集成技术2025，Vol.14Issue(4)：21-30,10.DOI:10.12146/j.issn.2095-3135.20241207001
基于冠豪猪优化器-改进双向时间卷积网络-长短期记忆网络和注意力机制的动力锂电池健康状态预测
Prediction of State of Health of Power Lithium Battery Based on Crested Porcupine Optimizer-Improved Bidirectional Temporal Convolutional Network-Long Short-Term Memory and Attention Mechanism
摘要
Abstract
In order to better monitor the health status of lithium power batteries,this paper proposes a method for predicting the health status of lithium batteries based on an improved bidirectional temporal convolutional network,a long short-term memory network,and an attentional mechanism,and uses the crested porcupine optimizer to find the optimal hyperparameters of the method.The proposed method is tested on the University of Maryland lithium battery charge/discharge dataset,and the capacity-related health features are extracted,and the health features with high correlation are screened by the Pearson correlation coefficient and used as inputs to the method.The experimental results show that the method can achieve high accuracy in the health state prediction of lithium batteries.Specifically,the root-mean-square error of the method in the health state prediction of 4 batteries does not exceed 0.020,the average absolute error does not exceed 0.017,and the coefficient of determination is greater than 0.995.关键词
健康状态/长短期记忆网络/动力锂电池/双向时间卷积网络/冠豪猪优化器Key words
state of health/long short-term memory network/power lithium battery/bidirectional temporal convolutional network/crested porcupine optimizer分类
信息技术与安全科学引用本文复制引用
邢泽铭,龚家元,陈鸿洋,陈安庆..基于冠豪猪优化器-改进双向时间卷积网络-长短期记忆网络和注意力机制的动力锂电池健康状态预测[J].集成技术,2025,14(4):21-30,10.基金项目
湖北省自然科学基金计划(十堰创新发展联合基金)培育项目(2024AFD116) （十堰创新发展联合基金）
湖北省教育厅科学技术研究计划重点项目(D20231805) （D20231805）
湖北汽车工业学院博士科研启动基金项目(BK202307,BK201604) （BK202307,BK201604）
湖北省自然科学基金项目(2023AFB481) This work is supported by Hubei Provincial Natural Science Foundation Shiyan Innovation and Development Joint Fund Project(2024AFD116),Key Project of Science and Technology Research Plan of Hubei Provincial Department of Education(D20231805),Doctoral Research Startup Fund Project of Hubei University of Automotive Technology(BK202307,BK201604)and Natural Science Foundation of Hubei Province(2023AFB481) （2023AFB481）
