Electric bus fleet size and mix problem with optimization of charging infrastructure

Rogge, Matthias; Hurk, Evelien van der; Larsen, Allan; Sauer, Dirk Uwe

doi:10.1016/j.apenergy.2017.11.051

Cited by 243 publications

(114 citation statements)

References 31 publications

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“…Some works have focused on the centralized overnight charging scheduling of EBs fleets [13][14][15][16][17][18][19][20][21]. Other works were dedicated to the optimization of charging infrastructure [22,23]. Some works have also addressed the battery aging issue in optimization by developing a capacity fade model for energy system that are not dedicated to transport [24,25].…”

Section: Introductionmentioning

confidence: 99%

“…The present paper differs from the referred ones [13][14][15][16][17][18][19][20][21][22][23][24][25] as it investigates more precisely the battery electro-thermal and aging behavior in order to minimize the battery aging cost. This work differs also from our previous results [26,27] that dealt with the reduction of the charging cost applied to a multi-objective small-scale EBs fleet and a mono-objective large-scale EBs fleet.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

et al. 2019

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“…As known, the main inputs to an economic input‐output‐based LCSA model are the unit costs of variables that are included in the system boundary under examination (Kucukvar, ). These unit costs are influenced by vehicle types and their technical specifications (Rogge, van der Hurk, Larsen, & Sauer, ). For example, truck platooning is regarded as a new mode in terms of cost structure (Meersman et al., ).…”

Section: Introductionmentioning

confidence: 99%

Life cycle sustainability assessment of autonomous heavy‐duty trucks

Sen

Küçükvar

Onat

et al. 2019

J of Industrial Ecology

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Connected and automated vehicles (CAVs) are emerging technologies expected to bring important environmental, social, and economic improvements in transportation systems. Given their implications in terms of air quality and sustainable and safer movement of goods, heavy-duty trucks (HDTs), carrying the majority of U.S. freight, are considered an ideal domain for the application of CAV technology. An input-output (IO) model is developed based on the Eora database-a detailed IO database that consists of national IO tables, covering almost the entire global economy. Using the Eora-based IO model, this study quantifies and assesses the environmental, economic, and social impacts of automated diesel and battery electric HDTs based on 20 macro-level indicators.The life cycle sustainability performances of these HDTs are then compared to that of a conventional diesel HDT. The study finds an automated diesel HDT to cause 18% more fatalities than an automated electric HDT. The global warming potential (GWP) of automated diesel HDTs is estimated to be 4.7 thousand metric tons CO 2 -eq. higher than that of automated electric HDTs. The health impact costs resulting from an automated diesel HDT are two times higher than that of an automated electric HDT. Overall, the results also show that automation brings important improvements to the selected sustainability indicators of HDTs such as global warming potential, life cycle cost, GDP, decrease in import, and increase in income. The findings also show that there are significant trade-offs particularly between mineral and fossil resource losses and environmental gains, which are likely to complicate decision-making processes regarding the further development and commercialization of the technology.

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“…Emission mitigation is one of the major topics of the 21st century [1]. The emissions from road transport have attracted wide publicity, as they cause a severe threat to inner-city air quality and contribute 22% of the global CO 2 emissions [2,3].…”

Section: Introductionmentioning

confidence: 99%

Carbon Asset of Electrification: Valuing the Transition from Fossil Fuel-Powered Buses to Battery Electric Buses in Beijing

Han

2019

Sustainability

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An increasing number of cities are transitioning from fossil fuel-powered buses for public transport to battery electric buses, but there is still much confusion about the economic evaluation of the electrification of buses, especially in terms of the carbon asset value for carbon emissions reduction in this transition. Taking Beijing as the example, this paper studies the economic value of the transition of public buses from fossil fuel-powered buses to battery electric buses from the perspective of carbon asset theory, and mainly focuses the analysis on direct carbon emissions. First, the theory and methodology of carbon asset evaluation are introduced for the transition from fossil fuel-powered buses to battery electric buses. Second, the internal determinants of the carbon assets for the transition from fossil fuel-powered buses to battery electric buses are studied. Third, the distinct impacts of the determinants of the carbon assets of the transition from fossil fuel-powered buses to battery electric buses are analysed. The results indicate that (1) the transition from fossil fuel-powered buses to battery electric buses has a carbon asset value; (2) the carbon asset value of the transition from fossil fuel-powered buses to battery electric buses is determined by the distance-specific CO2 emissions of fossil fuel-powered buses, the carbon price and the annual driving distances of the buses as well as the discounted rate of the carbon assets for buses and the termination time of the fossil fuel-powered or battery electric buses; and (3) the carbon assets contribute to the economic value of the transition from fossil fuel-powered buses to battery electric buses. This paper provides academic support for the economic evaluation of the transition from fossil fuel-powered buses to battery electric buses in a low-carbon society.

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Electric bus fleet size and mix problem with optimization of charging infrastructure

Cited by 243 publications

References 31 publications

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

Life cycle sustainability assessment of autonomous heavy‐duty trucks

Carbon Asset of Electrification: Valuing the Transition from Fossil Fuel-Powered Buses to Battery Electric Buses in Beijing

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