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Reducing the carbon footprint of urban bus fleets using multi-objective optimization

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  • Ribau, João P.
  • Sousa, João M.C.
  • Silva, Carla M.

Abstract

The electrification of road vehicles was introduced as a way to significantly reduce oil dependence, increase efficiency, and reduce pollutant emissions, especially in urban areas. The goal of this paper is to find the best alternative vehicle to replace a conventional diesel bus operating in urban environments, aiming to reduce the carbon footprint and still being financially advantageous. The multi-objective nondominated sorting genetic algorithm is used to perform the vehicle optimization, covering pure electric and fuel cell hybrid possibilities (with and without plug-in capability). The used multi-objective genetic algorithm optimizes the powertrain components (type and size) and the energy management strategy. Although multiple optimal solutions were successfully achieved, a decision method is implemented to select one unique solution. A global criterion approach, a pseudo-weight vector approach, and a new multiple criteria score approach are considered to choose a preferred optimal vehicle. Real and synthetic driving cycles are used to compare the optimized buses concerning their powertrain components, efficiency and life cycle of fuel and vehicle materials. The conflict between objectives and the importance of the decision considerations in the final solutions are discussed. Passengers load and air conditioning system influence in the solutions and its life cycle is addressed.

Suggested Citation

  • Ribau, João P. & Sousa, João M.C. & Silva, Carla M., 2015. "Reducing the carbon footprint of urban bus fleets using multi-objective optimization," Energy, Elsevier, vol. 93(P1), pages 1089-1104.
  • Handle: RePEc:eee:energy:v:93:y:2015:i:p1:p:1089-1104
    DOI: 10.1016/j.energy.2015.09.112
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    Cited by:

    1. Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2018. "Assessing life cycle impacts and the risk and uncertainty of alternative bus technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 569-579.
    2. Harris, Andrew & Soban, Danielle & Smyth, Beatrice M. & Best, Robert, 2020. "A probabilistic fleet analysis for energy consumption, life cycle cost and greenhouse gas emissions modelling of bus technologies," Applied Energy, Elsevier, vol. 261(C).
    3. Fei Ma & Wenlin Wang & Qipeng Sun & Fei Liu & Xiaodan Li, 2018. "Ecological Pressure of Carbon Footprint in Passenger Transport: Spatio-Temporal Changes and Regional Disparities," Sustainability, MDPI, vol. 10(2), pages 1-17, January.
    4. Xylia, Maria & Silveira, Semida, 2018. "The role of charging technologies in upscaling the use of electric buses in public transport: Experiences from demonstration projects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 399-415.
    5. Dennis Dreier & Semida Silveira & Dilip Khatiwada & Keiko V. O. Fonseca & Rafael Nieweglowski & Renan Schepanski, 2019. "The influence of passenger load, driving cycle, fuel price and different types of buses on the cost of transport service in the BRT system in Curitiba, Brazil," Transportation, Springer, vol. 46(6), pages 2195-2242, December.
    6. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    7. Cui, Shaohua & Gao, Kun & Yu, Bin & Ma, Zhenliang & Najafi, Arsalan, 2023. "Joint optimal vehicle and recharging scheduling for mixed bus fleets under limited chargers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).

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