A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Zhang, Le; Zeng, Ziling; Gao, Kun

doi:10.1108/jicv-07-2021-0009

Cited by 43 publications

(11 citation statements)

References 34 publications

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“…While the existing literature also presents models that incorporate emission considerations [28], extreme fast charging technologies [29], battery swap studies [30], charge scheduling [31] in car-sharing systems [32], intercity travels [33], among other innovative solutions (e.g., see [34], [35], [36], [37]), our paper specifically focuses on the charging location and pricing design given a set of user-centric factors. The main contribution of this paper is developing an integrated framework for the facility location design and dynamic pricing of EV chargers under UE decisions.…”

Section: Discussionmentioning

confidence: 99%

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Mirheli

Hajibabai

2023

IEEE Trans. Intell. Transport. Syst.

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This paper aims to investigate the electric vehicle (EV) charging network design and utilization management considering user-centric decisions. A hierarchical formulation is developed with the EV charging network design and demanddriven pricing scheme in the upper level and users' charging decisions to minimize their own travel costs and charging expenses in the lower level. The model aims to minimize the facility deployment cost and maximize the charging income of the network operator while minimizing the user-centric costs. We have converted the proposed bi-level formulation into an equivalent single-level model using the lower-level objective function as complementary equations. Then, we have developed an iterative active-set based solution technique to determine the strategic decisions on charging network design. To partially overcome the computational burden, the arc travel times are estimated using a macroscopic fundamental diagram concept. The proposed integrated methodology is applied to a hypothetical and an empirical case study to evaluate its performance and solution quality. The numerical results indicate that the proposed algorithm can solve the problem efficiently and outperform a system-level bi-level optimization benchmark. Our experiments show a CPU time of 2.3 hr for the proposed approach compared to 173.1 hr of the benchmark. Finally, a series of sensitivity analyses has been conducted to study the impact of input parameters on the solutions and draw managerial insights.

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Section: Discussionmentioning

confidence: 99%

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Mirheli

Hajibabai

2023

IEEE Trans. Intell. Transport. Syst.

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“…The optimization of BEB scheduling has been a topic of interest within the broader fleet management and charging infrastructure planning problems, as demonstrated by recent studies (An, 2020;Li et al, 2018;Wang et al, 2017;Xu et al, 2018Xu et al, , 2023Zhang et al, 2022;Zhou et al, 2023). In particular, a mixed-integer linear programming technique was presented by Wang et al (2017) to optimize the charging schedule of BEBs and to determine the ideal capacity and location of fast charging stations in a transit network with a single depot.…”

Section: Integrated Electric Bus Planningmentioning

confidence: 99%

Vehicle and Charging Scheduling of Electric Bus Fleets: A Comprehensive Review

Zhang,

Han,

Peng

et al. 2023

J. Int. Con. Veh.

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Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits it offers. However, the adoption of battery electric buses is still impeded by their limited flexibility, a constraint that necessitates adjustments to current bus scheduling plans. Consequently, this study aspires to offer a thorough review of articles focused on battery electric bus scheduling. Moreover, we provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain. Despite this extensive review, our findings reveal a paucity of research that takes into account the robustness of electric bus scheduling. Furthermore, we highlight the critical areas of considering diverse charging modes in electric bus scheduling and integrated planning of electric buses, which have not been adequately explored but hold the potential to greatly boost the effectiveness of electric bus systems. Through this synthesis, we hope that readers could acquire a thorough comprehension of the studies in this field and be motivated to address the identified research gaps, thus propelling the progress of transit electrification.

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“…The model can indicate the wear cost of transferring 1 kWh when cycled from one SOC value to another. The current emerging research for electric vehicle (EV) charging schedule optimization uses a battery aging model that is only applicable to full charging (Zhang et al 2021a(Zhang et al , 2022. However, the introduced model is suitable for capturing partial charging behavior with an arbitrary SOC range.…”

Section: Wear Cost Calculation To Improve Ebcs Modelmentioning

confidence: 99%

Limitations and suggestions of electric transit charge scheduling

Zeng¹,

Daniels²,

Qu³

2022

Prog. Energy

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A major factor hindering the popularization of electric buses in the current automotive market is the high ownership cost of batteries and its large upfront investment. For the daily maintenance of electric fleets, the amortized battery replacement cost is at least six times the charging cost. Thus, ensuring the healthy operation of the battery and prolonging the cycle life are some of the most concerned issues of the bus operators. In order to achieve the best operating mode, the operators are required to formulate an effective charging schedule with minimized battery wear. However, little quantitative formulation exists in prior literature to consider battery wear in bus charge scheduling. In this paper, a general formula is presented for battery wear cost consideration in charge scheduling based on the emerging literature. Then, the existing charge scheduling model is improved based on the proposed approach, and a case study illustrates the significant difference in operating costs between charging plans developed with or without consideration of battery wear. The focus of this commentary is to present the crucial factors to improve the efficiency of electric bus operations and help make the charge scheduling models more realistic.

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A bi-level optimization framework for charging station design problem considering heterogeneous charging modes

Cited by 43 publications

References 34 publications

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Charging Network Design and Service Pricing for Electric Vehicles With User-Equilibrium Decisions

Vehicle and Charging Scheduling of Electric Bus Fleets: A Comprehensive Review

Limitations and suggestions of electric transit charge scheduling

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