Planning of Fast-Charging Stations for a Battery Electric Bus System under Energy Consumption Uncertainty

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.

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Partial Power Processing Based Converter for Electric Vehicle Fast Charging Stations

Electronics ◽

10.3390/electronics10030260 ◽

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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Life-Cycle Assessment of Carbon Footprint of Bike-Share and Bus Systems in Campus Transit

Sustainability ◽

10.3390/su13010158 ◽

2020 ◽

Vol 13 (1) ◽

pp. 158

Author(s):

Sishen Wang ◽

Hao Wang ◽

Pengyu Xie ◽

Xiaodan Chen

Keyword(s):

Life Cycle Assessment ◽

Life Cycle ◽

Energy Consumption ◽

Carbon Footprint ◽

Co2 Emission ◽

Raw Material ◽

Transit System ◽

Two Systems ◽

Bus System ◽

Bike Share

Low-carbon transport system is desired for sustainable cities. The study aims to compare carbon footprint of two transportation modes in campus transit, bus and bike-share systems, using life-cycle assessment (LCA). A case study was conducted for the four-campus (College Ave, Cook/Douglass, Busch, Livingston) transit system at Rutgers University (New Brunswick, NJ). The life-cycle of two systems were disaggregated into four stages, namely, raw material acquisition and manufacture, transportation, operation and maintenance, and end-of-life. Three uncertain factors—fossil fuel type, number of bikes provided, and bus ridership—were set as variables for sensitivity analysis. Normalization method was used in two impact categories to analyze and compare environmental impacts. The results show that the majority of CO2 emission and energy consumption comes from the raw material stage (extraction and upstream production) of the bike-share system and the operation stage of the campus bus system. The CO2 emission and energy consumption of the current campus bus system are 46 and 13 times of that of the proposed bike-share system, respectively. Three uncertain factors can influence the results: (1) biodiesel can significantly reduce CO2 emission and energy consumption of the current campus bus system; (2) the increased number of bikes increases CO2 emission of the bike-share system; (3) the increase of bus ridership may result in similar impact between two systems. Finally, an alternative hybrid transit system is proposed that uses campus buses to connect four campuses and creates a bike-share system to satisfy travel demands within each campus. The hybrid system reaches the most environmentally friendly state when 70% passenger-miles provided by campus bus and 30% by bike-share system. Further research is needed to consider the uncertainty of biking behavior and travel choice in LCA. Applicable recommendations include increasing ridership of campus buses and building a bike-share in campus to support the current campus bus system. Other strategies such as increasing parking fees and improving biking environment can also be implemented to reduce automobile usage and encourage biking behavior.

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