Seungmin Jeong

Dynamic wireless charging (DWC) is an emerging technology that enables the batteries of electric vehicles (EVs) to charge automatically while the vehicles are in motion. The DWC-EV system addresses the challenges inherent in battery technology, such as the short driving range, long recharging time, and high price. Compared with conventional plug-in EVs, the DWC-EV can charge a battery more frequently because it can be done while the EV is in motion from the charging infrastructure installed on the road. In this paper, we analyze how this frequent-charging characteristic of DWC-EV can affect the battery lifetime in the DWC-EV. We first introduce a mathematical model to evaluate the economic cost of the DWC-EV for a given battery size. A battery degradation model is incorporated to account for the quantitative relationship between the installation of the charging infrastructure and battery life extension. We then use the model to analyze how the economic cost varies with the size of the battery. Our preliminary findings provide insight into the relationship between DWC from the charging infrastructure and the battery's lifetime.

This paper presents an initial investment cost analysis of public transportation systems operating with wireless charging electric vehicles (EVs). There are three different types of wireless charging systems, namely, stationary wireless charging (SWC), in which charging happens only when the vehicle is parked or idle, quasi-dynamic wireless charging (QWC), in which power is transferred when a vehicle is moving slowly or in stop-and-go mode, and dynamic wireless charging (DWC), in which power can be supplied even when the vehicle is in motion. This analysis compares the initial investment costs for these three types of charging systems for a wireless charging-based public transportation system. In particular, this analysis is focused on the energy logistics cost in transportation, which is defined as the cost of transferring and storing the energy needed to operate the transportation system. Performing this initial investment analysis is complicated, because it involves considerable tradeoffs between the costs of batteries in the EV fleet and different kinds of battery-charging infrastructure. Mathematical optimization models for each type of EV and infrastructure system are used to analyze the initial costs. The optimization methods evaluate the minimum initial investment needed to deploy the public transportation system for each type of EV charging solution. To deal with the variable cost estimates for batteries and infrastructure equipment in the current market, a cost-sensitivity analysis is performed. The goal of this analysis is to identify the market cost conditions that are most favorable for each type of wireless charging solution. Furthermore, the cost analysis quantitatively verifies the qualitative comparison of the three different wireless charging types conducted in the previous research.

In this paper, we provide the numerical analysis of the charging states for the dynamic wireless charging (DWC) electric vehicles (EVs), or simply called DWC-EVs, which charge the their batteries from the power source embedded under the road while the vehicles are in motion. The previous studies suggested the optimal allocation of the wireless power source called power track and economic size of the battery. However, these studies used the assumption that the vehicles are driving with a predetermined speed. This study relaxes the nominal speed assumption and instead assumes that the uncertainty in the vehicle speed. The goal of this study is to understand the how the uncertainty in driving causes the performance of the charging and operation of the vehicles for the DWC-EV based public transportation system. In this study, we used the operation data collected from the KAIST On-line Electric Vehicle (OLEV) currently operating in a Gumi City in South Korea, which is one of the first commercialized DWC-EVs. With the data, energy states are generated from the Markov-based energy consumption simulation model. The simulation results indicate that there is significant risk of running out of battery if the power tracks are allocated and battery size is selected with the nominal speed assumption.