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Characterization of battery electric transit bus energy consumption by temporal and speed variation

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  • Perugu, Harikishan
  • Collier, Sonya
  • Tan, Yi
  • Yoon, Seungju
  • Herner, Jorn

Abstract

De-carbonizing transport is an important strategy for combating climate change and reducing the health impacts of air pollutants. The transit bus sector is one of the highest potential categories to be electrified with Battery-Electric Buses (BEBs). The energy consumption and fueling patterns of BEBs, however, will be different from conventional technology buses, so research is needed to better understand these phenomena to make the BEB rollout successful. We have analyzed the BEB activity and charging data collected over a year from a transit fleet (consisting of 40-ft and 60-ft BEBs) undergoing complete electrification. The average energy consumption for 40-ft and 60-ft buses were 2.6 ± 0.3 kWh/mile and 3.6 ± 0.5 kWh/mile, respectively, over the year. The regenerative braking recovered a significant amount of energy spent and that accounts for 37.3% and 40.2% of the total average energy required for 40-ft and 60-ft bus operation, respectively. Bus speed has a significant effect on average energy consumption per mile; the higher speed resulted in less energy consumption for both types of buses. The variability of seasonal and intra-day energy consumption per mile can be attributed to increased use of air conditioner (A/C) and heater, which were controlled according to the ambient temperatures. This transit agency may incur 16.2 ± 2.1% (based on current fleet composition, energy tariffs) more energy costs in Summer compared to Winter, if no optimized operation is used. This effort provides reliable BEB energy consumption estimates that can be used for any transit fleet's energy efficiency objective.

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  • Perugu, Harikishan & Collier, Sonya & Tan, Yi & Yoon, Seungju & Herner, Jorn, 2023. "Characterization of battery electric transit bus energy consumption by temporal and speed variation," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222028006
    DOI: 10.1016/j.energy.2022.125914
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    2. Wang, Hongfei & Guan, Hongzhi & Qin, Huanmei & Zhao, Pengfei, 2024. "Assessing the sustainability of time-dependent electric demand responsive transit service through deep reinforcement learning," Energy, Elsevier, vol. 296(C).
    3. Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2023. "A novel method for co-optimizing battery sizing and charging strategy of battery electric bus fleets: An application to the city of Paris," Energy, Elsevier, vol. 285(C).
    4. Zvonimir Dabčević & Branimir Škugor & Ivan Cvok & Joško Deur, 2024. "A Trip-Based Data-Driven Model for Predicting Battery Energy Consumption of Electric City Buses," Energies, MDPI, vol. 17(4), pages 1-27, February.
    5. Zhang, Rui & Yu, Jilai, 2024. "Evaluating multi-dimensional response capability of electric bus considering carbon emissions and traffic index," Energy, Elsevier, vol. 286(C).

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