Ultracapacitor Assisted Battery Based HESS for Electric Vehicle Considering Battery Aging

In this paper, the enhanced performance of a Hybrid Energy Storage System (HESS) comprising Ultracapacitor (UC) and Battery for Electric Vehicle (EV) is achieved by effective use of battery aging information in Energy Management Strategy (EMS). The Model Predictive Control (MPC) algorithm is an efficient EMS that ensures the optimum operation of UC during dynamic operating conditions. The algorithm takes control of UC charging/discharging in such a way that it caters to the high frequency power component of the load while the battery manages the low frequency power component. Further, the EMS explores the impact of a driving cycle on battery life by considering the battery aging effect. During a drive cycle battery experiences charging/discharging phenomenon that depends upon charging rate (C-rate). Here the battery aging model is considered that estimates capacity fade by calculating C-rate for each sampled current for the given drive cycle. The performance of EMS considering battery aging is investigated and shows that battery life is extended if actual capacity fade is estimated. The actual capacity fade at the end of the drive cycle is estimated by variable C-rate rather than presumed constant C-rate. The simulation results obtained shows that performance enhancement is achieved by considering the real C-rate instead of assigning a single C-rate for complete drive cycle which is validated by experimental results.