{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T11:13:26Z","timestamp":1773054806470,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The state of health (SOH) estimation of lithium-ion batteries faces significant challenges under complex operating conditions due to transient disturbances and distribution shifts. This paper proposes a deep learning framework named Conformer-KAN, which integrates a convolution-augmented Transformer (Conformer) with a Kolmogorov\u2013Arnold Network (KAN). The method first constructs a unified input representation by fusing multi-view features including voltage, current, temperature, and incremental capacity. It then employs a Conformer encoder that combines gated local convolution units (GLCU) and multi-head self-attention (MHSA) to achieve joint modeling of local and global features. In addition, learnable spline-based activation functions are introduced within the KAN structure to enhance the model\u2019s capacity for capturing complex nonlinear degradation behaviors. Cross-battery and cross-condition evaluations conducted on two public datasets demonstrate that the proposed method achieves root mean square errors (RMSE) of 0.006 \u00b1 0.001 and 0.003 \u00b1 0.001, and coefficients of determination (R2) of 0.987 \u00b1 0.003 and 0.994 \u00b1 0.002, respectively. These results show that Conformer-KAN significantly outperforms existing mainstream approaches in both robustness and generalization performance.<\/jats:p>","DOI":"10.3390\/a19030203","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:58:45Z","timestamp":1773046725000},"page":"203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["State of Health Estimation for Lithium-Ion Batteries Based on Conformer-KAN"],"prefix":"10.3390","volume":"19","author":[{"given":"Yuchen","family":"Wang","sequence":"first","affiliation":[{"name":"College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, K., Luo, B., Chen, D., Wang, C., Chen, L., Li, F., Cao, Y., and Wang, C. 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