Control of electric vehicle hybrid energy storage systems considering aging and temperature effects

Abstract

Batteries are considered as the most important component of an electric vehicle in terms of their costs and weight and the assurance of their longevity is a major concern. One way to protect batteries from high currents, supercapacitor is employed to form a hybrid energy storage system (HESS). In these systems, a control algorithm called power distribution system (PDS) is employed to distribute the power demand between battery and supercapacitor efficiently. There are different energy management strategies reported in the literature in order to optimize these systems to achieve the best battery life and state of heath. There are off-line strategies which implement different driving cycles classified as high and low energy and are offered for different driving scenarios, which rely on predefined rules. Dynamic programing, predictive, fuzzy, stochastic, wavelet decomposition and online ones which use real driving conditions for power management. In this work, an electric bike and HESS are modelled and four different energy management systems are evaluated and compared for their effectiveness in improving battery life and traveled distance at two conditions; 20 degrees Celsius excluding battery and supercapacitor aging effects and 45 degrees Celsius including battery and SC aging effects. The results suggest that the proposed method is effective for improving battery life and vehicle driving range.