Estimating charging demand from the perspective of choice behavior: A framework combining rule-based algorithm and hybrid choice model
It is of great significance to study the generation and spatio-temporal variation of electric vehicle charging demand for guiding the planning, construction and operation of charging facilities. Based on users' daily travel laws and behaviors, this paper proposed a framework for charging demand...
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| Vydané v: | Journal of cleaner production Ročník 376; s. 134262 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
20.11.2022
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| Predmet: | |
| ISSN: | 0959-6526, 1879-1786 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | It is of great significance to study the generation and spatio-temporal variation of electric vehicle charging demand for guiding the planning, construction and operation of charging facilities. Based on users' daily travel laws and behaviors, this paper proposed a framework for charging demand estimation, which combines rule-based algorithm and discrete choice model. The generation process of charging demand is divided into two choice stages. Stage one is before travel, where users choose whether to charge in the trip chain. Stage two is during travel, where users choose the charging location. The hybrid choice model based on the mixed logit model is constructed for the two choice stages respectively. This model can comprehensively consider the influence of several factors on charging choice behavior, including individual attitudes, preference heterogeneity, electric vehicle status, traffic attributes, parking place attributes and charging service level. In addition, the rule-based algorithm can establish the correlation within a trip chain, which refers to influence of the previous episode on the current episode. It can also update the status changes of battery electric vehicles and trips. In this way, the spatio-temporal coupling estimation of charging demand is achieved. Next, two stated preference surveys are conducted through online platform and field survey. The estimation results show that both probabilistic models have achieved high goodness-of-fit, and the proposed model can both characterize the influence of psychological attitude and preference heterogeneity of electric vehicle users on choice behavior. The parameter calibration results show that the user's travel plan plays a very important role in the process of generating charging demand. Besides, the attributes of the parking place and the service quality of the charging facilities are important factors affecting the choice of the charging location. The research results can guide the formulation of charging facility planning and operation strategies.
•A charging demand estimation framework is proposed.•The method considers the psychological attitude and preference heterogeneity.•A two-stage revealed preference and stated preference survey is conducted.•The travel plan and BEV status play an important role in charging demand generation.•Recommendations on planning and operation of charging facilities are presented. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0959-6526 1879-1786 |
| DOI: | 10.1016/j.jclepro.2022.134262 |