{"id":{"repo_id":"qu-belfast","oai_identifier":"oai:pure.qub.ac.uk/portal:studenttheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57"},"canonical_url":"https://search.dev.ndltd.org/etd/qu-belfast/oai:pure.qub.ac.uk/portal:studenttheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57","repository":{"repo_id":"qu-belfast","name":"Queen's University Belfast","base_url":"https://pureadmin.qub.ac.uk/ws/oai"},"display":{"title":"A technology impact forecasting framework for the comparative life cycle cost and carbon assessment of alternative bus fleets","abstract":"A modal shift from private to public transport, such as increased use of buses, and adoption of alternative powertrain technologies (e.g. diesel-hybrids and battery-electric), are key solutions for mitigating the impacts of climate change, whilst reducing fossil energy dependency and urban congestion and improving air quality. However, there are risks and uncertainties in introducing alternative fleet technologies. Bus operators are faced with significant economic and environmental challenges in identifying technologies that reduce or achieve total-cost-of-ownership parity and mitigate air quality and greenhouse-gas emissions from a well-to-wheels or life cycle perspective. New technologies further present operational challenges; the need for sufficient vehicle range, recharging/refuelling capabilities and similar passenger capacities to meet public service requirements. Bus operators would benefit from a tool that rapidly and clearly analyses the costs and benefits of technologies that could affect these economic, environmental, and operational challenges they face. A need therefore exists for a system-wide assessment tool for quantifying the life cycle impacts of alternative bus fleet technologies.
The aim of this research was to design a novel framework to assist decision-makers in assessing the uncertainty (a state of limited knowledge) and risk (the probability of an outcome) of the life cycle impacts of alternative bus technologies. The developed framework combines technology impact forecasting, computer modelling and statistical analysis techniques, to enable a greater understanding of the direct and indirect impacts of technologies at an early stage of research and development, where all parameters may not be fully defined. This framework will assist with the complex decision-making process for stakeholders of bus fleets and further allow manufacturers to identify which technologies to invest in, decide where to assign resources appropriately to remain competitive in a challenging market.
The resulting framework is limited to comparative vehicle-by-vehicle and fleet-by-fleet assessments of diesel, micro-hybrid, mild-hybrid and battery-electric technologies over a life cycle boundary comprising, for life cycle costs: vehicle capital, fuel and electricity costs, maintenance and energy-storage-system replacements, infrastructure, driver labour, and social cost of carbon and comprising for life cycle greenhouse-gas emissions: carbon dioxide-equivalent emissions associated with vehicle manufacturing, well-to-wheels cycle, maintenance, energy-storage-system replacements and infrastructure.
A preliminary framework evaluated multiple diesel and battery-electric bus scenarios, including various battery technologies, well-to-tank pathways, charging infrastructure and auxiliary demands. For every electric bus scenario, there is an 80% confidence that life cycle greenhouse-gas emissions are mitigated compared to the baseline diesel bus, but life cycle costs are higher. Opportunity charged electric buses with lithium-titanate batteries are the most effective at mitigating greenhouse-gas emissions per additional cost to the operator. A trade-off between dependence on battery capacity and high-power charging infrastructure is highlighted. The framework enables stakeholders to make technology adoption and resource allocation decisions based on the risk of a scenario and provides a level of confidence in a technology’s ability to mitigate life cycle impacts.
The framework was further developed to enable risk and uncertainty quantification of diesel, micro-hybrid, mild-hybrid and battery-electric fleet scenarios for a UK-based case study. The peer-reviewed fleet analysis approach revealed decreased potential to reduce life cycle costs and greenhouse-gas emissions from battery-electric buses. At low risk levels, the micro-hybrid double-deck fleet delivers the largest life cycle cost savings (18.7%) of all scenarios. The largest life cycle greenhouse-gas emission savings come from the mild-hybrid lithium-titanate single-deck fleet (20.8%). Double-deck micro and mild hybrid fleets are the most effective at saving both life cycle costs and greenhouse-gas emissions. The modelling approach adds a novel probabilistic capability for making comparative fleet-wide assertions, supporting the decision-making process for implementing new sustainable fleet technologies. Proposed policy incentives were investigated, revealing no total-cost-of-ownership parity between battery-electric and diesel fleets. Hence, proposed incentives are insufficient in encouraging rapid electric fleet uptake. Current capital incentives make mild-hybrids the most cost-competitive option.
