AI RESEARCH
Battery health prognosis using Physics-informed neural network with Quantum Feature mapping
arXiv CS.LG
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ArXi:2604.10362v1 Announce Type: new Accurate battery health prognosis using State of Health (SOH) estimation is essential for the reliability of multi-scale battery energy storage, yet existing methods are limited in generalizability across diverse battery chemistries and operating conditions. The inability of standard neural networks to capture the complex, high-dimensional physics of battery degradation is a major contributor to these limitations. To address this, a physics-informed neural network with the Quantum Feature Mapping(QFM) technique (QPINN) is proposed.