Rese ar ch Hybrid Electric Vehicles (HEVs) Technologies and Reinforcement Look ahead Energy Management

: This paper presents a predictive energy management strategy for a parallel hybrid electric vehicle (HEV) based on velocity prediction and reinforcement learning (RL). The design procedure starts with modeling the parallel HEV as a systematic control-oriented model and defining a cost function. Fuzzy encoding and nearest neighbor approaches are proposed to achieve velocity prediction, and a finite-state Markov chain is exploited to learn transition probabilities of power demand. The significant motivators for shifting to EVs are reducing polluting engine emissions and reducing dependence on costly oil fuels. By the end of 2019, the global stock of EVs crossed the ten million mark. The growing acceptance of EVs is the outcome of several factors: technological advancements, rising storage capacity of traction batteries coupled with their falling cost, increased public charging facilities and Govt. incentives. The two EV technologies currently remain at the top are the battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEV). This paper gives an overview of various EV technologies, their features, limitations and challenges in their bulk deployment as a replacement to conventional vehicles.