Defining the accuracy of real-world range estimations of an electric vehicle

Abstract

Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way.Publisher's statement: "© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works." A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription.For more information, please contact the WRAP Team at: publications@warwick.ac.uk Abstract-Range anxiety is a major barrier for the mass adoption of electric vehicles (EVs), a contributing factor to this is the variability of the predicted range remaining presented to the driver in the vehicle. This study aims to better understand the causes of potential inaccuracies and how ITS can help resolve these issues. Eleven participants completed 141 logged journeys, with results showing that range (as predicted by the EV and presented to the driver) was overestimated by approximately 50% in comparison to journey distance. Driving style had the most significant impact on range prediction accuracy, where a more aggressive driving style led to greater inaccuracies. However, journey distance and type of road driven, which can be calculated from Satnav systems, were factors which were correlated with having a significant effect on range accuracy. Therefore incorporating these into future range prediction algorithms has the potential to increase the accuracy of information and subsequently increase driver trust.

Harnessing big data for estimating the energy consumption and driving range of electric vehicles

“…However, the market penetration rate of BEVs is lethargic, mainly because of high purchase prices, limited recharging infrastructures, limited driving range coupled with long recharging times, uncertainties concerning driving range and battery life, and risk aversion behavior in adopting new technologies (see, e.g., Birrell et al, 2014;Egbue and Long, 2012;Kihm and Trommer, 2014). It is clear that uncertainty plays a significant role in the (non-)choice of BEVs, especially when thinking about the driving range and the refueling costs and time with respect to a conventional car.…”

“…It is clear that uncertainty plays a significant role in the (non-)choice of BEVs, especially when thinking about the driving range and the refueling costs and time with respect to a conventional car. Uncertainty plays an even larger role when factoring in that customers have limited knowledge about the actual performances of BEVs and their sensitivity to driving environments, with this lack of knowledge adversely affecting the demand for BEV (Birrell et al, 2014;Jensen et al, 2013). Accordingly, providing insight into the factors that affect the energy consumption rate (ECR) and driving range of BEVs under different driving environments is very relevant to support on the one hand consumers in choosing appropriate 4 vehicles that suit their needs and, on the other hand, manufacturers in distinguishing and targeting different customers depending on the driving environments that the customers live and travel in.…”

“…Large differences were usually observed between the results of car manufacturers and the results observed in real-world (Huo et al, 2011), mainly because manufacturers test BEVs by performing a long and continue test drive from a fully charged battery to a completely flat battery, thus ignoring basic real-world energy expenditures such as the energy used to overcome the inertia force to propel a parked car and the energy used to cool down a car during each trip. A limited number of studies have focused on the ECR and the driving range of BEVs: the ECR of BEVs was evaluated by comparing the driving range reported by the manufacturer versus the actual driving range of drivers (Birrell et al, 2014); the ECR of BEVs was estimated by taking into account driving patterns and car features from GPS data, and in-city driving was deemed more energy efficient than freeway driving (Wu et al, 2015). However, these studies present limitations: (i) the study samples consisted respectively of one (Wu et al, 2015) and 11 drivers (Birrell et al, 2014), with obvious consequences on the possibility of generalizing any finding; (ii) the data collections did not cover the winter months, 6 with obvious consequences on the possibility of analyzing the effect of cold temperature on the ECR of BEVs; (iii) the data analyses did not control for possible confounders, with obvious consequences on the possibility of assessing whether the ECR differences were caused by other factors.…”

Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

“…, w i . As for bidding price of the latest start time lst i,k in the kth bid, it is smaller than the value by U (2, 4), i.e., p i (lst i,k ) = v i (lst i,k ) − U (2,4). For instance, an XOR bid could be < Q 1 , 10, $10 >XOR< Q 1 , 11, $8 >XOR< Q 1 , 12, $6 >, where the $10, $8 and $6 indicate the bidding prices for the lst i,k 10 a.m., 11 a.m. and 12 a.m. in each bid, respectively.…”

Driving electric vehicles at highway speeds: The effect of higher driving speeds on energy consumption and driving range for electric vehicles in Australia

“…[12], with new and fully charged batteries [13][14][15][16][17][18][19][20] and for urban driving with a short highway section [21][22][23][24], at the time of this study there is little information available on realistic EV use, energy consumption and vehicle range for travel on an electric highway between cities. In particular, there is a gap in the literature on the interaction of the combination of a limited fast-DC charge level of 80% capacity, increased energy consumption at highway speeds, increased loads due to headwinds, increased aerodynamic drag due to roof racks, additional vehicle weight, the absence of energy recovery and a battery discharge safety margin.…”