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[en] Highlights: • The only wind powered EV charging station reported in the literature. • The charging station maximally converts wind energy into electric energy. • Novel fast and highly accurate MPPT technique implemented in the EV charging station. • The charging station is grid-connected type with vehicle-to-grid (V2G) technology. • The charging station balances load demand in the grid connected to it. - Abstract: In this study, a novel grid-connected wind powered electric vehicle (EV) charging station with vehicle-to-grid (V2G) technology is designed and constructed. The wind powered EV charging station consists of a wind energy conversion system (WECS), a unidirectional DC/DC converter connected to the WECS, a maximum power point tracking (MPPT) controller, 15 bidirectional DC/DC converters dedicated to 15 charging stations provided for charging EVs, and a three-phase bidirectional DC/AC inverter connected to the grid. The contribution of this work is that the grid-connected wind powered EV charging station presented in this work is the only constructed EV charging station reported in the literature that uses wind energy as a renewable resource to produce electric energy for charging EVs, and moreover, it maximally converts wind energy into electric energy because it uses a novel fast and highly accurate MPPT technique proposed in this study. Other works are only simulated models without any new MPPT consideration. It is demonstrated that the constructed wind powered EV charging station is a perfect charging station that not only produces electric energy to charge EVs but also balances load demand in the grid connected to it.
[en] This paper details the development of an energy demand model for a hydrogen-electric vehicle fleet and the modelling of the fleet interactions with a clean energy hub. The approach taken is to model the architecture and daily operation of every individual vehicle in the fleet. A generic architecture was developed based on understanding gained from existing detailed models used in vehicle powertrain design, with daily operation divided into two periods: charging and travelling. During the charging period, the vehicle charges its Electricity Storage System (ESS) and refills its Hydrogen Storage System (HSS), and during the travelling period, the vehicle depletes the ESS and HSS based on distance travelled. Daily travel distance is generated by a stochastic model and is considered an input to the fleet model. The modelling of a clean energy hub is also presented. The clean energy hub functions as an interface between electricity supply and the energy demand (i.e. hydrogen and electricity) of the vehicle fleet. Finally, a sample case is presented to demonstrate the use of the fleet model and its implications on clean energy hub sizing. (author)
[en] This paper describes a number of different allocation methods for assigning greenhouse gas emissions from electricity generation to charging plug-in electric vehicles. These methods for calculating the carbon intensity of electricity are discussed in terms of merits and drawbacks and are placed into a framework to aid in understanding the relation with other allocation methods. Three independent decisions are used to define these methods (average vs. marginal, aggregate vs. temporally-explicit, and retrospective vs. prospective). This framework is important because the use of different methods can lead to very different carbon intensities and studies or analyses that do not properly identify the methods used can confuse policymakers and stakeholders, especially when compared to other studies using different methods. - Highlights: • Reviews literature of emissions from charging electric vehicles. • Examines multiple allocation methods for GHG emissions for electric vehicles. • Provides a framework for understanding various GHG impact studies. • A “best” allocation method for all situations and analyses does not exist
[en] Mobile applications have become widely popular for their ability to access real-time information. In electric vehicle (EV) mobility, these applications are used by drivers to locate charging stations in public spaces, pay for charging transactions, and engage with other users. This activity generates a rich source of data about charging infrastructure and behavior. However, an increasing share of this data is stored as unstructured text—inhibiting our ability to interpret behavior in real-time. In this article, we implement recent transformer-based deep learning algorithms, BERT and XLnet, that have been tailored to automatically classify short user reviews about EV charging experiences. We achieve classification results with a mean accuracy of over 91% and a mean F1 score of over 0.81 allowing for more precise detection of topic categories, even in the presence of highly imbalanced data. Using these classification algorithms as a pre-processing step, we analyze a U.S. national dataset with econometric methods to discover the dominant topics of discourse in charging infrastructure. After adjusting for station characteristics and other factors, we find that the functionality of a charging station is the dominant topic among EV drivers and is more likely to be discussed at points-of-interest with negative user experiences
[en] Highlights: • A scenario of vehicle-to-grid implementation within regional smart grid is discussed and mathematically formulated. • A double-layer optimal charging strategy for plug-in electric vehicles is proposed. • The proposed double-layer optimal charging algorithm aims to minimize power grid’s load variance. • The performance of proposed double-layer optimal charging algorithm is evaluated through comparative study. - Abstract: As an emerging new electrical load, plug-in electric vehicles (PEVs)’ impact on the power grid has drawn increasing attention worldwide. An optimal scenario is that by digging the potential of PEVs as a moveable energy storage device, they may not harm the power grid by, for example, triggering extreme surges in demand at rush hours, conversely, the large-scale penetration of PEVs could benefit the grid through flattening the power load curve, hence, increase the stability, security and operating economy of the grid. This has become a hot issue which is known as vehicle-to-grid (V2G) technology within the framework of smart grid. In this paper, a scenario of V2G implementation within regional smart grids is discussed. Then, the problem is mathematically formulated. It is essentially an optimization problem, and the objective is to minimize the overall load variance. With the increase of the scale of PEVs and charging posts involved, the computational complexity will become tremendously high. Therefore, a double-layer optimal charging (DLOC) strategy is proposed to solve this problem. The comparative study demonstrates that the proposed DLOC algorithm can effectively solve the problem of tremendously high computational complexity arising from the large-scaled PEVs and charging posts involved
