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Development of Wheel Loader Duty Cycle Using Hybrid Markov Chain and Genetic Algorithm

Journal Article
02-14-04-0034
ISSN: 1946-391X, e-ISSN: 1946-3928
Published May 10, 2021 by SAE International in United States
Development of Wheel Loader Duty Cycle Using Hybrid Markov Chain and
                    Genetic Algorithm
Citation: Karimi, G., Masih-Tehrani, M., and Pourbafarani, Z., "Development of Wheel Loader Duty Cycle Using Hybrid Markov Chain and Genetic Algorithm," SAE Int. J. Commer. Veh. 15(1):51-64, 2022, https://doi.org/10.4271/02-14-04-0034.
Language: English

References

  1. Dai , Z. , Niemeier , D. , and Eisinger , D. Driving Cycles: A New Cycle-Building Method that Better Represents Real-World Emissions U.C. Davis-Caltrans Air Qual. Proj. 66 66 2008 37
  2. Yang , Y. , Zhang , Q. , Wang , Z. , Chen , Z. et al. Markov Chain-Based Approach of the Driving Cycle Development for Electric Vehicle Application Energy Procedia 152 2018 502 507 10.1016/j.egypro.2018.09.201
  3. Zhao , J. , Gao , Y. , Guo , J. , and Chu , L. The Creation of a Representative Driving Cycle Based on Intelligent Transportation System (ITS) and a Mathematically Statistical Algorithm: A Case Study of Changchun (China) Sustain. Cities Soc. 42 May 2018 301 313 10.1016/j.scs.2018.05.031
  4. Zhang , B. , Zhang , X. , Xi , L. , and Sun , C. Development of a Representative Operation Cycle Characterized by Dual Time Series for Bulldozers Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 231 13 2017 1818 1828 10.1177/0954407016687452
  5. Zhang , M. , Shi , S. , Lin , N. , and Yue , B. High-Efficiency Driving Cycle Generation Using a Markov Chain Evolution Algorithm IEEE Trans. Veh. Technol. 68 2 2019 1288 1301 10.1109/TVT.2018.2887063
  6. Mayakuntla , S.K. and Verma , A. A Novel Methodology for Construction of Driving Cycles for Indian Cities Transp. Res. Part D Transp. Environ. 65 2018 725 735 10.1016/j.trd.2018.10.013
  7. Nezhadali , V. and Eriksson , L. Wheel Loader Optimal Transients in the Short Loading Cycle IFAC Proceedings Volumes 19 3 2014 7917 7922
  8. You , Y. , Sun , D. , and Qin , D. Shift Strategy of a New Continuously Variable Transmission Based Wheel Loader Mech. Mach. Theory 130 2018 313 329 10.1016/j.mechmachtheory.2018.08.004
  9. Dadhich , S. , Sandin , F. , Bodin , U. , Andersson , U. et al. Field Test of Neural-Network Based Automatic Bucket-Filling Algorithm for Wheel-Loaders Autom. Constr. 97 September 2018 2019 1 12 10.1016/j.autcon.2018.10.013
  10. Reno , F. Optimizing the Trajectory of a Wheel Loader Working in Short Loading Cycles Proceedings from 13th Scandinavian International Conference Fluid Power Linköping, Sweden 2013 92 307 317 10.3384/ecp1392a30
  11. Chen , Z. and Xiong , R. Driving Cycle Development for Electric Vehicle Application Using Principal Component Analysis and k-Means cluster: With the Case of Shenyang, China Energy Procedia 142 2017 2264 2269 10.1016/j.egypro.2017.12.628
  12. Fitriani , H. and Lewis , P. 2014 2014 2014 10.1061/9780784413517.063
  13. Nyberg , P. , Frisk , E. , and Nielsen , L. Driving Cycle Equivalence and Transformation IEEE Trans. Veh. Technol. 66 3 2017 1963 1974 10.1109/TVT.2016.2582079
  14. Huertas , J.I. , Quirama , L.F. , Giraldo , M.D. , and Díaz , J. Comparison of Driving Cycles Obtained by The Micro-Trips, Markov-Chains and MWD-CP Methods Int. J. Sustain. Energy Plan. Manag. 22 2019 109 120 10.5278/ijsepm.2554
  15. Kaymaz , H. , Korkmaz , H. , and Erdal , H. Development of a driving cycle for Istanbul bus rapid transit based on real-world data using stratified sampling method Transp. Res. Part D Transp. Environ. 75 August 2019 123 135 10.1016/j.trd.2019.08.023
  16. Masih-Tehrani , M. , Ebrahimi-Nejad , S. , and Dahmardeh , M. Combined Fuel Consumption and Emission Optimization Model for Heavy Construction Equipment Autom. Constr. 110 2020 103007 10.1016/j.autcon.2019.103007
