Abstract — There have been a number of methods proposed for accurate state of charge (SOC) estimation of a battery as an important role of the battery management System (BMS). However, adequate test data of batteries and BMS development costs must be taken into consideration before application in actual vehicles. In addition, different algorithms need to be adopted according to the type of energy storage device and system configuration. For a mild-HEV hybrid energy storage device that uses the ultracapacitor module and the VRLA battery, the small capacitance of ultracapacitor module makes accurate SOC difficult. Accordingly, this paper introduces a dynamic SOC estimation algorithm capable of SOC compensation of the ultracapacitor module even when there is current input and output. A cycle profile that simulates the operating conditions of a mild-HEV was applied to a vehicle simulator to verify the effectiveness of the proposed algorithm based on testing. I. INTRODUCTION Energy storage devices for Hybrid Electric Vehicles (HEVs) are required to have excellent power density and energy efficiency characteristics and guarantee long life cycle and usage even under frequent high-current charging and discharging conditions. According to these needs, energy storage devices with high power and longevity, such as Ni- MH or Li-Ion, have been proposed and used. In the case of mild-HEVs that only use limited functions of HEVs including idle-stop/start or regenerative braking, there have been a number of studies on using Value-Regulated Lead-Acid (VRLA) batteries that yield relatively high price-performance ratios. However, using a VRLA battery as a stand-alone energy storage device for a mild-HEV has disadvantages in terms of lifetime, power characteristics and weight, calling for improvements in the performance of the VRLA battery, which has its limitations. In turn, recent studies have suggested combining the VRLA battery with the ultracapacitor module capable of high efficiency and high-current charging and discharging to implement an energy storage device with improved efficiency and prolonged lifetime. [1][2][3] In order to effectively use the VRLA battery and the ultracapacitor module as a hybrid energy storage device, accurate State of Charge (SOC) estimation must be performed by the Battery Management System (BMS) that manages and monitors the energy storage device. SOC estimation is one of the most important issues for application products that use energy storage devices. Known methods for SOC estimation include using chemical reaction, voltage, current integration and internal impedance. [4] The chemical method involves estimating SOC by numerically representing the linear reduction of electrolyte’s specific gravity (SG) during battery discharging. SG is the ratio of the weight of a solution to the weight of an equal volume of water at a specified temperature. However, the chemical method can only be used when electrolyte can be directly accessed, such as in the case of lead-acid batteries. Another drawback of the chemical method is that it can yield different results according to the ambient temperature and the amount of electrolyte. The voltage method uses the correlation between SOC and the battery voltage during charging and discharging. Although this method provides accurate SOC based on Open Circuit Voltage (OCV) after a sufficient time for stabilization, different results are produced depending on the ambient temperature, discharging rate (C-rate), usage time, and variation in the characteristics of battery’s internal resistance. Also referred to as “Coulomb counting” or “Ampere-hour counting”, the current method calculates SOC by integrating battery’s input and output current. However, this method requires that an accurate account of initial capacity and current leakage and the sensing error are accumulated over time, gradually increasing the SOC error. The internal impedance method estimates SOC by measuring the variation in internal impedance according to battery’s charging and discharging. Drawbacks of the internal impedance method are that it is difficult to measure battery’s impedance while the cell is reacting and that impedance is sensitive to temperature. Since precise SOC estimation is difficult with aforementioned methods, new approaches such as Fuzzy logic, Kalman filtering and Neural network, are being proposed, but they call for accuracy in battery modeling and there are drawbacks such as significant amounts of test data and increased costs associated with using 32-bit MCUs for fast numerical processing. Therefore, accurate SOC estimation requires research efforts that utilize advantages of each method and do not use relatively large amounts of test data while taking into consideration the cost perspective. In a hybrid energy storage device, the ultracapacitor module is used as the main power source due to its A Study on the dynamic SOC compensation of an Ultracapacitor module for the Hybrid Energy Storage System Hyun-Sik Song 1 , Jin-Beom Jung 2 , Baek-Haeng-Lee 2 , Dong-Hyun Shin 2 , Byoung-Hoon Kim 2 , Yu-Cheol Park 2 , Hoon-Heo 1 , Hee-Jun Kim 3 1 Dept. of Control and Instrumentation Eng., Korea University, Korea 2 Electronic System R&D Center, Korea Automotive Technology Institute, Korea 3 Dept. of Electrical Eng., Hanyang University, Korea, E-mail: hjkim@hanyang.ac.kr 1-4244-2491-7/09/$20.00 ©2009 IEEE