Multi-Objective Optimal Allocation of Wireless Bus Charging Stations Considering Costs and the Environmental Impact

Abstract

In recent years, due to environmental concerns, there has been an increasing desire to develop alternative solutions to traditional energy sources. Since transportation is a significant fossil-fuel consumer, the development of electric vehicles, especially buses, has the potential to reduce fossil-fuel use and thus provide a better living environment. The aim of the current work was to develop an optimal allocation model for designing a system-wide network of wireless bus charging stations. The main advantages of wireless charging are the need for a much smaller battery and the fact that the charging process may occur under both static and dynamic (in-motion) conditions. The suggested approach consisted of a multi-objective model that selected the locations for the charging stations while (a) minimizing the costs, (b) maximizing the environmental benefit, and (c) minimizing the number of charging stations. The problem was formulated as a multi-objective non-linear optimization model with both deterministic and stochastic variations. An efficient genetic algorithm was introduced to solve the problem. A test case was used to demonstrate the model; accordingly, the decision-maker was provided with a solution set from which the best fit solution could be selected.

Optimal Deployment of Wireless Charging Infrastructure for Electric Tram with Dual Operation Policy

“…They analyzed the charging-facility-choice equilibrium of electric vehicles among the locations and two types of charging facilities. Nahum and Hadas [10] developed an optimal allocation model for wireless charging stations of electric bus. They suggested a non-linear optimization model with multiple objectives; (1) minimize the costs, (2) maximize the environmental benefit, (3) minimize the number of charging stations.…”

Effect of Coil Dimensions on Dynamic Wireless Power Transfer for Electric Vehicles

“…As previously mentioned, DWPT has advantages; Charging the EV dynamically (in-motion) can be translated to a significantly reduced battery size requirement, and the fact that the charging process may occur under both static and dynamic conditions, hence offering extended or even unlimited travel range. For example, the BYD K9 electric bus has a 324 kWh battery weighing 1500 kg which requires recharge of 6 hours every 250 km, compared to an the OLEV bus that uses a 13 kWh battery weighing only 130 kg that can be charged in less than 5 minutes [14]. As most public transport buses have fixed routes, it is possible to design an optimal allocation of charging stations along the route, considering the dwell time at the stops.…”

“…Several papers have dealt with the problem of finding the optimal location for installing transmitting coils from a logistical managerial point of view [15]- [18]. A recent paper by Nahum and Hadas [14] proposed an optimal allocation model for designing a systemwide network of wireless bus charging stations. In this work, the suggested approach consisted of a multi-objective model that selected the locations for the charging stations (i.e., road embedded transmitting coils) while (a) minimizing the costs, (b) maximizing the environmental benefit, and (c) minimizing the number of charging stations.…”

“…As an example, the multi-objective optimization model proposed by Nahum and Hadas [14] was adapted to examine the deployment of infrastructure in the shuttle route of Bar-Ilan University (a single route), with 17 bus stops. Based on dwell times, energy consumption profile and costs of batteries and charging stations, an optimal solution with a 1 kWh battery priced at $ 1500, and nine charging station, was found.…”

Improved Multi-Objective Optimization Model for Policy Design of Rental Housing Market

“…A multi-objective optimization algorithm is a hot research issue in the field of system management, which generally refers to an optimization problem with a target number greater than four. Its research aim is to solve a set of Pareto solution sets that converge to the real Pareto front and are evenly distributed as much as possible [21]. Scholars have developed the multi-objective optimization algorithm to solve practical engineering problems such as allocation of wireless bus charging stations [22] and allocation of hybrid renewable energy systems [23].…”