Lithium battery SOC estimation method based on exponentially weighted H∞ filtering algorithm

Abstract

Lithium battery state of charge (SOC) estimation has always been a core issue in power battery management system (BMS). The lithium battery system contains interference noise with unknown statistical characteristics. Although the traditional H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> filtering algorithm (HIF) has strong robustness, it ignores the influence of prior data, leading to increased SOC estimation error. In response to the above problems, this paper proposes an exponential weighted H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> filtering algorithm (EWMA-HIF), which fully considers the current innovation anc selects appropriate weight coefficients, which effectively reduces the SOC estimation error arised form the uncertainty of model accuracy, and ensures the stability of the filter. The simulation results show that, compared with the HIF algorithm, the EWMA-HIF algorithm improves the convergence speed of the algorithm, and has better robustness and higher estimation accuracy.