While electrification of heavy-duty freight is essential for reducing emissions, recent studies have shown that operational aspects have been neglected in strategic electrification decisions, and that adopting heavy-duty battery-electric trucks (BETs) requires routes and schedules to be re-optimized. In other words, internal combustion engine truck (ICETs) fleet operations are not representative of entirely nor partially electrified fleet operations. Additionally, logistics service providers (LSPs) have stated that available methods do not provide enough insight into how electrification affects their operations to make investment decisions. To address this, we have developed a framework to describe how incremental replacement of ICETs with one or more BETs impacts operations and total costs while accounting for operational heterogeneity through simulated diffusion and Electric Vehicle Routing Problem (EVRP) optimization. By re-optimizing operations for each combination of mixed BET and ICET fleets, we plot an “electrification scaling curve” that describes how cost performance changes as more ICETs are replaced while accounting for operational dynamics in routing, charging, and scheduling for each individual vehicle. To generate these scaling curves, a large number of optimization problems need to be solved, beginning with a fleet of diesel vehicles, the investment and routing problems are optimized according to varying levels of transportation demand to serve as a baseline, and then another set of optimizations are run for the same fleet but with one ICET replaced with a BET, and then this is repeated with incremental replacement of ICETs until the entire fleet is electrified (or until an upper limit is reached). This data-driven framework for generating electrification scaling curves supports decisions in phased diffusion of heavy-duty battery-electric trucks by providing a measure for the scalability of electrification performance. MAIN SUPERVISOR: Ou Tang, Linköping University