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Electric fleet charging management considering battery degradation and nonlinear charging profile

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  • Shi, Junzhe
  • Zeng, Teng
  • Moura, Scott

Abstract

The populations of commercial electric vehicles (EVs) and electric robots (ERs) have been growing rapidly in recent years. Yet, the availabilities and incoordination of the charging infrastructure still constrain the operations of all EVs/ERs, resulting in wasted waiting time and, thus, decreased total profits. Coordinating these electric machines as a fleet and identifying the optimal operation and charging schedules to maximize total profit is essential. On the other hand, the charging process usually consists of two charging stages, constant current (CC) and constant voltage (CV), which lead to a nonlinear charging profile. Other factors, such as the high charging current, may significantly accelerate battery degradation and lead to capacity fade. However, the high nonlinearities make the battery charging profile and the degradation model computationally difficult to be integrated into optimization problems. In this study, we propose an innovative fleet management strategy that maximizes the operation revenue and minimizes the cost of electricity and battery degradation while addressing the aforementioned nonlinear charging profile. By proposing two linearization methods to replace the nonlinear parts, we formulated a Mixed-Integer Linear Program (MILP). Furthermore, stemming from the numerical case study, two managerial insights, the impact of the battery SOH on fleet management and the selection of fast charging vs. normal charging modes, are outlined.

Suggested Citation

  • Shi, Junzhe & Zeng, Teng & Moura, Scott, 2023. "Electric fleet charging management considering battery degradation and nonlinear charging profile," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422302488x
    DOI: 10.1016/j.energy.2023.129094
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    References listed on IDEAS

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    1. Shi, Junzhe & Xu, Bin & Shen, Yimin & Wu, Jingbo, 2022. "Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition," Energy, Elsevier, vol. 243(C).
    2. Zhang, Le & Wang, Shuaian & Qu, Xiaobo, 2021. "Optimal electric bus fleet scheduling considering battery degradation and non-linear charging profile," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
    3. Zou, Yuan & Wei, Shouyang & Sun, Fengchun & Hu, Xiaosong & Shiao, Yaojung, 2016. "Large-scale deployment of electric taxis in Beijing: A real-world analysis," Energy, Elsevier, vol. 100(C), pages 25-39.
    4. He, HongWen & Zhang, YongZhi & Xiong, Rui & Wang, Chun, 2015. "A novel Gaussian model based battery state estimation approach: State-of-Energy," Applied Energy, Elsevier, vol. 151(C), pages 41-48.
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    Cited by:

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    2. Huang, Di & Zhang, Jinyu & Liu, Zhiyuan & He, Yiliu & Liu, Pan, 2024. "A novel ranking method based on semi-SPO for battery swapping allocation optimization in a hybrid electric transit system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).

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