Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.809514
Title: Intelligent charging strategies for battery electric vehicles
Author: Menz, L.
ISNI:       0000 0004 9352 5219
Awarding Body: University of Exeter
Current Institution: University of Exeter
Date of Award: 2020
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 Access from EThOS: Full text unavailable from EThOS. Thesis embargoed until 01 Jun 2023 Access from Institution:
Abstract:
Given the relevance of mobility in a globalised world, the development of \emph{sustainable} mobility describes one of the most prominent challenges of modern societies. Electric mobility in conjunction with renewable energy (RE) generation can contribute to the reduction of transportation-related greenhouse gas (GHG) emissions. The combination of RE generation and electric mobility is a nontrivial task. Fluctuations in RE generation and mobility-related energy demand are the products of independent processes. Among other objectives, smart charging'' concepts aim to align these processes by shifting electric vehicle (EV) charging into sensible periods, for instance during excessive RE availability. The original purpose of EVs, which is to provide mobility to their users, is significantly affected by charging events. EVs cannot fulfil their purpose for as long as they are charging. Hence, shifting charging into periods in which a user wants to be mobile would contradict the concept of electric mobility. Existing smart charging solutions often consider EV energy demand as generic input that needs to be provided by the user. A smart charging solution's dependency on user input, however, limits its applicability. To eliminate the necessity of manual charging data provision and promote a widespread application of smart charging solutions, this dissertation demonstrates how smart charging can be enhanced by individual user mobility prediction. As part of a joint framework, the thesis explores an improved method for human mobility prediction. The method combines a Markov model-based prediction scheme with kernel density estimation for departure time prediction. A neural network-based prediction method for atypical travel behaviour further enhances the framework's prediction performance. It is demonstrated that a generic scheduling scheme can schedule EV charging based on predicted mobility under consideration of existing charging infrastructure. The applicability of a smart charging framework on real-world data is used to demonstrate that it can avoid disutility and a user's adaption in mobility behaviour due to EV-related constraints is not necessary. The resulting framework does not only contribute to the wider adoption of smart charging but can also improve EV acceptance. Improved smart charging usability serves multiple higher purposes as it accelerates prevalent adoption to EV, advances sustainable mobility, reduces transportation-related GHG emissions and saves resources. Furthermore, insights about individual mobility behaviour are aggregated to gain knowledge about collective behaviour, which will be valuable information for utilities, grid operators and government energy policymakers.
Supervisor: Luo, C. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.809514  DOI: Not available
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