  17. 2019 https://www.lifewire.com/iphone-gps-set-up-1683393
  18. Gao , W. , Pan , S. , Gao , C. , Wang , Q. et al. Tightly Combined GPS and GLONASS for RTK Positioning with Consideration of Differential Inter-System Phase Bias Meas. Sci. Technol. 30 5 2019 054001 10.1088/1361-6501/ab03bc
  19. Ghodoosi , F. , Abu-Samra , S. , Zeynalian , M. , and Zayed , T. Maintenance Cost Optimization for Bridge Structures Using System Reliability Analysis and Genetic Algorithms J. Constr. Eng. Manag. 144 2 2018 1 10 10.1061/(ASCE)CO.1943-7862.0001435
  20. Weller , K. , Lipp , S. , Röck , M. , Matzer , C. et al. Real World Fuel Consumption and Emissions From LDVs and HDVs Front. Mech. Eng. 5 July 2019 1 22 10.3389/fmech.2019.00045
  21. Li , Y. , Peng , J. , He , H. , and Xie , S. The Study on Multi-scale Prediction of Future Driving Cycle Based on Markov Chain Energy Procedia 105 2017 3219 3224 10.1016/j.egypro.2017.03.709
  22. Roso , V.R. and Martins , M.E.S. Evaluation of a Real-World Driving Cycle and its Impacts on Fuel Consumption and Emissions SAE Technical Paper 2015-36-0195 2015 https://doi.org/10.4271/2015-36-0195
  23. Zhao , X. , Yu , Q. , Ma , J. , Wu , Y. et al. Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm J. Adv. Transp. 2018 2018 22 25 10.1155/2018/1890753
  24. Huertas , J.I. , Quirama , L.F. , Giraldo , M. , and Díaz , J. Comparison of Three Methods for Constructing Real Driving Cycles Energies 12 4 2019 10.3390/en12040665
  25. Triantafyllopoulos , G. , Kontses , A. , Tsokolis , D. , Ntziachristos , L. et al. Potential of Energy Efficiency Technologies in Reducing Vehicle Consumption under Type Approval and Real World Conditions Energy 140 2017 365 373 10.1016/j.energy.2017.09.023
  26. Huertas , J.I. , Giraldo , M. , Quirama , L.F. , and Díaz , J. Driving Cycles Based on Fuel Consumption Energies 11 11 2018 1 13 10.3390/en11113064
  27. Szamocki , N. , Kim , M.K. , Ahn , C.R. , and Brilakis , I. Reducing Greenhouse Gas Emission of Construction Equipment at Construction Sites: Field Study Approach J. Constr. Eng. Manag. 145 9 2019 10.1061/(ASCE)CO.1943-7862.0001690
  28. Frank , B. , Kleinert , J. , and Filla , R. Optimal Control of Wheel Loader Actuators in Gravel Applications Autom. Constr. 91 September 2017 2018 1 14 10.1016/j.autcon.2018.03.005
  29. Lee , H. , Kim , M. , and Yoo , W. Force-Balancing Algorithm to Remove the Discontinuity in Soil Force during Wheel Loader Excavation J. Mech. Sci. Technol. 32 10 2018 4951 4957 10.1007/s12206-018-0943-9
  30. Ozdemir , B. and Kumral , M. Stochastic Assessment of the Material Haulage Efficiency in the Earthmoving Industry J. Constr. Eng. Manag. 143 8 2017 1 9 10.1061/(ASCE)CO.1943-7862.0001336
  31. Nilsson , T. , Nyberg , P. , Sundström , C. , Frisk , E. et al. Robust Driving Pattern Detection and Identification with a Wheel Loader Application Int. J. Veh. Syst. Model. Test. 9 1 2014 56 76 10.1504/IJVSMT.2014.059156
  32. Rane , A.K. , Kumar , S. , and Maheshwari , S. Literature Review on Analysis of Wheel Loader and Its Various Components Mater. Today Proc. 5 9 2018 19049 19055 10.1016/j.matpr.2018.06.257
  33. Lin , M. , Yu , Z. , Zhao , L. , and Chen , Y. Working Cycle Identification-Based Braking Control Strategy and Its Application for Hydraulic Hybrid Loader Adv. Mech. Eng. 10 5 2018 1 12 10.1177/1687814018773160
  34. Liu , X. , Sun , D. , Qin , D. , and Liu , J. Achievement of Fuel Savings in Wheel Loader by Applying Hydrodynamic Mechanical Power Split Transmissions Energies 10 9 2017 10.3390/en10091267
  35. Frank , B. , Skogh , L. , and Alaküla , M. On Wheel Loader Fuel Efficiency Difference due to Operator Behaviour Distribution Commercial Vehicle Technology Symposium Kaiserslautern, Germany 2012 http://www.iea.lth.se/publications/Papers/Frank_2012.pdf
