A Novel Adaptive Particle Swarm Optimization Algorithm Based High Precision Parameter Identification and State Estimation of Lithium-Ion Battery

Lithium-ion batteries are widely used in new energy vehicles, storage systems, aerospace and other fields because of their high density, long cycle life high-cost performance. Accurate equivalent modeling, adaptive internal state characterization accurate charge estimation the cornerstones expanding application market lithium-ion batteries. According to highly nonlinear operating characteristics batteries, Thevenin model is characterize particle swarm optimization algorithm process measured data, strategy added improve global search ability particles, parameters identified innovatively. Combined with extended Kalman Sage-Husa filtering algorithm, state-of-charge lithium ion battery constructed. Aiming at influence fixed inaccurate noise initial value traditional on SOC results, adaptively correct system noise. The experimental results under HPPC condition show that maximum error less than 1.5%. Simulation two different conditions 0.05, which realizes high-precision parameter identification estimation.