A neural network constrained by an Arrhenius SEI/LLI degradation law, integrated forward cycle-by-cycle, gives lower SOH prediction error and more monotone extrapolation than MLP and PINN baselines on a 55-cell public dataset.
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PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction
A neural network constrained by an Arrhenius SEI/LLI degradation law, integrated forward cycle-by-cycle, gives lower SOH prediction error and more monotone extrapolation than MLP and PINN baselines on a 55-cell public dataset.