ENHANCING THERMAL PERFORMANCE AND LIFE TIME CYCLES OF LI-ION BATTERY IN ELECTRIC VEHICLE BASED ON HYBRID ENERGY STORAGE SYSTEM

Abstract

Hybrid energy storage system has essential priority in electric vehicle applications. Therefore, the design of an appropriate power sharing algorithm among energy storage components is necessary to improve the battery thermal performance and provide extra extension of battery lifetime cycles. The thesis presents an analytical study on the effect of using wavelet decomposition-based power sharing algorithm to force the high frequency component to be fed by the supercapacitor and accordingly reduces the thermal stress on the battery. Neural Networks pattern recognition tool is also applied to classify the driving cycle to the nearest reference cycles chosen to represent the different driving conditions which help to detect the appropriate wavelet decomposition level, achieving better battery thermal performance and battery lifetime cycles. The proposed approach was investigated by applying it on electric vehicle model in ADVISOR Tool/MATLAB using different driving profiles such as Urban Dynamometer Driving Schedule profile, Highway Fuel Economy Test, New York City Cycle, Los Angeles 1992 and new European driving cycle. The results declare that by using proposed power sharing algorithm, the working temperature of Li-ion battery decreases significantly while the battery lifetime cycles increase. For urban dynamometer driving schedule, the operating temperature of Li-ion battery is improved much at maximum decomposition levels reaching to only 25.6 °C compared to 35 °C when using the battery only. In addition, the battery lifetime cycles increased from 2213 to 2585 cycles.