CN116699412A - A method for estimating the remaining capacity of an energy storage battery module - Google Patents
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Abstract
本发明提供了一种储能电池模组的剩余容量估算方法，具体估算步骤如下：S1：储能电池模组剩余容量的数据采集；S2：储能电池模组剩余容量的数据预处理；S3：储能电池模组剩余容量的特征提取；S4：储能电池模组剩余容量的状态估计；S5：储能电池模组剩余容量的状态估算；S6：储能电池模组剩余容量的精确度提升；S7：储能电池模组剩余容量的反馈控制，通过上述的七个估算步骤，可以依次对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理，此外设计的储能电池模组剩余容量的精确度提升，可以提高对储能电池模组的剩余容量高效估算的精确度。The present invention provides a method for estimating the remaining capacity of an energy storage battery module. The specific estimation steps are as follows: S1: data collection of the remaining capacity of the energy storage battery module; S2: data preprocessing of the remaining capacity of the energy storage battery module; S3 : Feature extraction of the remaining capacity of the energy storage battery module; S4: State estimation of the remaining capacity of the energy storage battery module; S5: State estimation of the remaining capacity of the energy storage battery module; S6: Accuracy of the remaining capacity of the energy storage battery module Improvement; S7: Feedback control of the remaining capacity of the energy storage battery module. Through the above seven estimation steps, data collection, data preprocessing, feature extraction, state estimation, state estimation, and Accuracy improvement and feedback control can achieve efficient estimation and processing of the remaining capacity of the energy storage battery module. In addition, the accuracy of the remaining capacity of the designed energy storage battery module can be improved, which can improve the efficient estimation of the remaining capacity of the energy storage battery module. the accuracy.
Description
技术领域technical field
本发明涉及储能电池模组技术领域，具体为一种储能电池模组的剩余容量估算方法。The invention relates to the technical field of energy storage battery modules, in particular to a method for estimating the remaining capacity of an energy storage battery module.
背景技术Background technique
储能电池模组的剩余容量估算方法通常使用基于电池状态估计的技术，BSE是一种通过监测电池的电流、电压和温度等参数，来对电池当前状态进行估计的技术，常用的BSE技术包括卡尔曼滤波、粒子滤波、扩展卡尔曼滤波等。这些方法的核心思想是在估计电池状态时，将电池看作一个动态系统，并建立数学模型，通过观察电池的输入和输出信号，来不断更新电池状态的估计值，除了BSE技术外，还有一些基于机器学习的方法，如支持向量回归、人工神经网络等，也可以用于储能电池模组的剩余容量估算。这些方法可以利用历史数据来训练模型，并通过预测未来的电池行为来估计电池的剩余容量。The remaining capacity estimation method of the energy storage battery module usually uses a technology based on battery state estimation. BSE is a technology that estimates the current state of the battery by monitoring parameters such as battery current, voltage, and temperature. Commonly used BSE technologies include Kalman filter, particle filter, extended Kalman filter, etc. The core idea of these methods is to regard the battery as a dynamic system when estimating the battery state, and establish a mathematical model to continuously update the estimated value of the battery state by observing the input and output signals of the battery. In addition to BSE technology, there are Some methods based on machine learning, such as support vector regression, artificial neural network, etc., can also be used to estimate the remaining capacity of the energy storage battery module. These methods can use historical data to train models and estimate the remaining battery capacity by predicting future battery behavior.
然而，现有的储能电池模组的剩余容量估算方法存在以下的问题：精度欠佳：由于电池状态的估计和剩余容量的计算都涉及到多个参数，而且这些参数之间相互耦合，因此估算精度受到很大的影响，目前的估算方法尚难以达到高精度要求。为此，需要设计相应的技术方案解决存在的技术问题。However, the existing method for estimating the remaining capacity of the energy storage battery module has the following problems: poor accuracy: since the estimation of the battery state and the calculation of the remaining capacity involve multiple parameters, and these parameters are coupled with each other, so The estimation accuracy is greatly affected, and the current estimation method is still difficult to meet the high precision requirements. Therefore, it is necessary to design corresponding technical solutions to solve the existing technical problems.
