An Efficient Electric Vehicle Path-Planner That Considers the Waiting Time

Gareau, Jaël Champagne; Beaudry, Éric

doi:10.1145/3347146.3359064

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…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%

“…• Gt-mobisim: Artificial data simulators use Gt-mobisim simulator 6 for generating mobility traces for a large number of mobile agents moving in a constrained network. These data contain 1000 mobile agents, 2000 raw trajectories and 138107 GPS points.…”

Section: • Us Census Bureaumentioning

confidence: 99%

“…They don't consider the specificity of electric cars and the factors that influence their energy consumption. Thus, such road recommendations are not suitable for Electric Vehicles [6].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: • Us Census Bureaumentioning

confidence: 99%

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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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show abstract

“…For example, in 2019, inexpensive EVs typically covered a distance of approximately 250 km. Due to their restricted range, many recharge breaks are required for lengthy trips [12]. BEV users are concerned about the accessibility of charging infrastructure along travel routes, particularly for long-distance journeys [5].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is important to gain a deeper understanding of the charging choice behavior of BEV users to more accurately assess charging needs. For this purpose, research studies have employed methods, such as revealed preference (RP) studies [9][10][11][12] and stated preference (SP) studies [11,[13][14][15][16], to investigate the charging choice behavior of EV users. Many factors including charging duration [13,14,16,17], charging speed [16], the state of charge (SOC) [9,10,12,16], battery capacity [5], and the distance of the next trip [11,12] affect charging choice behavior.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Electric Vehicle Charging Intentions in Japan

Hanni,

Yamamoto,

Nakamura

2024

Sustainability

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This study focuses on charging-related decisions for fast charging at highway service and parking areas, slow charging at home, fast charging at commercial facilities, and fast and slow charging at workplaces. This research contributes to the existing literature by estimating the charging behavior variables, as well as understanding the role of explanatory variables in influencing charging-related decisions. Responses from the stated preference (SP) survey in Japan in 2021 were analyzed with a mixed logit model (MXL). The results showed that, (1) when the battery level is 75% or higher, users of battery electric vehicles (BEVs) are not keen to charge their vehicles, but when the next trip is anticipated to be 50 or more kilometers, they choose to charge their vehicles; (2) individuals are not willing to tolerate any waiting time for their vehicles to be charged at each location; and (3) the recurrence of charging at the target location affects the charging decision of BEV users. We found significant relationships between socioeconomic characteristics and charging decisions. Furthermore, we examined the practical applications of the empirical findings in this study for policymaking and charging infrastructure planning.

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A Fast Electric Vehicle Planner Using Clustering

Gareau

Beaudry

2021

Data Analysis and Rationality in a Complex World

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An Efficient Electric Vehicle Path-Planner That Considers the Waiting Time

Cited by 13 publications

References 12 publications

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

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

An Analysis of Electric Vehicle Charging Intentions in Japan

A Fast Electric Vehicle Planner Using Clustering

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