scholarly journals Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles

2020 ◽  
Vol 12 (8) ◽  
pp. 3439 ◽  
Author(s):  
Ye Yang ◽  
Zhongfu Tan ◽  
Yilong Ren

Due to the limited power cell performance of battery electric vehicles (BEVs), BEV drivers endure a short cruising range and a long charging time. Additionally, uneven charging facilities and unreasonable charging arrangements result in partial queuing and partial idling of charging stations. To solve these problems, it is critical to understand BEV charging behavior and its influential factors. Considering the urgency of BEV charging, BEV drivers tend to choose fast charging when BEV is in driving state. This study investigates fast charging behavior by utilizing private BEV connected data collected from Beijing. First, 130 private BEVs with travel rules were screened out. Using seven months of BEV data, a total of 15,752 trajectories were identified, among which 2161 have fast charging behavior. According to the relationship between fast charging behavior and some influential factors, including battery modeling, driving behavior, weather and environment, and even user habit, were empirically investigated. Moreover, the battery state of charge at the start time, time-origin, travel time duration, driving distance, driving speed, wind power, temperature, and last-fast-status are determined as significant influencing factors. Lastly, a prediction model based on the significant factors is proposed to estimate whether there is fast charging in a day trajectory. The proposed model achieves the best accuracy over compared models, i.e., univariate linear regression (ULR) with several factors and multivariate linear regression (MLR) model. The study is expected to help better understand fast charging behavior and further contribute to the future improvement of fast charging efficiency.

2022 ◽  
pp. 114-132
Author(s):  
Gagandeep Sharma ◽  
Vijay K. Sood

This chapter discusses the available charging systems for electric vehicles (EV) which include battery electric vehicles (BEV) and plugged hybrid electric vehicles (PHEV). These architectures are categorized as common DC bus charging (CDCB) station and common AC bus charging (CACB) station. CACB charging stations are generally used as slow chargers or semi-fast chargers (on-board chargers). CDCB charging stations are used as fast chargers (off-board chargers). These chargers are vital to popularize the electric vehicles (EVs) as a green alternative to the internal combustion engine (ICE) vehicles. Further, this chapter covers the power quality problems related to the grid-connected fast charging stations (FCS), AC-DC converter, control strategies for converters, proposed system of architectures, methodology, system results with comparisons, and finally, a conclusion.


Author(s):  
Mohammad Shohidul Islam ◽  
Sultana Easmin Siddika ◽  
S M Injamamul Haque Masum

Rainfall forecasting is very challenging task for the meteorologists. Over the last few decades, several models have been utilized, attempting the successful analysing and forecasting of rainfall. Recorded climate data can play an important role in this regard. Long-time duration of recorded data can be able to provide better advancement of rainfall forecasting. This paper presents the utilization of statistical techniques, particularly linear regression method for modelling the rainfall prediction over Bangladesh. The rainfall data for a period of 11 years was obtained from Bangladesh Meteorological department (BMD), Dhaka i.e. that was surface-based rain gauge rainfall which was acquired from 08 weather stations over Bangladesh for the years of 2001-2011. The monthly and yearly rainfall was determined. In order to assess the accuracy of it some statistical parameters such as average, meridian, correlation coefficients and standard deviation were determined for all stations. The model prediction of rainfall was compared with true rainfall which was collected from rain gauge of different stations and it was found that the model rainfall prediction has given good results.


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