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An iterative learning control (ILC) algorithm has been developed for a test cell electro-hydraulic, fully flexible valve actuation system to track valve lift profile under steady-state and transient operation. A dynamic model of the plant was obtained from experimental data to design and verify the ILC algorithm. The ILC is implemented in a prototype controller. The learned control input for two different lift profiles can be used for engine transient tests. Simulation and bench test are conducted to verify the effectiveness and robustness of this approach. The simple structure of the ILC in implementation and low cost in computation are other crucial factors to recommend the ILC. It does not totally depend on the system model during the design procedure. Therefore, it has relatively higher robustness to perturbation and modeling errors than other control methods for repetitive tasks.

This paper describes the advantage of the integrated simulation platform and presents the results of performance simulations and the feed-forward air-fuel ratio (AFR) controller design of a new concept stoichiometric compression ignition (SCI) engine based on this platform. In this integrated simulation environment, the SCI engine was modeled in GT-Power and a simplified production engine control module (ECM) is implemented in Simulink/Matlab for the performance simulation and AFR control. The integrated engine and controller model was used to investigate constant-speed load-acceptance (CSLA) performance. During performance simulation, searching for operating conditions is difficult but critical for performance analysis. Trial and error method would require a long time to do. Based on the integrated simulation, a proportional-integral (PI) controller was designed to find the accurate operating conditions.

Mean value engine model (MVEM) is the basis of control design for advanced internal combustion engines. The engine performance transient process usually takes a few cycles. The MVEM provides an adequate accurate description of the engine dynamics with reasonable approximation by ignoring the heat loss and sub-cycle events. MVEM is very important for engine system control development, especially when the modern engine becomes more and more complicated when equipped with throttle, turbocharger and after-treatment systems. Usually the MVEM is developed based on data from engine tests, which is a costly and time consuming process. In this paper, the air path MVEM modeling method based on the 1D detail model is discussed for a turbocharged diesel engine. Simulation is applied to demonstrate the effectiveness of this new method. This approach could be used to get the MVEM for control design even before the prototype engine is available.

This work is to propose a new Iterative Learning Observer (ILO)-based strategy for State Of Charge (SOC) estimation. The ILO is able to estimate the SOC in real time while identifying modeling errors and/or disturbances at the same time. An Electrical-Circuit Model (ECM) is adopted to characterize the Lithium-ion battery behavior. The ILO is designed based on this ECM and the stability is proved. Several experiments are conducted and the collected data is used to extract ECM parameters. The effectiveness of the estimated SOCs via ILO is verified by the experimental results. This implies that the ILO-based SOC determination scheme is effective to identify the SOC in real time.