{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:30:57Z","timestamp":1786354257865,"version":"build-2736575974"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Batteries"],"abstract":"<jats:p>Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and may produce non-physical degradation trends due to the absence of domain knowledge constraints. To address these limitations, this work proposes a monotonic Physics-Informed Residual MLP neural network framework for SOH estimation using the NASA battery dataset (B0005, B0006, B0007, and B0018). The proposed model incorporates a physics-based monotonic degradation constraint by penalizing positive gradients of SOH with respect to cycle index, thereby enforcing physically consistent capacity fade behavior. A loss function is employed to improve robustness and enhance late-cycle learning. Experimental results demonstrate that the proposed approach achieves an RMSE of 0.0287, MAE of 0.0181, and MAPE of 2.69%, indicating accurate and stable SOH prediction across multiple degradation patterns. The use of physics-informed constraints markedly enhances deterioration consistency and diminishes overfitting relative to solely data-driven models. The proposed structure offers a faithful solution for State of Health estimation in practical battery management systems.<\/jats:p>","DOI":"10.3390\/batteries12080294","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T08:08:53Z","timestamp":1786349333000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SOH Estimation of Lithium-Ion Batteries Using a Residual Multilayer Perceptron-Based, Physics-Informed Neural Network for the Battery Management System"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9088-4406","authenticated-orcid":false,"given":"Radhika","family":"G R","sequence":"first","affiliation":[{"name":"Department of Electronics and Instrumentation Engineering, Government College of Technology, Coimbatore 641013, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanthalakshmi","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6265","DOI":"10.22214\/ijraset.2023.53171","article-title":"Sustainable Energy Development: Transitioning towards a Cleaner Future","volume":"11","author":"Arulkumar","year":"2023","journal-title":"Int. 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