State-of-charge estimation of lithium-ion batteries using a tree seed and genetic algorithm-optimized generalized mixture minimum error entropy-based square root cubature Kalman filter

TL;DR

This paper introduces a novel robust state-of-charge estimation method for lithium-ion batteries using a generalized mixture minimum error entropy-based square-root cubature Kalman filter optimized with a hybrid tree seed and genetic algorithm, enhancing accuracy and stability.

Contribution

It proposes a GMMEE-SRCKF algorithm with improved robustness and numerical stability, and a TSGA method for automatic kernel parameter optimization, addressing limitations of previous MEE-based filters.

Findings

Achieves RMSE less than 0.5% in experiments.

Outperforms existing robust filters in accuracy.

Demonstrates improved numerical stability and adaptability.

Abstract

The cubature Kalman filter based on minimum error entropy (MEE-CKF) offers accurate and robust performance in state of charge (SOC) estimation. However, due to the inflexibility of the minimum error entropy (MEE), this algorithm demonstrates limited robustness when confronted with more complex noise environments. To address these limitations, this paper proposes a generalized mixture minimum error entropy-based (GMMEE) square-root cubature Kalman filter (GMMEE-SRCKF). The square-root algorithm ensures improved numerical stability and avoids covariance degeneration, while the GMMEE criterion with two flexible kernels adapts effectively to non-Gaussian noise. Moreover, a hybrid tree seed and genetic algorithm (TSGA) is introduced to optimize the kernel parameters automatically. Experimental results confirm that the TSGA-optimized GMMEE-SRCKF outperforms existing robust filters, achieving the root mean square error (RMSE) of less than 0.5%.