Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

Siddiqi, Umair F.; Shiraishi, Yasuyuki; Sait, Sadiq M.

doi:10.1109/isda.2011.6121687

Cited by 34 publications

(21 citation statements)

References 12 publications

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“…These problems only consider routing in a network without charging facilities. Kobayashi et al (2011) and Siddiqi et al (2011) further include battery charging stations in their models and propose heuristic techniques as solution methodologies. Note that assuming the electricity as a commodity similar to gasoline, the algorithms mentioned above for MCPP-CV can also be used as solution methodologies for MCPP-EV.…”

Section: Related Literaturementioning

confidence: 99%

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

Arslan

Yıldız

Karaşan

2015

Transportation Research Part E: Logistics and Transportation Re

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a b s t r a c tWe introduce a practically important and theoretically challenging problem: finding the minimum cost path for PHEVs in a road network with refueling and charging stations. We show that this problem is NP-complete and present a mixed integer quadratically constrained formulation, a discrete approximation dynamic programming heuristic, and a shortest path heuristic as solution methodologies. Practical applications of the problem in transportation and logistics, considering specifically the long-distance trips, are discussed in detail. Through extensive computational experiments, significant insights are provided. In addition to the charging infrastructure availability, a driver's stopping tolerance arises as another critical factor affecting the transportation costs.

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Section: Related Literaturementioning

confidence: 99%

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

Arslan

Yıldız

Karaşan

2015

Transportation Research Part E: Logistics and Transportation Re

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“…This work, however, does not consider the presence of charging stations modeled as nodes in the network. Charging times are incorporated into a multi-constrained optimal path planning problem in [4], which aims to minimize the length of an EV's route and meet constraints on total traveling time, total time delay due to signals, total recharging time, and total recharging cost; a particle swarm optimization algorithm is then used to find a suboptimal solution. In this formulation, however, recharging times are simply treated as parameters and not as controllable variables.…”

Section: Introductionmentioning

confidence: 99%

Optimal routing and charging of energy‐limited vehicles in traffic networks

Pourazarm

Cassandras

Wang

2015

Intl J Robust & Nonlinear

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SUMMARYWe study the problem of routing vehicles with energy constraints through a network where there are at least some charging nodes. We seek to minimize the total elapsed time for vehicles to reach their destinations by determining routes, as well as recharging amounts when the vehicles do not have adequate energy for the entire journey. For a single vehicle, we formulate a mixed-integer nonlinear programming problem and derive properties of the optimal solution allowing it to be decomposed into two simpler problems. For a multi-vehicle problem, where traffic congestion effects are included, we seek to optimize a system-wide objective and formulate the problem by grouping vehicles into 'subflows'. We also provide an alternative flow optimization formulation leading to a computationally simpler problem solution with minimal loss in accuracy. Because the problem size increases with the number of subflows, a proper selection of this number is essential to render the problem computationally manageable and reflects a trade-off between proximity to optimality and computational effort needed to solve the problem. We propose a criterion and procedure leading to an appropriate choice of the number of subflows. We also quantify the 'price of anarchy' for this problem and compare user-optimal to system-optimal performance. Finally, when the system consists of both electric vehicles (EVs) and non-electric vehicles, we formulate a system-centric optimization problem for optimal routing of both non-electric vehicles and EVs along with an optimal policy for charging EVs along the way if needed. Numerical results are included to illustrate these approaches.

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“…incorporated into a multi-constrained optimal path planning problem in [4], which aims to minimize the length of an EV's route and meet constraints on total traveling time, total time delay due to signals, total recharging time and total recharging cost. A particle swarm optimization algorithm is used to find a suboptimal solution.…”

Section: Introductionmentioning

confidence: 99%

Energy-aware Vehicle Routing in Networks with Charging Nodes

Tao

Cassandras

Pourazarm

2014

IFAC Proceedings Volumes

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Abstract-We study the problem of routing vehicles with energy constraints through a network where there are at least some charging nodes. We seek to minimize the total elapsed time for vehicles to reach their destinations by determining routes as well as recharging amounts when the vehicles do not have adequate energy for the entire journey. For a single vehicle, we formulate a mixed-integer nonlinear programming (MINLP) problem and derive properties of the optimal solution allowing it to be decomposed into two simpler problems. For a multi-vehicle problem, where traffic congestion effects are included, we use a similar approach by grouping vehicles into "subflows." We also provide an alternative flow optimization formulation leading to a computationally simpler problem solution with minimal loss in accuracy. Numerical results are included to illustrate these approaches.

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Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

Cited by 34 publications

References 12 publications

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

Minimum cost path problem for Plug-in Hybrid Electric Vehicles

Optimal routing and charging of energy‐limited vehicles in traffic networks

Energy-aware Vehicle Routing in Networks with Charging Nodes

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