Magnetic Field Assisted Smart Nano Fluids for Electric Vehicle (EV) Performance Test for Defence Application
Abstract
Modern days electric vehicle (EV) technology is moving towards in the research and development area and also it can be more useful in upcoming years. The research paper is based on intelligent EV which is using smart nanofluid in its braking system. The smart fluid can substantially change the EV break, clutch and damping system. The EV moves through lot of obstacles in its path, the EV can adaptively change its position without getting disturbed to reach its destination. The regenerative braking system (RBS) is developed for EV using electrical energy storage system and run by brushless DC (BLDC) motor. While run-time of EV when it takes brake, the BLDC acts as a generator. A magneto-sensitive fluid (MSF) is a stable colloidal suspension of magnetic particles in a carrier fluid. A wide selection of liquid bases and magnetic particles is available: e.g. hydrocarbons, fluorocarbons, esters, organometallics, polyphenol ethers; and magnetic particles like metallic oxides (Fe3O4, Fe2O3), metals and their alloys (Fe, Co, Ni). Brownian motion keeps the particles suspended indefinitely, so that there is no precipitation under gravity or a magnetic field. Because the particle concentrations are only about 3-10% by volume, there is little effect on the physical and chemical characteristics of the fluid. Particles are stabilized with an absorbent coating, and various proprietary fluids have been tailored using specific particles and liquid bases. At this time fluid will play vital role during stoppage it will be easier stability and more smooth braking of the system without disturbances (like slippage and too much time in stop). To provide a reliable and smooth braking regeneration system, the braking force distribution is realized through a soft computing technique. The EV experiment result confirms high capacity of the future smart nanofluid technology.
References
Awad. F and Shamroukh.R , “ EV Detection by Intelligent Machine Urban Search and Rescue Using Image Processing and Neural Networks” International Journal of Intelligent Science , pp.4 39-53, 2014.
Dennis, Jacob D., et al. (2013), "Rheological characterization of monomethylhydrazine gels", Journal of Propulsion and Power 29.2,313-320.
Gold.J and Koren.K, “Hybrid vehicle with Obstacle Avoidance with Ultrasonic Sensors”. IEEE Journal of Robotics and Automation, vol. RA-4, No.2, pp. 213-218, 2018.
Hook.A, and Koren. K, “The vector field histogram-fast obstacle avoidance for EV, IEEE Trans. on Robotics and Automation, pp.501-518, 2011.
Harrol. J and Latombe .J.C, “Hybrid Machine motion planning: a distributed representation Approach”, International Journal of Robotics Research, vol.10 no.6, pp. 628-649, 2018.
H. M. Luan C. Korman and N. Willenbacher, (1996) Rheometry on magnetorheological (MR) fluids, Rheol. Acta., 35, 417.
KatiyarA., Singh A. N., Shukla P., and Nandi T., (2012), "Rheological behaviour of magnetic nanofluids containing spherical nanoparticles of Fe–Ni" Powder technology224,86-89.
Mongin.A.K , Chatterji.B.N, Ray A.K . “Design of a real-time tracking system for fast-moving objects with Solar EV” IETE J. Res 43, pp.359-369, 2017.
Nick.D and Forsyth.A, “Finding and tracking people from the autonous electric machine”, in Proceeding of Computer Vision and Pattern Recognition (CVPR), Madison Wisconnin , June 2019.
Nian, X. Peng, F. and Zhang, H. “Regenerative braking system of EV driven by brushless DC motor,” IEEE Trans. Ind. Electron., vol. 61, no. 10, pp. 5798–5808, Jan. 2014.
Prassler, E. Bank, D. Kluge, B. and Hagek, H. “Key technologies in robot assistants: Motion co-ordination between a human and a mobile robot,” proc. 30thint .sym. Robotics, 2001, pp. 410-415, Mar. 2001.
Schulz, W. Burgard, D. and Fox, “People tracking with mobile robots using Sample-based joint probilistic data association filters”, International Journal of Robotics Research pp.99-116, Sept. 2003.
Urzelai, J. and Ezkerra, J, M. “Fuzzy controller for wallfollowing with a non- holonomous mobile robot” FUZZY-IEEE / IFES, BARCELONA, July, 1997.
Yadav, A. Chaturvedi, D, K. “Design and development of safe intelligent moving machine”. International Journal of Electrical Machines & Drives(IJEMD). vol.2, pp 16-21, Feb. 2016.
Yadav, A. Chaturvedi, D, K. “Mathematical Model for Ground EV Using Matlab-Simulink”. International Journal of Automatic Control System (IJACS). Vol. 2, pp 26-34, April 2016. 16. Yadav, A. Yadav, N. Chaturvedi, D, K. “An Intelligent EV (IEV)Using LabVIEW”. IEEE 2nd International Conference on Communication, Control and Intelligent Systems (CCIS) vol.2, ISSN 978-5090 Nov. 2016.
Yang, Y, P. Liu, L. Wang, T, J. Kuo, K, C.and Hsu, P, E.“An electric gearshift with ultracapacitors for the power train of an EV with a directly driven wheel motor,” IEEE Trans. Veh. Technol., vol. 56, no. 5, pp. 2421–2431, Sep. 2007.
Zhou, H. and Sakane,S. “Sensor planning for mobile EV localization based of probalistic inference using bayesian network,” proc. of the 4th IEEE International Symposium on Assembly and Task Planning, pp. 7-12, May 2001.
