Joint design of electric bus transit service and wireless charging facilities

Abstract

Cities worldwide are deploying electric buses rapidly to enhance eco-friendly public transportation. Most existing electric transit systems rely solely on stationary charging at terminals and have to equip buses with large-sized batteries to maintain operation in networks. Consequently, the stationary-charging electric bus (SE-Bus) systems are subject to high battery costs, limited range per charge, long charging time (layover time), etc. These limitations may be addressed by integrating the new wireless power transfer technology, which provides contactless charging to the operating buses without extra delays. To guide wireless-charging electric bus (WE-Bus) designs in corridors, this paper proposes an optimization model to jointly optimize (i) the electric supply system and (ii) the transit service that are interdependent.Specifically, we construct parsimonious models for system metrics using five design variables/functions, i.e., battery size, the number of wireless power transfer (WPT) pads, stationary charging rate, service headways, and stop densities. Accordingly, we formulate the optimal design problem to minimize the generalized system cost as the sum of the transit agency’s and patrons’ costs. The problem is attacked by first decomposing into two sub-problems regarding (i) electric supply system design and (ii) transit service design and then solving them iteratively. The (i) sub-problem is converted to a linear programming problem by linearization, and the (ii) sub-problem is analyzed with optimal conditions. Numerical tests show the proposed solution approach produces accurate results.Extensive numerical experiments unveil several findings. First, the WE-Bus systems outperform the SE-Bus in various scenarios except when wireless charging facilities are too costly. Second, the optimized WE-Bus designs can reduce costs not only to the agency (with smaller-sized bus battery and reduced fleet cost) but also to patrons (who enjoy improved services with shorter headways). Additional parameter analyses also investigate the impacts of demand levels, wireless charging rates, depths of discharge, etc. Lastly, a case study in Chengdu (China) illustrates the application of the proposed model.