PIER
 
Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 139 > pp. 671-687

ON-ROAD MAGNETIC EMISSIONS PREDICTION OF ELECTRIC CARS IN TERMS OF DRIVING DYNAMICS USING NEURAL NETWORKS

By A. Wefky, F. Espinosa, F. Leferink, A. Gardel, and R. Vogt-Ardatjew

Full Article PDF (636 KB)

Abstract:
This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

Citation:
A. Wefky, F. Espinosa, F. Leferink, A. Gardel, and R. Vogt-Ardatjew, "On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks," Progress In Electromagnetics Research, Vol. 139, 671-687, 2013.
doi:10.2528/PIER13040405
http://www.jpier.org/PIER/pier.php?paper=13040405

References:
1. Hansen, J. Q., M. Winther, and S. C. Sorenson, "The influence of driving patterns on petrol passenger car emissions," Science of the Total Environment, Vol. 169, No. 1-3, 129-139, 1995.
doi:10.1016/0048-9697(95)04641-D

2. Joumard, R., et al., "Hot passenger car emissions modelling as a function of instantaneous speed and acceleration," Science of the Total Environment, Vol. 169, No. 1-3, 167-174, 1995.
doi:10.1016/0048-9697(95)04645-H

3. Sjodin, A. and M. Lenner, "On-road measurements of single vehicle pollutant emissions, speed and acceleration for large fleets of vehicles in different tra±c environments," Science of the Total Environment, Vol. 169, No. 1-3, 157-165, 1995.
doi:10.1016/0048-9697(95)04644-G

4. Barth, M., et al., "Analysis of modal emissions from diverse in-use vehicle fleet," Transportation Research Record: Journal of the Transportation Research Board, Vol. 1587, 73-84, 1997.
doi:10.3141/1587-09

5. Nesamani, K. S. and K. P. Subramanian, "Impact of real-world driving characteristics on vehicular emissions," JSME International Journal Series B: Fluids and Thermal Engineering, Vol. 49, No. 1, 19-26, 2006.
doi:10.1299/jsmeb.49.19

6. Washington, S., J. Wolf, and R. Guensler, "Binary recursive partitioning method for modeling hot-stabilized emissions from motor vehicles," Transportation Research Record: Journal of the Transportation Research Board, Vol. 1587, 96-105, 1997.
doi:10.3141/1587-11

7. Barth, M. J., et al., "Development of a comprehensive modal emissions model,", Monograph Record, 2000.

8. Sorenson, S. C., et al., "Individual and public transportation - Emissions and energy consumption models,", Lyngby, 1992.

9. Holmen, B. A. and D. A. Niemeier, "Characterizing the effects of driver variability on real-world vehicle emissions," Transportation Research Part D: Transport and Environment, Vol. 3, No. 2, 117-128, 1998.
doi:10.1016/S1361-9209(97)00032-1

10. Tong, H., W. Hung, and C. Cheung, "On-road motor vehicle emissions and fuel consumption in urban driving conditions," Journal of the Air & Waste Management Association, Vol. 50, No. 4, 543-554, 2000.
doi:10.1080/10473289.2000.10464041

11. Espinosa, F., et al., "Design and implementation of a portable electronic system for vehicle-driver-route activity measurement," Measurement, Vol. 44, No. 2, 326-337, 2011.
doi:10.1016/j.measurement.2010.10.006

12. Silva, F. and M. Aragon, "Electromagnetic interferences from electric/hybrid vehicles," 2011 XXXth URSI General Assembly and Scientific Symposium, 1-4, 2011.

13. Ruddle, A. R., D. A. Topham, and D. D. Ward, "Investigation of electromagnetic emissions measurements practices for alternative powertrain road vehicles," 2003 IEEE International Symposium on Electromagnetic Compatibility, Vol. 2, 543-547, 2003.
doi:10.1109/ISEMC.2003.1236660

14. Agency, N. S., Allied Environmental Conditions and Tests Publication, AECTP 500, 4th edition, January 2011.

15. Ptitsyna, N. and A. Ponzetto, "Magnetic fields encountered in electric transport: Rail systems, trolleybus and cars," 2012 International Symposium on Electromagnetic Compatibility (EMC EUROPE), 1-5, 2012.
doi:10.1109/EMCEurope.2012.6396902

16. Halgamuge, M. N., C. D. Abeyrathne, and P. Mendis, "Measurement and analysis of electromagnetic fields from trams, trains and hybrid cars," Radiation Protection Dosimetry, Vol. 141, No. 3, 255-268, 2010.
doi:10.1093/rpd/ncq168

17. Wefky, A., et al., "Electrical drive radiated emissions estimation in terms of input control using extreme learning machines," Mathematical Problems in Engineering, Vol. 2012, 11, 2012.

