Deployment of battery-swapping stations: Integrating travel chain simulation and multi-objective optimization for delivery electric micromobility vehicles

Abstract

As the delivery sector increasingly relies on electric micromobility vehicles (EMVs), the urgency for efficient battery-swapping infrastructure becomes critical. This study develops a comprehensive framework for predicting battery-swapping demand for delivery EMVs (DEMVs) based on an activity-based travel chain simulation model and devises a multi-objective optimization model for the strategic placement of battery-swapping stations. The simulation model integrates submodules such as the EMV generation and attraction model, Agent-based EMV travel chain, and EMV and battery-swapping behavior model to capture the nuanced travel patterns and battery-swapping demand. Leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the study optimizes the network design of battery-swapping stations considering both construction and travel costs. A case study in Nanjing City, representative of the diverse delivery sector's operations, substantiates the simulation's accuracy, maps out the spatiotemporal distribution of swapping demand, and analyzes the Pareto optimal set derived from the optimization model. Sensitivity analysis focuses on the facility planning model, assessing how uncertainties in swapping demand and battery charging rates within stations impact operational efficacy. This research melds demand forecasting with infrastructure optimization, providing actionable insights for the planning and management of battery-swapping stations.