Comparison of Three Real-Time Implementable Energy Management Strategies for Multi-mode Electrified Powertrain

Three real-time implementable energy management system (EMS) strategies have been articulated for forward simulation vehicle model with an electrified powertrain. Rulebased strategy and equivalent consumption minimization strategy (ECMS) have been profoundly used as a competent real-time implementable EMS strategy for electrified powertrain. Reinforcement learning (RL) is relatively new as a real-time EMS controller. All these three controllers have been articulated for model-in-the-loop (MIL) simulation. A comparison among state-of-the art RL-based controller, widely accredited ECMS, and rule-based control strategies is very crucial in order to analyze strengths and weaknesses of each of these strategies at the MIL and to make them apposite for the subsequent phases of utilitarian controller development.