A wireless charging or inductive charging electric vehicle (EV) is a type of EVs with a battery that is charged from a charging infrastructure, using a wireless power transfer technology. Wireless charging EVs are classified as stationary or dynamic charging EVs. Stationary charging EVs charge wirelessly when they are parked, and dynamic charging EVs can charge while they are in motion. The online electric vehicle developed at the Korea Advanced Institute of Science and Technology is an example of a commercially available dynamic charging transportation system. Numerous studies have reported that one of the benefits of dynamic charging is that it allows smaller and lighter batteries to be used, due to frequent charging using the charging infrastructure embedded under roads. In this paper, we quantitatively analyze the benefits of dynamic charging with an economic model of battery size and charging infrastructure allocation, using a mathematical optimization model. Particularly, we analyze by how much battery size can be reduced and what the cost saving of reducing the battery size is with the model. We also show that the dynamic charging can be beneficial to battery life.

The Online Electric Vehicle (OLEV) is an innovative electric transportation system developed by the Korea Advanced Institute of Science and Technology (KAIST), Daejon, Korea, which remotely picks up electricity from power transmitters buried underground. Unlike a conventional electric vehicle that requires significant recharging downtime, the battery in the OLEV can be charged while the vehicle is in motion. Selected as one of “the 50 Best Innovations of 2010” by TIME Magazine, the OLEV is considered as a potential solution for the next-generation electric public transportation system in South Korea. The prototype of the OLEV has been developed, and the commercialization process is now in progress. One of the main tasks to achieve the successful commercialization of the system is to determine economically how to allocate the power transmitters on the given routes and how to evaluate the right battery capacity for the vehicle. The allocation of the power transmitters and the size of the battery capacity directly affect the initial infrastructure cost. In this paper, we first introduce the system design issues of the mass transportation system operating with OLEV. We then present a mathematical model and an optimization method to allocate economically the power transmitters and to determine the battery capacity of the OLEV-based mass transportation system. The particle swarm optimization (PSO) algorithm is used as the solution method for the optimization problem. Numerical problems with sensitivity analysis are presented to show the validity of the mathematical model and solution procedure.

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.

Dynamic wireless charging (DWC) technology, a novel way of supplying vehicles with electric energy, allows the vehicle battery to be recharged remotely while it is moving over power tracks, which are charging infrastructures installed beneath the road. DWC systems mitigate the range limitation of electric vehicles by using power tracks as additional sources of electric energy. This paper proposes a model and algorithm for optimally designing DWC electric vehicle (EV) systems, particularly those operating in multiple-route environments. Multi-route system comprises several single routes that share common road segments, and the vehicles operating on a specific route are equipped with identical batteries. We build a general model to optimally allocate power tracks and determine the vehicle battery size for each route. Then, we apply a particle swarm optimization algorithm to solve the given multi-route DWC-EV system optimization problem. A numerical example is solved to illustrate the characteristics of the multi-route model, and we show that the proposed modeling approach and algorithm are effective, compared with a mixed integer programming-based exact solution approach. We also conduct a sensitivity analysis to examine the solution behavior of the problem.

We introduce a new type of electric transit bus (ETB) system that uses the innovative wireless power transfer technology developed by the Korea Advanced Institute of Technology (KAIST), which is called on-line electric vehicle (OLEV). In the ETB system, the wireless-charging infrastructure installed under the road charges the fleet of electric buses that are operative over that road. The technology is innovative in that the battery in the bus is charged while it is moving over the charging infrastructure. Unlike conventional electric vehicles, the OLEV-based ETB system is a road-vehicle integrated system. Since charging occurs while the vehicle is operational, the performance of the operation depends on the system integration of the vehicle and the road in which the charging infrastructure is embedded. In this paper, we qualitatively analyze the benefits of the OLEV-based ETB system from the energy logistics perspective. We then present two analytical economic design optimization models. The first model is for an ETB system operating in a “closed environment” with no traffic and no heavy vehicle interactions. The OLEV-based shuttle bus currently operating on the KAIST campus constitutes such a case. The second model is the “open environment model” and considers an ETB system operating in normal traffic conditions. We also present the result of numerical case studies for the optimization models. The goal of this paper is to present an innovative ETB system and a logical design framework for commercializing and deploying that system.