficient; Performing heat dissipation evaluation on the energy storage system according to the power-temperature correlation coefficient and the hotspot location information to obtain a heat dissipation efficiency evaluation result; The power data, the temperature distribution data, the hotspot location information and the heat dissipation efficiency evaluation results are integrated to obtain corresponding real-time measurement results, including integrating instantaneous power data, temperature distribution data, hotspot location information and heat dissipation efficiency evaluation results according to a predefined data format and communication protocol.

3. The method for detecting the electric quantity of the energy storage system according to claim 1, characterized in that: The cumulative power calculation and correction processing based on the real-time measurement result to obtain the corrected power data includes: Integrating the current data in the real-time measurement result to obtain initial accumulated electricity data; Matching the real-time measurement result with a preset temperature correction coefficient table to obtain a corresponding temperature correction coefficient; Performing a product operation on the initial accumulated power data and the temperature correction coefficient to obtain temperature corrected power data; Performing self-discharge compensation calculation on the temperature-corrected electrical quantity data according to a preset self-discharge rate and time interval to obtain self-discharge corrected electrical quantity data; Matching the real-time measurement result with a preset voltage correction coefficient table to obtain a corresponding voltage correction coefficient; Perform voltage correction calculation on the self-discharge correction power data and the voltage correction coefficient to obtain voltage correction power data; Performing efficiency calculation on the real-time measurement result according to a preset charge and discharge efficiency lookup table to obtain a current charge and discharge efficiency value; A correction operation is performed on the voltage-corrected electric quantity data and the current charge-discharge efficiency value to obtain the corrected electric quantity data.

4. The method for detecting the electric quantity of the energy storage system according to claim 1, characterized in that: The step of acquiring the open circuit voltage data of the energy storage system and performing a table lookup estimation on the open circuit voltage data based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value includes: Performing open circuit voltage measurement and sampling processing on the energy storage system to obtain multiple groups of real-time open circuit voltage sampling data; Calculate the average value according to the multiple groups of real-time open circuit voltage sampling data to obtain corresponding average open circuit voltage data; Interpolation calculation is performed based on a pre-established voltage-SOC correspondence table, which is obtained through experimental testing and records the corresponding standard open-circuit voltage values ​​under different SOC states; interpolation calculation uses a linear interpolation method to perform numerical estimation between known voltage points to obtain a preliminary estimated SOC value; Performing exponential weighted processing on historical SOC data of the energy storage system to obtain a historical SOC trend value; The SOC preliminary estimated value and the historical SOC trend value are fused to obtain a fused SOC value, and the SOC preliminary estimated value and the historical trend value are weighted and fused. The fusion coefficient is determined according to the credibility of the two. The weight of the SOC preliminary estimated value is 0.7, and the weight of the historical trend value is 0.

3. The fused SOC value is obtained through weighted calculation; The fused SOC value is adaptively corrected according to a preset SOC correction coefficient to obtain a final SOC estimation value.

5. The method for detecting the electric quantity of the energy storage system according to claim 1, characterized in that: The constructing a curve according to the reconstructed power data and the SOC estimation value to obtain a corresponding power SOC curve includes: Performing data smoothing on the reconstructed electric quantity data to obtain smoothed reconstructed electric quantity data; Establishing a corresponding relationship between the reconstructed power data and the SOC estimation value to obtain an initial power SOC data pair; Performing least squares fitting on the initial power SOC data pair to obtain an initial power SOC curve equation; According to the initial power SOC curve equation, abnormal points are eliminated to obtain a corrected power SOC data pair; Performing spline interpolation on the corrected power SOC data pair to obtain interpolated power SOC data points; Performing curve fitting based on the interpolated power SOC data points to obtain a final power SOC curve equation; A curve image is generated based on the final power SOC curve equation to obtain the power SOC curve.

6. The method for detecting the electric quantity of the energy storage system according to claim 1, characterized in that: The data recording of the power display information and the real-time measurement result to obtain corresponding comprehensive recorded data includes: The power display information and the real-time measurement results are subjected to data fusion processing to obtain a fused multidimensional data set. The power display information and the real-time measurement results are subjected to data fusion processing. During the data fusion process, multidimensional data integration technology is introduced to eliminate the possible deviations and limitations of a single data source by establishing a cross-dimensional data association model. The fused multidimensional data set not only contains power values, but also comprehensively considers time and environmental influencing factors, laying a solid foundation for subsequent analysis. Perform feature extraction based on the fused multidimensional data set to obtain key data features; Performing time series analysis on the key data features to obtain a time series feature pattern; Constructing a power variation law matrix according to the time series characteristic pattern to obtain power variation law data; Performing nonlinear interpolation processing on the power change law data to obtain a power change curve; Performing numerical differentiation operation according to the electric quantity change curve to obtain electric quantity change rate data; Performing threshold judgment based on the power change rate data to obtain power abnormality mark data; The multidimensional data set, the key data features, the time series feature patterns, the power quantity change law data, the power quantity change rate data and the power quantity abnormality mark data are integrated to obtain the comprehensive record data.

