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State of health estimation of lithium-ion batteries based on Mixers-bidirectional temporal convolutional neural network

文献类型：期刊论文

作者Gao, Jingyi6; Yang, Dongfang5; Wang, Shi4; Li, Zhaoting3; Wang, Licheng2; Wang, Kai1,6
刊名JOURNAL OF ENERGY STORAGE
出版日期2023-12-20
卷号73页码:15
关键词Lithium-ion battery State of health estimation Bidirectional temporal convolutional neural network Mixers Transfer learning
ISSN号2352-152X
DOI10.1016/j.est.2023.109248
英文摘要Accurate state of health (SOH) estimation is essential for designing a safe and reliable battery management systems (BMS). Although data-driven methods have achieved great accuracy and satisfied robustness in SOH estimation, for most neural networks, it is a challenge for SOH estimation to learn long dependencies in the training process due to the lack of the scalability for modeling long sequences. In this study, a novel SOH estimation framework combing Mixers and bidirectional temporal convolutional neural network (BTCN) is proposed, which not only takes the greatest advantage of local and global properties of input features to estimate SOH of lithium-ion batterie (LIBs), but also eases the redundancy of temporal and channel information. In the data pre-processing, the voltage change in the equal time interval is extracted from the measured data of the constant current (CC) charging stage, which is easily obtained in the real-world charging scenario. Then, the features that are highly correlated with SOH are selected by Pearson correlation coefficient (PCC), and all the features are normalized by minimum-maximum scaling method to speed up the convergence process and reduce the initialization requirement of learning-rate. After pre-processing, all features are input into the Mixers-BTCN model. We carry out experiments on aging data from two public datasets, NASA and CALCE. The simulations results indicate that the R2 for each dataset is above 0.768. The mean absolute error (MAE) and root mean square error (RMSE) that are both held within 2.34 %, which proves the accuracy and stability of the proposed SOH estimation. In addition, the introduction of transfer learning technology verifies the robustness of the proposed model to different ambient temperatures.
资助项目Guangdong Provincial Key Lab of Green Chemical Product Technology[GC202111] ; Zhejiang Province Natural Science Foundation[LY22E070007] ; National Natural Science Foundation of China[52007170]
WOS研究方向Energy & Fuels
语种英语
WOS记录号WOS:001097429700001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/38113]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yang, Dongfang; Wang, Kai
作者单位1.Shandong Suoxiang Intelligent Technol Co Ltd, Weifang 261101, Peoples R China
2.Zhejiang Univ Technol, Sch Informat Engn, Hangzhou 310014, Hangzhou, Peoples R China
3.Brown Univ, Sch Engn, Providence, RI 02912 USA
4.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing, Peoples R China
5.Shaanxi Univ Sci & Technol, Haojing Coll, Xian 712046, Peoples R China
6.Qingdao Univ, Weihai Innovat Res Inst, Sch Elect Engn, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Jingyi,Yang, Dongfang,Wang, Shi,et al. State of health estimation of lithium-ion batteries based on Mixers-bidirectional temporal convolutional neural network[J]. JOURNAL OF ENERGY STORAGE,2023,73:15.
APA Gao, Jingyi,Yang, Dongfang,Wang, Shi,Li, Zhaoting,Wang, Licheng,&Wang, Kai.(2023).State of health estimation of lithium-ion batteries based on Mixers-bidirectional temporal convolutional neural network.JOURNAL OF ENERGY STORAGE,73,15.
MLA Gao, Jingyi,et al."State of health estimation of lithium-ion batteries based on Mixers-bidirectional temporal convolutional neural network".JOURNAL OF ENERGY STORAGE 73(2023):15.

入库方式： OAI收割

来源：计算技术研究所

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