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.
Highlights
Vehicle emission has attracted global concerns for a long time
The emerging dynamic wireless power transfer (DWPT) technology, allowing the Battery electric bus (BEB) charged in motion, seems applicable to alleviate the range anxiety concerned by bus operators [8], [9]
It is found that as the scale of network size increases, the computation time consumed by LINGO exponentially increases, while the times consumed by GA and nested genetic algorithm (NGA) are significantly less than LINGO
Summary
Vehicle emission has attracted global concerns for a long time. The transport sector emissions represented 23% of the global emissions, with road transport emissions accounting for 75% of the total emissions in the sector [1], [2]. The emerging dynamic wireless power transfer (DWPT) technology, allowing the BEB charged in motion, seems applicable to alleviate the range anxiety concerned by bus operators [8], [9]. More cost could be incurred by deploying DWPT devices if the bus routes, locations of DWPT and battery capacity were not strategically optimized [12]. G. Chen et al.: Optimizing Battery-Electric-Feeder Service and Wireless Charging Locations by the DWPT technology. (1) A mixed integer non-linear model is developed to jointly optimize a feeder bus network, consisting of bus routes and service frequencies, locations of DWPT devices and battery capacity.
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