System Identification of Lithium-Ion Battery Equivalent Circuit Models Using Ensemble Kalman Inversion

TL;DR

This paper presents an ensemble Kalman inversion method for accurate and stable system identification of nonlinear lithium-ion battery models with electro-thermal dynamics, validated through simulations and experiments.

Contribution

It introduces a novel application of ensemble Kalman inversion for lithium-ion battery parameter estimation, handling nonlinearities and multi-physics coupling effectively.

Findings

Achieves rapid convergence in parameter estimation.

Demonstrates high accuracy in simulation and experimental validation.

Shows potential for broader application to other battery models.

Abstract

System identification remains an intriguing challenge for lithium-ion batteries, as many models are nonlinear, exhibit multi-physics coupling, and involve a large number of parameters. In this paper, we address this challenge using the ensemble Kalman inversion (EnKI) method for battery system identification. EnKI performs maximum a posteriori parameter estimation through successive local Gaussian approximations, enabling an iterative and incremental search for unknown parameters. The search combines Monte Carlo sampling with Kalman-type updates to evolve an ensemble of samples, thereby offering empirical stability and the ability to handle strongly nonlinear models. We validate the proposed approach on two equivalent circuit models with coupled electro-thermal dynamics, through both simulation and experiments. The results demonstrate that the proposed approach achieves accurate parameter estimation with rapid iterative convergence, and it shows strong potential for application to other battery models.