Adaptive Cruise Control For Electric Vehicles

Adaptive Cruise Control (ACC) eases driving operations by reducing fatigue and providing comfort when driving on highway roads. ACC system consisting of upper-level and lower-level controllers is developed for an electric vehicle using the Fuzzy Logic Controller (FLC). The ACC is designed in MATLAB...

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Main Author:	Naina Mohamed, Abdul Razak
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	T Technology (General)
Online Access:	http://eprints.utem.edu.my/id/eprint/25552/1/Adaptive%20Cruise%20Control%20For%20Electric%20Vehicles.pdf http://eprints.utem.edu.my/id/eprint/25552/2/Adaptive%20Cruise%20Control%20For%20Electric%20Vehicles.pdf
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id	my-utem-ep.25552
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Phuman Singh, Amrik Singh
topic	T Technology (General) T Technology (General)
spellingShingle	T Technology (General) T Technology (General) Naina Mohamed, Abdul Razak Adaptive Cruise Control For Electric Vehicles
description	Adaptive Cruise Control (ACC) eases driving operations by reducing fatigue and providing comfort when driving on highway roads. ACC system consisting of upper-level and lower-level controllers is developed for an electric vehicle using the Fuzzy Logic Controller (FLC). The ACC is designed in MATLAB Simulink together with longitudinal dynamics model and Burckhardt tyre model. The upper-level controller is a combination of feedforward and fuzzy feedback controllers. The feedforward controller determines the desired acceleration and headway distance. The feedback controller equipped with FLC determines the additional longitudinal force. The lower-level controller distributes the desired tyre longitudinal force and converts the forces into wheel torque commands. The evaluation process is to determine appropriate parameters and fuzzy rules set for the controllers. Feedforward and feedback controllers are evaluated in the selected driving simulation scenario. The feedforward controller had achieved the desired headway distance with reduced relative velocity. The vehicle response has improved more with the combination of feedforward and feedback controllers. The performance is better when the relative velocity is reduced carlier during the same simulation. The simulation results also show the headway distance corresponding to the preceding vehicle speed. The feedforward and feedback controllers’ combination gives safe distance compared to the feedforward controller usage.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Naina Mohamed, Abdul Razak
author_facet	Naina Mohamed, Abdul Razak
author_sort	Naina Mohamed, Abdul Razak
title	Adaptive Cruise Control For Electric Vehicles
title_short	Adaptive Cruise Control For Electric Vehicles
title_full	Adaptive Cruise Control For Electric Vehicles
title_fullStr	Adaptive Cruise Control For Electric Vehicles
title_full_unstemmed	Adaptive Cruise Control For Electric Vehicles
title_sort	adaptive cruise control for electric vehicles
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Mechanical Engineering
publishDate	2020
url	http://eprints.utem.edu.my/id/eprint/25552/1/Adaptive%20Cruise%20Control%20For%20Electric%20Vehicles.pdf http://eprints.utem.edu.my/id/eprint/25552/2/Adaptive%20Cruise%20Control%20For%20Electric%20Vehicles.pdf
_version_	1747834138761625600
