Dynamic WPT Chargers: State of the Art

Hamed, Ahmed M.; Fadil, H. El; Lassioui, Abdellah; Nady, Soukaina; Jeilani, Sidina El

doi:10.1007/978-3-031-29860-8_79

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…As shown in Figure 4, when a bus moves in segment n on the m-th departure in a day, its velocity v d (n) is modeled as Equation (9). As a common assumption, v d (n) keeps unchanged in the segment.…”

Section: Energy Modelmentioning

confidence: 99%

“…The Electric Bus Dynamic Wireless Charging (EB-DWC) system [7] is a bus charging system that operates on the principle of DWC, enabling electric buses to receive power wirelessly from ground-based electromagnetic induction devices while driving or parked, eliminating the need for traditional plug-in charging. Developed by a research team at the Korea Advanced Institute of Science and Technology (KAIST) [7,9], EB-DWC has undergone pilot implementations in public transportation systems in some cities. The advancement of EB-DWC represents an innovative approach to electric vehicle charging, promising a more convenient and efficient charging method, which could contribute to the widespread adoption and usage of electric vehicles in the future [10].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Optimization of an Electric Bus Dynamic Wireless Charging System

Zhu,

Fan,

Zhang

et al. 2024

WEVJ

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The Electric Bus Dynamic Wireless Charging (EB-DWC) system is a bus charging system that enables electric buses to receive power wirelessly from ground-based electromagnetic induction devices. In this system, how to optimally configure the charging infrastructures while considering the unpredictable nature of bus movement is a great challenge. This paper presents an optimization problem for an EB-DWC system in urban settings, addressing stochastic elements inherent in the vehicle speed, initial charging state, and dwell time at bus stops. We formulate a stochastic planning problem for the EB-DWC system by integrating these uncertainties and apply Monte Carlo sampling techniques to effectively solve this problem. The proposed method can improve the system’s robustness effectively.

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Section: Energy Modelmentioning

confidence: 99%