{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T23:57:02Z","timestamp":1773878222931,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,15]],"date-time":"2022-10-15T00:00:00Z","timestamp":1665792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Science and Technology of Henan Province","award":["212102210008"],"award-info":[{"award-number":["212102210008"]}]},{"name":"Department of Science and Technology of Henan Province","award":["222102240116"],"award-info":[{"award-number":["222102240116"]}]},{"name":"Department of Science and Technology of Henan Province","award":["2020CXZX0046"],"award-info":[{"award-number":["2020CXZX0046"]}]},{"name":"Zhengzhou Science and Technology Bureau","award":["212102210008"],"award-info":[{"award-number":["212102210008"]}]},{"name":"Zhengzhou Science and Technology Bureau","award":["222102240116"],"award-info":[{"award-number":["222102240116"]}]},{"name":"Zhengzhou Science and Technology Bureau","award":["2020CXZX0046"],"award-info":[{"award-number":["2020CXZX0046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Battery state of health (SOH) estimating is essential for the safety and preservation of electric vehicles. The degradation mechanism of batteries under different aging conditions has attracted considerable attention in SOH prediction. In this article, the discharge voltage curve early in the cycle is considered to be strongly characteristic during cell aging. Therefore, the battery aging state can be quantitatively characterized by an incremental capacity analysis (ICA) of the voltage distribution. Due to the interference of vibration noise of the test platform, the discrete wavelet transform (DWT) methods are accustomed to soften the premier incremental capacity curves in different hierarchical decompositions. By analyzing the battery aging mechanism, the peak of the curve and its corresponding voltage are used in the characterization of capacity decay by grey relation analysis (GRA) and to optimize the input of the deep learning model, and finally, the double-layer long short-term memory network (LSTM) model is used to train the data. The results demonstrate that the proposed model can predict the SOH of a single battery cycle using only small batch data and the relative error is less than 2%. Further, by freezing the LSTM layer for transfer learning, it can be used for battery health estimation in different loading modes. The results of training and verification show that this method has high accuracy and reliability in SOH estimation.<\/jats:p>","DOI":"10.3390\/s22207835","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"7835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3507-8209","authenticated-orcid":false,"given":"Lei","family":"Yao","sequence":"first","affiliation":[{"name":"Henan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3329-7819","authenticated-orcid":false,"given":"Jishu","family":"Wen","sequence":"additional","affiliation":[{"name":"Henan Engineering Research Center of New Energy Vehicle Lightweight Design and Manufacturing, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Shiming","family":"Xu","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Jie","family":"Zheng","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"given":"Junjian","family":"Hou","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1254-0891","authenticated-orcid":false,"given":"Zhanpeng","family":"Fang","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-0537","authenticated-orcid":false,"given":"Yanqiu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100097","DOI":"10.1016\/j.etran.2020.100097","article-title":"Battery Thermal Management System Based on the Forced-Air Convection: A Review","volume":"7","author":"Qin","year":"2021","journal-title":"eTransportation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100169","DOI":"10.1016\/j.etran.2022.100169","article-title":"Critical Review of Life Cycle Assessment of Lithium-Ion Batteries for Electric Vehicles: A Lifespan Perspective","volume":"12","author":"Lai","year":"2022","journal-title":"eTransportation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"227275","DOI":"10.1016\/j.jpowsour.2019.227275","article-title":"A Multi-Fault Diagnosis Method Based on Modified Sample Entropy for Lithium-Ion Battery Strings","volume":"446","author":"Shang","year":"2020","journal-title":"J. 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