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

TL;DR

This paper introduces a novel deep reinforcement learning framework combined with optimal experimental design to improve the accuracy and efficiency of electrochemical parameter identification in lithium-ion batteries.

Contribution

It proposes a DRL-based method using TD3 algorithm for optimized excitation input, outperforming traditional methods in accuracy and resource efficiency.

Findings

Higher Fisher information compared to traditional methods

Lower parameter estimation errors

Reduced experimental time and computational resources

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.