Study on rotary valve system (RVS) for automotive engines has been carried out over many years. Recent researches have already successfully implemented the idea, but none of design can provide variable valve timing and flow area control simultaneously. Therefore, a RVS with variable valve control system is proposed in this paper. The system design and dynamic analysis of the proposed RVS are first presented. Moreover, the selection of the proportional, integral and derivative (PID) controller parameters for the valve control based on an emerging artificial intelligent technique, cuckoo search (CS), is also discussed. Experimental and simulation results show that the proposed tuning method is better than the traditional Ziegler and Nichols method, and the proposed variable rotary valve control system is feasible.
UysalF. and SagirogluS., The effects of a pneumatic-driven variable valve timing mechanism on the performance of an otto engine, Strojniski Vestnik-Journal of Mechanical Engineering61 (2015), 632–640.
2.
KocherL., KoeberleinE., Van AlstineD.G., StrickerK. and ShaverG., Physically based volumetric efficiency model for diesel engines utilizing variable intake valve actuation, International Journal of Engine Research13 (2012), 169–184.
3.
GolcuM., SekmenY., ErduranliP. and SalmanV.S., Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine, Applied Energy81 (2005), 187–197.
4.
BasbousT., YounesR., IlincaA. and PerronJ., Required time response of a variable valve actuator equiping a hybrid pneumatic-combustion engine, International Journal of Engine Research13 (2012), 514–528.
5.
AbagnaleC., MigliaccioM. and PennacchiaO., New mechanical variable valve actuation systems for motorcycle engines, Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science229 (2015), 716–734.
6.
AbagnaleC., MigliaccioM. and PennacchiaO., Design of a new mechanical variable valve actuation system for motorcycle engines, Proceedings of the ASME 11th Biennial Conference on Engineering Systems Design and Analysis3 (2013), 625–636.
7.
NagayaK., KobayashiH. and KoikeK., Valve timing and valve lift control mechanism for engines, Mechatronics16 (2006), 121–129.
SaridemirS. and SaruhanH., Experimental analysis of maximum valve lift effects in cam-follower system for internal combustion engines, Journal of Mechanical Science and Technology28 (2014), 3443–3448.
10.
JiaM., XieM.Z. and WangT.Y., Numerical investigation of the influence of intake valve lift profile on a diesel premixed charge compression ignition engine with a variable valve actuation system at moderate loads and speeds, International Journal of Engine Research14 (2013), 151–179.
11.
LumleyJ.L., Engines: An introduction, Cambridge University Press, 1999.
12.
FlierlR., KauschC., KairiesD., JubeltM. and HannibalW., Potential of a mechanical fully-variable valve train. Results of simulated and actual engine operation for the new univalve system, SAE Technical Paper 2005-01-0768, 2005.
13.
WongP.K., TamL.M. and LiK., Modeling and simulation of a dual-mode electrohydraulic fully variable valve train for four-stroke engines, International Journal of Automotive Technology9 (2008), 509–521.
14.
SunZ. and ClearyD., Dynamics and control of an electro-hydraulic fully flexible valve actuation system, Proceedings of the 2003 American Control Conference4 (2003), 3119–3124.
15.
PadenB.A., SnyderS.T., PadenB.E. and RicciM.R., Modeling and control of an electromagnetic variable valve actuation system, IEEE/ASME Transactions on Mechatronics20 (2015), 2654–2665.
16.
DuchaudJ.L., HliouiS., LoufF., OjedaJ. and GabsiM., Modeling and optimization of a linear actuator for a two-stage valve tappet in an automotive engine, IEEE Transactions on Vehicular Technology64 (2015), 4441–4448.
17.
PeliksB. and SuhN.P., Rotary valve revolution, SAE Technical Paper 2005-01-1522, 2005.
18.
GimelliA., MuccilloM. and PennacchiaO., Study of a new mechanical variable valve actuation system: Part I valve train design and friction modeling, International Journal of Engine Research16 (2015), 750–761.
19.
GimelliA., MuccilloM. and PennacchiaO., Study of a new mechanical variable valve actuation system: Part II estimation of the actual fuel consumption improvement through one-dimensional fluid dynamic analysis and valve train friction estimation, International Journal of Engine Research16 (2015), 762–772.
20.
WrightP. and MatthewsT., Formula 1 Technology, Society of Automotive Engineers Inc, 2001.
21.
CrossP., HansenC. and MartinJ., A rotary valve combustion system with throttleless inlet charge control, SAE Technical Paper 940813, 1994.
22.
MurokiT., MoriyoshiY. and SekizukaS., A study of rotary valve for a single cylinder engine, SAE Technical Paper 1999-01-3322, 1999.
23.
LawesK., The rotating cylinder valve 4-stroke engine a practical alternative, SAE Technical Paper 2002-32-1828, 1828.
24.
NiimiK., Internal combustion engine, Google Patents, 2006.
25.
MatsuokaK., EsumiM., IkeguchiK.B., KasaharaJ., MatsuoA. and FunakiI., Optical and thrust measurement of a pulse detonation combustor with a coaxial rotary valve, Combustion and Flame159 (2012), 1321–1338.
26.
PalmisanoR. and NgH., Design analysis and comparison between standard and rotary porting systems for IC engine, International Journal of Automotive Technology13 (2012), 175–191.
27.
DavisR.S., LeeJ.R. and MacfarlaneG.R., Rotary valve and system for duration and phase control, Google Patents, 1992.
28.
SchechterM.M. and KaufmanW.F., Variable engine valve control system, Google Patents, 1993.
29.
KhoobanM.H., AlfiA. and AbadiD.N.M., Teaching-learning-based optimal interval type-2 fuzzy PID controller design: A nonholonomic wheeled mobile robots, Robotica31 (2013), 1059–1071.
30.
AmeliK., AlfiA. and AghaebrahimiM., A fuzzy discrete harmony search algorithm applied to annual cost reduction in radial distribution systems, Engineering Optimization (2015), 1–21.
31.
AlfiA., KalatA.A. and KhoobanM.H., Adaptive fuzzy sliding mode control for synchronization of uncertain non-identical chaotic systems using bacterial foraging optimization, Journal of Intelligent & Fuzzy Systems26 (2014), 2567–2576.
32.
MousaviY. and AlfiA., A memetic algorithm applied to trajectory control by tuning of Fractional Order Proportional-Integral-Derivative controllers, Applied Soft Computing36 (2015), 599–617.
33.
AlfiA. and FatehM.M., Identification of nonlinear systems using modified particle swarm optimisation: A hydraulic suspension system, Vehicle System Dynamics49 (2011), 871–887.
34.
Shokri-GhalehH. and AlfiA., Optimal synchronization of teleoperation systems via cuckoo optimization algorithm, Nonlinear Dynamics78 (2014), 2359–2376.
35.
YangX.-S. and DebS., Cuckoo search: Recent advances and applications, Neural Computing and Applications24 (2014), 169–174.
36.
CiviciogluP. and BesdokE., A conceptual comon of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms, Artificial Intelligence Review39 (2013), 315–346paris.
37.
WongP.K., WongK.I., VongC.M. and CheungC.S., Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search, Renewable Energy74 (2015), 640–647.
38.
DashP., SaikiaL.C. and SinhaN., Comparison of performances of several Cuckoo search algorithm based 2DOF controllers in AGC of multi-area thermal system, International Journal of Electrical Power & Energy Systems55 (2014), 429–436.
