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The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network\u2019s output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application.<\/jats:p>","DOI":"10.1007\/s40747-024-01458-4","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T02:01:55Z","timestamp":1715220115000},"page":"5489-5511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A knowledge distillation based cross-modal learning framework for the lithium-ion battery state of health estimation"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4009-0632","authenticated-orcid":false,"given":"Wei","family":"Xie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4343-5560","authenticated-orcid":false,"given":"Yuyu","family":"Zeng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,9]]},"reference":[{"key":"1458_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.118348","volume":"308","author":"S Khaleghi","year":"2022","unstructured":"Khaleghi S, Hosen MS, Karimi D, Behi H, Beheshti SH, Van Mierlo J, Berecibar M (2022) Developing an online data-driven approach for prognostics and health management of lithium-ion batteries. 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