On-line energy management for HEV based on Particle Swarm Optimization

This study considers a Hybrid Electrical Vehicle supplied by a Fuel Cell stack and supercapacitances used as Storage Element. In such an application, real time energy management is of paramount importance in order to increase autonomy and be able to deal on-line with perturbed power demand. Many off-line power flow optimization principles are available but on-line algorithms are preferred and should be derived for optimal management of the instantaneous power splitting between the different available power sources. Based on particle swarm optimization algorithm, this study defines the parameters tuning of such algorithm in real time. The final power splitting allows not only recovering energy braking but also is robust to some disturbances occurring during the trip. The solution provides good-quality and high-robustness results in a certain class of mission profile and power disturbance.