卡尔曼滤波器
电池(电)
人工神经网络
锂离子电池
国家(计算机科学)
锂(药物)
控制理论(社会学)
计算机科学
能量(信号处理)
离子
扩展卡尔曼滤波器
卷积神经网络
荷电状态
人工智能
算法
化学
数学
物理
心理学
统计
功率(物理)
控制(管理)
有机化学
量子力学
精神科
作者
Jin Li,Shunli Wang,Lei Chen,Yangtao Wang,Heng Zhou,Josep M. Guerrero
标识
DOI:10.1016/j.est.2024.110750
摘要
To achieve accurate State of Energy (SOE) estimation of Battery Management System (BMS), the Adaptive Kalman Filter and self-designed Early Stopping Optimized Convolutional Neural Network (AKF-ESCNN) is innovatively introduced. It is based on a self-designed Early Stopping (ES) strategy to optimize the training of Convolutional Neural Network (CNN) models, addressing the issue of network overfitting. By integrating Adaptive Kalman Filtering (AKF) for smoothing and filtering the network outputs, it reduces erroneous abrupt variations in results, ultimately achieving precise estimation of SOE. After different experimental data verification (5 °C, 10 °C and 25 °C), compared the loss values of model training. AKF-ESCNN model training accuracy is 10 % higher than CNN. In the whole temperature range of this paper, AKF-ESCNN also has a better performance. At cold −5 °C the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of AKF-ESCNN in the HPPC working condition are 0.268 % and 0.449 %, while the MAE and RMSE of CNN before optimization are 1.411 % and 1.973 %, and the estimation accuracy has been significantly improved. AKF-ESCNN provides a new way to solve the problems faced by data-driven SOE estimation of lithium-ion batteries.
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