Charge-Discharge Coupling Strategy for Dispatching Problems with Electric Tractors at Airports

TL;DR

This paper develops a charge-discharge coupling strategy for electric tractor dispatching at airports, introducing an accurate model and an improved genetic algorithm to optimize operational efficiency considering nonlinear charge-discharge processes.

Contribution

It presents a novel charge-discharge model and an enhanced genetic algorithm tailored for airport electric tractor dispatching, addressing nonlinearities and operational diversity.

Findings

Improved genetic algorithm outperforms traditional methods in accuracy and speed.

Adaptive charging strategies are effective with increased flight scale.

Dispersed aircraft stands require higher pacing strategies.

Abstract

Airports worldwide are actively promoting the transition of ground service vehicles from traditional fuel-powered vehicles to electric vehicles. The key to the successful implementation of this transition lies in the development of efficient electric vehicle dispatching models that comprehensively consider the charge-discharge processes of electric vehicles. However, due to the nonlinear characteristics of charge-discharge processes, finding precise solutions poses a significant challenge. Previous researchers have often used traditional energy consumption models and constant charging rates to simplify calculations, but this has resulted in inaccurate estimates of the remaining battery charge level. Furthermore, the lack of diverse pacing and charging strategies for airport ground service vehicles necessitates more adaptable solutions to enhance operational efficiency. To address these challenges, this paper uses airport electric tractors as a case study, develops an accurate model that takes into account the start-stop process and a piecewise linear charging function, designs an improved genetic algorithm that incorporates a greedy algorithm and an adaptive strategy, and develops charge-discharge coupling strategies for different configuration scenarios at Nanjing Lukou Airport to meet current and future needs. The research results indicate that compared to traditional genetic algorithms, the proposed improved genetic algorithm significantly enhances solution accuracy and convergence speed. Additionally, with the increase in flight scale, airports can appropriately enhance their charging strategies; airports with dispersed aircraft stands should devise higher pacing strategies compared to those with dense aircraft stands.