[en] The 'Hydrogen Economy' is a proposed system where hydrogen is produced from carbon dioxide free energy sources and is used as an alternative transportation fuel. Application of hydrogen on board fuel cell vehicles can significantly decrease air pollutants and greenhouse gases emission from transportation. There must be significant transition of infrastructure in order to achieve the Hydrogen Economy with investment required in both production and distribution infrastructure. This research is focused on the projected demands for infrastructure transition of 'Hydrogen Economy' in Ontario, Canada. Three potential hydrogen demand and distribution system development scenarios are examined to estimate hydrogen fuel cell vehicle market penetration, as well as the associated hydrogen production and distribution. Demand of transportation hydrogen is estimated based on the type of hydrogen fuel cell vehicle. Finally, an estimate of hydrogen demand from fuel cell vehicles in Ontario and the resulting cost of delivered hydrogen are investigated. (author)
[en] The evolution of electric drive technologies from 1988, at the 9"t"h International Electric Vehicle Symposium (EVS 9) in Toronto, to 2007 at EVS 23 in Anaheim, is described. Total hybridization of Canada's fleet of light, medium and heavy duty vehicles would result in greenhouse reductions savings of 30 Mt of CO_2E per year, similar to the saving from a 25% reduction in vehicle weight. Further savings in greenhouse reductions from plug-in hybrids require a battery cost similar to that needed for electric vehicles. Further development of both ultracapacitors and batteries is needed as is work on other parts of the electric drive supply chain. (author)
[en] Highlights: • A new model is developed to optimise the performance of a PEV aggregator in the power market. • PEVs aggregator can combine the PEVs and manage the charge/discharge of their batteries. • A new approach to calculate the satisfaction/motivation of PEV owners is proposed. • Several uncertainties are taken into account using a two-stage stochastic programing approach. • The proposed model is proficient in significantly improving the short- and long-term behaviour. - Abstract: In this paper, a new model is developed to optimise the performance of a plug-in Electric Vehicle (EV) aggregator in electricity markets, considering both short- and long-term horizons. EV aggregator as a new player of the power market can aggregate the EVs and manage the charge/discharge of their batteries. The aggregator maximises the profit and optimises EV owners’ revenue by applying changes in tariffs to compete with other market players for retaining current customers and acquiring new owners. On this basis, a new approach to calculate the satisfaction/motivation of EV owners and their market participation is proposed in this paper. Moreover, the behaviour of owners to select their supplying company is considered. The aggregator optimises the self-scheduling programme and submits the best bidding/offering strategies to the day-ahead and real-time markets. To achieve this purpose, the day-ahead and real-time energy and reserve markets are modelled as oligopoly markets, in contrast with previous works that utilised perfectly competitive ones. Furthermore, several uncertainties and constraints are taken into account using a two-stage stochastic programing approach, which have not been addressed in previous works. The numerical studies show the effectiveness of the proposed model
[en] The transport sector represents a serious energy consumer in most energy systems across the EU and wider. In Croatia for example, the transport sector accounted for 32.8 percent of the final energy consumption in 2011 making it the second most energy demanding sector right after buildings with 43 percent and a large portion of that energy demand is linked to road transport and personal vehicles. Because of their higher efficiency, a modal switch from conventional internal combustion engines (ICE) to electric vehicles (EVs) has the potential to greatly reduce the overall energy demand of the transport sector. Our previous work has shown that a widespread electrification of the personal vehicle fleet could reduce the total final energy demand of the transport sector in Croatia by roughly 53 percent by the year 2050 when compared with a business as usual scenario. This represents a saving of 89 PJ of energy. If a modal split from road and air to rail transport is taken into account as well, this savings could be increased to 59 percent or 99 PJ. A potential issue regarding a high penetration of EVs is their impact on the electricity demand, especially peak demand. In order to properly analyse this impact it is crucial to model the hourly distribution of energy demand of EVs, and with this data analyse their impact on the electricity grid. The goal of this work is to model the hourly distribution of the energy consumption of EVs and use the calculated electricity load curves to test their impact on the Croatian energy system. The hourly demand for the transport sector has been calculated using the agent-based modelling tool MATSim on a simplified geographic layout. The impact EVs have on the energy system has been modelled using the EnergyPLAN advanced energy system analyses tool. (author).
[en] A push for electric vehicles has occurred in the past several decades due to various concerns about air pollution and the contribution of emissions to global climate change. Although electric cars and buses have been the focus of much of electric vehicle development, smaller vehicles are used extensively for transportation and utility purposes in many countries. In order to explore the viability of fuel cell - battery hybrid electric vehicles, empirical fuel cell system data has been incorporated into the NREL's vehicle design and simulation tool, ADVISOR (ADvanced Vehicle SimulatOR), to predict the performance of a low-speed, fuel cell - battery electric vehicle through MATLAB Simulink. The modelling and simulation provide valuable feedback to the design optimization of the fuel cell power system. A sampling based optimization algorithm was used to explore the viability and options of a low cost design for urban use. (author)