<ul><li>This paper presents a novel physical parameter estimation framework for on-site model characterization, using a two-phase modelling strategy with Physics-Informed Neural Networks (PINNs) and transfer learning (TL).</li><li>In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations.</li><li>In the second phase, the majority of the PINN parameters are frozen, while critical electrochemical parameters are set as trainable and adjusted using real-world voltage profile data.</li><li>The proposed approach significantly reduces computational costs, making it suitable for real-time implementation on Battery Management Systems (BMS).</li></ul>

On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach

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