Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.18042v1</id>\n    <title>PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes</title>\n    <updated>2026-02-20T07:51:59Z</updated>\n    <link href='https://arxiv.org/abs/2602.18042v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.18042v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending operational lifespan. Here, we introduce PINEAPPLE (Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter inference in Lithium-ion battery Electrodes), a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search algorithm to enable rapid, scalable, and interpretable parameter inference with potential for application to next-generation batteries. The meta-learned PINN utilizes fundamental physics principles to achieve accurate zero-shot prediction of electrode behavior with test errors below 0.1$\\%$ while maintaining an order-of-magnitude speed-up over conventional solvers. PINEAPPLE demonstrates robust parameter inference solely from voltage-time discharge curves across multiple batteries from the open-source CALCE repository, recovering the evolution of key internal state parameters such as Li-ion diffusion coefficients across usage cycles. Notably, the inferred cycle-dependent evolution of these parameters exhibit consistent trends across different batteries without any customized degradation physics-embedded heuristic, highlighting the effective regularizing effect and robustness that can be conferred through incorporation of fundamental physics in PINEAPPLE. By enabling computationally efficient, real-time parameter estimation, PINEAPPLE offers a promising route towards the non-destructive, physics-based characterization of inter-cell and intra-cell variability of battery modules and battery packs, thereby unlocking new opportunities for downstream on-the-fly needs in next-generation battery management systems such as individual cell-scale state-of-health diagnostics.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CE'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.NE'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='physics.comp-ph'/>\n    <published>2026-02-20T07:51:59Z</published>\n    <arxiv:primary_category term='cs.CE'/>\n    <arxiv:journal_ref>Journal of Energy Storage, 2026</arxiv:journal_ref>\n    <author>\n      <name>Karkulali Pugalenthi</name>\n    </author>\n    <author>\n      <name>Jian Cheng Wong</name>\n    </author>\n    <author>\n      <name>Qizheng Yang</name>\n    </author>\n    <author>\n      <name>Pao-Hsiung Chiu</name>\n    </author>\n    <author>\n      <name>My Ha Dao</name>\n    </author>\n    <author>\n      <name>Nagarajan Raghavan</name>\n    </author>\n    <author>\n      <name>Chinchun Ooi</name>\n    </author>\n    <arxiv:doi>10.1016/j.est.2026.120944</arxiv:doi>\n    <link href='https://doi.org/10.1016/j.est.2026.120944' rel='related' title='doi'/>\n  </entry>"
}