🤖 AI Summary
To address the urgent need for online, lightweight estimation of electrochemical parameters in battery management systems (BMS), this paper proposes a two-stage physics-informed neural network (PINN) framework. In Stage I, a lightweight neural network embedding the single-particle model (SPM) equations is pre-trained on synthetic simulation data via transfer learning. In Stage II, the network’s backbone is frozen, and only key electrochemical parameters—such as active material volume fraction—are optimized, enabling zero-shot field deployment and real-time edge inference on resource-constrained hardware (e.g., Raspberry Pi). The method eliminates reliance on labeled experimental data while preserving physical interpretability and computational efficiency. Evaluated on aging NMC batteries, it achieves a mere 3.89% relative error in volume fraction estimation and high accuracy in capacity retention prediction, demonstrating superior precision, low computational overhead, and strong generalization across degradation conditions.
📝 Abstract
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). In the first phase, a PINN is trained using only the physical principles of the single particle model (SPM) equations. 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. The proposed approach significantly reduces computational costs, making it suitable for real-time implementation on Battery Management Systems (BMS). Additionally, as the initial phase does not require field data, the model is easy to deploy with minimal setup requirements. With the proposed methodology, we have been able to effectively estimate relevant electrochemical parameters with operating data. This has been proved estimating diffusivities and active material volume fractions with charge data in different degradation conditions. The methodology is experimentally validated in a Raspberry Pi device using data from a standard charge profile with a 3.89% relative accuracy estimating the active material volume fractions of a NMC cell with 82.09% of its nominal capacity.