Agent-based Learning Approach to Electric Vehicle Routing Problem with Vehicle-to-Grid Supply and Battery Swapping

Narayanan, Ajay; Misra, Prasant; Ojha, Ankush; Gupta, Abhinav; Ghosh, Supratim; Vasan, Arunchandar

doi:10.1145/3570991.3571049

Cited by 8 publications

(5 citation statements)

References 12 publications

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Section: Accounting Charging Problem Of Evsmentioning

confidence: 99%

“…The results provide direction regarding the optimal fleet size and total distance traveled while minimizing associated costs. Narayanan et al 41 analyzed the issue of vehicle routing for an EV fleet with vehicle‐to‐grid and battery switching with the goal of minimizing the total delivery cost. Moreover, several other applications of designing a proper recharging infrastructure are also observed while encouraging effective routing plans of EVs 36,42,43 …”

Section: Introductionmentioning

confidence: 99%

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A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

Aggarwal

Rani

Bedi

et al. 2023

Concurrency and Computation

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SummaryTraveling has contributed a lot to the evolution of mankind. Today, electric vehicles (EVs) are being preferred due to their greater efficiency, comfort, and environment‐friendly qualities. The EVs' contribution to future mobility is projected to rise exponentially in years to come. To make this innovative technology more successful, there is a dire need to install a sufficient number of charging stations (CSs). As the EVs are limited by their cruising range, they require multiple recharging to cover long distances (especially in the case of logistics delivery services). Thus, there is a great need to develop an efficient and cost‐effective EV route optimization approach considering multiple recharging options and time‐of‐use (ToU) energy prices. In this regard, a novel mat‐heuristic approach named (firefly with ant colony algorithm) has been proposed to solve the problem of EVRPTW (electric vehicle routing problem with time windows) incorporating detailed modeling of multiple charging flexibility (i.e., battery swapping, partial recharge, and different charging levels) and ToU energy prices. Our proposed approach aims to minimize the total cost of traveling, which is highly influenced by the cost of recharging. Ant colony algorithm (ACA) serves as the basic optimization framework in the proposed approach, while the firefly approach explores hitherto unexplored solution space and avoids local optima. The computation performance of the proposed approach is compared with existing state‐of‐the‐art similar domain approaches such as variable neighborhood search (VNS) and ant colony optimization using local search (ACO‐LS) which has average deviation of nearly 20%–25% from with optimal solution achieved by the proposed . The proposed approach yields a near‐optimal solution with a faster convergence rate (approximately 50%) compared to other existing approaches. Moreover, the multiple recharging options modeled in our proposed approach justify their significance in terms of cost‐effectiveness for most scenarios.

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Section: Accounting Charging Problem Of Evsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

Aggarwal

Rani

Bedi

et al. 2023

Concurrency and Computation

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show abstract

“…And plug-in charging can be categorized as fast or slow based on the charging speed, and as complete or partial based on the charging strategy. Felipe et al [11] were the first to introduce multiple recharging technologies such as fast charging, slow charging, and partial charging strategy in EVRP. In addition to determining the travel route, this study also decided on the charging method and energy replenishment.…”

Section: Electric Vehicle Routing Problem With Multiple Recharging Op...mentioning

confidence: 99%

A review on the electric vehicle routing problem and its variations

2023

FSST

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As an important extension of the traditional Vehicle Routing Problem (VRP), the Electric Vehicle Routing Problem (EVRP) has attracted much attention of scholars. In this paper, we systematically summarize the literature on EVRP at home and abroad, and provide a detailed summary of the research on EVRP considering time windows, different recharging options and capacitated stations. Then, a variety of solving methods are introduced, including exact methods and heuristic methods. Finally, this study also provides some prospects for the future development trends of EVRP.

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“…Next, if ETs arrive at depot, the set of constraints (15) deal with ETs' charging and discharging options. Also, the set of constraints ( 16)- (18) ensure that ETs can be charged or discharged within the battery capacity, the remaining battery level, or the battery level above 0. Lastly, all ETs are dispatched at fully charged battery level at depot in the set of constraints (19).…”

Section: Model Presentationmentioning

confidence: 99%

“…Teir analysis highlighted the prevalence of metaheuristic techniques in most studies, with limited attention given to emerging machine learning approaches. Tey suggest a future direction wherein not only machine learning but also reinforcement learning [18], distributed systems, the Internet of Vehicles (IoV), and innovative fuel technologies play signifcant roles in advancing GVRP research. Furthermore, to enhance the economic viability of electric vehicles, a strategy involving the utilization of V2G technology can be examined as a means of generating additional revenue [19].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Electric Truck Routing and Charging with Soft Time Windows Using Vehicle-to-Grid Technology

Ryu,

Hwang

2023

Journal of Advanced Transportation

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Due to the rapid increase in the use of electric vehicles and instability in energy supply, the application of vehicle-to-grid (V2G) technology has gained attention in the freight transportation sector. V2G has the potential to increase the efficiency of power grid and make additional profits by utilizing surplus power from electric vehicle batteries. This paper proposes an optimization model for electric trucks (ETs) to provide operational decision-making support for the freight transportation sector. The objective of the model is to minimize the total net cost, which includes charging cost, discharging reward, and time penalties, while considering changes in ET charging cost and the system marginal price. Furthermore, we conduct sensitivity analysis in the vehicle routing problem with soft time windows using ETs in the V2G system.

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Agent-based Learning Approach to Electric Vehicle Routing Problem with Vehicle-to-Grid Supply and Battery Swapping

Cited by 8 publications

References 12 publications

A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

A review on the electric vehicle routing problem and its variations

Optimizing Electric Truck Routing and Charging with Soft Time Windows Using Vehicle-to-Grid Technology

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