Abstract
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.
Highlights
In recent years, the number of motor vehicles worldwide exceeded 1 billion
We find that the start-state of charge (SOC) is negatively correlated with the fast charging behavior
The results show that the start-SOC, time-origin, travel time duration, driving distance, driving speed, and temperature are significant influential factors
Summary
The number of motor vehicles worldwide exceeded 1 billion. The massive amount of motor vehicle possession has increased people’s concerns over petroleum dependence, energy security, and environmental quality of cities [1,2]. Due to zero carbon-based emissions and high energy-efficiency, battery electric vehicles (BEVs) have substantial potential for fleet applications [3]. Based on these considerations, many countries carried out the national policies to support and promote the BEVs, and the number of BEVs has increased rapidly [4]. The uneven distribution of charging facilities leads to the phenomenon of some charging queuing and some idle [13] This severely limits the charging of BEVs and further affects the promotion and application of electric vehicles. This paper will use the historical data of Beijing’s electric private BEVs to extract and analyze the trajectory data and fast charging behavior information of each vehicle every day. We conclude this paper with a few perspectives for future research
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