The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …

Abstract Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical
systems for automation tasks. It is important to record the developing trends in DRL's …

The advanced cruise control system has expanded the energy-saving potential of the hybrid
electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only …

Deep reinforcement learning (DRL)-based energy management strategy (EMS) is attractive
for fuel cell vehicle (FCV). Nevertheless, the fuel economy and lifespan durability of proton …

Deep Reinforcement Learning (DRL) has recently been applied to eco-driving to intelligently
reduce fuel consumption and travel time. While previous studies synthesize simulators and …

Hybrid vehicles (HVs) that equip at least two different energy sources have been proven to
be one of effective and promising solutions to mitigate the issues of energy crisis and …

In response to the discrepancy between the mechanical-electric torque distribution rules
used in the shifting calculation of plug-in hybrid electric vehicles and the actual operating …

Eco-driving methods incorporating lateral motion exhibit enhanced energy-saving prospects
in multi-lane traffic contexts, yet the randomly distributed obstructing vehicles and sparse …

Electric vehicles are considered the most effective solution to the petroleum crisis and
reduction of air pollution. In order to enhance energy efficiency and battery lifetime, this …

An advanced eco-driving technology is widely recognized as having enormous potential to
reduce the vehicle fuel consumption. However, most research on eco-driving focuses on the …