Joint battery state of charge and parameter estimation using Gaussian integral based Kalman filtering

Abstract

Accurate estimation of battery state of charge (SOC) and model parameters is essential for ensuring the safety, reliability, and efficiency of modern battery management systems. Conventional filtering methods such as the extended Kalman filter rely on local approximations that can degrade in accuracy when nonlinearities are significant. This paper proposes a Gaussian integral-based filtering framework for joint SOC and parameter estimation of lithium-ion batteries modeled with an equivalent circuit representation. The key advantage lies in exploiting the polynomial structure of the SOC–OCV (open-circuit voltage) relation, which enables exact evaluation of Gaussian integrals for mean and covariance propagation. The model is validated against experimental data, and performance is assessed under an urban dynamometer driving schedule. The obtained results demonstrate that the proposed filter achieves more accurate SOC estimation and parameter tracking than the other conventional Kalman filter variants, confirming its effectiveness as a practical solution for real-time battery management under dynamic operating conditions.