Active Disturbance Rejection Based High-Dilution Low-Temperature Combustion Control For P-DI Gasoline Engines

The controlled auto-ignition(CAI) or SI-CAI hybrid combustion has great potential in reducing the fuel consumption and emissions for gasoline engines, simultaneously. However, its ignition and combustion are extremely sensitive to boundary conditions, making its control challenging. In this paper, an active disturbance rejection based self-optimizing controller is proposed, combining the model-based feedforward controller, fast disturbance rejection controller, and long-term self-tuning controller, aiming at solving the control problems with high sensitivity to boundary condtions, with complicated cross-coupling and with difficulty in accurate modeling.First, in order to establish the model for feedforward control, experiments are carried out focusing on the coupling effects between temperature and composition, the control authority of spark timing on combustion phasing, as well as the impact of the direct injection timing on heat release, based on a four cylinder gasoline engine equipped with Port fuel injection(PFI) and gasoline Direct injection(GDI) dual injection system, and with intake and exhaust dual VVT system. A spark-timing correction factor is introduced into the CAI combustion phasing prediction model, and consequently, a SI-CAI hybrid combustion prediction model is developed for CA50 and IMEP feedforward control. The main parameters are identified using nonlinear least square method. Furthermore, two self-learning factors that are designed for model prediction error reduction and feedforward effect improvement in the operation, are introduced for the CA50 and air mass sub-models, respectively, making the model self-optimzing available.Secondly, to accurately coordinate the temperature and composition, a combustion prediction inverse model based feedforward decoupling controller is proposed; besides, a fast disturbance observation algorithm based on extended states observer(ESO) is proposed. In order to mitigate the disturbance of CA50 cyclic variation on ESO observation, the adaptive ESO is adopted. Simplified ESO tuning in λ and IMEP loop is realized, by using the bandwidth-based parameterization tuning method. The resulted feedforward-based hybrid disturbance decoupling controller is then implemented in Simulink, then runs in MicroAutoBox prototyping controller, to be validated on the four cylinder gasoline engine test bench. Results demonstrate that during the IMEP step reponse test, the CA50 mean deviation is less than 4 oCA, and λ deviation less than 0.08, in 1200r/min to 1600 r/min engine speed and 0.225 MPa to 0.5MPa IMEP range.A recursive least square method based model-parameter online self-optimizing algorithm is proposed, which improves the accuracy of both the model and the decoupling by adapting the self-learning factor in real-time. An Extermum Seeking method based DI timing self-optimizing algorithm is proposed, which not only avoids the time consuming off-line calibration, but also compensates for optimizing DI timing offset resulting from the boundary condition variation. The boundary condition variation caused by the cooling system desposition is simulated by increasing the cylinder wall temperature, and the control performance-improving effect that results from model adaption is validated. Results show that the CA50, IMEP and λ tracking error reduced at least 16% in the dynamic process. Experimental results confirmed that the long-term observation algrithom can reduce the λ tracking error over 27%, and the optimization of DI timing can finish within 300 cycles at fastest speed, compensating the changes in spark timing and the engine load.In a word, the proposed controller can effectively solve the CA50 and IMEP tracking and optimizing problem in the transient process, for SI-CAI multi-cylinder engines. It builds up a solid foundation for the SI-CAI high-dilution low-temperature combustion control.