发明内容Contents of the invention
本发明的目的在于提供一种储能电池模组的剩余容量估算方法，解决了现阶段储能电池模组的剩余容量存在精度欠佳：由于电池状态的估计和剩余容量的计算都涉及到多个参数，而且这些参数之间相互耦合，因此估算精度受到很大的影响，目前的估算方法尚难以达到高精度要求，这一技术问题，可以对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理，此外设计的储能电池模组剩余容量的精确度提升，可以提高对储能电池模组的剩余容量高效估算的精确度。The purpose of the present invention is to provide a method for estimating the remaining capacity of the energy storage battery module, which solves the problem of poor accuracy of the remaining capacity of the energy storage battery module at the present stage: since the estimation of the battery state and the calculation of the remaining capacity involve many parameters, and these parameters are coupled with each other, so the estimation accuracy is greatly affected, and the current estimation method is still difficult to meet the high-precision requirements. Preprocessing, feature extraction, state estimation, state estimation, accuracy improvement, and feedback control can achieve efficient estimation and processing of the remaining capacity of the energy storage battery module. In addition, the accuracy of the remaining capacity of the designed energy storage battery module can be improved. Improve the accuracy of efficient estimation of the remaining capacity of the energy storage battery module.
为实现上述目的，本发明提供如下技术方案：一种储能电池模组的剩余容量估算方法，具体估算步骤如下：In order to achieve the above purpose, the present invention provides the following technical solution: a method for estimating the remaining capacity of an energy storage battery module, the specific estimation steps are as follows:
S1：储能电池模组剩余容量的数据采集；S1: Data acquisition of the remaining capacity of the energy storage battery module;
S2：储能电池模组剩余容量的数据预处理；S2: Data preprocessing of the remaining capacity of the energy storage battery module;
S3：储能电池模组剩余容量的特征提取；S3: Feature extraction of the remaining capacity of the energy storage battery module;
S4：储能电池模组剩余容量的状态估计；S4: State estimation of the remaining capacity of the energy storage battery module;
S5：储能电池模组剩余容量的状态估算；S5: State estimation of the remaining capacity of the energy storage battery module;
S6：储能电池模组剩余容量的精确度提升；S6: The accuracy of the remaining capacity of the energy storage battery module is improved;
S7：储能电池模组剩余容量的反馈控制；S7: Feedback control of the remaining capacity of the energy storage battery module;
通过上述的七个估算步骤，可以依次对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理，此外设计的储能电池模组剩余容量的精确度提升，可以提高对储能电池模组的剩余容量高效估算的精确度。Through the above seven estimation steps, the data acquisition, data preprocessing, feature extraction, state estimation, state estimation, accuracy improvement and feedback control of the remaining capacity of the energy storage battery module can be performed sequentially, and the accuracy of the energy storage battery module can be achieved. In addition, the accuracy of the remaining capacity of the designed energy storage battery module is improved, which can improve the accuracy of the efficient estimation of the remaining capacity of the energy storage battery module.
作为本发明的一种优选方式，S1：储能电池模组剩余容量的数据采集，通过电池检测仪将检测线路与待测储能电池模组相连接，通过监测电池的电流、电压、温度等参数，采集电池的实时数据，并将数据上传至电脑端备用。As a preferred mode of the present invention, S1: data collection of the remaining capacity of the energy storage battery module, the detection circuit is connected to the energy storage battery module to be tested through the battery tester, and the current, voltage, temperature, etc. of the battery are monitored parameters, collect real-time data of the battery, and upload the data to the computer for backup.
作为本发明的一种优选方式，S2：储能电池模组剩余容量的数据预处理，对采集到的原始数据进行滤波、修正等处理，以消除噪声和非线性效应，提高数据质量。As a preferred method of the present invention, S2: data preprocessing of the remaining capacity of the energy storage battery module, filtering and correcting the collected raw data to eliminate noise and nonlinear effects and improve data quality.
作为本发明的一种优选方式，S3：储能电池模组剩余容量的特征提取，根据电池的物理特性和工作状态，选择合适的特征参数，如电荷状态、内阻、容量衰减率等，用于建立电池状态模型。As a preferred method of the present invention, S3: feature extraction of the remaining capacity of the energy storage battery module, according to the physical characteristics and working state of the battery, select appropriate characteristic parameters, such as charge state, internal resistance, capacity decay rate, etc., and use to build a battery state model.