18. Wefky, A. M., et al., "Modeling radiated electromagnetic emissions of electric motorcycles in terms of driving profile using mlp neural networks," Progress In Electromagnetics Research, Vol. 135, 231-244, 2013.

19. Miyajima, C., et al., "Cepstral analysis of driving behavioral signals for driver identification," 2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 Proceedings, 2006.

20. Wefky, A. M., et al., "Alternative sensor system and MLP neural network for vehicle pedal activity estimation," Sensors, Vol. 10, No. 4, 3798-3814, 2010.
doi:10.3390/s100403798

21. Wefky, A., et al., "Comparison of neural classifiers for vehicles gear estimation," Applied Soft Computing, Vol. 11, No. 4, 3580-3599, 2011.
doi:10.1016/j.asoc.2011.01.030

22. Ahn, K., et al., "Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels," Journal of Transportation Engineering, Vol. 128, No. 2, 182-190, 2002.
doi:10.1061/(ASCE)0733-947X(2002)128:2(182)

23. Dong, X., et al., "Detection and identification of vehicles based on ," IEEE Transactions on Electromagnetic Compatibility, Vol. 48, No. 4, 752-759, 2006.
doi:10.1109/TEMC.2006.882841

24. Tsai, C. Y., E. J. Rothwell, and K. M. Chen, "Target discrimination using neural networks with time domain or spectrum magnitude response," Journal of Electromagnetic Waves and Applications, Vol. 10, No. 3, 341-382, 1996.
doi:10.1163/156939396X00450

25. Atkins, R. G., R. T. Shin, and J. A. Kong, "A neural network method for high range resolution target classification," Progress In Electromagnetics Research, Vol. 4, 255-292, 1991.

26. Koroglu, S., et al., "An approach to the calculation of multilayer magnetic shielding using artificial neural network," Simulation Modelling Practice and Theory, Vol. 17, No. 7, 1267-1275, 2009.
doi:10.1016/j.simpat.2009.05.001

27. Aunchaleevarapan, K., K. Paithoonwatanakij, W. Khan-Ngern, and S. Nitta, "Novel method for predicting PCB configurations for near field and far field radiated EMI using a neural network," IEICE Trans. Commun., Vol. E86-B, No. 4, 1364-1376, 2003.

28. Chahine, I., et al., "Characterization and modeling of the suscep-tibility of integrated circuits to conducted electromagnetic disturbances up to 1 GHz," IEEE Transactions on Electromagnetic Compatibility, Vol. 50, No. 2, 285-293, 2008.
doi:10.1109/TEMC.2008.918983

29. Sujintanarat, P., P. Dangkham, S. Chaichana, K. Aunchaleevarapan, and P. Teekaput, "Recognition and identification of radiated EMI for shielding aperture using neural network," PIERS Online, Vol. 3, No. 4, 444-447, 2007.
doi:10.2529/PIERS060907082540

30. Luo, M. and K.-M. Huang, "Prediction of the electromagnetic field in metallic enclosures using artificial neural networks," Progress In Electromagnetics Research, Vol. 116, 171-184, 2011.

31. Zaharis, Z. D., K. A. Gotsis, and J. N. Sahalos, "Comparative study of neural network training applied to adaptive beamforming of antenna arrays," Progress In Electromagnetics Research, Vol. 126, 269-283, 2012.
doi:10.2528/PIER12012408

32. Zaharis, Z. D., K. A. Gotsis, and J. N. Sahalos, "Adaptive beamforming with low side lobe level using neural networks trained by mutated boolean PSO," Progress In Electromagnetics Research, Vol. 127, 139-154, 2012.
doi:10.2528/PIER12022806

33. Li, X. and J. Gao, "Pad modeling by using artificial neural network," Progress In Electromagnetics Research, Vol. 74, 167-180, 2007.
doi:10.2528/PIER07041201

34. Al Salameh, M. S. and E. T. Al Zuraiqi, "Solutions to lectromagnetic compatibility problems using artificial neural networks representation of vector finite element method," IET Microwaves, Antennas & Propagation, Vol. 2, No. 4, 348-357, 2008.
doi:10.1049/iet-map:20060189

35. Bermani, E., S. Caorsi, and M. Raffetto, "An inverse scattering approach based on a neural network technique for the detection of dielectric cylinders buried in a lossy half-space," Progress In Electromagnetic Research, Vol. 26, 69-90, 2000.

