Comprehensive energy footprint benchmarking of commercial electrified powertrains

Abstract

This work presents a comprehensive energy management framework applied to electrified commercial powertrains with in-depth energy footprint analysis. We use our recently proposed algorithm, validated real-world powertrain system models, and Pareto-optimal analysis to optimize fuel consumption and harmful pollutant emissions. The approach involves dynamic optimization of 13 states and 4 control levers capturing complex subsystem interactions in parallel P2 and series range-extender commercial medium-duty electrified trucks. These subsystems exhibit vehicular (eco-driving), thermal, electrical, and mechanical dynamics at different time scales, and contain kinematic and combinatorial constraints, integer- and real-valued variables, interpolated look-up tables, and data maps. A Pareto-optimal solution is found by carefully optimizing fuel and NOx emissions to understand the energy footprint. Presented results exhibit rich powertrain behavior to unearth up to 6% improvement in energy demand, 2% in fuel economy and 18% in pollutant NOx reduction when compared to Dynamic Programming baseline from a coarsely modeled powertrain system. Furthermore, internal energy flow in the powertrains is analyzed to benchmark the optimal energy consumption and realize the multi-objective trade-off. Finally, a transient study problem is presented comparing cold versus warm after-treatment start conditions. It optimizes engine switching, eco-driving, and powersplit controls revealing adequate dynamic response and complex system interactions.