nach oben

International Journal of Automotive Technology

07.11.2024 | Engine and Emissions, Fuels and Lubricants, Heat Transfer, Fluid and Thermal Engineering

Application of Hybrid Engine Modeling Method Based on Neural Network Group and PSO with Adaptive Inertia Factor in Engine Calibration

verfasst von: Xiuyong Shi, Jiande Wei, Haoyu Wang, Hua Liu, Degang Jiang

Erschienen in: International Journal of Automotive Technology

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The traditional hybrid engine calibration method has low efficiency and high cost of manpower and material resources, which can not meet the calibration requirements of complex engine electronic control system. The calibration method based on a mathematical model can greatly reduce the test workload and improve efficiency. Therefore, the black-box model of the engine is constructed by using the results of Spearman correlation analysis, and nine variables are selected as input, at the same time five variables are used as outputs. The improved RSR-BPNNG neural network group method is used to construct the hybrid engine economy and emission model. The model prediction results show that the R² value of fuel consumption prediction reaches 0.9975, and the R² value of NOx emission prediction reaches 0.9933, which achieves high precision modeling. On this basis, the performance of the engine under the WHSC cycle is simulated and optimized by the improved adaptive PSO algorithm. The optimization results show that the NOx emission of the engine is reduced by 8.16%, and the fuel consumption is reduced by 4.55%.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

ATZelectronics worldwide

ATZlectronics worldwide is up-to-speed on new trends and developments in automotive electronics on a scientific level with a high depth of information.

Order your 30-days-trial for free and without any commitment.

Jetzt informieren

ATZelektronik

Die Fachzeitschrift ATZelektronik bietet für Entwickler und Entscheider in der Automobil- und Zulieferindustrie qualitativ hochwertige und fundierte Informationen aus dem gesamten Spektrum der Pkw- und Nutzfahrzeug-Elektronik.

Lassen Sie sich jetzt unverbindlich 2 kostenlose Ausgabe zusenden.

Jetzt informieren

Atashkari, K., Nariman-Zadeh, N., Gölcü, M., Khalkhali, A., & Jamali, A. (2007). Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms. Energy Convers Manag, 48(3), 1029–1041. https://doi.org/10.1016/j.enconman.2006.07.007CrossRef

Benjamin Berger, F. R., & Lohmann, B. (2011). Analysing gaussian processes for stationary black-box combustion engine modelling. IFAC Proc Vol, 44(1), 10633–10640. https://doi.org/10.3182/20110828-6-IT-1002.01160CrossRef

Berger, B., & Rauscher, F. (2012). Robust Gaussian process modelling for engine calibration. IFAC Proc Vol, 45(2), 159–164. https://doi.org/10.3182/20120215-3-at-3016.00028CrossRef

Cho, J., & Song, S. (2020). Prediction of hydrogen-added combustion process in T-GDI engine using artificial neural network. Applied Thermal Engineering. https://doi.org/10.1016/j.applthermaleng.2020.115974CrossRef

Dipankar Kakati, S. R., & Banerjee, R. (2019). Development of an artificial neural network based virtual sensing platform for the simultaneous prediction of emission-performance-stability parameters of a diesel engine operating in dual fuel mode with port injected methanol. Energy Convers Manag, 184, 488–509. https://doi.org/10.1016/j.enconman.2019.01.087CrossRef

Francesco de Nola, G. G., Gimelli, A., Molteni, A., Muccillo, M., Picariello, R., & Tornese, D. (2018). Reduction of the experimental effort in engine calibration by using neural networks and 1D engine simulation. Energy Proc, 148, 344–351. https://doi.org/10.1016/j.egypro.2018.08.087CrossRef

Hashemi, N., & Clark, N. N. (2007). Artificial neural network as a predictive tool for emissions from heavy-duty diesel vehicles in Southern California. International Journal of Engine Research, 8(4), 321–336. https://doi.org/10.1243/14680874jer00807CrossRef

Hossein, A., & Hassan, M. A. A. (2009). A method for pre-calibration of DI diesel engine emissions and performance using neural network and multi-objective genetic algorithm. Iranian Journal of Chemistry & Chemical Engineering, 28, 61–70.

Kesgin, U. (2004). Genetic algorithm and artificial neural network for engine optimisation of efficiency and NOx emission. Fuel, 83(7–8), 885–895. https://doi.org/10.1016/j.fuel.2003.10.025CrossRef

Langouët, H., Métivier, L., Sinoquet, D., & Tran, Q.-H. (2011). Engine calibration: multi-objective constrained optimization of engine maps. Optimization and Engineering, 12(3), 407–424. https://doi.org/10.1007/s11081-011-9140-8CrossRef

Ma, H. (2013). Control oriented engine modelling and engine multi-objective optimal feedback control. Birmingham: University of Birmingham.

