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Abstract Despite policy support and technological progress, consumer adoption of electric vehicles remains limited globally. One important barrier to electric vehicle adoption may be limited consumer awareness. We investigate trends in consumer awareness, familiarity, and experience with electric vehicles by comparing cross-sectional survey responses from two representative samples of Canadian new vehicle-buyers collected in 2013 (n = 2922) and in 2017 (n = 1808). While a significantly higher proportion of 2017 respondents have ‘heard of’ key electric vehicle models, stated familiarity and experience are low for both samples. Further, about three-quarters of respondents in both samples are confused about the basic notion of how to refuel (or recharge) electric vehicles—and how these vehicles differ from hybrids. Conversely, over half of 2017 respondents report having seen at least one electric vehicle charger in public, which is more than double the proportion reported in the 2013 sample. These trends hold in analyses of three Canadian provinces, including two that have engaged in significant consumer outreach activities over this time frame. Overall, in contrast to expectations, our results suggest that consumer awareness remains low and stagnant, which may hinder market growth and inhibit the climate mitigation potential of electric vehicles.

With the growing fleet of a new generation electric vehicles (EVs), it is essential to develop an adequate high power charging infrastructure that can mimic conventional gasoline fuel stations. Therefore, much research attention must be focused on the development of off-board DC fast chargers which can quickly replenish the charge in an EV battery. However, use of the service transformer in the existing fast charging architecture adds to the system cost, size and complicates the installation process while directly connected to medium-voltage (MV) line. With continual improvements in power electronics and magnetics, solid state transformer (SST) technology can be adopted to enhance power density and efficiency of the system. This paper aims to review the current state of the art architectures and challenges of fast charging infrastructure using SST technology while directly connected to the MV line. Finally, this paper discusses technical considerations, challenges and introduces future research possibilities.

The future smart grids (SGs) consist of considerable amount of renewable energy sources (RESs), electrical vehicles (EVs), and energy storage systems (ESSs). The uncertainties associated with EVs and uncontrollable nature of RESs have magnified voltage stability challenges and the importance of an effective energy management system (EMS) in SGs as a practical solution. This study presents a multi‐agent transactive energy management system (TEMS) to control demand and supply in the presence of high levels of RESs and EVs, and maximises profit of each participant in addition to satisfying voltage regulation constraints. For this purpose, a real‐time pricing is considered based on Cournot oligopoly competition model for demand and merit order effect for production to compensate RESs’ fluctuations in real time by an indirect control method. Simulations are conducted in the modified IEEE 37‐bus test system with 1141 customers, 670 EVs, two solar plants, four wind turbines, and one ESS. The results show that the proposed multi‐agent TEMS can indirectly control EVs, elastic loads, and ESSs to balance the RESs oscillation, minimise customers cost, and regulate voltage in a real‐time manner.

Carbon trading is an effective measure for the road transportation to reduce energy consumption and carbon emissions. Carbon emission quotas are the primary concern to ensuring the efficiency of carbon trading. However, the existing studies have mostly focused on carbon emission quotas in different regions, i.e., countries and provinces. Few literature studies simulate carbon quota allocation in the road transportation. A novel approach from the perspective of carbon emission intensity of vehicle is proposed, on the basis of data envelopment analysis (DEA) model. Unlike other studies, the idea of allocation of baseline excitation is introduced and the intensity is included in the model as the baseline. Firstly, the Delphi method is employed to select input and output indicators. Secondly, carbon emission intensity is determined by the cumulative distribution function (CDF). Furthermore, the carbon emission quotas in road transportation in 30 provinces of China are used to validate the model. The results show that (1) the carbon emission intensity of commercial trucks and buses in China’s road transport industry is 75.04 g/t·km and 13.12 g/p·km, respectively; (2) the provinces of Shanghai, Guangdong, and Xinjiang have the greatest carbon reduction potential and Henan, Hunan, and Anhui have the largest increase in emission quotas; (3) compared with traditional “history responsibility” and “baseline” methods, the proposed approach increases allocation efficiency by 19% and 14%, respectively; and (4) the approach can make the carbon emission quotas play the role of incentive while taking fairness into account and can more effectively promote the implementation of carbon trading system in road transportation.

Powertrain electrification has heightened the need for an energy management strategy, which has been a continuing concern in the development of electrified vehicles. The energy management control unit manages power flow between different energy sources in an electrified powertrain that directly affects vehicle performance. Developing an energy management strategy that is compatible with different real-world driving scenarios has opened a significant field of study for researchers. Recent advances and progress in intelligent control approaches have facilitated developing an intelligent energy management strategy. However, there are inadequate numbers of studies on the latest energy management strategies. The presented review paper aims to provide the requirements of intelligent energy management strategies as well as a new categorization of them into principle-based, data-driven, and composite methods. Besides, enabling technologies for implementing an energy management system with a comparison of different controller chips are described to give readers an experimental view. Future trends and existing challenges are presented, which generate fresh insight into energy management strategies.

Abstract: The usage of multi-objective cost functions (MOCFs) in sizing and energy management strategy (EMS) of fuel cell hybrid electric vehicles (FCHEVs) has expanded due to the participation of multiple technological and economic disciplines. To better understand the impact of price fluctuation on the component size and EMS of an FCHEV, this article proposed a sensitivity analysis methodology. First, a two-step optimization approach that considers hydrogen consumption, system degradation, and trip cost is used to minimize a MOCF of the Can-Am Spyder electric motorcycle simulator. Then, an effect analysis is carried out for the cost-optimal results under two driving profiles to understand the link between cost variation and system performance. These simulations indicate that each might result in different system sizes and EMS compromise. After that, an online optimization EMS based on sequential quadratic programming is used on a reduced-scale hardware-in-the-loop configuration to evaluate the simulation results with varied weights. Experimental results indicate that when an adequate size is used for each pair of weights, the EMS results in a 6% decrease in the trip cost.

The energy stored in electric vehicles (EVs) would be made available to commercial buildings to actively manage energy consumption and costs in the near future. These concepts known as vehicle-to-building (V2B) and vehicle-to-grid (V2G) technologies have the potential to provide storage capacity to benefit both EV and building owners respectively, by reducing some of the high cost of EVs, buildings’ energy cost, and providing reliable emergency backup services. In this study, we considered a vehicle-to-buildings/grid (V2B/V2G) system simultaneously for peak shaving and frequency regulation via a combined multi-objective optimization strategy which captures battery state of charge (SoC), EV battery degradation, EV driving scenarios, and operational constraints. Under these assumptions, we showed that the electricity usage/bill can be reduced by a difference of 0.1 on a scale of 0 to 1 (with 1 the normalized original electricity cost), and that EV batteries can also achieve superior economic benefits under controlled SoC limits (e.g., when kept between the SoC range of SoCmin > 30% and SoCmax < 90%) and subjected to very restricted charge-discharge battery cycling.