Using learning curves to guide the energy transition with the example of heavy electric trucks

Abstract

Learning curves accurately predict the continuing progress of clean energy and mobility technologies but are not systematically used as a basis for evidence-based policy. We present a systems-level learning model for electric trucks to illustrate how this can be done. Focussing on Europe, we use an approach based on learning curves for eTruck drivetrain and battery pack design; battery developments in cost, durability and composition; energy efficiency and CO 2 emissions; weights of all components; electricity and diesel costs; charging costs in different scenarios; and the use of an eTruck fleet with different ranges. Our model shows several tipping points that can lead to fast eTruck adoption. Policies could leverage these tipping points by rewarding longer range, faster charging, vehicle-to-grid capabilities, and an open and interoperable network of eTruck fast-chargers to drive a rapid and cost-effective transition to eTrucks.