Ma, H., Li, Z., Tayarani, M., Lu, G., Xu, H., & Yao, X. (2018a). Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines. Journal of Dynamic Systems, Measurement, and Control. https://doi.org/10.1115/1.4037835CrossRef

Ma, H., Li, Z., Tayarani, M., Lu, G., Xu, H., & Yao, X. (2018b). Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration. J Autom Eng, 233(6), 1391–1402. https://doi.org/10.1177/0954407018776743CrossRef

Men, Y., Haskara, I., & Zhu, G. (2018). Multi-zone reaction-based modeling of combustion for multiple-injection diesel engines. International Journal of Engine Research, 21(6), 1012–1025. https://doi.org/10.1177/1468087418788488CrossRef

Millo, F., Arya, P., & Mallamo, F. (2018). Optimization of automotive diesel engine calibration using genetic algorithm techniques. Energy, 158, 807–819. https://doi.org/10.1016/j.energy.2018.06.044CrossRef

Mosbach, S., Braumann, A., Man, P. L. W., Kastner, C. A., Brownbridge, G. P. E., & Kraft, M. (2012). Iterative improvement of Bayesian parameter estimates for an engine model by means of experimental design. Combustion and Flame, 159(3), 1303–1313. https://doi.org/10.1016/j.combustflame.2011.10.019CrossRef

Nishio, Y., Murata, Y., Yamaya, Y., & Kikuchi, M. (2018). Optimal calibration scheme for map-based control of diesel engines. Sci China Inform Sci. https://doi.org/10.1007/s11432-017-9381-6CrossRef

Pal, A., Zhu, L., Wang, Y., & Zhu, G. (2022). Data-driven model-based calibration for optimizing electrically boosted diesel engine performance. International Journal of Engine Research, 24(4), 1515–1529. https://doi.org/10.1177/14680874221090307CrossRef

Piano, A., Millo, F., Sapio, F., & Pesce, F. C. (2018). Multi-objective optimization of fuel injection pattern for a light-duty diesel engine through numerical simulation. SAE International Journal of Engines, 11(6), 1093–1107. https://doi.org/10.4271/2018-01-1124CrossRef

Picerno, M., Lee, S.-Y., Pasternak, M., Siddareddy, R., Franken, T., Mauss, F., & Andert, J. (2021). Real-time emission prediction with detailed chemistry under transient conditions for hardware-in-the-loop simulations. Energies. https://doi.org/10.3390/en15010261CrossRef

Riccio, A., Monzani, F., & Landi, M. (2022). Towards a powerful hardware-in-the-loop system for virtual calibration of an off-road diesel engine. Energies. https://doi.org/10.3390/en15020646CrossRef

Ritzmann, J., Chinellato, O., Hutter, R., & Onder, C. (2021). Optimal integrated emission management through variable engine calibration. Energies. https://doi.org/10.3390/en14227606CrossRef

Seo, J., Yun, B., Park, J., Park, J., Shin, M., & Park, S. (2021). Prediction of instantaneous real-world emissions from diesel light-duty vehicles based on an integrated artificial neural network and vehicle dynamics model. Sci Tot Environ. https://doi.org/10.1016/j.scitotenv.2021.147359CrossRef

Taghavi, M., Gharehghani, A., Nejad, F. B., & Mirsalim, M. (2019). Developing a model to predict the start of combustion in HCCI engine using ANN-GA approach. Energy Convers Manag, 195, 57–69. https://doi.org/10.1016/j.enconman.2019.05.015CrossRef

Tong, H., Pei, J., Zhang, Q., Liu, J., Feng, X., & Wu, F. (2021). Learning boosts optimisation. Surrogate-Assisted Real Engine Calibration 2021 IEEE Symposium Series on Computational Intelligence (SSCI)

Wong, P. K., Tam, L. M., & Ke, L. (2012). Automotive engine power performance tuning under numerical and nominal data. Control Engineering Practice, 20(3), 300–314. https://doi.org/10.1016/j.conengprac.2011.11.006CrossRef

Wong, K. I., Wong, P. K., Cheung, C. S., & Vong, C. M. (2013). Modeling and optimization of biodiesel engine performance using advanced machine learning methods. Energy, 55, 519–528. https://doi.org/10.1016/j.energy.2013.03.057CrossRef

Wong, P. K., Wong, K. I., Vong, C. M., & Cheung, C. S. (2015). Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renewable Energy, 74, 640–647. https://doi.org/10.1016/j.renene.2014.08.075CrossRef

Wu, M. H., Lin, W., & Duan, S. Y. (2008). Investigation of a multi-objective optimization tool for engine calibration. J Autom Eng, 222(2), 235–249. https://doi.org/10.1243/09544070jauto398CrossRef

Zhou, Q., Gullitti, A., Xiao, J., & Huang, Y. (2008). Neural network-based modeling and optimization for effective vehicle emission testing and engine calibration. Chemical Engineering Communications, 195(6), 706–720. https://doi.org/10.1080/00986440701568830CrossRef

Zhu, Q., Wang, Q., Zeng, X., Yu, Y., & Wang, P. (2007). Development platform for HEV energy management system based on V-mode and its application. Journal of Jilin University. https://doi.org/10.3229/j.cnki.jdxbgxb2007.06.020CrossRef

Zhu, L., Wang, Y., Pal, A., & Zhu, G. G. (2022). Adaptive design of experiments for automotive engine applications using concurrent Bayesian optimization. ASME Lett Dyn Syst Control. https://doi.org/10.1115/1.4054222CrossRef

Ziółkowski, J., Oszczypała, M., Małachowski, J., & Szkutnik-Rogoż, J. (2021). Use of artificial neural networks to predict fuel consumption on the basis of technical parameters of vehicles. Energies. https://doi.org/10.3390/en14092639CrossRef

Titel: Application of Hybrid Engine Modeling Method Based on Neural Network Group and PSO with Adaptive Inertia Factor in Engine Calibration
verfasst von: Xiuyong Shi
Jiande Wei
Haoyu Wang
Hua Liu
Degang Jiang
Publikationsdatum: 07.11.2024
Verlag: The Korean Society of Automotive Engineers
Erschienen in: International Journal of Automotive Technology
Print ISSN: 1229-9138
Elektronische ISSN: 1976-3832
DOI: https://doi.org/10.1007/s12239-024-00186-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

ATZelectronics worldwide

ATZelektronik