Data-driven analysis of strategic–operational interfaces in freight electrification under deep uncertainty

Abstract

Battery electric trucks (BETs) offer environmental benefits but have been challenged by technical and economic viability compared to internal combustion engine trucks (ICETs). Meanwhile, fleet owner-operators have difficulties making strategic decisions of freight electrification based on Total Cost-of-Ownership (TCO) due to inaccurate representations of fleet operations, high uncertainty, and lack of generalizability. We therefore develop a data-driven analytical framework for BET and ICET fleets evaluating over 100,000 scenarios to measure the effects of strategic decisions, operational optimization, and deep uncertainty on TCO and competitiveness. We find that electrification becomes more efficient at scale, enabling optimization of routing, charging and scheduling to better mitigate operational constraints, thus improving service level and utilization. Partial electrification in large networks shows to be a robust pathway towards uncertainties such as battery prices and future battery technology. The study results provide guidelines for fleet operators realizing electrification and thereby their sustainability goals in operations.