Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

Khan, Zadid; Khan, Sakib Mahmud; Chowdhury, Mashrur; Safro, Ilya; Ushijima‐Mwesigwa, Hayato

doi:10.1111/mice.12439

Cited by 16 publications

(9 citation statements)

References 48 publications

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“…It is expected that the influence of range anxiety is considered in more depth and detail in the future work. Wireless charging technology for an electric bus is helpful for solving the issue of limited driving range (Khan et al., 2019). Besides, the charging schedule and the size of the charging station may cause bus to queue in the station (Mencía et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

Optimal route design of electric transit networks considering travel reliability

Tong

Yan

Wang

et al. 2021

Computer aided Civil Eng

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Travel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl's road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase.

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Section: Discussionmentioning

confidence: 99%

Optimal route design of electric transit networks considering travel reliability

Tong

Yan

Wang

et al. 2021

Computer aided Civil Eng

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“…Sharma et al (2019) [92] compared the park-and-ride lot choice behaviour of users based on the random utility maximization model and random regret minimization model and indicated a trade-off relationship. Khan et al (2019) [93] compared the solution qualities and computation times of 12 global mixed-integer non-linear programming solvers and proposed a wireless charging utility maximization framework for electric vehicles. Bachmann (2019) [94] calculated the impact of changes in transport networks and spatial economies on interregional and international trade patterns based on two models.…”

Section: Utility Analysis Methodsmentioning

confidence: 99%

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Gui

2020

Sustainability

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The transportation utility values calculated by traditional utility methods are not comprehensive. Some objects and factors are ignored in traditional utility methods, and this narrow perspective is their primary drawback. In intelligent transportation systems, it is necessary to calculate transportation utility for promoting public traffic planning and management. To build a sustainable intelligent transportation system, modified utility methods are essential to analyze transportation utility in a comprehensive way with innovative technologies and efficient communication systems. To solve the disadvantages of traditional utility methods, it is necessary to establish a new method to build sustainable public transport in the future. In this study, the Multiple Utility Method and Transportation Utility Method are proposed for public transport planning and management from multiple perspectives. A sample is presented to provide a better description, and 69,174 GPS-equipped taxi data in Haikou are adopted for the application of the Transportation Utility Method. The results show that the transportation utility values calculated by the Transportation Utility Method are more comprehensive than the transportation utility calculated by traditional utility methods. This indicates that it is necessary to calculate transportation utility from multiple perspectives based on the Transportation Utility Method. Future directions could include improving the methods, considering more factors, expanding the data used, and extrapolating this research to other cities around the world with similar urban metrics and urban form.

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“…Most current research efforts in VSPs concentrate on conventional fuel buses, such as the scheduling problem overview (Adler, 2014;Bunte & Kliewer, 2009;Ceder, 2002;Laporte, 2009;Meng & Qu, 2013;H. Wang & Shen, 2007; S. Wang et al, 2018), the multiple depot VSPs (Hadjar et al, 2006), vehicle scheduling with multi-vehicle types (Ceder, 2011), vehicle and crew scheduling problem (Amberg et al, 2019;Kliewer et al, 2012), integrated approach to timetabling and vehicle scheduling (Ibarra-Rojas et al, 2014;Schmid & Ehmke, 2015), dynamic control method (Bie et al, 2020;Khan et al, 2019;M. Li et al, 2011;Xie & Jiang, 2016), reliability of trip times (Liu et al, 2013;Naumann et al, 2011;Shen et al, 2017), and other intelligent transportation systems (Adeli & Ghosh-Dastidar, 2004;Adeli & Jiang, 2009;Gao et al, 2020Gao et al, , 2021Ghosh-Dastidar & Adeli, 2006;X.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Bie

Wang

et al. 2021

Computer aided Civil Eng

112

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This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption.First, a model for estimating the trip energy consumption is proposed based on field-collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs.

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Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

Cited by 16 publications

References 48 publications

Optimal route design of electric transit networks considering travel reliability

Optimal route design of electric transit networks considering travel reliability

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

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