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.

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.

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 accelerated adoption of electric vehicles (EVs) has intensified technical demands on traction inverters, particularly regarding power density, energy efficiency, and thermal management. Silicon Carbide (SiC) MOSFET power modules, with their superior switching performance and thermal characteristics, offer significant advantages but introduce new thermal challenges due to their increased power density and compact packaging. The primary objective of this study is to enhance real-time thermal control and prediction in a SiC-based dual traction inverter through the design and experimental validation of a high-performance cooling system integrated into a modular electric drive platform. To achieve this, multiple cold plate configurations were designed and subsequently evaluated using computational fluid dynamics (CFD) simulations, focusing on junction temperature, thermal uniformity, and pressure drop. To enable real-time thermal predictions, an artificial neural network (ANN) was developed and trained on datasets generated from high-fidelity CFD simulations and a CFD-derived reduced-order model (ROM) to estimate junction temperatures dynamically. Integrated into the inverter control system and supported by real-time sensor feedback, the ANN achieved prediction errors below 3.5% within 1 second across all operating conditions. The proposed framework offers a scalable and intelligent thermal management solution, supporting predictive maintenance and enhancing the reliability and efficiency of next-generation EV powertrains.