Intelligent prediction on air intake flow of spark ignition engine by a chaos radial basis function neural network

Abstract

To ensure the control of the precision of air-fuel ratio (AFR) of port fuel injection (PFI) spark ignition (SI) engines, a chaos radial basis function (RBF) neural network is used to predict the air intake flow of the engine. The data of air intake flow is proved to be multidimensionally nonlinear and chaotic. The RBF neural network is used to train the reconstructed phase space of the data. The chaos algorithm is employed to optimize the weights of output layer connection and the radial basis center of Gaussian function in hidden layer. The simulation results obtained from Matlab/Simulink illustrate that the model has higher accuracy compared to the conventional RBF model. The mean absolute error and the mean relative error of the chaos RBF model can reach 0.0017 and 0.48, respectively.

Keywords

intake air flow / spark ignition engine / chaos / RBF neural network

References

[1] ChenJ, XuW, ZuoH, WuX, Jia-QiangE, WangT, ZhangF, LuN. System development and environmental performance analysis of a solar-driven supercritical water gasification pilot plant for hydrogen production using life cycle assessment approach [J]. Energy Conversion and Management, 2019, 184: 60-73

[2] MaW, XueX, LiuG. Technoeconomicevaluation for hybrid renewable energy system: Application and merits [J]. Energy, 2018, 159: 385-409

[3] LiuG, LiM, ZhouB, ChenY, LiaoS. General indicator for techno-economic assessment of renewable energy resources [J]. Energy Conversion and Management, 2018, 156: 416-426

[4] LiY, TangW, ChenY, LiuJ, LeeC F F. Potential of acetone-butanol-ethanol (ABE) as a biofuel [J]. Fuel, 2019, 242: 673-686

[5] ZhaoX, Jia-QiangE, WuG, DengY, HanD, ZhangB, ZhangZ. A review of studies using graphenes in energy conversion, energy storage and heat transfer development [J]. Energy Conversion and Management, 2019, 184: 581-599

[6] ZuoH, LiuG, Jia-QiangE, ZuoW, WeiK, HuW, TanJ, ZhongD. Catastrophic analysis on the stability of a large dish solar thermal power generation system with wind-induced vibration [J]. Solar Energy, 2019, 183: 40-49

[7] Jia-QiangE, LiuG, LiuT, ZhangZ, ZuoH, HuW, WeiK. Harmonic response analysis of a large dish solar thermal power generation system with wind-induced vibration [J]. Solar Energy, 2019, 181: 116-129

[8] Jia-QiangE, ZhaoX, XieL, ZhangB, ChenJ, ZuoQ, HanD, HuW, ZhangZ. Performance enhancement of microwave assisted regeneration in a wall-flow diesel particulate filter based on field synergy theory [J]. Energy, 2019, 169: 719-729

[9] ZhangB, Jia-QiangE, GongJ, YuanW, ZhaoX, HuW. Influence of structural and operating factors on performance degradation of the diesel particulate filter based on composite regeneration [J]. Applied Thermal Engineering, 2017, 121: 838-852

[10] WuG, LuZ, PanW, GuanY, LiS, JiC Z. Experimental demonstration of mitigating self-excited combustion oscillations using an electrical heater [J]. Applied Energy, 2019, 239: 331-342

[11] WU Gang, WU Deng, LI Yue-lin, MENG Li. Effect of acetone-n-butanol-ethanol (ABE) as an oxygenate on combustion, performance, and emission characteristics of a spark ignition engine [J]. Journal of Chemistry, 2020, 7468651.

[12] PayriR, GimenoJ, MataC, VieraA. Rate of injection measurements of a direct-acting piezoelectric injector for different operating temperatures [J]. Energy Conversion & Management, 2017, 154: 387-393

[13] KumarM, ShenT. In-cylinder pressure-based air-fuel ratio control for lean burn operation mode of SI engines [J]. Energy, 2017, 120: 106-116

[14] DengY, ZhengW, Jia-QiangE, ZhangB, ZhaoX, ZuoQ, ZhangZ, HanD. Influence of geometric characteristics of a diesel particulate filter on its behavior in equilibrium state [J]. Applied Thermal Engineering, 2017, 123: 61-73

[15] DengY, CuiJ, Jia-QiangE, ZhangB, ZhaoX, ZhangZ, HanD. Investigations on the temperature distribution of the diesel particulate filter in the thermal regeneration process and its field synergy analysis [J]. Applied Thermal Engineering, 2017, 123: 92-102

[16] Jia-QiangE, XieL, ZuoQ, ZhangG. Effect analysis on regeneration speed of continuous regeneration-diesel particulate filter based on NO₂-assisted regeneration [J]. Atmospheric Pollution Research, 2016, 7(1): 9-17

[17] LiuZ, ZuoQ, WuG, LiY. An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol-gasoline blends [J]. Advances in Mechanical Engineering, 2018, 10(1): 1-11

[18] ZuoQ, ZhuX, LiuZ, WuG, LiY. Prediction of the performance and emissions of a spark ignition engine fueled with butanol-gasoline blends based on support vector regression [J]. Environmental Progress & Sustainable Energy, 2019, 38(3): 1-9

[19] SayinC, ErtuncH M, HosozM, KilicaslanI, CanakciM. Performance and exhaust emissions of a gasoline engine using artificial neural network [J]. Applied Thermal Engineering, 2007, 27(1): 46-54

[20] NajafiG, GhobadianB, TavakoliT, ButtsworthD R, YusafT F, FaizollahnejadM. Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network [J]. Applied Energy, 2009, 86(5): 630-639

[21] KianiM K D, GhobadianB, TavakoliT, NikbakhtA M, NajafiG. Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol-gasoline blends [J]. Energy, 2010, 35(1): 65-69

[22] LiJ, MengX. Temperature decoupling control of double-level air flow field dynamic vacuum system based on neural network and prediction principle [J]. Engineering Applications of Artificial Intelligence, 2013, 26(4): 1237-1245

[23] ManzieC, PalaniswamiH, WatsonH. Model predictive control of a fuel injection system with a radial basis function network observer [J]. Journal of Dynamic Systems Measurement & Control, 2002, 124(4): 359-364

[24] WangS, YuD L. Adaptive RBF network for parameter estimation and stable air-fuel ratio control [J]. Neural Networks, 2008, 21(1): 102-112

[25] ShiY, YuD, TianY, ShiY. Air-fuel ratio prediction and NMPC for SI engines with modified Volterra model and RBF network [J]. Engineering Applications of Artificial Intelligence, 2015, 45(1): 313-324

[26] AblayG. Sliding mode control of uncertain unified chaotic systems [J]. Nonlinear Analysis: Hybrid Systems, 2009, 3(4): 531-535

[27] AbdechiriM, FaezK, AmindavarH. Visual object tracking with online weighted chaotic multiple instance learning [J]. Neurocomputing, 2017, 247: 16-30

[28] Jia-QiangE, LiY, GongJ. Function chain neural network prediction on heat transfer performance of oscillating heat pipe based on grey relational analysis [J]. Journal of Central South University of Technology, 2011, 18(5): 1733-1737