SOC Estimation of Lithium-ion Battery based on Weight Selection Particle Filter Algorithm

Aiming at the estimation of the state of charge (SOC) of lithium-ion power batteries, this paper took ternary lithium (MNC) batteries as the research object, selected Thevenin equivalent circuit model to establish the state equation and observation equation of the battery model and completed the theoretical derivation of recursive least squares method (FFRLS). Hybrid pulse power characteristic test (HPPC test) on battery cells was performed, online parameter identification of battery model was achieved by using test data and FFRLS algorithm, and the feasibility of the algorithm was verified by the battery terminal voltage. On this basis, a weighted selection particle filter (WSPF) algorithm was proposed to realize the SOC estimation of lithium-ion batteries. All particles in the algorithm participate in the particle filter process, but only the particles of which weight are better are used for battery state estimation, thereby solving the problem of particle degradation of particle filtering and improving the diversity of particles. Through HPPC test and dynamic working condition test (DST) result verification, the estimation accuracy of WSPF algorithm can be controlled within 2%. Compared with that of the resampling particle filter (SIR-PF) algorithm, the estimation accuracy of the WSPF algorithm is high and the robustness is good.