Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Zhuge, Chengxiang (LSGI) | en_US |
| dc.creator | Yang, Xiong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/13514 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Electric vehicle charging infrastructure planning : a data-driven micro-simulation approach | en_US |
| dcterms.abstract | Transportation electrification has been recognized as one of the most effective strategies in combating energy crises and climate change in the transport sector. While significant progress has been made in the adoption of Electric Vehicles (EVs) within the road transportation sector, further development is impeded by challenges such as range anxiety and inadequate charging infrastructure. These potential obstacles can be overcome through effective charging infrastructure deployment and management, which requires a good understanding and proper modeling of the mobility and charging behavior and patterns of EV users. With an increasing adoption rate of EVs, it becomes possible to collect large-scale real EV usage data, which contains real rich mobility and charging information and would be more suitable for such EV studies than traditional survey data and big data on conventional vehicles (e.g., petrol cars). In response, this thesis will develop a comprehensive analytical and modeling framework for a large-scale one-month GPS trajectory dataset collected from over 76,000 actual private EVs in Beijing (with a sample rate of 68%). The framework comprises a big data analytical framework for EV user behavior and pattern analysis and modeling, a data-driven EV mobility and charging simulation model, and data-driven and simulation-based charging infrastructure deployment and management models. | en_US |
| dcterms.abstract | Firstly, an analytical framework was developed for the EV trajectory data to characterize the mobility and charging behavior and patterns of private EV users, such as key characteristics of mobility patterns, differences between Conventional Vehicles (CVs) and EVs in mobility patterns, and charging behavior and patterns by dominant charging location, where EV users get their EVs recharged more frequently. New insights have been obtained: private EV users tended to have both regular travel and activity patterns, perform activities within a small geographical area, and have unique mobility traces; private EV users tended to make more trips and travel longer distances per day than their CV counterparts; over 50% of private EV users were Home Dominated (HD) users with most charging events occurring around home; there are significant differences in charging patterns among private EV users from different groups by dominant charging location. | en_US |
| dcterms.abstract | Secondly, based on the empirical findings of private EV users’ mobility and charging behavior and patterns obtained above, a data-driven and activity-based EV micro-simulation (DAEVSim) model was developed to well represent their mobility and charging behavior and patterns at the micro-scale. The DAEVSim model comprised three key data-driven approaches: an initial travel demand generation method, an EV energy consumption model, and an EV charging model. Its performance was evaluated in a case study of Beijing. The calculated coincidence rates with a 99% confidence interval indicated a high level of consistency between real-world observations and simulation results in terms of mobility and charging patterns, suggesting the effectiveness of the DAEVSim model. Furthermore, the simulation results about the usage of charging posts indicated the inappropriate deployment of public charging stations and the great potential of sharing private charging posts. | en_US |
| dcterms.abstract | Thirdly, the well-validated DAEVSim model was further used to explore the potential of private home charging post sharing (PHCPS) and deploy public charging stations (PCSs), respectively. For PHCPS, its potential was quantified with the scenarios with and without PHCPS, both of which were simulated using the DAEVSim model. Furthermore, baseline and “what-if” scenarios were developed, again in the case study of Beijing, to explore how different settings in PHCPS would influence its potential. The results suggested that PHCPS could provide more charging opportunities for EV users and decrease their dependence on PCSs. For example, in the baseline scenario, PHCPS resulted in a 7.8% increase in the proportion of parking events that were accessible to any usable charging posts and a 22.5% decrease in the average electricity provided by a public charging post on a working day. For the deployment of PCSs, a simulation-based optimization approach was developed based on the DAEVSim model. The optimization model considered the influence of different layouts of PCSs on both the annual net profit for the PCS operator and public charging opportunities for private EV users. The model was also applied in Beijing and tested through sensitivity analysis. The results showed that, compared to the PCS deployment without PHCPS, coupling the PCS deployment with PHCPS tended to increase public charging opportunities for private EV users while resulting in a reduction in the annual net profit for the PCS operator. Also, changes in parameter values related to the capital cost of constructing PCSs and the government subsidy would significantly influence the annual net profit for the PCS operator, but would have nearly no influence on public charging opportunities for private EV users. The outcomes of this study are expected to be useful for policymaking and infrastructure planning and management for a transportation system undergoing electrification. | en_US |
| dcterms.extent | xviii, 191 pages : color illustrations, maps | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.LCSH | Electric vehicles -- Power supply | en_US |
| dcterms.LCSH | Battery charging stations (Electric vehicles) | en_US |
| dcterms.LCSH | City planning -- Data processing | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
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