Battery health prognosis using Physics-informed neural network with Quantum Feature mapping
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. QPINN projects raw battery sensor data into a high-dimensional Hilbert space, creating a highly expressive feature set that effectively captures subtle, non-li...
| Authors | Muhammad Imran Hossain, Md Fazley Rafy, Sarika Khushalani Solanki et al. |
|---|---|
| Primary category | cs.LG |
| Published | 11 April 2026 |
| Last updated at source | 17 April 2026 |
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