Integrated Route and Charging Planning for Electric Vehicles Considering Nonlinear Charging Functions

Ying, Zhang; Ma, Qunfei; Zhang, Xin; Gao, Minghe; Yang, Puhai; He, Hui; Hu, Xiang; Aliya, Baltabay

doi:10.1109/cscwd.2018.8465298

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…On the other hand, the problem of EVs route planning with charging is generally classified to be NP-complete [10] and it widely uses the well-known Traveling Salesman Problem (TSP) as a design model, which is a typical non-deterministic polynomial (NP) hard problem [30]. As it's often impossible to solve these problems in a reasonable time, we used the approximation NSGA-II algorithm to solve our BEV multi-objective path planning and charging problem to yield near-optimal solutions in a satisfactory time.…”

Section: Discussionmentioning

confidence: 99%

“…Travel distance, time and charging costs, also, play an important role in route planning. This makes the problem even more complex and classified as NP-complete [10]. Hulagu et al [11] propose a model dependent on the driving resistances against the motion such as air, rolling, acceleration/deceleration and on the regenerative braking energy rather than being linearly dependent on distance driven.…”

Section: Introductionmentioning

confidence: 99%

“…Travel distance, time and charging costs, also, play an important role in route planning. This makes the problem even more complex and classified as NP‐complete [10]. Hulagu et al.…”

Section: Introductionmentioning

confidence: 99%

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Route planning for electric vehicles

Abbes

Rejeb

Baati

2022

IET Intelligent Trans Sys

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This work addresses the multi‐objective route planning and charging problem for Battery Electric Vehicles (BEVs). The proposed solution is based on the NSGA‐II algorithm to derive the Pareto‐optimal set of eco‐routes with the minimum travel and charging time, distance, energy consumption and charging costs while complying with the constraints related to the battery State of Charge (SoC) and the status of charging stations. Influencing factors, such as, vehicle and battery parameters, weather conditions, driver behaviour, road grade, Time Of Use (TOU) electricity cost, and Charging Stations (CS) status and type, are taken into account. The final results of the proposed method are compared with the well‐established simulator SUMO.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Route planning for electric vehicles

Abbes

Rejeb

Baati

2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Besides, battery drops were considered in [12] but they did not include the waiting time at the charging station nor the charging time. Although, several studies focused on finding optimal routes regarding the charging station location, recharging time [13,14], and waiting time at the charging station [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

Sebai

Rejeb

Denden

et al. 2022

IJECER

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Electric Vehicles (EVs) are regarded to be among the most environmentally and economically efficient transportation solutions. However, barriers and range limitations hinder this technology’s progress and deployment. In this paper, we examine EV route planning to derive optimal routes considering energy consumption by analyzing historical trajectory data. More specifically, we propose a novel approach for EV route planning that considers real-time traffic incidents, road topology, charging station locations during battery failure, and finally, traffic flow prediction extracted from historical trajectory data to generate energy maps. Our approach consists of four phases: the off-line phase which aims to build the energy graph, the application of the A* algorithm to deliver the optimal EV path, the NEAT trajectory clustering which aims to produce dense trajectory clusters for a given period of the day, and finally, the on-line phase based on our algorithm to plan an optimal EV path based on real traffic incidents, dense trajectory clusters, road topology information, vehicle characteristics, and charging station locations. We set up experiments on real cases to establish the optimal route for electric cars, demonstrating the effectiveness and efficiency of our proposed algorithm.

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“…In [28][29][30], the authors propose modeling mixed vehicular traffic where conventional and unconventional mobility participates; furthermore, it is suggested that multiple charging terminals be considered according to homologation according to the requirements needed by each user. Several variables such as limited capacity, path chance, trip time, flow conservation, charging time, spatio-temporal collaboration, terminal types, and traffic events are considered in [31][32][33], and the main aspects to obtain robust solutions that respond to the different situations where unconventional mobility practices exist are introduced.…”

Section: Ev Charging Station Infrastructurementioning

confidence: 99%

Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities

2021

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Achieving high penetration of electric vehicles (EVs) is one of the objectives proposed by the scientific community to mitigate the negative environmental impact caused by conventional mobility. The limited autonomy and the excessive time to recharge the battery discourage the final consumer from opting for new environmentally friendly mobility alternatives. Consequently, it is essential to provide the urban road network with charging infrastructure (CI) to ensure that the demand generated by EV users is met. The types of terminals to be considered in charging stations (CS) are fast and ultra-fast. The high energy requirements in these types of terminals could stress the electrical systems, reducing the quality of service. To size and forecast the resources needed in CI, it is of great interest to model and predict the maximum number of EVs, in each hour, that each CS will have to serve according to the geographic area in which they are located. Our proposal is not based on an assumed number of vehicles to be served by each CS, but rather it is based on the analysis of vehicular traffic in geo-referenced areas, so that the load managers can design the topology of the CS. The maximum vehicular concentration is determined by some considerations such as the road system, direction of the route, length of the road segment, the intersections, and the economic zone to which it belongs. The topology of the road map is freely extracted from OpenStreetMap to know the latitude and longitude coordinates. Vehicular traffic will be modeled through the topology obtained from OpenStreetMap and other microscopic variables to understand the traffic engineering constraints. In addition, the Hungarian algorithm is used as a minimum cost decision tool to allocate demand to the CS by observing vehicular traffic as a cost variable. The multi commodity flow problem (MCFP) algorithm aims to make commodities flow through the road network with the minimum cost without exceeding the capacities of the road sections. Therefore, it is proposed to solve the transportation problem by directing the vehicular flow to the CS while minimizing the travel time. This situation will contribute significantly to the design of the topology of each CS, which will be studied in future research.

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Integrated Route and Charging Planning for Electric Vehicles Considering Nonlinear Charging Functions

Cited by 7 publications

References 11 publications

Route planning for electric vehicles

Route planning for electric vehicles

Optimal Electric Vehicles Route Planning with Traffic Flow Prediction and Real-Time Traffic Incidents

Optimal Sizing of Electric Vehicle Charging Stations Considering Urban Traffic Flow for Smart Cities

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