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

arXiv: https://arxiv.org/abs/2504.15578 · HTML: https://mlsanity.com/paper/2504.15578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization](https://mlsanity.com/paper/2405.10750/) (2024)
- [Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics](https://mlsanity.com/paper/2411.12935/) (2024)
- [Optimal design of experiments for a lithium-ion cell: parameters identification of an isothermal single particle model with electrolyte dynamics](https://mlsanity.com/paper/1811.08656/) (2018)

## Built on by (most cited first, 1 of 1)

- [Augmented Physics-Based Li-ion Battery Model via Adaptive Ensemble Sparse Learning and Conformal Prediction](https://mlsanity.com/paper/2507.00353/) (2025)

## Abstract (arXiv)

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.