Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning

2025/06/23 by Mehmet Fatih Ozkan, Ozkan, Mehmet Fatih, Samuel Filgueira da Silva +7
Engineering · #Advanced Algorithms and Applications #Advanced Battery Technologies Research #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2506.19146

openalex publication_date 2025/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforcement learning (DRL) with an optimal experimental design (OED) framework to identify key electrochemical parameters of LiB cell models. The proposed method utilizes the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize input excitation, thereby increasing the sensitivity of the system response to electrochemical parameters. The performance of this DRL-based approach is evaluated against a nonlinear model predictive control (NMPC) method and conventional tests. Results indicate that the DRL-based method provides superior information content, reflected in higher Fisher information (FI) values and lower parameter estimation errors compared to the NMPC design and conventional test practices. Additionally, the DRL approach offers a substantial reduction in experimental time and computational resources.

Citations

• Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization
• Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey
• Global Sensitivity Methods for Design of Experiments in Lithium-ion Battery Context
• Addressing Function Approximation Error in Actor-Critic Methods

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