# Special Issue \"Plug-In Hybrid Electric Vehicle (PHEV)\"
## 2 Drive Trains and Energy Management  
The first paper, authored by [PERSON] [4], proposes an energy management strategy for a power-split plug-in hybrid electric vehicle (PHEV) based on reinforcement learning (RL). Firstly, a control-oriented power-split PHEV model was built, and then the RL method was employed based on the Markov decision process (MDP) to find the optimal solution according to the built model. During the strategy search, several different standard driving schedules were chosen, and the transfer probability of the power demand was derived based on the Markov chain. Accordingly, the optimal control strategy was found by the Q-learning (QL) algorithm, which can decide suitable energy allocation between the gasoline engine and the battery pack. Simulation results indicate that the RL-based control strategy could not only lessen fuel consumption under different driving cycles but also limit the maximum discharge power of the battery, compared with the charging depletion/charging sustaining (CD/CS) method and the equivalent consumption minimization strategy (ECMS) [4]. [PERSON] and his co-authors compared different energy management methods in their paper [5]. This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV). Based on detailed analysis of the vehicle driveline, quadratic convex functionsare presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speeds and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA) algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP)-based method and the charging depleting-charging sustaining (CD/CS) method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC) values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save fuel consumption and reduce the computation burden noticeably [5]. [PERSON] describes in his paper the development of DC/DC multiport converters (MPC) [6]. These converters are gaining interest in the field of hybrid electric drivetrains (i.e., vehicles or machines), where multiple sources are combined to enhance their capabilities and performances in terms of efficiency, integrated design, and reliability. This hybridization will lead to more complexity and high development/design time. Therefore, a proper design approach is needed to optimize the design of the MPC as well as its performance and to reduce development time. In this research article, a new design methodology based on a multi-objective genetic algorithm (MOGA) for non-isolated interleaved MPCs is developed to minimize the weight, losses, and input current ripples that have a significant impact on the lifetime of the energy sources. The inductor parameters obtained from the optimization framework are verified by the finite element method (FEM) COMSOL software, which shows that inductor weight of optimized design is lower than that of the conventional design. The comparison of input current ripples and losses distribution between optimized and conventional designs are also analyzed in detail, which validates the perspective of the proposed optimization method, taking into account emerging technologies, such as wide-bandgap semiconductors (SiC, GaN) [6]. The last paper in the domain of drive trains and energy management is from [PERSON] et al. [7]. A drive system with an open-end winding permanent magnet synchronous motor (OW-PMSM) fed by a dual inverter and powered by two independent power sources is suitable for electric vehicles.