Optimal Control Strategy to Maximize the Performance of Hybrid Energy Storage System for Electric Vehicle Considering Topography Information

This research designed an energy management system involving a battery-supercapacitor Hybrid Energy Storage System (HESS) for electric vehicles (EV). The objective is to improve the performance of the HESS by combining battery and supercapacitor features, accounting for topographical information to guarantee continuous hybridization during the drive cycle. Contour Positioning System (CPS) was used to determine the slope of the rode travelled by the vehicle. Two adaptive algorithms were designed for a rule-based controller to control the energy shared between the battery and the supercapacitor; an optimal adaptive controller and fuzzy adaptive controller. The HESS model, electric vehicle and controllers were tested using MATLAB/Simulink with three real drive cycles, namely, uphill, downhill and city tour, in three different speeds 50Km/h, 60Km/h and 70 Km/h. The results proved the controllers managed to extend battery life-cycle by reducing the stress on the battery for the drive cycles. The results were compared in terms of energy consumption for the optimal adaptive rule-based controller and fuzzy adaptive rule-based controller. The optimal adaptive rule-based controller guaranteed the HESS was able to operate continuously and extend the number of drive cycles in a wide range of speeds and road slopes.