Forklift truck performance simulation and fuel consumption estimation

作者: Luigi d'Apolito , Hanchi Hong

DOI: 10.1108/JEDT-06-2019-0165

关键词: CruiseFuel efficiencyComputer scienceDriving cycleAutomotive engineeringHydraulic circuitTruckArtificial neural networkPowertrainReduction (mathematics)

摘要: Forklift trucks are generally operated with frequent accelerations and stops, reverse operations of load handling. This way operation increases the energy losses consequently need for reduction fuel consumption from forklift customers. study aims to build a model replicate performance forklifts during real estimate without building prototype.,AVL Cruise has been used simulate powertrain hydraulic circuit. The driving cycles this were in accordance standard VDI 2198. Artificial neural networks (ANNs), trained by results AVL simulations, have forecast large set possible cycles.,The comparison between simulated experimental data verified that was able forklifts, but only valid specified cycle. ANNs, certain number cycles, found effective larger following prescriptions 2198.,A new method based on ANN, simulation results, introduced consumption, reducing computational time cost tests.

参考文章(24)
Guizhi Sun, Minxiang Wei, Jinju Shao, Man Pei, Automotive Powertrain Modeling and Simulation Based on AMESim SAE Technical Paper Series. ,(2007) , 10.4271/2007-01-3464
Paul Bowles, Michael Tiller, Hilding Elmqvist, Dag Brück, Sven Erik Mattsson, Andreas Möller, Hans Olsson, Martin Otter, Feasibility of Detailed Vehicle Modeling SAE 2001 World Congress. ,(2001) , 10.4271/2001-01-0334
Frank Böhler, Phillip Thiebes, Marcus Geimer, Julien Santoire, Richard Zahoransky, Hybrid Drive Systems for Industrial Applications 9th International Conference on Engines and Vehicles. ,(2009) , 10.4271/2009-24-0061
G. Stein, K. Meitz, W. Kriegler, Martin Wichmann, Powertrain Optimization Using Simulation - Example of Engine Selection for Fork Lift Trucks SAE transactions. ,vol. 113, pp. 630- 635 ,(2004) , 10.4271/2004-01-2727
Mehrsa Ehsani, Abbas Ahmadi, Dawud Fadai, Modeling of vehicle fuel consumption and carbon dioxide emission in road transport Renewable & Sustainable Energy Reviews. ,vol. 53, pp. 1638- 1648 ,(2016) , 10.1016/J.RSER.2015.08.062
A. Amer, Ahmed Abdalla, A. Noraziah, Ainul Azila Che Fauzi, Prediction of Vehicle Fuel Consumption Model Based on Artificial Neural Network Applied Mechanics and Materials. ,vol. 492, pp. 3- 6 ,(2014) , 10.4028/WWW.SCIENTIFIC.NET/AMM.492.3
Dongyun Wang, Cheng Guan, Shuangxia Pan, Minjie Zhang, Xiao Lin, Performance analysis of hydraulic excavator powertrain hybridization Automation in Construction. ,vol. 18, pp. 249- 257 ,(2009) , 10.1016/J.AUTCON.2008.10.001
Elnaz Siami-Irdemoosa, Saeid R. Dindarloo, Prediction of fuel consumption of mining dump trucks: A neural networks approach Applied Energy. ,vol. 151, pp. 77- 84 ,(2015) , 10.1016/J.APENERGY.2015.04.064
Burak Gokalp, H. Metin Ertunc, Murat Hosoz, H. Ibrahim Sarac, Performance prediction of a CI engine using artificial neural network for various SME and diesel fuel blends International Journal of Vehicle Design. ,vol. 54, pp. 156- ,(2010) , 10.1504/IJVD.2010.035357
Lorenzo Damiani, Matteo Repetto, Alessandro Pini Prato, Improvement of powertrain efficiency through energy breakdown analysis Applied Energy. ,vol. 121, pp. 252- 263 ,(2014) , 10.1016/J.APENERGY.2013.12.067