  36. Kancharla , S.R. and Ramadurai , G. Incorporating Driving Cycle Based Fuel Consumption Estimation in Green Vehicle Routing Problems Sustain. Cities Soc. 40 2018 214 221 10.1016/j.scs.2018.04.016
  37. Giakoumis , E.G. Driving and Engine Cycles Athens Springer 2016
  38. Jalaei , F. and Jrade , A. Association between Construction Contracts and Relational Contract Theory Constr. Res. Congr. 2014 2008 2014 140 149 10.1061/9780784413517.176
  39. ECOpoint Inc. 2016 https://www.dieselnet.com/standards/eu/nonroad.php
  40. Montazeri , M. Obtaining the First Tehran Bus Driving Cycle Using Markov Chain and Transition Analysis First Spec. Conf. Environ. Eng. 10 1385 1
  41. Hereijgers , K. , Silvas , E. , Hofman , T. , and Steinbuch , M. Effects of Using Synthesized Driving Cycles on Vehicle Fuel Consumption IFAC-PapersOnLine 50 1 2017 7505 7510 10.1016/j.ifacol.2017.08.1183
  42. Luna-Romera , J.M. , Martínez-Ballesteros , M. , García-Gutiérrez , J. , and Riquelme , J.C. External Clustering Validity Index Based on Chi-Squared Statistical Test Inf. Sci. (Ny). 487 2019 1 17 10.1016/j.ins.2019.02.046
  43. Kamble , S. , Mathew , T. , and Sharma , G. I2.Development of Real-World Driving Cycle: Case Study of Pune, India Transp. Res. Part D Transp. Environ. 14 2 2009 132 140 10.1016/j.trd.2008.11.008
  44. Jing , Z. , Wang , G. , Zhang , S. , and Qiu , C. Building Tianjin Driving Cycle Based on Linear Discriminant Analysis Transp. Res. Part D Transp. Environ. 53 2017 78 87 10.1016/j.trd.2017.04.005
  45. Mohseni , H. , Setunge , S. , Zhang , G. , and Wakefield , R. Markov Process for Deterioration Modeling and Asset Management of Community Buildings J. Constr. Eng. Manag. 143 6 2017 04017003 10.1061/(ASCE)CO.1943-7862.0001272
  46. Bi , L. , Ren , B. , Zhong , D. , and Hu , L. Real-Time Construction Schedule Analysis of Long-Distance Diversion Tunnels Based on Lithological Predictions Using a Markov Process J. Constr. Eng. Manag. 141 2 2015 04014076 10.1061/(ASCE)CO.1943-7862.0000935
  47. Pouresmaeili , M.A. , Aghayan , I. , and Taghizadeh , S.A. Development of Mashhad Driving Cycle for Passenger Car to Model Vehicle Exhaust Emissions Calibrated Using On-Board Measurements Sustain. Cities Soc. 36 June 2017 2018 12 20 10.1016/j.scs.2017.09.034
  48. Wang , Z. , Zhang , J. , Liu , P. , Qu , C. et al. Driving Cycle Construction for Electric Vehicles Based on Markov Chain and Monte Carlo Method: A Case Study in Beijing Energy Procedia 158 2019 2494 2499 10.1016/j.egypro.2019.01.389
  49. Zhang , J. , Wang , Z. , Liu , P. , Zhang , Z. et al. Driving Cycles Construction for Electric Vehicles Considering Road Environment: A Case Study in Beijing Appl. Energy 253 5 2019 113514 10.1016/j.apenergy.2019.113514
  50. Shi , S. et al. Research on Markov Property Analysis of Driving Cycles and Its Application Transp. Res. Part D Transp. Environ. 47 2016 171 181 10.1016/j.trd.2016.05.013
  51. Ji , W. and Abourizk , S.M. Data-Driven Simulation Model for Quality-Induced Rework Cost Estimation and Control Using Absorbing Markov Chains J. Constr. Eng. Manag. 144 8 2018 1 10 10.1061/(ASCE)CO.1943-7862.0001534
  52. Razavialavi , S. and Abourizk , S. Genetic Algorithm-Simulation Framework for Decision Making in Construction Site Layout Planning J. Constr. Eng. Manag. 143 1 2017 1 13 10.1061/(ASCE)CO.1943-7862.0001213
  53. Masih-Tehrani , M. and Ebrahimi-Nejad , S. Hybrid Genetic Algorithm and Linear Programming for Bulldozer Emissions and Fuel-Consumption Management Using Continuously Variable Transmission J. Constr. Eng. Manag. 144 7 2018 04018053 10.1061/(ASCE)CO.1943-7862.0001490
  54. Turkmen , A.C. , Solmaz , S. , and Celik , C. Analysis of Fuel Cell Vehicles with ADVISOR Software Renew. Sustain. Energy Rev. 70 July 2015 2017 1066 1071 10.1016/j.rser.2016.12.011

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