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To improve the air quality in urban areas, diesel buses are getting replaced by battery electric buses (BEBs). This conversion introduces several challenges, such as the proper control of the charging process and a reduction in the operational costs, which can be addressed by introducing smart charging concepts for BEB fleets. Therefore, this paper proposes a real-time scheduling and optimization (RTSO) algorithm for the charging of multiple BEBs in a depot. The algorithm assigns a variable charging current to the different time slots the charging process of each BEB is divided to provide an optimal charging schedule that minimizes the charging cost, while satisfying the power limitations of the distribution network and maintaining the operation schedule of the BEBs. A genetic algorithm is used to solve the formulated cost function in real time. Several charging scenarios are tested in simulation, which show that a reduction in the charging cost up to 10% can be obtained under a dynamic electricity price scheme. Furthermore, the RTSO is implemented in a high-level charging management system, a new feature required to enable smart charging in practice, to test the developed algorithm with existing charging infrastructure. The experimental validation of the RTSO algorithm has proven the proper operation of the entire system.

The recent breakthrough of battery electric buses (BEBs) in cities is leading to an increased load demand on the grid. Hence, smart and efficient charging infrastructure is required. Emerging wide bandgap (WBG) semiconductors, such as SiC and GaN, will enable future charging systems to operate at higher switching frequencies and therefore increase the efficiency of the charging process, while smart control algorithms will introduce new features like grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations to optimize the charging behaviour. Therefore, this paper proposes the modelling of a SiC technology-based off-board charging system for BEBs and the design of its bi-level controller. The low-level control system is used to ensure the battery’s charging or discharging modes and to achieve the best total harmonic distortion (THD) and unity power factor (PF). Therefore, a linear and nonlinear simulation model are developed in MATLAB/Simulink to validate the PQ control theory-based performances. Additionally, a high-level charging management strategy (HL-CMS), based on a genetic algorithm (GA), provides an optimal charging (G2V) and discharging (V2G) scheduling for the entire charging/discharging process, considering the limits and requirements of the grid and bus operators. The results demonstrate that both controllers operate properly under different circumstances.

In the upcoming years, the implementation of smart charging strategies will be required to deal with the increasing number of electric vehicles (EVs). This paper investigates the impact of smart charging on the reliability and the lifetime of the charging infrastructure. An electro-thermal model of a high-power off-board charger was developed to estimate the thermal stress response of the switches on the applied smart charging current profile. The resulting junction temperature profiles were used to estimate the lifetime of the charger. The results showed that due to smart charging, the lifetime of the high-power off-board charger improved by a factor of 2.4 compared to a conventional uncoordinated charging strategy.

This paper describes smart power management and charging scheduling strategy for a multiple port electric vehicle (EV) charging station, connected to battery storage systems and renewable energy sources. The charging station can charge different types of EVs, like electric scooters and passenger cars at different power levels. The energy management optimizes the usage of the power sources to fulfill the charging demand by analyzing the overall load demand, the electricity tariff, and the information provided by the EV user. The station's control system is set up to satisfy the charging demand primarily with a solar photovoltaic (PV) array and an energy storage system (ESS) as a battery. In the case of PV power generation and battery power shortage, it draws power from the grid to fulfill the charging demand. Additionally, a charging scheduling strategy is described in this paper in which the charging current setpoint for each charger is estimated by an optimization process in the local charger controller. The overall system management and charging scheduling are simulated and verified in the MATLAB/Simulink environment.