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.
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