Optimized allocation of scooter battery swapping station under demand uncertainty. (August 2021)

• Record Type: Journal Article
• Authors: Lin, Min-Der; Liu, Ping-Yu; Yang, Ming-Der; Lin, Yu-Hao
• Is Part Of: Sustainable cities and society. Volume 71(2021)
• Publication Date: 2021-08
• Subjects: Battery swapping station (BSS) -- Electric scooter (ES) -- Monte Carlo simulation (MCS) -- Stochastic optimization -- Uncertainty; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.102963
• Languages: English
• ISSNs: 2210-6707

Abstract

Graphical abstract: Highlights: Use of electric scooter of battery swapping can reduce air pollutant emission. Proper battery swapping station (BSS) allocation can promotes the electric scooter. Main novelty is to propose an optimal scooter BSS allocation model with uncertainty. Monte Carol simulation was used to solve the uncertainty of battery swapping event. Optimized BSS locations and capacity was displayed in GIS for decision reference. Abstract: Appropriately allocating battery swapping stations (BSSs) encourages drivers to use battery-operated scooters (ES) for reducing air pollution. The stochastic nature of battery swapping (BS) has not been widely discussed. Also, relatively few models have been proposed to, with particular reference to the possible demand for battery swapping, optimize the BSS locations and the appropriate number of batteries provided for the users of ESs to swap depleted batteries. Hence, this study aims to develop an optimized allocation model of grid-based scooter BSS (OAMSBSS) to be used to solve the abovementioned problem. First, using Monte Carlo simulation for problem-solving operations, along with the consideration given to the traffic flow and population distribution, the stochastic BS model used to predict the demand of stochastic BS was proposed to estimate the various possible scenarios of BSD. Each scenario involved the location decisions and the time distribution for battery swapping demand. Optimizers were adopted to optimally allocate the BSS to both satisfy the BS demand scenarios and achieve the minimal BSS construction cost. The optimized locations of BSSs are considered to not only cost lower land rentals but also help a large number of drivers faced with the problem of the demand for BS services.