spelling	my-utem-ep.255522022-01-06T12:38:42Z Adaptive Cruise Control For Electric Vehicles 2020 Naina Mohamed, Abdul Razak T Technology (General) TL Motor vehicles. Aeronautics. Astronautics Adaptive Cruise Control (ACC) eases driving operations by reducing fatigue and providing comfort when driving on highway roads. ACC system consisting of upper-level and lower-level controllers is developed for an electric vehicle using the Fuzzy Logic Controller (FLC). The ACC is designed in MATLAB Simulink together with longitudinal dynamics model and Burckhardt tyre model. The upper-level controller is a combination of feedforward and fuzzy feedback controllers. The feedforward controller determines the desired acceleration and headway distance. The feedback controller equipped with FLC determines the additional longitudinal force. The lower-level controller distributes the desired tyre longitudinal force and converts the forces into wheel torque commands. The evaluation process is to determine appropriate parameters and fuzzy rules set for the controllers. Feedforward and feedback controllers are evaluated in the selected driving simulation scenario. The feedforward controller had achieved the desired headway distance with reduced relative velocity. The vehicle response has improved more with the combination of feedforward and feedback controllers. The performance is better when the relative velocity is reduced carlier during the same simulation. The simulation results also show the headway distance corresponding to the preceding vehicle speed. The feedforward and feedback controllers’ combination gives safe distance compared to the feedforward controller usage. 2020 Thesis http://eprints.utem.edu.my/id/eprint/25552/ http://eprints.utem.edu.my/id/eprint/25552/1/Adaptive%20Cruise%20Control%20For%20Electric%20Vehicles.pdf text en public http://eprints.utem.edu.my/id/eprint/25552/2/Adaptive%20Cruise%20Control%20For%20Electric%20Vehicles.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117879 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Mechanical Engineering Phuman Singh, Amrik Singh 1. Abdeen, A. A., Ibrahim, K. and Nasr, A. M. (2018) Active Suspension System Design Using Fuzzy Logic Control and Linear Quadratic Regulator . International Conference on Advanced Intelligent Systems and Informatics 2018 , 152-166. 2. Abdelhady, M. B. A. (2003) A Fuzzy Controller for Automotive Active Suspension Systems’. SAE Technical Paper 2003-01-1417. 3. Abdullah, R., Hussain, A., Warwick, K. and Zayed, A. (2008) ‘Neurocomputing Autonomous Intelligent Cruise Control using a Novel Multiple-Controller Framework Incorporating Fuzzy-Logic-Based Switching and Tuning’. Neurocomputing, 71, pp. 2727-2741. 4. Buckholtz, K. R. (2002) ‘Use of Fuzzy Logic in Wheel Slip Assignment- Part I: Yaw Rate Control’. SAE Technical Paper 2002-01-1221. 5. Chen, L., Bian, M., Luo, Y., Qin, Z. and Li, K. (2015) ‘Tire-Road Friction Coefficient Estimation Based on the Resonance Frequency of In-Wheel Motor Drive System’. Vehicle System Dynamics, 54(1), pp. 1-19. 6. Chen, X., Zhang, J. and Liu, Y. (2016) ‘Research on the Intelligent Control and Simulation of Automobile Cruise System Based on Fuzzy System’. Hindawi Publishing Corporation Mathematical Problems in Engineering, 2016 (Article ID 9760653), pp. 1-12. 7. Denton, T. (2004) Automobile Electrical & Electronic System. 3rd edn. Great Britain: Elsevier Butterworth-Heinemann. Department of Occupational Safety and Health (DOSH) Malaysia (2010) 'Code of Practice for Road Transport Activities 2010'. [Online]. Available at: http://www.dosh.gov.my/ index.php /legislation/codes-of-practice/transportation. (Accessed on 30 January 2020). 8. Elaphe (2019) Elaphe Technology [Online]. Available at: https://in-wheel.com/technology/ (Accessed on 12 September 2019). 9. Erjavec, J. and Thompson, R. (2017) Automotive Technology: A Systems Approach. 6th edn. United States: Cengage Learning. 10. Fancher, P. and Bareket, Z. (1994) Evaluating Headway Control using Range Versus Range-Rate Relationships . Vehicle System Dynamics, 23(1), pp. 575-596. 11. Fancher, P., Bareket, Z. and Ervin, R. (2001) ‘Human-Centered Design of an ACC-WithBraking and Forward-Crash-Waming System’. Vehicle System Dynamics,36(2-3), pp. 203-223. 