Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model

• URL: http://arxiv.org/abs/2504.15578v1
• Date: Tue, 22 Apr 2025 04:25:50 GMT
• Title: Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model
• Authors: Ian Mikesell, Samuel Filgueira da Silva, Mehmet Fatih Ozkan, Faissal El Idrissi, Prashanth Ramesh, Marcello Canova,
• Abstract summary: This paper describes a Reinforcement Learning based approach that dynamically tailors the current profile applied to a LiB cell to optimize the parameters identifiability of the electrochemical model.<n>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.
• Score: 1.4843690728082002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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