发电技术2025，Vol.46Issue(6)：1154-1163,10.DOI:10.12096/j.2096-4528.pgt.24068
基于改进自适应蜜獾算法优化时间卷积网络的车载锂离子电池健康状态估计
State of Health Estimation of On-Board Lithium-Ion Batteries Using Temporal Convolutional Network Optimized by Improved Self-Adaptive Honey Badger Algorithm
摘要
Abstract
[Objectives]Lithium-ion batteries,as an important power source for new energy vehicles,require accurate state of health(SOH)estimation to design safe and reliable battery management systems.Traditional methods often overlook issues such as capacity recovery and insufficient feature effectiveness,which significantly affects estimation accuracy.To address these issues,a novel SOH estimation method for lithium-ion batteries that considers battery capacity recovery is proposed.[Methods]By combining the median absolute deviation with the Savitzky-Golay filter in the data preprocessing stage,the model effectively removes outliers and noise to improve the effectiveness of the features.Subsequently,feature decomposition is performed to remove redundant information and alleviate the computational load of the model.Highly correlated features are then selected as inputs for the temporal convolutional network model,reducing data dimensionality and simplifying the computational complexity of the neural network.Furthermore,an improved self-adaptive honey badger algorithm is proposed to optimize the hyperparameters of the network,accelerating model convergence and enhancing network performance.[Results]The proposed method has a high level of accuracy,with both the root mean squared error and the mean absolute error being lower than 0.007.[Conclusions]The proposed method exhibits high robustness,enabling effective SOH estimation of on-board lithium-ion batteries and meeting requirements of practical application.关键词
储能/新能源/电动汽车/锂离子电池/健康状态估计/改进蜜獾算法/时间卷积网络/数据驱动/荷电状态Key words
energy storage/new energy/electric vehicles/lithium-ion battery/state of health estimation/improved honey badger algorithm/temporal convolutional network/data-driven/state of charge分类
能源科技引用本文复制引用
张效伟,衣振晓,王凯..基于改进自适应蜜獾算法优化时间卷积网络的车载锂离子电池健康状态估计[J].发电技术,2025,46(6):1154-1163,10.基金项目
国家自然科学基金项目(52037005).Project Supported by National Natural Science Foundation of China(52037005). （52037005）
