SI and HCCI Combustion Mode Transition Control of a Multi-Cylinder HCCI Capable SI Engine via Iterative Learning

2011 ◽  
Author(s):  
Xiaojian Yang ◽  
Guoming G. Zhu
Keyword(s):  
Control Strategy ◽  
Iterative Learning ◽  
Mode Transition ◽  
Combustion Mode ◽  
Operational Parameters ◽  
Ic Engine ◽  
Operational Conditions ◽  
Transition Control ◽  
Hcci Combustion ◽  
Mode Transition Control

The combustion mode transition between spark ignition (SI) and homogeneously charged compression ignition (HCCI) combustions of an internal combustion (IC) engine is challenging due to the distinct engine operational parameters over these two combustion modes and the cycle-to-cycle residue gas dynamics of the HCCI combustion. The control problem becomes even more complicated when multi-cylinder operation is involved. This paper studies the combustion mode transition problem of a multi-cylinder IC engine with dual-stage valve lifts and electrical variable valve timing systems. A control oriented engine model was used to develop a multistep mode transition control strategy via iterative learning for combustion mode transition between SI to HCCI with minimal engine torque fluctuations. The hardware-in-the-loop (HIL) simulations demonstrated the effectiveness of the developed control strategy for the combustion mode transition under both constant load and transient engine operational conditions.

Model-Based Mode Transition Control Between SI and HCCI Combustion

2014 ◽  
Author(s):  
Shupeng Zhang ◽  
Guoming G. Zhu
Keyword(s):  
External Disturbance ◽  
Thermal Conditions ◽  
Spark Ignition Engine ◽  
Intake Manifold ◽  
Mode Transition ◽  
Electronic Throttle ◽  
Engine Torque ◽  
Transition Control ◽  
Hcci Combustion ◽  
Mode Transition Control

While the homogeneous charge compression ignition (HCCI) combustion has its advantages of high thermal efficiency with low emissions, its operational range is limited in both engine speed and load. To utilize the advantage of the HCCI combustion an HCCI capable SI (spark ignition) engine needs to be developed. One of the key challenges of developing such an engine is how to achieve smooth mode transition between SI and HCCI combustion, where the in-cylinder thermal conditions are quite different due to the distinct combustion characteristics. In this paper, an SI-HCCI mode transition control strategy was developed for an HCCI capable SI engine equipped with electrical variable valve timing (EVVT) systems with two step-lift valves and electronic throttle control (ETC) system, and the developed strategy was validated in hardware-in-the-loop (HIL) simulations. During the mode transition, a MAP (manifold air pressure) controller was used to track the desired intake manifold pressure for charge air management, and a fuel management controller is used to provide the desired engine torque. HIL simulation results show that the developed controller is able to achieve smooth combustion mode transition under unmodeled dynamics and external disturbance.

Robust mode transition control of four-wheel-drive hybrid electric vehicles based on radial basis function neural network estimation-a simulation study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ling Li ◽  
Fazhan Tao ◽  
Zhumu Fu
Keyword(s):  
Dynamic Performance ◽  
Transition Process ◽  
Mode Transition ◽  
Robust Controller ◽  
Content Type ◽  
Engine Torque ◽  
Transition Control ◽  
Wheel Drive ◽  
Mode Transition Control ◽  
Four Wheel Drive

Purpose The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty. Design/methodology/approach First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform. Findings Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system. Originality/value The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

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