A novel adaptive H-infinity filtering method for the accurate SOC estimation of lithium-ion batteries based on optimal forgetting factor selection

Accurate estimation of the state of charge (SOC) of lithium-ion batteries is quite crucial to battery safety monitoring and efficient use of energy; to improve the accuracy of lithium-ion battery SOC estimation under complicated working conditions, the research object of this study is the ternary lithium-ion battery; the forgetting factor recursive least square (FFRLS) method optimized by particle swarm optimization (PSO) and adaptive H-infinity filter (HIF) algorithm are adopted to estimate battery SOC. The PSO algorithm is improved with dynamic inertia weight to optimize the forgetting factor to solve the contradiction between FFRLS convergence speed and anti-noise ability. The noise covariance matrixes of the HIF are improved to realize adaptive correction function and improve the accuracy of SOC estimation. To verify the rationality of the joint algorithm, a second-order Thevenin model is established to estimate the SOC under three complex operating conditions. The experimental results show that the absolute value of the maximum estimation error of the improved algorithm under the three working conditions is 0.0192, 0.0131, and 0.0111, respectively, which proves that the improved algorithm has high accuracy and offers a theoretical basis for the safe and efficient operation of the battery management system.