36. Khare, M. and S. M. S. Nagendra, Artificial Neural Networks in Vehicular Pollution Modelling, Springer, 2007.

37. Winter, W. and M. Herbrig, "Time domain measurements a novel method for qualification of electronics," 2010 15th International Conference on Microwave Techniques (COMITE), 19-24, 2010.
doi:10.1109/COMITE.2010.5481261

38. Winter, W. and M. Herbrig, "Time domain measurements in automotive applications," 2009 IEEE International Symposium on Electromagnetic Compatibility, 109-115, 2009.
doi:10.1109/ISEMC.2009.5284604

39. Karabetsos, E., et al., "EMF measurements in hybrid technology cars," Proceedings of 6th International Workshop on Biological Effects of Electromagnetic Fields, Istambul, 2010.

40. Concha Moreno-Torres, P., et al., "Evaluation of the magnetic field generated by the inverter of an electric vehicle," IEEE Transactions on Magnetics, Vol. 49, No. 2, 837-844, 2013.
doi:10.1109/TMAG.2012.2214787

41. Berisha, S., et al., "Magnetic field generated from different electric vehicles,", SAE Technical Paper 951934, 1995.

42. Ptitsyna, N., et al., "Analysis of magnetic fields onboard electric transport systems in regard to human exposure," 2012 International Symposium on Electromagnetic Compatibility (EMC EUROPE), 1-5, 2012.
doi:10.1109/EMCEurope.2012.6396902

43. Kopytenko, Y. A., et al., "Monitoring and analysis of magnetic fields onboard transport systems: Waveforms and exposure Assessment," 2007 7th International Symposium on Electromagnetic Compatibility and Electromagnetic Ecology, 331-333, 2007.
doi:10.1109/EMCECO.2007.4371725

44. Zegeye, S., et al., "Model-based traffic control for the reduction of fuel consumption, emissions, and travel time," 12th IFAC Symposium on Control in Transportation Systems, 149-154, 2009.

45. Hagan, M. T. and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5, No. 6, 989-993, 1994.
doi:10.1109/72.329697

46. Moler, M. F., "A scaled conjugate gradient algorithm for fast supervised learning," Neural Networks, Vol. 6, No. 4, 525-533, 1993.
doi:10.1016/S0893-6080(05)80056-5

47. Battiti, R., "First-and second-order methods for learning: Between steepest descent and Newton's method," Neural Computation, Vol. 4, No. 2, 141-166, 1992.
doi:10.1162/neco.1992.4.2.141

48. Setiono, R. and L. C. K. Hui, "Use of a quasi-Newton method in a feedforward neural network construction algorithm," IEEE Transactions on Neural Networks, Vol. 6, No. 1, 273-277, 1995.
doi:10.1109/72.363426

49. Huang, G.-B., D.Wang, and Y. Lan, "Extreme learning machines: A survey," International Journal of Machine Learning and ybernetics, Vol. 2, No. 2, 107-122, 2011.
doi:10.1007/s13042-011-0019-y

50. Huang, G.-B., L. Chen, and C.-K. Siew, "Universal approximation using incremental constructive feedforward networks with random hidden nodes," IEEE Transactions on Neural Networks, Vol. 17, No. 4, 879-892, 2006.
doi:10.1109/TNN.2006.875977

51. Haykin, S. S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 1999.

52. Hagan, M. T., H. B. Demuth, and M. H. Beale, Neural Network Design, PWS Pub., 1996.

53. Demuth, H. and M. Beale, "Neural network toolbox: For use with MATLAB ®,", Mathworks, 2001.


© Copyright 2014 EMW Publishing. All Rights Reserved