Comprehensive Energy Footprint Benchmarking of Strong Parallel Electrified Powertrain

TL;DR

This paper conducts a comprehensive energy footprint analysis of an electrified strong parallel commercial vehicle, optimizing fuel and emissions using advanced modeling and Pareto analysis, resulting in significant efficiency and pollution reductions.

Contribution

It introduces a detailed dynamic optimization framework for complex hybrid powertrains, achieving improved fuel efficiency and reduced NOx emissions compared to coarser models.

Findings

7% fuel consumption improvement

29% reduction in NOx emissions

In-depth analysis of powertrain subsystem interactions

Abstract

This work presents comprehensive energy management and in-depth energy footprint analysis of an electrified strong parallel commercial vehicle. We use the PS3 framework, 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 with complex interactions between multiple subsystems for a parallel hybrid electric pick-up and delivery truck. These subsystems exhibit 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 of the electrified powertrain. The presented results exhibit rich analysis and complex interactions among the powertrain subsystems to unearth a 7% improvement in its fuel consumption and 29% pollutant NOx reduction when compared to solution from a coarsely modeled powertrain system.