How to Cite
Abstract
Reinforcement learning is a machine learning (ML) method under debate due to handling the optimal energy management strategy in the connected hybrid electric vehicles (CHEVs). Within the recent few years, a pronounced amount of studies has been presented to explain EMSs based on reinforcement and predictive learning methods. The primary intent of the RL-based methods is to develop an oversight virtual inspector for setting the optimal energy management strategy. These approaches are capable of updating the anticipated energy management strategies with real-time data and models. Various approaches have been introduced in recent years to model the energy consumption and the EV driving agents emulate using reinforcement learning strategies. EVs are a promising transportation solution to reduce pollutants and other greenhouse gases associated with the traditional internal combustion engine vehicles. They incorporate numerous advantages such as controllability, high efficiency, and energy renewal and hence have been an acute focus of attention in the automotive industry.
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