This paper provides a comprehensive overview of the state-of-the-art related to the implementation of battery electric buses (BEBs) in cities. In recent years, bus operators have started focusing on the electrification of their fleet to reduce the air pollutants in cities, which has led to a growing interest from the scientific community. This paper presents an analysis of the BEB powertrain topology and the charging technology of BEBs, with a particular emphasis on the power electronics systems. Moreover, the different key technical requirements to facilitate the operation of BEBs are addressed. Accordingly, an in-depth review on vehicle scheduling, charger location optimization and charging management strategies is carried out. The main findings concerning these research fields are summarized and discussed. Furthermore, potential challenges and required further developments are determined. Based on this analysis, it can be concluded that an accurate energy consumption assessment of their BEBs is a must for bus operators, that real-time, multi-objective smart charging management strategies with V2X features should be included when performing large bus fleet scheduling and that synchronized opportunity charging, smart green depot charging, and electric bus rapid transit can further reduce the impact on the grid. This review paper should help to enable a smarter and more efficient integration of BEBs in cities in the future.

This paper proposes a co-design optimization procedure of a high-power off-board charger for electric vehicle (EV) applications. The primary purpose is to design a 175 kW SiC DC-charging system with high power density to achieve high efficiency at a wide operating range. For the active part of the DC offboard charger, a three-phase active front end (AFE) rectifier topology is considered in the design optimization and the modelling. The design methodology focuses on the optimal design of the passive filters, accurate electro-thermal modelling of the converter, inductor design, capacitor selection, loss and geometric modelling of the passive filters and control system design. The design optimization of the high-power charging system is performed in MATLAB Simulink using a closed-loop dynamic electro-thermal simulation of the off-board charger. The switching frequency, loss and temperature-dependent efficiency of the charger is investigated in parallel. Through this proposed technique, efficiency greater than 96% is achieved at a switching frequency of 40 kHz, along with a smaller size and lower weight of the system. Moreover, it operates with a current total harmonic distortion (THDi) below 3% and a power factor (PF) above 99% at rated power condition.INDEX TERMS co-design, thermal modelling, AFE rectifier, wide bandgap, SiC, passive filter, electric vehicles, fast DC charger

Vehicle-to-Grid (V2G) technology is viewed as a viable solution to offer auxiliary power system services. Currently, V2G operation is only possible through DC chargers using the CHAdeMO connector with the necessary communication protocol. However, in Europe, for high-power DC charging (>50 kW), the Combined Charging Service (CCS) Type 2 is preferred over CHAdeMO. Therefore, this work presents the development of a V2G testing system with a Combo CCSType 2 charger including communication via the ISO 15118-2 protocol. The BOSCH passenger car with a 400 V battery pack is used to test and validate the technical feasibility of V2G charging via a Combo CCS Type 2 connector standard. The V2G feature is characterized in terms of efficiency, signal delay, response proportionality, magnitude accuracy and noise precision. A data driven V2G charger simulation model based on the real-time data is also developed in MATLAB/Simulink. The performance under various operating settings is presented in the outcomes, emphasizing the need for appropriate hardware calibration, and understanding while delivering standard-compliant grid control services using V2G technology. Finally, the results of the simulation model are compared with the real hardware results in terms of error, noise level and data magnitude accuracy.