Analysis and prediction of charging behaviors for private battery electric vehicles with regular commuting: A case study in Beijing. (15th August 2022)

• Record Type: Journal Article
• Authors: Ren, Yilong; Lan, Zhengxing; Yu, Haiyang; Jiao, Gangxin
• Is Part Of: Energy. Volume 253(2022)
• Publication Date: 2022-08-15
• Subjects: Battery electric vehicles -- Charging behaviors -- Nested logit model -- Trip chain; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.124160
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Battery electric vehicles (BEVs) assume a critical role in the promotion of transportation electrification. Accurate analysis and prediction of BEVs charging behaviors are essential to solving the issues, such as electricity supply imbalance stemming from the BEVs increasing volume. To achieve that, the agent-based trip chain model (ABTCM) and nested logit model (NL) are proposed in this study based on meter-level real-world data. In our investigation, not only the general charging patterns including trip chains distributions and dynamic attributes, but also the different charging strategies influencing mechanisms are profoundly estimated. The results demonstrate that most BEVs dispense with charging in the chain during one-day trips and users generally hold moderate range psychology before departure. For charging patterns, the longer people travel, the more inclined they are to adopt the fast charging strategy. The start moment SOC, consumed SOC, travel distance, the speed and weather, as well as all last charging status, are common significant factors for both slow charging and fast charging. The argument reveals that it is more applicable to consider charging scene context when exploring BEVs charging behaviors. Furthermore, the task of charging behaviors is conducted by the united NL model, which displays the effectiveness with accessible accuracy. Highlights: Real-world BEVs traveling and charging data are used to analyze. The agent-based trip chain model and the nested logit model are developed. Charging behaviors and charging strategies influencing mechanisms are estimated. The longer people travel, the more inclined they are to adopt fast charging. Charging scene context is a significant impact on the choice of charging patterns.