Understanding favourable technological and operational parameter combinations informs how new fleets can be effectively utilised to reduce life cycle impacts, where design improvement can be made and where the modelling methodology can be improved. Response surface methodology techniques were employed to identify key influencing life cycle factors. Bus fleet life cycle impacts are significantly influenced by operational demands, highlighting the need to carefully assess technologies on a case-by-case basis. Improving efficiency of diesel drivetrain components is a clear way to improving fuel economy with further gains available from smarter electrified auxiliaries than engine stop-start functionalities. Vehicle light-weighting and powertrain efficiency improvements for electric fleets has a similar greenhouse-gas emission mitigating influence as decarbonising the electricity grid. Well-to-wheel and manufacturing phases should be prioritised in future model developments.","abstract_html":"A modal shift from private to public transport, such as increased use of buses, and adoption of alternative powertrain technologies (e.g. diesel-hybrids and battery-electric), are key solutions for mitigating the impacts of climate change, whilst reducing fossil energy dependency and urban congestion and improving air quality. However, there are risks and uncertainties in introducing alternative fleet technologies. Bus operators are faced with significant economic and environmental challenges in identifying technologies that reduce or achieve total-cost-of-ownership parity and mitigate air quality and greenhouse-gas emissions from a well-to-wheels or life cycle perspective. New technologies further present operational challenges; the need for sufficient vehicle range, recharging/refuelling capabilities and similar passenger capacities to meet public service requirements. Bus operators would benefit from a tool that rapidly and clearly analyses the costs and benefits of technologies that could affect these economic, environmental, and operational challenges they face. A need therefore exists for a system-wide assessment tool for quantifying the life cycle impacts of alternative bus fleet technologies.<br/><br/>The aim of this research was to design a novel framework to assist decision-makers in assessing the uncertainty (a state of limited knowledge) and risk (the probability of an outcome) of the life cycle impacts of alternative bus technologies. The developed framework combines technology impact forecasting, computer modelling and statistical analysis techniques, to enable a greater understanding of the direct and indirect impacts of technologies at an early stage of research and development, where all parameters may not be fully defined. This framework will assist with the complex decision-making process for stakeholders of bus fleets and further allow manufacturers to identify which technologies to invest in, decide where to assign resources appropriately to remain competitive in a challenging market.<br/><br/>The resulting framework is limited to comparative vehicle-by-vehicle and fleet-by-fleet assessments of diesel, micro-hybrid, mild-hybrid and battery-electric technologies over a life cycle boundary comprising, for life cycle costs: vehicle capital, fuel and electricity costs, maintenance and energy-storage-system replacements, infrastructure, driver labour, and social cost of carbon and comprising for life cycle greenhouse-gas emissions: carbon dioxide-equivalent emissions associated with vehicle manufacturing, well-to-wheels cycle, maintenance, energy-storage-system replacements and infrastructure.<br/><br/>A preliminary framework evaluated multiple diesel and battery-electric bus scenarios, including various battery technologies, well-to-tank pathways, charging infrastructure and auxiliary demands. For every electric bus scenario, there is an 80% confidence that life cycle greenhouse-gas emissions are mitigated compared to the baseline diesel bus, but life cycle costs are higher. Opportunity charged electric buses with lithium-titanate batteries are the most effective at mitigating greenhouse-gas emissions per additional cost to the operator. A trade-off between dependence on battery capacity and high-power charging infrastructure is highlighted. The framework enables stakeholders to make technology adoption and resource allocation decisions based on the risk of a scenario and provides a level of confidence in a technology’s ability to mitigate life cycle impacts.<br/><br/>The framework was further developed to enable risk and uncertainty quantification of diesel, micro-hybrid, mild-hybrid and battery-electric fleet scenarios for a UK-based case study. The peer-reviewed fleet analysis approach revealed decreased potential to reduce life cycle costs and greenhouse-gas emissions from battery-electric buses. At low risk levels, the micro-hybrid double-deck fleet delivers the largest life cycle cost savings (18.7%) of all scenarios. The largest life cycle greenhouse-gas emission savings come from the mild-hybrid lithium-titanate single-deck fleet (20.8%). Double-deck micro and mild hybrid fleets are the most effective at saving both life cycle costs and greenhouse-gas emissions. The modelling approach adds a novel probabilistic capability for making comparative fleet-wide assertions, supporting the decision-making process for implementing new sustainable fleet technologies. Proposed policy incentives were investigated, revealing no total-cost-of-ownership parity between battery-electric and diesel fleets. Hence, proposed incentives are insufficient in encouraging rapid electric fleet uptake. Current capital incentives make mild-hybrids the most cost-competitive option.