Electric Vehicle Battery SOC Estimation based on GNL Model Adaptive Kalman Filter

Abstract

With the efficient development of the electric vehicle, it is urgent to recycle and utilize the decommissioned power battery which increases rapidly in quantity year by year. Accurate and reliable state of charge (SOC) estimation of the battery is the key technology to realize the battery cascade utilization. The traditional estimation methods do not take the self-discharge factors into account which affect the aging battery to a great extent. This research adopts the GNL circuit equivalent model, which considers the self-discharge factor and discretizes its state space equation by matrix quadratic form. The adaptive unscented Kalman filter algorithm (AUKF) is used to estimate and update the SOC in time. The experimental comparison verifies the effectiveness of AUKF for aging batteries. The results show that the proposed method can obtain less error of the state estimated value and fast following features which meets the actual demand of SOC estimation.