Adaptive Battery State-of-Charge Estimation Method for Electric Vehicle Battery Management System

In this paper, an adaptive battery state-of-charge (SOC) estimation method for electric vehicle (EV) battery management system (BMS) is presented. In these days, many parts of EV have been developed with electrical systems, and it makes a growth of energy storage system named battery. Therefore, to make many type of batteries safer and more reliable, BMS is employed and implemented together in EV. The BMS monitors many kinds of battery states and is responsible to manage its charging and discharging. SOC is a key parameter in judging by BMS, and therefore it is certainly important to estimate the SOC accurately. Many SOC estimation methods have been studied, and extended Kalman-filter (EKF) based methods show the best performance. However, they have high computation complexity. In this paper, adaptively combination of EKF and conventional Coulomb counting method is proposed. Finally, the proposed adaptive method shows within 2% error with 70% decreased complexity compared to EKF.

I. Introduction

As the electric vehicles (EV) have been increased, many automobile manufacturers are investing in the research, development, and promotion of electric systems. One of the most important part in EV is electrical energy storage elements, that is battery actually comprise hundreds of cells linked in series and parallel. As the growth of the battery capacity, many fire accidents occur frequently, which have the reason turned out the fault of battery or according system [1]–[2].