This article reviews the design and evaluation of different DC-DC converter topologies for Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). The design and evaluation of these converter topologies are presented, analyzed and compared in terms of output power, component count, switching frequency, electromagnetic interference (EMI), losses, effectiveness, reliability and cost. This paper also evaluates the architecture, merits and demerits of converter topologies (AC-DC and DC-DC) for Fast Charging Stations (FCHARs). On the basis of this analysis, it has found that the Multidevice Interleaved DC-DC Bidirectional Converter (MDIBC) is the most suitable topology for high-power BEVs and PHEVs (> 10kW), thanks to its low input current ripples, low output voltage ripples, low electromagnetic interference, bidirectionality, high efficiency and high reliability. In contrast, for low-power electric vehicles (<10 kW), it is tough to recommend a single candidate that is the best in all possible aspects. However, the Sinusoidal Amplitude Converter, the Z-Source DC-DC converter and the boost DC-DC converter with resonant circuit are more suitable for low-power BEVs and PHEVs because of their soft switching, noise-free operation, low switching loss and high efficiency. Finally, this paper explores the opportunity of using wide band gap semiconductors (WBGSs) in DC-DC converters for BEVs, PHEVs and converters for FCHARs. Specifically, the future roadmap of research for WBGSs, modeling of emerging topologies and design techniques of the control system for BEV and PHEV powertrains are also presented in detail, which will certainly help researchers and solution engineers of automotive industries to select the suitable converter topology to achieve the growth of projected power density.
The paper presents use case simulations of fleets of electric buses in two cities in Europe, one with a warm Mediterranean climate and the other with a Northern European (cool temperate) climate, to compare the different climatic effects of the thermal management strategy and charging management strategy. Two bus routes are selected in each city, and the effects of their speed, elevation, and passenger profiles on the energy and thermal management strategy of vehicles are evaluated. A multi-objective optimization technique, the improved Simple Optimization technique, and a “brute-force” Monte Carlo technique were employed to determine the optimal number of chargers and charging power to minimize the total cost of operation of the fleet and the impact on the grid, while ensuring that all the buses in the fleet are able to realize their trips throughout the day and keeping the battery SoC within the constraints designated by the manufacturer. A mix of four different types of buses with different battery capacities and electric motor specifications constitute the bus fleet, and the effects that they have on charging priority are evaluated. Finally, different energy management strategies, including economy (ECO) features, such as ECO-comfort, ECO-driving, and ECO-charging, and their effects on the overall optimization are investigated. The single bus results indicate that 12 m buses have a significant battery capacity, allowing for multiple trips within their designated routes, while 18 m buses only have the battery capacity to allow for one or two trips. The fleet results for Barcelona city indicate an energy requirement of 4.42 GWh per year for a fleet of 36 buses, while for Gothenburg, the energy requirement is 5 GWh per year for a fleet of 20 buses. The higher energy requirement in Gothenburg can be attributed to the higher average velocities of the bus routes in Gothenburg, compared to those of the bus routes in Barcelona city. However, applying ECO-features can reduce the energy consumption by 15% in Barcelona city and by 40% in Gothenburg. The significant reduction in Gothenburg is due to the more effective application of the ECO-driving and ECO-charging strategies. The application of ECO-charging also reduces the average grid load by more than 10%, while shifting the charging towards non-peak hours. Finally, the optimization process results in a reduction of the total fleet energy consumption of up to 30% in Barcelona city, while in Gothenburg, the total cost of ownership of the fleet is reduced by 9%.
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