作为本发明的一种优选方式，S4：储能电池模组剩余容量的状态估计，利用BSE技术或机器学习方法，对电池的状态进行估计，并更新电池的状态估计值，可以根据具体情况选择卡尔曼滤波、支持向量回归方法。As a preferred method of the present invention, S4: Estimation of the state of the remaining capacity of the energy storage battery module, using BSE technology or machine learning methods to estimate the state of the battery and update the estimated value of the state of the battery, which can be selected according to specific circumstances Kalman filtering, support vector regression methods.
作为本发明的一种优选方式，S5：储能电池模组剩余容量的状态估算，根据当前估计的电池状态和历史使用情况，利用数学模型计算电池的剩余容量，并输出估算结果，可以利用基于神经网络的模型、统计模型方法来实现。As a preferred method of the present invention, S5: Estimation of the state of the remaining capacity of the energy storage battery module, according to the current estimated battery state and historical usage, use a mathematical model to calculate the remaining capacity of the battery, and output the estimated result, which can be used based on Neural network model, statistical model method to achieve.
作为本发明的一种优选方式，S6：储能电池模组剩余容量的精确度提升，改进特征提取方法：选择更准确、更具代表性的特征参数，如使用基于频谱分析的方法来提取内阻参数，以提高模型的拟合度和预测精度，优化状态估计算法：使用复杂的BSE算法或机器学习方法进行状态估计，进一步提高估算精度，例如，可以利用卡尔曼滤波的改进算法，如无迹卡尔曼滤波，对电池状态进行估计，引入温度补偿：电池的工作温度对其容量有较大影响，因此可以通过引入温度修正项，对电池容量进行实时修正，以提高估算精度，数据融合技术：将多种数据源的信息进行融合，如结合电池历史使用记录及当前的实时数据，并将相关因素进行分析，以提高估算结果的精度，适应不同电池类型：针对不同品牌、不同类型、不同规格的电池，建立相应的模型，根据其特征参数进行估算，以提高估算精度，实时监测和反馈控制：通过实时监测电池状态、剩余容量等参数，对储能电池模组进行适当的反馈控制，保障其安全和性能。As a preferred method of the present invention, S6: Improve the accuracy of the remaining capacity of the energy storage battery module, improve the feature extraction method: select more accurate and representative feature parameters, such as using a method based on spectrum analysis to extract content Resistance parameters to improve the fitting degree and prediction accuracy of the model, optimize the state estimation algorithm: use complex BSE algorithm or machine learning method for state estimation, and further improve the estimation accuracy, for example, the improved algorithm of Kalman filter can be used, such as no Trace Kalman filter to estimate the battery state and introduce temperature compensation: the operating temperature of the battery has a great influence on its capacity, so the battery capacity can be corrected in real time by introducing a temperature correction item to improve the estimation accuracy. Data fusion technology : Integrate information from multiple data sources, such as combining historical battery usage records and current real-time data, and analyze relevant factors to improve the accuracy of estimation results and adapt to different battery types: for different brands, types, and Standard battery, establish corresponding model, estimate according to its characteristic parameters to improve estimation accuracy, real-time monitoring and feedback control: through real-time monitoring of battery status, remaining capacity and other parameters, conduct appropriate feedback control on the energy storage battery module, guarantee its safety and performance.
作为本发明的一种优选方式，S7：储能电池模组剩余容量的反馈控制，根据估算的剩余容量值，对储能电池模组进行适当的反馈控制，以保障电池的安全和性能。As a preferred method of the present invention, S7: Feedback control of the remaining capacity of the energy storage battery module. According to the estimated remaining capacity value, an appropriate feedback control is performed on the energy storage battery module to ensure the safety and performance of the battery.
