A State of Charge Estimation Method Based on Adaptive Extended Kalman-Particle Filtering for Lithium-ion Batteries

Abstract

A state of charge (SOC) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order resistor-capacitor (RC) Equivalent Circuit Model (ECM) is used to identify dynamic parameters of the battery. After testing (include Dynamic Stress test (DST), New European Driving Cycle (NEDC), Federal Urban Dynamic Schedule (FUDS), Urban Dynamometer Driving Schedules (UDDS), etc.) at different temperatures and times, it was found that the AEKPF exhibits greater tolerance for high system noise (10% or higher) and provides more accurate estimations under common operating conditions.