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In this paper recent control developments for an electromechanical valve actuator will be presented. The model-based control methodology utilizes position feedback, a nonlinear observer that provides virtual sensing of the armature velocity and current, and cycle-to-cycle learning. The controller is based on a nonlinear state-space description of the actuator that is derived based on physical principles and parameter identification. A bench-top experimental setup and a rapid control prototyping system are used to quantify the actuator performance. Experiments are conducted to measure valve release timing, transition times, and contact velocities for open- and closed-loop control schemes. Simulation results are presented for a feed-forward cycle-to-cycle learning controller.

Previously fuel consumption on a drive cycle has been shown to be proportional to traction work, with an offset for powertrain losses. This model had different transfer functions for different drive cycles, performance levels, and applied powertrain technologies. Following Soltic it is shown that if fuel usage and traction work are both expressed in terms of cycle average power, a wide range of drive cycles collapse to a single transfer function, where cycle average traction power captures the drive cycle and the vehicle size. If this transfer function is then normalized by weight, i.e. by working in cycle average power/weight (P/W), a linear model is obtained where the offset is mainly a function of rated performance and applied technology. A final normalization by rated power/weight as the primary performance metric further collapses the data to express the cycle average fuel power/rated power ratio as a function of cycle average traction power/rated power ratio.