<br/><br/>Understanding favourable technological and operational parameter combinations informs how new fleets can be effectively utilised to reduce life cycle impacts, where design improvement can be made and where the modelling methodology can be improved. Response surface methodology techniques were employed to identify key influencing life cycle factors. Bus fleet life cycle impacts are significantly influenced by operational demands, highlighting the need to carefully assess technologies on a case-by-case basis. Improving efficiency of diesel drivetrain components is a clear way to improving fuel economy with further gains available from smarter electrified auxiliaries than engine stop-start functionalities. Vehicle light-weighting and powertrain efficiency improvements for electric fleets has a similar greenhouse-gas emission mitigating influence as decarbonising the electricity grid. Well-to-wheel and manufacturing phases should be prioritised in future model developments.","abstract_has_math":false,"creators":["Harris, Andrew"],"institution":"Queen's University Belfast","degree_name":"Doctor of Philosophy","degree_level":"Doctoral Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Soban, Danielle","Smyth, Beatrice"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-7","date_published":"2021-7","updated_at":"2026-07-24T03:55:38Z","subjects":["Technology impact forecasting","life cycle cost","greenhouse gas emissions","bus fleets","life cycle analysis","battery electric vehicles","hybrid vehicles","risk and uncertainty","public transport"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.qub.ac.uk/portal:studenttheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57"],"render_values":[{"text":"oai:pure.qub.ac.uk/portal:studenttheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57","href":null,"code":true}]}]},"links":{"outbound_url":"https://pure.qub.ac.uk/en/studentTheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Soban, Danielle","Smyth, Beatrice"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Northern Ireland Department for the Economy","Wrights Group Limited"]},{"key":"dc:creator","label":"Author","values":["Harris, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-7"]},{"key":"dc:date.issued","label":"Date","values":["2021-7"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Mechanical and Aerospace Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Queen's University Belfast"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://pure.qub.ac.uk/en/studentTheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral Thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Technology impact forecasting","life cycle cost","greenhouse gas emissions","bus fleets","life cycle analysis","battery electric vehicles","hybrid vehicles","risk and uncertainty","public transport"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.qub.ac.uk/portal:studenttheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57","https://pure.qub.ac.uk/en/studentTheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://pure.qub.ac.uk/files/223416431/AHarris_Thesis_Final.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A modal shift from private to public transport, such as increased use of buses, and adoption of alternative powertrain technologies (e.g. diesel-hybrids and battery-electric), are key solutions for mitigating the impacts of climate change, whilst reducing fossil energy dependency and urban congestion and improving air quality. However, there are risks and uncertainties in introducing alternative fleet technologies. Bus operators are faced with significant economic and environmental challenges in identifying technologies that reduce or achieve total-cost-of-ownership parity and mitigate air quality and greenhouse-gas emissions from a well-to-wheels or life cycle perspective. New technologies further present operational challenges; the need for sufficient vehicle range, recharging/refuelling capabilities and similar passenger capacities to meet public service requirements. Bus operators would benefit from a tool that rapidly and clearly analyses the costs and benefits of technologies that could affect these economic, environmental, and operational challenges they face. A need therefore exists for a system-wide assessment tool for quantifying the life cycle impacts of alternative bus fleet technologies.
The aim of this research was to design a novel framework to assist decision-makers in assessing the uncertainty (a state of limited knowledge) and risk (the probability of an outcome) of the life cycle impacts of alternative bus technologies. The developed framework combines technology impact forecasting, computer modelling and statistical analysis techniques, to enable a greater understanding of the direct and indirect impacts of technologies at an early stage of research and development, where all parameters may not be fully defined. This framework will assist with the complex decision-making process for stakeholders of bus fleets and further allow manufacturers to identify which technologies to invest in, decide where to assign resources appropriately to remain competitive in a challenging market.
The resulting framework is limited to comparative vehicle-by-vehicle and fleet-by-fleet assessments of diesel, micro-hybrid, mild-hybrid and battery-electric technologies over a life cycle boundary comprising, for life cycle costs: vehicle capital, fuel and electricity costs, maintenance and energy-storage-system replacements, infrastructure, driver labour, and social cost of carbon and comprising for life cycle greenhouse-gas emissions: carbon dioxide-equivalent emissions associated with vehicle manufacturing, well-to-wheels cycle, maintenance, energy-storage-system replacements and infrastructure.
