Optimised charging scheduling and predictive modelling to reduce emissions from battery electric buses

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dc.contributor.advisorArbelaez, Alejandro
dc.contributor.advisorCliment, Laura
dc.contributor.advisorBrown, Kenneth
dc.contributor.authorJarvis, Padraighen
dc.contributor.funderSustainable Energy Authority of Irelanden
dc.contributor.funderInsight SFI Research Centre for Data Analyticsen
dc.date.accessioned2026-05-20T09:42:18Z
dc.date.available2026-05-20T09:42:18Z
dc.date.issued2025-07-31
dc.date.submitted2025-07-31
dc.description.abstractBattery Electric Vehicles provide an opportunity to decarbonise the transportation sector, which has proven to be a large contributor to Greenhouse Gases. This decarbonisation proves even more effective for Battery Electric Buses, as public transport services expand worldwide to help mitigate climate change. However, while Battery Electric Buses do not produce emissions themselves, the electricity used to power such vehicles may come from polluting sources, such as diesel or natural gas, resulting in lifecycle emissions. Reduction of these lifecycle emissions can aid in meeting carbon emission targets set by countries across the world. The Battery Electric Bus Charging Schedule Problem aims to create charging schedules for Battery Electric Bus to enable the continued operation of established bus routes while considering the restrictions tied to such vehicles, such as a lower operational range. The focus of this thesis is the modelling of this problem to prioritise charging of Battery Electric Buses using low-emission electricity, thus lowering lifecycle emissions. Furthermore, the impact on service quality is also considered to reduce any potential delays caused by charging. The first contribution made by this thesis is the evaluation of Deep Learning predictive model configurations for imbalanced regression problems to estimate the availability of low-emission electricity. Six Deep Learning models are considered, along with three resampling strategies and three loss metrics. The resulting 54 configurations are empirically evaluated using multiple test metrics. It was determined that Convolution Neural Networks trained with a Squared Error Relevance Area loss function performed best when estimating the availability of low-emission energy, such as excess wind-generated electricity. However, the same configuration using an Inverse-Weighted Mean Squared Error loss function appears to perform well in estimating extremes in excess wind-generated electricity. The second contribution creates a Battery Electric Bus Charging Schedule Problem to reduce the consumption of non-low-emission energy, utilising predictive models. The performance of the models was compared to a naïve scenario, and an ideal scenario with 100% accurate information, low-emission energy information. Use of the predictive models showed improvement of up to 10.04% compared to the naïve scenario. Furthermore, there was only an average discrepancy of 4.89% between the predictive and ideal scenarios. The final contribution made in this dissertation is the extension of the previously developed Battery Electric Bus Charging Schedule Problem to consider potential negative service impact. The problem aims to reduce the daily operational costs of a bus fleet, considering fuel costs, carbon emissions, and passengers’ Value of Time expressed as a financial value. Furthermore, a combination of aspects which have not been modelled before for a Battery Electric Bus Charging Schedule Problem is used to accurately represent the problem. Evaluation on a large dataset with over 1000 buses showed that carbon emissions can be reduced by up to 64%, with savings in fuel costs reaching 50% per day, while only inducing an average delay of 8.8 seconds.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationJarvis, P. J. W. 2025. Optimised charging scheduling and predictive modelling to reduce emissions from battery electric buses. PhD Thesis, University College Cork.
dc.identifier.endpage188
dc.identifier.urihttps://hdl.handle.net/10468/18794
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectSustainable Energy Authority of Ireland (Grant no. 19/RDD/519)
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /
dc.rights© 2025, Padraigh Jarvis.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBattery Electric Bus
dc.subjectDeep learning
dc.subjectMixed interger programming
dc.subjectImbalanced regression
dc.subjectCarbon emissions
dc.subjectOptimisation
dc.subjectLifecycle emissions
dc.titleOptimised charging scheduling and predictive modelling to reduce emissions from battery electric buses
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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