• Energy Research
• other engineering and technologies close
• CA close
• BE close
• Transport Research close

Demand for pure electric vehicles has been found to be increasing over the years. This has necessitated the development of a model that would serve as a predicting machine for manufacturing different types of pure electric vehicles. Direct Artificial Neural Network approach was used for predictions of nine different parameters commonly found in pure electric cars. Predictions were found to be of high degree of accuracy while using unit and overall model errors as the basis of performance measurement. The mean absolute error, mean square error and root mean square error of the model were 0.109, 0.218 and 0.467, respectively, when the combined electric charge consumption was used for modeling. For the model formation, using the same variable, the losses for the training and testing were 3.9132 × 10−6 and 9.698 × 10−7, respectively. The model was also evaluated using redefined datasets. The developed model can be used by manufacturers and engineers to simulate future designs when certain parameters are given.

This study examines the results of a survey of 1,000 Canadian internal combustion engine (ICE) vehicle owners to assess factors that would encourage them to purchase an electric vehicle (EV). Further to the work of Peters and Dutschke (2014) and (Matthews et al. (2017) we combine the various drivers of EV adoption, independently identified in the literature, into one model in order to investigate their influence on the intent to purchase an EV. Through correlations and a series of probit regression modelling, we provide evidence to support additional policies that could establish greater relative advantages for owning an EV. These include the promotion of the communication of those advantages through experiential awareness initiatives such as improved access to EV test drives and consumer information. We suggest that car dealerships are important partners in this journey and their association is critical for greater diffusion of EVs in the market. Our findings have implications for EV manufacturers and government policy makers.

This paper analyses the environmental impacts of electric automated minibuses operated in public transportation systems. The results are based on an environmental life cycle assessment study, which uses data from a minibus producer and field data from several European cities. It is shown that electric automated minibuses promise a reduction in environmental impacts within specific conditions and in several future settings. Their environmental performance largely depends on their average utilisation, lifetime and total mileage, the electricity mix used, and the substituted means of transport. Automated components affect environmental performance but are not its most important driver. The minibus’s contribution to climate change in near-future use cases is expected at 78 g carbon dioxide equivalents per passenger kilometre on average and only 39 g at peak operation.

Big cities in the world are burdened by heavy traffic and all the resulting negative consequences. A partial solution of this problem is achieved by the introduction of high-quality public urban transport of passengers that enables faster and more efficient transport. Since the introduction of individual forms of public urban transport depends on several factors, such as economic, traffic, environmental and technical ones, the problem is often a very complex one. The implementation of trolleybuses represents one of the forms of passenger transportation in public urban transport. It is the most favourable electrical vehicle for public urban transport when the implementation of a rail system is not affordable. Looking through history, the trolleybus has experienced significant changes in the implementation, in a large number of world urban environments. This paper points out the unjustifiable neglect of trolleybus in the implementation for the public urban transport and the renewed increase in the interest for its implementation. The advantages and drawbacks of the trolleybus are listed in relation to buses, as well as the reasons due to which some cities in the world are introducing it or increasing its implementation. The paper also emphasises and provides arguments of the ecological and energy advantages of trolleybuses in relation to buses as crucial in advanced public urban transport of passengers. Also, advanced technical solutions are pointed out that contribute to safer, faster and more comfortable transport of passengers. KEY WORDS: trolleybus, bus, public urban transport, environment, economics

This article investigates the combined low voltage (LV) and medium voltage (MV) residential grid impact for slow and fast electric vehicle (EV) charging, for an increasing local penetration rate and for different residential slow charging strategies. A realistic case study for a Flemish urban distribution grid is used, for which three residential slow charging strategies are modeled: uncoordinated charging, residential off-peak charging, and EV-based peak shaving. For each slow charging strategy, the EV hosting capacity is determined, with and without the possibility of fast charging, while keeping the grid within its operating limits. The results show that the distribution grid impact is much less sensitive to the presence of fast charging compared to the slow charging strategy. EV-based peak shaving results in the lowest grid impact, allowing for the highest EV hosting capacity. Residential off-peak charging has the highest grid impact, due the load synchronization effect that occurs, resulting in the lowest EV hosting capacity. Therefore, the EV users should be incentivized to charge their EVs in a more grid-friendly manner when the local EV penetration rate becomes significant, as this increases the EV hosting capacity much more than the presence of fast charging decreases it.

Electric vehicles (EV) are becoming increasingly popular due to their efficiency and potentials to reduce greenhouse gas emission. However, penetration of a very large number of EVs can have negative impacts on power systems. This study proposes optimal vehicle-to-grid (V2G) models to incorporate the EV penetration by minimizing multiple objectives including the peak demand, the variance of load profile, the battery degradation cost and the EV charging/discharging cost based on real-time pricing (RTP). The proposed models incorporate EV driving patterns including driving distance, driving periods, and charging/discharging levels and locations. A nonlinear battery degradation cost function is linearized and incorporated into the optimal models. In addition, a distributed control algorithm is developed to implement the optimal models. One-day simulation results show that the proposed approach can reduce the peak demand and the variance of the load profile by 7.8% and 81.9%, which can significantly improve power system stability and energy efficiency. In addition, the sum of EV charging/discharging cost and battery degradation cost is decreased from $251 to -$153. In fact, 100 EVs earn $153 in the day from the V2G program. The approaches can be used by a load aggregator or a utility to effectively incorporate EV penetration to power systems to unlock V2G opportunities and mitigate negative impacts.

The growing penetration of electric vehicles (EVs) introduces both opportunities and challenges for power grid operators. Incentivization is considered a viable option to tempt EV owners to participate in supporting the grid during peak load intervals while receiving compensation for their services. Therefore, this study proposes a two-step incentive mechanism to reduce the peak load of the grid by enabling power trading among the microgrid, EVs and the utility grid. In the first step, an incentive price is determined for EVs considering the grid-loading conditions during different hours of the day. In the second step, a multi-objective optimization problem is formulated to optimize trading among different entities, such as EVs, the microgrid and the utility grid. The two objectives considered in this study are the operation cost of the microgrid and the revenue of EVs. Monte Carlo simulations are used to deal with uncertainties associated with EVs. Simulations are conducted to analyze the impact of different weight parameters on the energy-trading amount and operation cost of EVs and MG. In addition, a sensitivity analysis is conducted to analyze the impact of changes in the EV fleet size on the energy-trading amount and operation cost.