12. Gao, Z. and Song, H. (2017) Study on the Electric Vehicle Adaptive Cruise Control Based on the Model Predictive Control Algorithm., in Wang, W., Bengler, K. and Jiang, X. (eds) Green Intelligent Transportation Systems. GITSS 2017. Lecture Notes in Electrical Engineering, Volume 503. Singapore: Springer Singapore, pp. 39-51. Gaspar, P. and Nemeth, B. (2019) Predictive Cruise Control for Road Vehicles Using Road and Traffic Information. Switzerland: Springer International Publishing. 13. Gillespie, T. D. (1992) Fundamentals of Vehicle Dynamics. Warrendale, PA: Society of Automotive Engineers. 14. Gorzelany, J. (2018) EV’s That Offer Advanced Safety Systems [Online]. Available at: https://www.myev.com/research/interesting-fmds/evs-that-offer-advanced-safety-systems (Accessed on 2 October 2019). 15. He, P., Yi, Y., Cai, J., Zhang, L., and Dai, Z. (2015) ‘Effect of Reducer Gear Ratio on Efficiency of In-Wheel Motor’. Materials Research Innovations, 19 (S6), pp.l16-120. 16. Hindiyeh, R. Y. (2013) Dynamics and Control of Drifting in Automobiles. PhD Thesis, Stanford University, United States of America. 17. Hooda, D.S. and Raich, I. (2017) Fuzzy Logic Models and Fuzzy Control: An Introduction. Oxford, United Kingdom: Alpha Science International Limited. 18. Husain, I. (2011) Electric and Hybrid Vehicles: Design Fundamentals. Florida: CRC Press. 19. Jabatan Keselamatan Jalan Raya (JKJR) Malaysia (2019) "Buku Statistik Keselamatan (kemaskini 22.07.2019) , Jabatan Keselamatan Jalan Raya Malaysia, (22.07.2019), p. 34. [Online]. Available at: http://www.jkjr.gov.my/ms/muat_turun/Statistik Statistic/lang,msmy/ (Accessed on 2 October 2019). 20. Jager, R. (1995) Fuzzy Logic in Control. PhD Thesis, Delft University of Technology, Netherlands. 21. Karjalainen, M. (2016) Real-Time Estimation of Tire Stiffness. MSc Thesis, Linkoping University, Sweden. 22. Kiencke, U. and Nielsen, L. (2005) Automotive Control Systems: For Engine, Driveline, and Vehicle. 2nd edn. Berlin, Heidelberg: Springer-Verlag. 23. Klomp, M. (2010) Longitudinal Force Distribution and Road Vehicle Handling. PhD Thesis, Chalmers University of Technology, Gothenburg, Sweden. 24. Lee, C. C. (1990) Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I’. IEEE Transactions on Systems, Man, and Cybernetics,20(2), pp. 404 418. 25. Mamdani, E. H. (1974) ‘Application of Fuzzy Algorithms for Control of Simple Dynamic Plant’. Proceedings of the Institution of Electrical Engineers, 121(12), pp. 1585-1588. 26. Mamdani, E. H. and Assilian, S. (1975) ‘An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller’. International Journal of Man-Machine Studies, 7(1), pp. 1-13. 27. March, C. and Shim, T. (2007) ‘Integrated Control of Suspension and Front Steering to Enhance Vehicle Handling’. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 221(4), pp. 377-391. 28. Mashadi, B. and Crolla, D. (2011) Vehicle Powertrain Systems: Integration and Optimization. United Kingdom: Wiley. 29. Murata, S. (2012) Innovation by In-Wheel-Motor Drive Unit . Vehicle System Dynamics, 50(6), pp. 807-830. 30. Nagai, M. and Raksincharoensak, P. (2019) Adaptive Cruise Control System. In-Vehicle Dynamics of Modem Passenger Cars (Lugner, P., Volume 582), pp. 281-289. Vienna, Austria: Springer International Publishing. 31. de Novellis, L., Somiotti, A., Gruber, P., Shead, L., Ivanov, V. and Hoepping, K. (2012) Torque Vectoring for Electric Vehicles with Individually Controlled Motors: State-Of-TheArt and Future Developments . World Electric Vehicle Journal, 5(2), pp. 617-628. 32. de Novellis, L., Somiotti, A., Gruber, P., Orus, J., Rodriguez Fortun, J.M., Theunissen, J. and De Smet, J. (2015) ‘Direct Yaw Moment Control Actuated Through Electric Drivetrains and Friction Brakes: Theoretical Design and Experimental Assessment’. Mechatronics. Elsevier Ltd, 26, pp. 1-15. 33. Omae, M., Fukuda, R., Ogitsu, T. and Chiang, W. P. (2013) ‘Spacing Control of Cooperative Adaptive Cruise Control for Heavy-Duty Vehicles’. IFAC Proceedings Volumes, 7 (Part 1), pp. 58-65. 34. Pacejka, H. B. (2012) Tire and Vehicle Dynamics. 