Thesis

Analyzing the Range Barrier to Electric Vehicle Adoption – The Case of Range Anxiety

November 2016

Authors:

Matthias Eisel

Georg-August-Universität Göttingen

Improved Range Prediction for Electric Vehicles by a Smart Tire Pressure Monitoring System

Conference Paper

Nov 2018

ResearchGate has not been able to resolve any references for this publication.

Conference Paper

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Investigating Electric Vehicle (EV) Charging Station Locations for Agartala, India

December 2017

Selecting the location for installing electric vehicles charging stations is important to ensure EV adoption and also to address some of the inherent risks such as battery cost and degradation, economic risks, lack of charging infrastructure, risky maintenance of EVs, problems of its integration in smart grid, range anxiety, auxiliary loads and motorist attitude. In this article, we ... [Show full abstract] investigate these problems by studying three aspects – 1) three types of electrical vehicle charging stations (Level 1, Level 2 and DC), 2) different types of batteries and 3) different types of electric vehicles. We compared and contrasted the features of these charging stations, batteries and EV to identify the best choice for a given scenario. We applied the framework proposed in [1], and used Agartala, India as a case study to identify location for charging stations in and around Agartala suburbs.

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

October 2014

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 ... [Show full abstract] 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.

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Analyzing the Range Barrier to Electric Vehicle Adoption – The Case of Range Anxiety

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