Energy management strategy is an important factor in determining the fuel economy of hybrid electric vehicles; thus, much research on how to distribute the required power to engines and motors of hybrid vehicles is required. Recently, various studies have been conducted based on reinforcement learning to optimally control the hybrid electric vehicle. In fact, the fundamental control approach of reinforcement learning shares many control frameworks with the control approach by using deterministic dynamic programming or stochastic dynamic programming. In this study, we compare the reinforcement learning based strategy by using these dynamic programming-based control approaches. For optimal control of hybrid electric vehicle, each control method was compared in terms of fuel efficiency by performing simulation by using various driving cycles. Based on our simulations, we showed the reinforcement learning-based strategy can obtain global optimality in the optimal control problem with an infinite horizon, which can also be obtained by stochastic dynamic programming. We also showed that the reinforcement learning-based strategy can present a solution close to the optimal one using deterministic dynamic programming, while a reinforcement learning-based strategy is more appropriate for a time variant controller with boundary value constraints. In addition, we verified the convergence characteristics of the control strategy based on reinforcement learning, when transfer learning was performed through value initialization using stochastic dynamic programming.

This study reports a polymer electrolyte fuel cell based on polydimethylsiloxane coated with a flexible current-collecting layer of Ag nanowire percolation networks. The reactive area of the bendable fuel cell was 9 cm² and showed the maximum absolute power of 639 mW (the power density was 71 mW cm⁻²) under various bending conditions. Impedance spectra of the operating cell revealed that ohmic and Faradaic resistances decreased under the bent condition. Overall, the degree of bending improves the cell performances. The structural modeling result showed that decrease of the resistance and corresponding performance enhancement were due to the increased compressive force normal to the membrane electrode assembly, which was investigated through finite element simulation of the stress within the bendable fuel cell.

With the development of autonomous vehicles, research on energy-efficient eco-driving is becoming increasingly important. The optimal control problem of determining the speed profile of the vehicle for minimizing energy consumption is a challenging problem that necessitates the consideration of various aspects, such as the vehicle energy consumption, slope of the road, and driving environment, e.g., the traffic and other vehicles on the road. In this study, an approach using reinforcement learning was applied to the eco-driving problem for electric vehicles considering road slopes. A novel model-based reinforcement learning algorithm for eco-driving was developed, which separates the vehicle’s energy consumption approximation model and driving environment model. Thus, the domain knowledge of vehicle dynamics and the powertrain system is utilized for the reinforcement learning process, while model-free characteristics are maintained by updating the approximation model using experience replay. The proposed algorithm was tested via a vehicle simulation and compared with a solution obtained using dynamic programming (DP), and as well as conventional cruise control driving with constant speed. The simulation results indicated that the speed profile optimized using model-based reinforcement learning had similar behavior to the global solution obtained via DP and energy saving performance compared with cruise control.