Environmental issues have become a global concern recently. Countries worldwide are making efforts for carbon neutrality. In the automotive industry, focus has shifted from internal combustion engine vehicle to eco-friendly vehicles such as Electric Vehicles (EVs), Hybrid Electric Vehicles (HEVs), and Fuel Cell Electric Vehicles (FCEVs). For driving strategy, research on vehicle driving method that can reduce vehicle energy consumption, called eco-driving, has been actively conducted recently. Conventional cruise mode driving control is not considered an optimal driving strategy for various driving environments. To maximize energy efficiency, this paper conducted research on eco-driving strategy for EVs-based on reinforcement learning. A longitudinal dynamics-based electric vehicle simulator was constructed using MATLAB Simulink with a road slope. Reinforcement learning algorithms, specifically Deep Deterministic Policy Gradient (DDPG) and Deep QNetwork (DQN), were applied to minimize energy consumption of EVs with a road slope. The simulator was trained to maximize rewards and derive an optimal speed profile. In this study, we compared learning results of DDPG and DQN algorithms and confirmed tendencies by parameters in each algorithm. The simulation showed that energy efficiency of EVs was improved compared to that of cruise mode driving.

Durability evaluations were conducted using polymer electrolyte membrane fuel cells in a marine environment. Deionised water and 3.5 wt% of NaCl solution were supplied to the cathode using an ultrasonic vibrator. Performance and electrochemical impedance spectroscopy of fuel cells were measured to evaluate the electrochemical behaviors. Additionally, long-term stability evaluations of PEMFCs were carried out at 0.65 V for 20 h. Following the experiments, scanning electron microscope analysis was conducted to confirm the presence of NaCl on membrane electrode assembly and micro porous layer of fuel cells.