Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations with Nested Genetic Algorithm

Document Type

Article

Publication Date

1-1-2020

Abstract

The technology of dynamic wireless power transfer (DWPT) has been recognized as an effective way to recharge battery electric bus and overcome some drawbacks (e.g. high battery cost and limited service range) with opportunity charging. This study develops a mixed integer non-linear model to optimize a feeder bus transit powered by DWPT. The decision variables consist of bus route networks, service frequency, locations of DWPT devices and battery capacity. The objective is to minimize total cost, including the costs of charging devices, battery, operation and travel time. A tangible nested genetic algorithm (NGA) is developed to find the optimal solution. The computational efficiency of NGA is demonstrated through numerical comparisons to the solutions founded by LINGO and GA. It was found that with NGA the solution converges to an acceptable level faster than using LINGO and GA. A real-world bus network is employed to explore the relation between the minimized costs and decision variables. The result suggested that DWPT outperforms terminal charging technology in terms of the least total cost, and that the yielded total infrastructure cost with DWPT is 16.6% less than that with terminal charging technology.

Identifier

85083820337 (Scopus)

Publication Title

IEEE Access

External Full Text Location

https://doi.org/10.1109/ACCESS.2020.2985168

e-ISSN

21693536

First Page

67166

Last Page

67178

Volume

8

Grant

300102220204

Fund Ref

National Natural Science Foundation of China

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