{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T02:23:29Z","timestamp":1778034209056,"version":"3.51.4"},"reference-count":32,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,5]],"date-time":"2022-11-05T00:00:00Z","timestamp":1667606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Existing data-driven technology for prediction of state of health (SOH) has insufficient feature extraction capability and limited application scope. To deal with this challenge, this paper proposes a battery SOH prediction model based on multi-feature fusion. The model is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). The CNN can learn the cycle features in the battery data, the LSTM can learn the aging features of the battery over time, and regression prediction can be made through the full-connection layer (FC). In addition, for the aging differences caused by different battery operating conditions, this paper introduces transfer learning (TL) to improve the prediction effect. Across cycle data of the same battery under 12 different charging conditions, the fusion model in this paper shows higher prediction accuracy than with either LSTM and CNN in isolation, reducing RMSPE by 0.21% and 0.19%, respectively.<\/jats:p>","DOI":"10.3390\/s22218530","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T03:02:22Z","timestamp":1667790142000},"page":"8530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["State-of-Health Prediction Using Transfer Learning and a Multi-Feature Fusion Model"],"prefix":"10.3390","volume":"22","author":[{"given":"Pengyu","family":"Fu","sequence":"first","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chu","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoran","family":"Hou","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqi","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin University, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1820-8999","authenticated-orcid":false,"given":"Jincheng","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119975","DOI":"10.1016\/j.fuel.2020.119975","article-title":"Effects analysis on soot combustion performance enhancement of an improved catalytic gasoline particulate filter regeneration system with electric heating","volume":"290","author":"Xie","year":"2021","journal-title":"Fuel"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110624","DOI":"10.1016\/j.rser.2020.110624","article-title":"Real-world automotive emissions: Monitoring methodologies, and control measures","volume":"137","author":"Agarwal","year":"2021","journal-title":"Renew. 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