Optimization and Control of a Dual-Motor Hybrid Opposed Piston Engine

In hybrid electric vehicles (HEV), the added degree of freedom from multiple power sources providing the demanded traction power allows for more efficient utilization of a downsized internal combustion engine (ICE). This thesis investigates the opportunity for hybridization to maximize the potential benefits of the opposed piston (OP) engine, leveraging the unique downsizing capabilities, reduced heat transfer losses, and increased power to weight ration of this engine by advancing the state of modeling and developing novel control algorithms. The powertrain architecture studied is a dual motor series hybrid design. Each crankshaft is directly coupled to a single electric motor, 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. To realize these potential benefits, coordination between the engine, control actuators, and motor torque is necessary. For intra-cycle operation, meaning the operation of the engine within a single cycle, a novel scheme utilizing nonlinear optimization of a 0-D model iteratively coupled to a high fidelity model is formulated to capture the system dynamics while also computing the optimal crankshaft motion profile which maximizes the work generated by the system. This iterative approach reduces the model complexity used in the optimal control problem (OCP) while capturing the gas exchange dynamics critical to the 2-stroke cycle of the OP engine. Results from this optimization process show that a crankshaft motion profile with near constant motor torque maximizes the work extraction efficiency of the system. Model uncertainty created challenges for linear quadratic state feedback control used to regulate the desired piston motion profile. Fortunately, we could leverage the repetitive nature of the OP engine to develop an iterative trajectory optimization (ITO) algorithm. This allows a new tracking reference of the crankshaft motion to be determined in real-time which maximizes the system efficiency in the presence of disturbances and model uncertainty. Experimental results demonstrate 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. While the general ITO algorithm developed here has application beyond the hybrid OP engine, the intra-cycle analysis showed that the electric machine efficiency was the dominant factor in system efficiency. Therefore, a piston motion profile which minimizes the motor torque amplitude maximizes the system efficiency more so than an engine-centric profile. This thesis also considered the inter-cycle operation of this system. In a series HEV, the engine operating setpoint, including the engine speed, load, and phasing of the crankshafts, is decoupled from the instantaneous power demands of the vehicle. The engine calibration problem is addressed by implementing an onboard setpoint optimization algorithm, highlighting the ability to use crankshaft phasing as a controllable parameter impacting scavenging dynamics and thermal efficiency. Finally, an optimization framework for the component sizing and energy management of a vehicle utilizing a dual-motor controlled OP engine in a series hybrid format is introduced. The onboard setpoint optimization enables ECL and crankshaft motion profiles to support higher system level operation such as hybrid topology design and fuel agnostic operation as these control actuators have little influence on the OP engine performance under normal operating conditions.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/175708/1/drallmei_1.pd