Accurately identifying the parameters of electrochemical models of li-ion battery (LiB) cells is a critical task for enhancing the fidelity and predictive ability. Traditional parameter identification methods often require extensive data collection experiments and lack adaptability in dynamic environments. This paper describes a Reinforcement Learning (RL) based approach that dynamically tailors the current profile applied to a LiB cell to optimize the parameters identifiability of the electrochemical model. The proposed framework is implemented in real-time using a Hardware-in-the-Loop (HIL) setup, which serves as a reliable testbed for evaluating the RL-based design strategy. The HIL validation confirms that the RL-based experimental design outperforms conventional test protocols used for parameter identification in terms of both reducing the modeling errors on a verification test and minimizing the duration of the experiment used for parameter identification.
@article{arxiv.2504.15578,
title = {Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model},
author = {Ian Mikesell and Samuel Filgueira da Silva and Mehmet Fatih Ozkan and Faissal El Idrissi and Prashanth Ramesh and Marcello Canova},
journal= {arXiv preprint arXiv:2504.15578},
year = {2025}
}