3rd edn. Oxford: Butterworth Heinemann. 35. Rajamani, R. (2012) Vehicle Dynamics and Control. 2nd edn. Boston, Massachusetts: Springer. 36. Raksincharoensak, P., Khaisongkram, W., Nagai, M., Shimosaka, M., Mori, T. and Sato, T. (2010) ‘Integrated Driver Modelling Considering State Transition Feature for Individual Adaptation of Driver Assistance Systems’. Vehicle System Dynamics, 48(S1), pp. 55-71. 37. Regolin, E., Incremona, G. P. and Ferrara, A. (2017) Longitudinal Vehicle Dynamics Control Via Sliding Modes Generation. In Sliding Mode Control of Vehicle Dynamics (Ferrara, A.,), pp. 33-76. London: The Institution of Engineering and Technology. 38. Reif, K. (2014) Brakes, Brake Control and Driver Assistance Systems: Function, Regulation and Components, Wiesbaden: Springer Fachmedien. 39. Ren, Y., Zheng, L., Yang, W. and Li, Y. (2018) Potential Field-Based Hierarchical Adaptive Cruise Control for Semi-Autonomous Electric Vehicle . Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 233(10), pp. 2479-2491. 40. Rill, G. (2011) Road Vehicle Dynamics: Fundamentals and Modelling. Florida: CRC Press. 41. Russell, H. E. B. and Gerdes, J. C. (2015) Design of Variable Vehicle Handling Characteristics’. IEEE Transactions on Control Systems Technology, 24(5), pp. 1-12. 42. Shahrieel, M., Najib, S., Chee, E. S. H., Azmira, I. and Hendra, M. (2009) ‘Comparison of Fuzzy Control Rules using MATLAB Toolbox and Simulink for DC Induction Motor-Speed Control’. SoCPaR 2009 - Soft Computing and Pattern Recognition, (May 2014), pp. 711-715. 43. Shakouri, P., Duran, O., Ordys, A. and Collier, G. (2013) ‘Teaching Fuzzy Logic Control Based on a Robotic Implementation’. IFAC Proceedings Volumes, 10 (Part 1), pp. 192-197. 44. Shakouri, P., Ordys, A. and Askari, M. R. (2012) Adaptive Cruise Control with Stop & Go Function Using the State-Dependent Nonlinear Model Predictive Control Approach. ISA Transactions. Elsevier, 51(5), pp. 622-631. 45. Sharkawy, A. B. (2005) ‘Fuzzy and Adaptive Fuzzy Control for the Automobiles’ Active Suspension System’. Vehicle System Dynamics, 43(11), pp. 795-806. 46. Shim, T. and Ghike, C. (2006) ‘Understanding the Limitations of Different Vehicle Models for Roll Dynamics Studies’. Vehicle System Dynamics, 45(3), pp. 1-26. 47. Suzuki, Y., Kano, Y. and Abe, M. (2014) ‘A Study on Tyre Force Distribution Controls for Full Drive-By-Wire Electric Vehicle . Vehicle System Dynamics, 52(supl), pp. 235-250. 48. Tahami, F., Farhangi, S. and Kazemi, R. (2004) A Fuzzy Logic Direct Yaw-Moment Control System for All-Wheel- Drive Electric Vehicles’. Vehicle System Dynamics, 41(August 2013), pp. 203-221. 49. Toyota (2019) Toyota Camry 2.5V Brochure [Online]. Available at: https://toyota.com.my/media/document/attachment/335/All-new-Camry-2018-brochure- 3.pdf (Accessed on 24 November 2019). 50. Vos, R. (2010) Influence of In-Wheel Motors on the Ride Comfort of Electric Vehicles. MSc Thesis, Eindhoven University of Technology. 51. Wang, L. (2015) Adaptive Cruise Control on an Electric Robotic Vehicle. MASc Thesis McMaster University, Canada. 52. Wong, J. Y. (2001) Theory of Ground Vehicles. 3rd edn. New York: John Wiley & Sons Inc. 53. Yoshimura, T. and Emoto, Y. (2003) Steering and Suspension System of a Full Car Model Using Fuzzy Reasoning Based on Single Input Rule Modules’. International Journal of Vehicle Autonomous Systems, 1(2), pp. 237-255. 54. Zadeh, L. A. (1965) ‘Fuzzy Sets’. Information and Control, 8, pp. 338-353. 55. Zeyada, Y., Kamopp, D., El-arabi, M. and El-Behiry, E. (1998) ‘A Combined ActiveSteering Differential-Braking Yaw Rate Control Strategy for Emergency Maneuvers’. SAE Technical Paper 980230. 56. Zheng, P. and McDonald, M. (2005) ‘Manual vs Adaptive Cruise Control - Can Driver’s Expectation Be Matched?’. Transportation Research Part C: Emerging Technologies,13(56), pp. 421-431.

Adaptive Cruise Control For Electric Vehicles

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