# Parameter Identification for Electrochemical Models of Lithium-Ion Batteries Using Bayesian Optimization

arXiv: https://arxiv.org/abs/2405.10750 · HTML: https://mlsanity.com/paper/2405.10750/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 5 of 5)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Unexpected Improvements to Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2310.20708/) (2023)
- [Bayesian Optimization with Adaptive Kernels for Robot Control](https://mlsanity.com/paper/2402.07021/) (2024)
- [Improvement of PSO algorithm by memory based gradient search - application in inventory management](https://mlsanity.com/paper/1410.5652/) (2014)

## Built on by (most cited first, 3 of 3)

- [Improving Low-Fidelity Models of Li-ion Batteries via Hybrid Sparse Identification of Nonlinear Dynamics](https://mlsanity.com/paper/2411.12935/) (2024)
- [Real-Time Optimal Design of Experiment for Parameter Identification of Li-Ion Cell Electrochemical Model](https://mlsanity.com/paper/2504.15578/) (2025)
- [Optimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning](https://mlsanity.com/paper/2506.19146/) (2025)

## Abstract (arXiv)

Efficient parameter identification of electrochemical models is crucial for accurate monitoring and control of lithium-ion cells. This process becomes challenging when applied to complex models that rely on a considerable number of interdependent parameters that affect the output response. Gradient-based and metaheuristic optimization techniques, although previously employed for this task, are limited by their lack of robustness, high computational costs, and susceptibility to local minima. In this study, Bayesian Optimization is used for tuning the dynamic parameters of an electrochemical equivalent circuit battery model (E-ECM) for a nickel-manganese-cobalt (NMC)-graphite cell. The performance of the Bayesian Optimization is compared with baseline methods based on gradient-based and metaheuristic approaches. The robustness of the parameter optimization method is tested by performing verification using an experimental drive cycle. The results indicate that Bayesian Optimization outperforms Gradient Descent and PSO optimization techniques, achieving reductions on average testing loss by 28.8% and 5.8%, respectively. Moreover, Bayesian optimization significantly reduces the variance in testing loss by 95.8% and 72.7%, respectively.