In this paper, a simplified power train model for an electric vehicle is developed by using the coast-down parameters published by the EPA to model the vehicle loads and by using Argonne National Laboratory (ANL) data to validate the model. The coast-down parameters provide a more accurate estimation of the road-load and vehicle no-load spin losses than could be estimated using other public information. The model is built up using engineering assumptions on the electromechanical power train, applied to the 2012 Nissan Leaf and validated against the Argonne experimental test data published in 2012. Excellent correlation is demonstrated between the model predictions and the experimental data for fuel economy, fuel consumption and range
This study presents a novel energy management strategy (EMS) which outperforms the published strategies developed for an international technology challenge, IEEE Vehicular Technology Society (VTS) Motor Vehicles Challenge 2017. The objective of the strategy is to minimise the cost of ownership of a low-power (15 kW) fuel cell (FC)-battery electric vehicle. Both the fuel consumption cost and power sources degradation costs are combined to represent the total cost of ownership. The simple adaptive rule-based strategy optimises the FC operation during low-traction power operation and switches to battery charge-sustaining operation for high traction power operation. This minimises fuel consumption and increases the lifetimes of the FC and of the battery. The strategy is then compared with the EMS of the 2015 Toyota Mirai, and the challenge vehicle model is modified to capture the learnings from the Mirai. Finally, a cost-benefit analysis for a plug-in FC vehicle is considered in order to improve FC lifetimes and to reduce costs for short drive cycles.
UK greenhouse gas (GHG) emissions are mandated by law to be 80% lower in 2050 than in 1990. In an effort to reduce transport emissions, vehicle manufacturers have recently introduced new electric vehicle technologies to the UK market. A number of empirical studies have shown that consumer attitudinal barriers are inhibiting the adoption of new vehicle technologies. This study was established to develop software tools that could be used to minimise these barriers. Four dynamic models were developed to examine vehicle CO 2 emissions, all electric vehicle range, factors that determine vehicle energy requirements and vehicle cost of ownership. Seven vehicles representing diesel, battery only electric vehicles and range extended electric vehicle technologies were compared using the software tools.The results of the study show that relatively simple models, based on standard office spreadsheet software, can be used to demonstrate the significant CO 2 emissions reduction possible with electric vehicles and that vehicle range is largely determined by the charging infrastructure. The results also suggest that the higher purchase price for these technologies may be recovered based on the fuel cost savings, congestion charge savings and potential higher retained value at end of life. The models also have the potential to demonstrate the impact of auxiliary loads, payload and battery charger efficiency on the vehicles fuel consumption.
Index Terms-Attitudes, Cost of Ownership, ElectricVehicles, Fleet Vehicles, Modelling CO 2 Emissions, Plug-in Hybrid Electric Vehicles, Range Anxiety, Range Extended Electric Vehicle.
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