与现有技术相比，本发明的有益效果如下：Compared with the prior art, the beneficial effects of the present invention are as follows:
本发明通过对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理，此外从数据处理、模型建立、状态估计、温度补偿、数据融合、电池类型适应性、实时监测及反馈控制等方面进行优化和改进，从而提高储能电池模组剩余容量的精确度，有效的解决剩余容量的计算都涉及到多个参数，而且这些参数之间相互耦合，一处参数存在误差导致整个估算结果不准确的情况出现。The present invention can efficiently estimate and process the remaining capacity of the energy storage battery module through data collection, data preprocessing, feature extraction, state estimation, state estimation, accuracy improvement, and feedback control of the remaining capacity of the energy storage battery module. In addition, optimize and improve data processing, model building, state estimation, temperature compensation, data fusion, battery type adaptability, real-time monitoring, and feedback control, so as to improve the accuracy of the remaining capacity of the energy storage battery module and effectively solve the problem. The calculation of the remaining capacity involves multiple parameters, and these parameters are coupled with each other. Errors in one parameter lead to inaccurate estimation results.
具体实施方式Detailed ways
实施例1：Example 1:
一种储能电池模组的剩余容量估算方法，具体估算步骤如下：A method for estimating the remaining capacity of an energy storage battery module, the specific estimating steps are as follows:
S1：储能电池模组剩余容量的数据采集；S1: Data acquisition of the remaining capacity of the energy storage battery module;
S2：储能电池模组剩余容量的数据预处理；S2: Data preprocessing of the remaining capacity of the energy storage battery module;
S3：储能电池模组剩余容量的特征提取；S3: Feature extraction of the remaining capacity of the energy storage battery module;
S4：储能电池模组剩余容量的状态估计；S4: State estimation of the remaining capacity of the energy storage battery module;
S5：储能电池模组剩余容量的状态估算；S5: State estimation of the remaining capacity of the energy storage battery module;
S6：储能电池模组剩余容量的精确度提升；S6: The accuracy of the remaining capacity of the energy storage battery module is improved;
S7：储能电池模组剩余容量的反馈控制；S7: Feedback control of the remaining capacity of the energy storage battery module;
通过上述的七个估算步骤，可以依次对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理，此外设计的储能电池模组剩余容量的精确度提升，可以提高对储能电池模组的剩余容量高效估算的精确度。Through the above seven estimation steps, the data acquisition, data preprocessing, feature extraction, state estimation, state estimation, accuracy improvement and feedback control of the remaining capacity of the energy storage battery module can be performed sequentially, and the accuracy of the energy storage battery module can be achieved. In addition, the accuracy of the remaining capacity of the designed energy storage battery module is improved, which can improve the accuracy of the efficient estimation of the remaining capacity of the energy storage battery module.
S1：储能电池模组剩余容量的数据采集，通过电池检测仪将检测线路与待测储能电池模组相连接，通过监测电池的电流、电压、温度等参数，采集电池的实时数据，并将数据上传至电脑端备用。S1: Data collection of the remaining capacity of the energy storage battery module. Connect the detection line to the energy storage battery module to be tested through the battery tester, and collect real-time data of the battery by monitoring the battery's current, voltage, temperature and other parameters, and Upload the data to the computer for backup.
S2：储能电池模组剩余容量的数据预处理，对采集到的原始数据进行滤波、修正等处理，以消除噪声和非线性效应，提高数据质量。S2: Data preprocessing of the remaining capacity of the energy storage battery module, filtering and correcting the collected raw data to eliminate noise and nonlinear effects and improve data quality.
S3：储能电池模组剩余容量的特征提取，根据电池的物理特性和工作状态，选择合适的特征参数，如电荷状态、内阻、容量衰减率等，用于建立电池状态模型。S3: Feature extraction of the remaining capacity of the energy storage battery module. According to the physical characteristics and working state of the battery, select appropriate characteristic parameters, such as charge state, internal resistance, capacity decay rate, etc., to establish a battery state model.
S4：储能电池模组剩余容量的状态估计，利用BSE技术或机器学习方法，对电池的状态进行估计，并更新电池的状态估计值，可以根据具体情况选择卡尔曼滤波、支持向量回归方法。S4: Estimation of the state of the remaining capacity of the energy storage battery module. Use BSE technology or machine learning methods to estimate the state of the battery and update the estimated value of the state of the battery. Kalman filter and support vector regression methods can be selected according to the specific situation.
