Drallmeier, Joseph (University of Michigan), Solbrig, Charles E. (University of Michigan), Middleton, Robert (University of Michigan), Siegel, Jason (University of Michigan), Stefanopoulou, Anna G. (Univ Of Michigan)

Maximizing Work Extraction Efficiency of a Hybrid Opposed Piston Engine through Iterative Trajectory Optimization

Scheduled for presentation during the Invited Session "MODELLING AND CONTROL METHODS FOR ADVANCED VEHICLE CONTROL" (MoBT5), Monday, August 29, 2022, 17:10−17:30, Pfhal Hall 202

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 23, 2024

Abstract

This paper presents the real-time optimization of the crankshaft motion in a hybridized opposed piston (OP) engine using an iterative learning-based trajectory optimization scheme. The powertrain is oriented in a series hybrid design with each crankshaft directly coupled to electric motors, eliminating the conventional geartrain linking the two crankshafts along with the associated friction and weight. In this way, the electric motors can directly extract the work generated by the engine and regulate the crankshaft dynamics, introducing the capability to dynamically vary compression ratio, combustion volume, and scavenging dynamics on an inter-cycle basis. This control freedom increases the system’s maximum potential efficiency, yet requires highly optimized intra-cycle crankshaft motion profiles to realize the improved work extraction efficiency of the dual motor-controlled OP engine. Leveraging the repetitive nature of the internal combustion engine, an iterative trajectory optimization (ITO) algorithm is used to define the optimal crankshaft motion profile in real-time for steady state operation. We demonstrate experimentally the rapid convergence and near optimal crankshaft motion profiles for the ITO strategy as well as its proficiency under both motored and fired cycle operation.