A gaussian particle swarm optimized particle filter estimation method, along with the second-orderresistance-capacitance model, is proposed for the state of charge estimation of lithium-ion battery inelectric vehicles. Based on the particle filter method, it exploits the strong optimality-seeking ability ofthe particle swarm algorithm, suppressing algorithm degradation and particle impoverishment byimproving the importance distribution. This method also introduces normally distributed decay inertiaweights to enhance the global search capability of the particle swarm optimization algorithm, whichimproves the convergence of this estimation method. As can be known from the experimental resultsthat the proposed method has stronger robustness and higher filter efficiency with the estimation errorsteadily maintained within 0.89% in the constant current discharge experiment. This method isinsensitive to the initial amount and distribution of particles, achieving adaptive and stable tracking inthe state of charge for lithium-ion batteries.
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