A novel fast-charging stations locational planning model for electric bus transit system

Energy ◽  
2021 ◽  
Vol 224 ◽  
pp. 120106
Author(s):  
Xiaomei Wu ◽  
Qijin Feng ◽  
Chenchen Bai ◽  
Chun Sing Lai ◽  
Youwei Jia ◽  
...  
Keyword(s):  
Planning Model ◽  
Transit System ◽  
Bus Transit ◽  
Electric Bus ◽  
Fast Charging ◽  
Charging Stations

scholarly journals Fast-Charging Infrastructure Planning Model for Urban Electric Vehicles

2021 ◽  
Author(s):  
Tran Van Hung
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Future Research ◽  
Planning Model ◽  
Transit System ◽  
Charging Infrastructure ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Fast Charging ◽  
Battery Degradation

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles and will be a sustainable substitution to conventional vehicles. As the number of electric vehicles in cities increases, the charging demand has surged. The optimal location of the charging station plays an important role in the electric vehicle transit system. This chapter discusses the planning of electric vehicle charging infrastructure for urban. The purpose of this work develops an electric vehicle fast-charging facility planning model by considering battery degradation and vehicle heterogeneity in driving range, and considering various influencing factors such as traffic conditions, user charging costs, daily travel, charging behavior, and distribution network constraints. This work identifies optimal fast-charging stations to minimize the total cost of the transit system for deploying fast-charging networks. Besides, this chapter also analyzes some optimization modeling approach for the fast charging location planning, and point out future research directions.

scholarly journals Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

2018 ◽  
Vol 8 (7) ◽  
pp. 1130 ◽  
Author(s):  
Haixiang Zang ◽  
Yuting Fu ◽  
Ming Chen ◽  
Haiping Shen ◽  
Liheng Miao ◽  
...  
Keyword(s):  
Queuing Theory ◽  
Planning Model ◽  
Travel Pattern ◽  
Charging Infrastructure ◽  
Factors Affecting ◽  
Satisfaction Index ◽  
Fast Charging ◽  
Total Social Cost ◽  
Charging Stations ◽  
The Impact

The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user’s travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.

scholarly journals Analysis of an Optimal Planning Model for Electric Vehicle Fast-Charging Stations in Al Ain City, United Arab Emirates

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Madathodika Asna ◽  
Hussain Shareef ◽  
Achikkulath Prashanthi ◽  
Hazlie Mohklis ◽  
Rachid Errouissi ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
United Arab Emirates ◽  
Optimal Planning ◽  
Planning Model ◽  
Al Ain ◽  
Fast Charging ◽  
Charging Stations

scholarly journals Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lei Wang ◽  
Wanjing Ma ◽  
Ling Wang ◽  
Yongli Ren ◽  
Chunhui Yu
Keyword(s):  
Autonomous Vehicles ◽  
Job Shop ◽  
Optimal Solution ◽  
Transit System ◽  
Transit Systems ◽  
Bus Transit ◽  
Electric Bus ◽  
Minimum Number ◽  
First Come First Serve ◽  
High Level

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

scholarly journals Joint Optimization of Regular Charging Electric Bus Transit Network Schedule and Stationary Charger Deployment considering Partial Charging Policy and Time-of-Use Electricity Prices

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xinghua Li ◽  
Tianzuo Wang ◽  
Lingjie Li ◽  
Feiyu Feng ◽  
Wei Wang ◽  
...  
Keyword(s):  
Traffic Noise ◽  
Joint Optimization ◽  
Maintenance Cost ◽  
Adaptive Genetic Algorithm ◽  
Electricity Prices ◽  
Transit System ◽  
Transit Network ◽  
Bus Transit ◽  
Electric Bus ◽  
Network Schedule

Electric buses (EBs) have been implemented worldwide and exhibited great potential for air pollution reduction and traffic noise control. In regular charging scenarios, the deployment of charging facilities and the operational scheduling of the transit system is crucial to bus transit system management. In this paper, we proposed a joint optimization model of regular charging electric bus transit network schedule and stationary charger deployment considering partial charging policy and time-of-use electricity prices. The objective of the model is to minimize the total investment cost of the transit system including the capital and maintenance cost of EBs and chargers, the power consumption cost, and time-related in-service cost. A solving procedure based on the improved adaptive genetic algorithm (AGA) is further designed and a transit network at inner Anting Town, Shanghai, with 8 individual bus routes and 867 daily service trips is adopted for the model validation. The validation results illustrated that the methodology considering the partial charging policy can arrange the charging schedule adaptive to the time-of-use electricity prices. Compared with the benchmark of single line separate scheduling, the proposed model can yield 3 million RMB investment saving by highly utilizing EBs and battery chargers.

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals Partial Power Processing Based Converter for Electric Vehicle Fast Charging Stations

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 260
Author(s):  
Jon Anzola ◽  
Iosu Aizpuru ◽  
Asier Arruti
Keyword(s):  
Electric Vehicle ◽  
Power Converters ◽  
Power Ratio ◽  
Charging Station ◽  
Fast Charging ◽  
Full Power ◽  
Processing Architectures ◽  
Charging Stations ◽  
Fast Charging Station ◽  
Partial Power Processing

This paper focuses on the design of a charging unit for an electric vehicle fast charging station. With this purpose, in first place, different solutions that exist for fast charging stations are described through a brief introduction. Then, partial power processing architectures are introduced and proposed as attractive strategies to improve the performance of this type of applications. Furthermore, through a series of simulations, it is observed that partial power processing based converters obtain reduced processed power ratio and efficiency results compared to conventional full power converters. So, with the aim of verifying the conclusions obtained through the simulations, two downscaled prototypes are assembled and tested. Finally, it is concluded that, in case galvanic isolation is not required for the charging unit converter, partial power converters are smaller and more efficient alternatives than conventional full power converters.

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