S5：储能电池模组剩余容量的状态估算，根据当前估计的电池状态和历史使用情况，利用数学模型计算电池的剩余容量，并输出估算结果，可以利用基于神经网络的模型、统计模型方法来实现。S5: Estimation of the state of the remaining capacity of the energy storage battery module. According to the current estimated state of the battery and historical usage, use a mathematical model to calculate the remaining capacity of the battery, and output the estimated result. You can use a neural network-based model and a statistical model method to accomplish.
S6：储能电池模组剩余容量的精确度提升，改进特征提取方法：选择更准确、更具代表性的特征参数，如使用基于频谱分析的方法来提取内阻参数，以提高模型的拟合度和预测精度，优化状态估计算法：使用复杂的BSE算法或机器学习方法进行状态估计，进一步提高估算精度，例如，可以利用卡尔曼滤波的改进算法，如无迹卡尔曼滤波，对电池状态进行估计，引入温度补偿：电池的工作温度对其容量有较大影响，因此可以通过引入温度修正项，对电池容量进行实时修正，以提高估算精度，数据融合技术：将多种数据源的信息进行融合，如结合电池历史使用记录及当前的实时数据，并将相关因素进行分析，以提高估算结果的精度，适应不同电池类型：针对不同品牌、不同类型、不同规格的电池，建立相应的模型，根据其特征参数进行估算，以提高估算精度，实时监测和反馈控制：通过实时监测电池状态、剩余容量等参数，对储能电池模组进行适当的反馈控制，保障其安全和性能。S6: Improve the accuracy of the remaining capacity of the energy storage battery module and improve the feature extraction method: select more accurate and representative feature parameters, such as using a method based on spectrum analysis to extract internal resistance parameters to improve model fitting degree and prediction accuracy, optimize the state estimation algorithm: use complex BSE algorithm or machine learning method for state estimation, further improve the estimation accuracy, for example, you can use the improved algorithm of Kalman filter, such as unscented Kalman filter, to perform Estimate, introduce temperature compensation: the operating temperature of the battery has a great influence on its capacity, so the battery capacity can be corrected in real time by introducing a temperature correction item to improve the estimation accuracy, data fusion technology: combine information from multiple data sources Fusion, such as combining historical battery usage records and current real-time data, and analyzing relevant factors to improve the accuracy of estimation results and adapt to different battery types: build corresponding models for batteries of different brands, types, and specifications, Estimate according to its characteristic parameters to improve estimation accuracy, real-time monitoring and feedback control: through real-time monitoring of battery status, remaining capacity and other parameters, perform appropriate feedback control on the energy storage battery module to ensure its safety and performance.
S7：储能电池模组剩余容量的反馈控制，根据估算的剩余容量值，对储能电池模组进行适当的反馈控制，以保障电池的安全和性能。S7: Feedback control of the remaining capacity of the energy storage battery module. According to the estimated remaining capacity value, an appropriate feedback control is performed on the energy storage battery module to ensure the safety and performance of the battery.
本发明通过对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理，此外从数据处理、模型建立、状态估计、温度补偿、数据融合、电池类型适应性、实时监测及反馈控制等方面进行优化和改进，从而提高储能电池模组剩余容量的精确度，有效的解决剩余容量的计算都涉及到多个参数，而且这些参数之间相互耦合，一处参数存在误差导致整个估算结果不准确的情况出现。The present invention can efficiently estimate and process the remaining capacity of the energy storage battery module through data collection, data preprocessing, feature extraction, state estimation, state estimation, accuracy improvement, and feedback control of the remaining capacity of the energy storage battery module. In addition, optimize and improve data processing, model building, state estimation, temperature compensation, data fusion, battery type adaptability, real-time monitoring, and feedback control, so as to improve the accuracy of the remaining capacity of the energy storage battery module and effectively solve the problem. The calculation of the remaining capacity involves multiple parameters, and these parameters are coupled with each other. Errors in one parameter lead to inaccurate estimation results.