A preliminary framework evaluated multiple diesel and battery-electric bus scenarios, including various battery technologies, well-to-tank pathways, charging infrastructure and auxiliary demands. For every electric bus scenario, there is an 80% confidence that life cycle greenhouse-gas emissions are mitigated compared to the baseline diesel bus, but life cycle costs are higher. Opportunity charged electric buses with lithium-titanate batteries are the most effective at mitigating greenhouse-gas emissions per additional cost to the operator. A trade-off between dependence on battery capacity and high-power charging infrastructure is highlighted. The framework enables stakeholders to make technology adoption and resource allocation decisions based on the risk of a scenario and provides a level of confidence in a technology’s ability to mitigate life cycle impacts.
The framework was further developed to enable risk and uncertainty quantification of diesel, micro-hybrid, mild-hybrid and battery-electric fleet scenarios for a UK-based case study. The peer-reviewed fleet analysis approach revealed decreased potential to reduce life cycle costs and greenhouse-gas emissions from battery-electric buses. At low risk levels, the micro-hybrid double-deck fleet delivers the largest life cycle cost savings (18.7%) of all scenarios. The largest life cycle greenhouse-gas emission savings come from the mild-hybrid lithium-titanate single-deck fleet (20.8%). Double-deck micro and mild hybrid fleets are the most effective at saving both life cycle costs and greenhouse-gas emissions. The modelling approach adds a novel probabilistic capability for making comparative fleet-wide assertions, supporting the decision-making process for implementing new sustainable fleet technologies. Proposed policy incentives were investigated, revealing no total-cost-of-ownership parity between battery-electric and diesel fleets. Hence, proposed incentives are insufficient in encouraging rapid electric fleet uptake. Current capital incentives make mild-hybrids the most cost-competitive option.
Understanding favourable technological and operational parameter combinations informs how new fleets can be effectively utilised to reduce life cycle impacts, where design improvement can be made and where the modelling methodology can be improved. Response surface methodology techniques were employed to identify key influencing life cycle factors. Bus fleet life cycle impacts are significantly influenced by operational demands, highlighting the need to carefully assess technologies on a case-by-case basis. Improving efficiency of diesel drivetrain components is a clear way to improving fuel economy with further gains available from smarter electrified auxiliaries than engine stop-start functionalities. Vehicle light-weighting and powertrain efficiency improvements for electric fleets has a similar greenhouse-gas emission mitigating influence as decarbonising the electricity grid. Well-to-wheel and manufacturing phases should be prioritised in future model developments."]},{"key":"dc:title","label":"Title","values":["A technology impact forecasting framework for the comparative life cycle cost and carbon assessment of alternative bus fleets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Soban, Danielle","Smyth, Beatrice"],"dc:contributor.sponsor":["Northern Ireland Department for the Economy","Wrights Group Limited"],"dc:creator":["Harris, Andrew"],"dc:date":["2021-7"],"dc:date.issued":["2021-7"],"dc:description.abstract":["A modal shift from private to public transport, such as increased use of buses, and adoption of alternative powertrain technologies (e.g. diesel-hybrids and battery-electric), are key solutions for mitigating the impacts of climate change, whilst reducing fossil energy dependency and urban congestion and improving air quality. However, there are risks and uncertainties in introducing alternative fleet technologies. Bus operators are faced with significant economic and environmental challenges in identifying technologies that reduce or achieve total-cost-of-ownership parity and mitigate air quality and greenhouse-gas emissions from a well-to-wheels or life cycle perspective. New technologies further present operational challenges; the need for sufficient vehicle range, recharging/refuelling capabilities and similar passenger capacities to meet public service requirements. Bus operators would benefit from a tool that rapidly and clearly analyses the costs and benefits of technologies that could affect these economic, environmental, and operational challenges they face. A need therefore exists for a system-wide assessment tool for quantifying the life cycle impacts of alternative bus fleet technologies.
The aim of this research was to design a novel framework to assist decision-makers in assessing the uncertainty (a state of limited knowledge) and risk (the probability of an outcome) of the life cycle impacts of alternative bus technologies. The developed framework combines technology impact forecasting, computer modelling and statistical analysis techniques, to enable a greater understanding of the direct and indirect impacts of technologies at an early stage of research and development, where all parameters may not be fully defined. This framework will assist with the complex decision-making process for stakeholders of bus fleets and further allow manufacturers to identify which technologies to invest in, decide where to assign resources appropriately to remain competitive in a challenging market.
