The foundation of these technologies is based on advancements in communication systems and power electronics converters. These power electronics-based solutions are reviewed in [4] from the point of view of the integration of photovoltaic energy sources and smart grid with charging systems for electric vehicles and plug-in hybrid electric vehicles. Regarding the energy management methods for optimizing the power handling in electric vehicles, some papers presented works whose objective evolved from the initial idea of this Special Issue. Optimized energy management systems will be needed in future microgrids where electric vehicles will to be connected and disconnected at different nodes and times. The work presented in [5] coped with that problem, making use of the concepts of fixed and variable virtual impedances. Fixed virtual impedance is related to the distance from each house to the power inverter and variable virtual impedance is associated with the distance from each electric vehicle to it. The proposed control of the inverter seeks to regulate the voltage where the electric vehicles create variations in the impedances. This novel control is useful for the future microgrids to adapt and share active and reactive power, regardless of the distance where electric vehicles are connected. In spite of the new research lines opened in this Special Issue, there are works that have been adjusted to the objective of this Special Issue (the design of novel energy management systems for hybrid electric vehicles), with interesting results that should be highlighted. In the work of [PERSON] and [PERSON] [6], an Energy Management System was presented with the objective of addressing the inherent problems of traditional fuzzy energy management systems: although they are easy to understand and easy to implement in the control chip, they cannot realize self-learning and have poor self-adaptability. Thus, there are previous studies, prior to [6], that improved the adaptability of fuzzy energy management systems to some extent by optimizing fuzzy control rules and membership functions, but they did not consider the impact of driving cycles. The research in [6] showed that the proposed neural network fuzzy Energy Management Strategy was able to perform adaptive optimization of the fuzzy membership function and control rules under different driving cycles. Consequently, the proposed control had strong robustness and practicability under different driving cycles. energy management systems can also be designed to improve the overall efficiency of new powertrain systems, for example, the dual-input coupling powertrain system for pure electric tractors presented in [7]. In this case, the energy management system operation depended on two crucial elements: a good choice of operating mode (dual-motor or single-motor operation) and, in dual-motor operation, a well-selected power distribution. The optimization of its parameters showed an improvement in the overall efficiency of the system by about 9.8%. Another issue that energy management systems must face is a proper regenerative braking performance. In [8], with the aim of guaranteeing comprehensive regenerative braking performance, a revised regenerative braking control strategy was introduced and a method of the multi-objective optimization algorithm for tuning the parameter of the control strategy was proposed to balance the braking performance, regenerative braking loss efficiency, and battery capacity loss rate. The results of the sensitivity analysis showed that after parameter optimization, the revised regenerative braking control strategy was proved to perform better road adaptability regarding the distribution of solutions. All the proposed energy management systems, independently from the kind of power system they are going to control (hybrid electric vehicles or microgrids), require a proper method to estimate the battery state of charge for their correct operation.