实施例2：Example 2:
一种储能电池模组的剩余容量估算方法，具体估算步骤如下：A method for estimating the remaining capacity of an energy storage battery module, the specific estimating steps are as follows:
S1：储能电池模组剩余容量的数据采集；S1: Data acquisition of the remaining capacity of the energy storage battery module;
S2：储能电池模组剩余容量的数据预处理；S2: Data preprocessing of the remaining capacity of the energy storage battery module;
S3：储能电池模组剩余容量的特征提取；S3: Feature extraction of the remaining capacity of the energy storage battery module;
S4：储能电池模组剩余容量的状态估计；S4: State estimation of the remaining capacity of the energy storage battery module;
S5：储能电池模组剩余容量的状态估算；S5: State estimation of the remaining capacity of the energy storage battery module;
S6：储能电池模组剩余容量的反馈控制；S6: Feedback control of the remaining capacity of the energy storage battery module;
通过上述的六个估算步骤，可以依次对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理。Through the above six estimation steps, the data acquisition, data preprocessing, feature extraction, state estimation, state estimation and feedback control of the remaining capacity of the energy storage battery module can be performed sequentially, and the remaining capacity of the energy storage battery module can be efficiently estimated. Estimate processing.
S1：储能电池模组剩余容量的数据采集，通过电池检测仪将检测线路与待测储能电池模组相连接，通过监测电池的电流、电压、温度等参数，采集电池的实时数据，并将数据上传至电脑端备用。S1: Data collection of the remaining capacity of the energy storage battery module. Connect the detection line to the energy storage battery module to be tested through the battery tester, and collect real-time data of the battery by monitoring the battery's current, voltage, temperature and other parameters, and Upload the data to the computer for backup.
S2：储能电池模组剩余容量的数据预处理，对采集到的原始数据进行滤波、修正等处理，以消除噪声和非线性效应，提高数据质量。S2: Data preprocessing of the remaining capacity of the energy storage battery module, filtering and correcting the collected raw data to eliminate noise and nonlinear effects and improve data quality.
S3：储能电池模组剩余容量的特征提取，根据电池的物理特性和工作状态，选择合适的特征参数，如电荷状态、内阻、容量衰减率等，用于建立电池状态模型。S3: Feature extraction of the remaining capacity of the energy storage battery module. According to the physical characteristics and working state of the battery, select appropriate characteristic parameters, such as charge state, internal resistance, capacity decay rate, etc., to establish a battery state model.
S4：储能电池模组剩余容量的状态估计，利用BSE技术或机器学习方法，对电池的状态进行估计，并更新电池的状态估计值，可以根据具体情况选择卡尔曼滤波、支持向量回归方法。S4: Estimation of the state of the remaining capacity of the energy storage battery module. Use BSE technology or machine learning methods to estimate the state of the battery and update the estimated value of the state of the battery. Kalman filter and support vector regression methods can be selected according to the specific situation.
S5：储能电池模组剩余容量的状态估算，根据当前估计的电池状态和历史使用情况，利用数学模型计算电池的剩余容量，并输出估算结果，可以利用基于神经网络的模型、统计模型方法来实现。S5: Estimation of the state of the remaining capacity of the energy storage battery module. According to the current estimated state of the battery and historical usage, use a mathematical model to calculate the remaining capacity of the battery, and output the estimated result. You can use a neural network-based model and a statistical model method to accomplish.
S6：储能电池模组剩余容量的反馈控制，根据估算的剩余容量值，对储能电池模组进行适当的反馈控制，以保障电池的安全和性能。S6: Feedback control of the remaining capacity of the energy storage battery module. According to the estimated remaining capacity value, an appropriate feedback control is performed on the energy storage battery module to ensure the safety and performance of the battery.
本发明通过对储能电池模组剩余容量的数据采集、数据预处理、特征提取、状态估计、状态估算、精确度提升和反馈控制，可以达到对储能电池模组的剩余容量高效估算处理。The present invention can efficiently estimate and process the remaining capacity of the energy storage battery module through data collection, data preprocessing, feature extraction, state estimation, state estimation, accuracy improvement and feedback control of the remaining capacity of the energy storage battery module.
最后应说明的是：以上所述仅为本发明的优选实施例而已，并不用于限制本发明，尽管参照前述实施例对本发明进行了详细的说明，对于本领域的技术人员来说，其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。Finally, it should be noted that: the above is only a preferred embodiment of the present invention, and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, it still The technical solutions recorded in the foregoing embodiments may be modified, or some technical features thereof may be equivalently replaced. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.
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