Impact of autonomous solutions on earthmoving electrification using machine learning: Case study

Purpose: This research aims to investigate the adoption of future technologies in earthmoving applications. The increased development in automated driving systems (ADS) has opened up significant opportunities to revolutionize mobility and to set the path for technologies, such as electrification. The research also aims to explore the impact of automation on electromobility in earthmoving applications. Design/methodology/approach: This paper adopts a multi-objective simulation-based optimization approach using machine learning in earthmoving applications. Findings: This study concludes that ADS is “conditionally” an enabler for electrification. The study highlights and explains how local and global factors affect this conclusion. In addition to that, the research explores the impact of the equipment size on the integration of future mobility technologies. The shift from “elephant to ants” in the fleet selection resulted in improved feasibility from the integration of ADS in electrification. Originality/value: This research provides fundamental considerations in the assessment of the impact of autonomous driving solutions on electromobility in the construction industry.