Journal article Open Access
Sadaf Rakshan; M S Aspalli
Electric vehicles (EVs) enabled by high efficiency electric motors and controllers and powered by alternative energy sources provide the means for a clean, efficient, and environmentally friendly system. The power demanded by an EV is very variable. Hence HESS (Hybrid energy storage system) as an alternative source have been investigated with the objective of improving the storage of electrical energy. In these systems, two (or more) energy sources work together to create a superior device in comparison with a single source. In batteries and ultra-capacitors have complementary characteristics that make them attractive for a hybrid energy storage system. But the result of this combination is fundamentally related to how the sources are interconnect and controlled. Hybrid Electric Vehicle (HEV) is the most advance technology in automobile industries but long drive range in HEV is still a problem due to limited battery life. For increasing of battery life, two methods are widely used in HEV; one is with fuzzy logic-based battery management strategy and second is through improvement in regenerative braking system. Regenerative braking system used in HEV is to give backup power in deceleration mode which not only make HEV to drive longer but also increase the battery life cycle by charging of ultra-capacitor. The present work is for controlling the source of the motor present in the EV during different driving load conditions and storage of energy by implementing regenerative braking. In the proposed control action, motor speed plays a major role in switch the energy sources in HESS. To attain the objective, another controller has been designed with four math functions corresponding to the speed of the motor termed as Math Function Based (MFB) controller. The MFB controller works based on the motor’s speed and this controller creates the closed loop operation of the overall system with smooth operation between the energy sources. Thereafter the designed MFB controller combined with a Fuzzy Logic controller applied to the entire circuit at different load conditions. In the same way, MFB with Artificial Neural Network controller also applied to the circuit. Finally, comparative analysis has been done between two controllers. The motor has been applied with 6 different types of load and simulated. The MATLAB results of MFB with FLC and MFB with ANN has been attained and compared, discussed.