Extended Kalman Filter based battery state of charge(SOC) estimation for electric vehicles

This paper proposed a battery state of charge (SOC) estimation methodology utilizing the Extended Kalman Filter. First, Extended Kalman Filter for Li-ion battery SOC was mathematically designed. Next, simulation models were developed in MATLAB/Simulink, which indicated that the battery SOC estimation with Extended Kalman filter is much more accurate than that from Coulomb Counting method. This is coincident with the mathematical analysis. At the end, a test bench with Lithium-Ion batteries was set up to experimentally verify the theoretical analysis and simulation. Experimental results showed that the average SOC estimation error using Extended Kalman Filter is <;1%.

I. INTRODUCTION

A great factor in determining the stability of Li-ion battery packs lies within the state-of-charge (SOC) estimation. Failing to predict battery SOC will cause overcharge or over discharge during cycles, which potentially will bring irreversible permanent damage to the battery cell. The most accurate method to measure the SOC is the direct relation of OCV, however this can only be accurately measured offline when the battery has no current transfer. Presently the most commonly applied online SOC estimation is the coulomb counting (CC) method [2] [6], which measures the battery current, integrates it with represent to time, and determines an estimated battery SOC. When the measured battery current has some error, the long-run integration will bring unpredictable deviation of SOC, which will lead to the overcharge or undercharge of the battery.