7. The method for detecting the electric quantity of the energy storage system according to claim 1, characterized in that: The comprehensive safety detection of the comprehensive recorded data based on the preset safety threshold is performed to obtain a corresponding comprehensive detection result, and a corresponding system status early warning report is generated according to the comprehensive detection result, including: Performing characteristic time trend analysis on the comprehensive recorded data to obtain a corresponding trend characteristic vector. In the data characteristic time trend analysis stage, the system selects multi-dimensional key parameters of the energy storage device, such as voltage, temperature, current, and insulation resistance, to construct a time series data set; performing statistical analysis on these data to extract the data's rate of change, fluctuation amplitude, and periodicity characteristics to form a multi-dimensional trend characteristic vector; Mapping the trend feature vector according to a preset multi-dimensional safety threshold space to obtain a system state point; Performing spatial position analysis on the system state point to obtain a system safety distance index; Performing interpolation calculation on a preset risk level curve according to the system safety distance index to obtain a system risk level; In the multi-dimensional safety threshold space mapping phase, a high-dimensional safety threshold space is pre-set, which includes the voltage safety range, temperature control range, current load limit, and insulation resistance critical value. The trend feature vector will be accurately located in this complex space coordinate system and mapped to form a specific system state point. The calculation of the system safety distance index is based on the specific position of the state point in the safety threshold space. By calculating the Euclidean distance from the state point to the boundary of the safety area, the degree of deviation of the system from the safety area is quantified. The larger the distance, the closer the system is to the danger boundary; the smaller the distance, the system is in a relatively safe central area. The risk level is determined by using a nonlinear interpolation method to accurately map the preset risk level curve according to the system safety distance index. The risk level curve is pre-constructed through a large amount of experimental data and theoretical models, forming a continuous risk assessment model from safety level, slight risk, medium risk to high risk; Perform multi-factor coupling analysis based on the system risk level and the trend characteristic vector to obtain a set of potential risk factors; Prioritizing the potential risk factor set to obtain a risk factor sequence; A system status warning report is generated according to the system risk level and the risk factor sequence.

8. An electric quantity detection device for an energy storage system, characterized in that: The method for detecting the electric quantity of the energy storage system applied to any one of claims 1 to 7 above comprises: An acquisition module, which is used to acquire the electric energy data and temperature data of the energy storage system, and perform real-time measurement processing to obtain corresponding real-time measurement results; An analysis module, the analysis module is used to calculate the accumulated power and perform correction processing based on the real-time measurement result to obtain corrected power data; An association module, the association module is used to obtain the open circuit voltage data of the energy storage system, and perform a table lookup estimation on the open circuit voltage data based on a preset voltage-SOC correspondence relationship to obtain a corresponding SOC estimation value; A processing module, the processing module is used to comprehensively analyze the corrected power data and the SOC estimated value to obtain corresponding power display information; A control module, the control module is used to record the power display information and the real-time measurement results to obtain corresponding comprehensive record data; An execution module, the execution module is used to perform a comprehensive safety test on the comprehensive record data based on a preset safety threshold, obtain a corresponding comprehensive test result, and generate a corresponding system status early warning report according to the comprehensive test result; The step of comprehensively analyzing the corrected power data and the SOC estimated value to obtain corresponding power display information includes: The corrected electric quantity data is subjected to wavelet transform processing to obtain electric quantity characteristic coefficients, including: the first stage of signal processing is electric quantity characteristic coefficient extraction; by performing wavelet transform on the corrected electric quantity data, local characteristics of the signal can be obtained in the time-frequency domain; the wavelet transform selects appropriate basis functions to decompose complex electric quantity data into multi-scale characteristic coefficients; Adaptively adjusting the threshold of the power characteristic coefficient according to the SOC estimation value to obtain the adjusted power characteristic coefficient, including: the adaptive threshold adjustment of the characteristic coefficient is the key link of the method, and the threshold of the wavelet coefficient is dynamically adjusted in combination with the SOC estimation value obtained in real time; the adjustment mechanism is based on the current charge and discharge state, temperature environment and historical operation data of the battery; when the SOC is in different intervals, the threshold adjustment strategy will change accordingly to ensure that effective information can be accurately extracted under different states of battery power; Performing inverse wavelet transform on the adjusted power characteristic coefficient to obtain reconstructed power data; Constructing a curve according to the reconstructed power data and the SOC estimation value to obtain a corresponding power SOC curve; Performing piecewise linear fitting on the power SOC curve to obtain piecewise fitting parameters; Correcting the reconstructed electrical quantity data according to the segmented fitting parameters to obtain corrected electrical quantity data; The corrected power data is subjected to information generation based on a preset display rule to obtain the power display information.

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