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Battery sizing for plug-in hybrid electric buses considering variable route lengths

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  • Shaobo, Xie
  • Qiankun, Zhang
  • Xiaosong, Hu
  • Yonggang, Liu
  • Xianke, Lin

Abstract

For plug-in hybrid electric buses, optimizing the battery size is beneficial for lowering the capital cost and improving the efficiency of energy split between multiple power sources. In addition to the energy management strategy, the optimization of battery size is highly dependent on expected trip mileage. For a fleet of urban buses, the travel distance varies between different routes or even within the same route due to random events. The strategy proposed in this paper addresses uncertainty in route length by a method that coordinates the energy management strategy and battery aging to determine optimal battery size. The route length is assumed to follow a normal distribution, and the overall speed profile along the route is composed of basic driving cycles established using a Markov chain model. The co-optimization of battery size, energy management, and battery aging is formulated as a convex programming problem. The optimal battery size for a given expectation value of route length is identified by minimizing the total weighted cost of energy consumption and battery aging over the whole route length distribution. The results, when assuming a variable route length and calculating a weighted cost, are significantly different compared to the case when a fixed route length is used. When optimal depth-of-discharge is not considered, the weighted cost versus route length has a shallow parabolic shape, and an optimal battery size corresponding to minimum weighted cost can be identified. If the optimal depth-of-discharge is taken into account, the optimal battery size is roughly constant versus route length.

Suggested Citation

  • Shaobo, Xie & Qiankun, Zhang & Xiaosong, Hu & Yonggang, Liu & Xianke, Lin, 2021. "Battery sizing for plug-in hybrid electric buses considering variable route lengths," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006174
    DOI: 10.1016/j.energy.2021.120368
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    References listed on IDEAS

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    2. Alcázar-García, Désirée & Romeral Martínez, José Luis, 2022. "Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles," Energy, Elsevier, vol. 254(PA).
    3. Tomáš Settey & Jozef Gnap & František Synák & Tomáš Skrúcaný & Marek Dočkalik, 2021. "Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    4. Kim, Dong-Min & Lee, Soo-Gyung & Kim, Dae-Kee & Park, Min-Ro & Lim, Myung-Seop, 2022. "Sizing and optimization process of hybrid electric propulsion system for heavy-duty vehicle based on Gaussian process modeling considering traction motor characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. García, Antonio & Monsalve-Serrano, Javier & Lago Sari, Rafael & Martinez-Boggio, Santiago, 2022. "Energy assessment of an electrically heated catalyst in a hybrid RCCI truck," Energy, Elsevier, vol. 238(PA).
    6. Tobias Frambach & Ralf Kleisch & Ralf Liedtke & Jochen Schwarzer & Egbert Figgemeier, 2022. "Environmental Impact Assessment and Classification of 48 V Plug-in Hybrids with Real-Driving Use Case Simulations," Energies, MDPI, vol. 15(7), pages 1-21, March.
    7. Basma, Hussein & Mansour, Charbel & Haddad, Marc & Nemer, Maroun & Stabat, Pascal, 2022. "Energy consumption and battery sizing for different types of electric bus service," Energy, Elsevier, vol. 239(PE).
    8. Zhou, Xingyu & Sun, Chao & Sun, Fengchun & Zhang, Chuntao, 2023. "Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency," Applied Energy, Elsevier, vol. 344(C).

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