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 off-board 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.

Active Front-End (AFE) rectifiers have regained momentum as the demand for highpower Electric Vehicle (EV) charging infrastructure increases exponentially. AFE rectifiers have high efficiency and reliability, and they minimize the disturbances that could be generated due to the operation of the EV charging systems by reducing harmonic distortion and operating close to the Unity Power Factor (UPF). The purpose of this review is to present the current state-of-the-art AFE rectifiers used in fast chargers, focusing on the comparison between different AFE topologies and their components, as well as modular AFE solutions. Furthermore, different control strategies of AFE converters are presented and compared. Some of their more widely used control techniques, namely Voltage Oriented Control (VOC), Direct Power Control (DPC), Hysteresis Current Control (HCC), and Model Predictive Control (MPC), have been implemented, and their performance compared. Centralized and distributed control systems are compared for operating parallel AFE rectifiers for modular, fast charging systems. An overview of cooling systems and reliability evaluation tools is also presented. Finally, trends and future outlooks are analyzed.

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.

Nowadays, the implementation of smart charging concepts and management strategies with vehicle-to-everything (V2X) functionalities, is required to address the increasing number of battery electric buses (BEBs) in cities. However, the introduction of these new functionalities to the charging systems might affect the lifetime of the charging infrastructure. This has not been investigated yet, although it is an important aspect for the BEB operators. Therefore, this paper performs a detailed reliability assessment to study the impact of smart and bidirectional (V2X) charging on the lifetime of SiC-based high-power off-board charging infrastructure used for BEBs in a depot for overnight charging. In this paper, four different charging current profiles, generated by a smart charging algorithm, are considered. In addition, an electro-thermal model of the charging system is developed to accurately estimate the junction temperature of the switching devices when subjected to the applied charging current profiles. The thermal stress is converted into a number of cycles to failures and accumulated damage by means of a rainflow cycle counting algorithm, a lifetime model and Miner’s damage rule. Finally, a Monte Carlo analysis and a Weibull probability function fit are applied to obtain the system reliability. The results have demonstrated that smart charging strategies can improve the lifetime of the charging system by at least a factor of three compared to conventional uncoordinated charging. Moreover, an uncoordinated charging strategy fails to fulfill the lifetime requirements in the parts per million range, while bidirectional charging could even further enhance the lifetime with a factor of one and a half.

This work aims to tune multiple controllers at the same time for a HVDC system by using a self-generated (SG) simulation-based optimization technique. Online optimization is a powerful tool to improve performance of the system. Proportion integral (PI) controllers of Multi-infeed HVDC systems are optimized by the evaluation of objective functions in time simulation design (TSD). Model based simulation setup is applied for rapid selection of optimal PI control parameters, designed in PSCAD software. A multiple objective function (OF), i.e. Integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), integral time square error (ITSE), and integral square time error (ISTE), is assembled for testing the compatibility of OFs with nonlinear self-generated simplex algorithm (SS-SA). Improved control parameters are achieved after multiple iterations. All OFs generate optimum responses and their results are compared with each other by their minimized numerical values. Disturbance rejection criteria are also proposed to assess the designed controller performance along with robustness of system. Results are displayed in form of graphs and tables in this paper.