The resulting framework is limited to comparative vehicle-by-vehicle and fleet-by-fleet assessments of diesel, micro-hybrid, mild-hybrid and battery-electric technologies over a life cycle boundary comprising, for life cycle costs: vehicle capital, fuel and electricity costs, maintenance and energy-storage-system replacements, infrastructure, driver labour, and social cost of carbon and comprising for life cycle greenhouse-gas emissions: carbon dioxide-equivalent emissions associated with vehicle manufacturing, well-to-wheels cycle, maintenance, energy-storage-system replacements and infrastructure.
A preliminary framework evaluated multiple diesel and battery-electric bus scenarios, including various battery technologies, well-to-tank pathways, charging infrastructure and auxiliary demands. For every electric bus scenario, there is an 80% confidence that life cycle greenhouse-gas emissions are mitigated compared to the baseline diesel bus, but life cycle costs are higher. Opportunity charged electric buses with lithium-titanate batteries are the most effective at mitigating greenhouse-gas emissions per additional cost to the operator. A trade-off between dependence on battery capacity and high-power charging infrastructure is highlighted. The framework enables stakeholders to make technology adoption and resource allocation decisions based on the risk of a scenario and provides a level of confidence in a technology’s ability to mitigate life cycle impacts.
The framework was further developed to enable risk and uncertainty quantification of diesel, micro-hybrid, mild-hybrid and battery-electric fleet scenarios for a UK-based case study. The peer-reviewed fleet analysis approach revealed decreased potential to reduce life cycle costs and greenhouse-gas emissions from battery-electric buses. At low risk levels, the micro-hybrid double-deck fleet delivers the largest life cycle cost savings (18.7%) of all scenarios. The largest life cycle greenhouse-gas emission savings come from the mild-hybrid lithium-titanate single-deck fleet (20.8%). Double-deck micro and mild hybrid fleets are the most effective at saving both life cycle costs and greenhouse-gas emissions. The modelling approach adds a novel probabilistic capability for making comparative fleet-wide assertions, supporting the decision-making process for implementing new sustainable fleet technologies. Proposed policy incentives were investigated, revealing no total-cost-of-ownership parity between battery-electric and diesel fleets. Hence, proposed incentives are insufficient in encouraging rapid electric fleet uptake. Current capital incentives make mild-hybrids the most cost-competitive option.
Understanding favourable technological and operational parameter combinations informs how new fleets can be effectively utilised to reduce life cycle impacts, where design improvement can be made and where the modelling methodology can be improved. Response surface methodology techniques were employed to identify key influencing life cycle factors. Bus fleet life cycle impacts are significantly influenced by operational demands, highlighting the need to carefully assess technologies on a case-by-case basis. Improving efficiency of diesel drivetrain components is a clear way to improving fuel economy with further gains available from smarter electrified auxiliaries than engine stop-start functionalities. Vehicle light-weighting and powertrain efficiency improvements for electric fleets has a similar greenhouse-gas emission mitigating influence as decarbonising the electricity grid. Well-to-wheel and manufacturing phases should be prioritised in future model developments."],"dc:identifier":["oai:pure.qub.ac.uk/portal:studenttheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57","https://pure.qub.ac.uk/en/studentTheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57"],"dc:identifier.uri":["https://pure.qub.ac.uk/files/223416431/AHarris_Thesis_Final.pdf"],"dc:language":["eng"],"dc:publisher.department":["School of Mechanical and Aerospace Engineering"],"dc:publisher.institution":["Queen's University Belfast"],"dc:relation.isreferencedby":["https://pure.qub.ac.uk/en/studentTheses/dcf2eb59-a073-4dbe-8b78-d7b5c27cae57"],"dc:subject":["Technology impact forecasting","life cycle cost","greenhouse gas emissions","bus fleets","life cycle analysis","battery electric vehicles","hybrid vehicles","risk and uncertainty","public transport"],"dc:title":["A technology impact forecasting framework for the comparative life cycle cost and carbon assessment of alternative bus fleets"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral Thesis"],"dc:type.qualificationname":["Doctor of Philosophy"]},"updated_at":"2026-07-24T03:55:38Z"}