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A Preliminary Study of Virtual Humidity Sensors for Vehicle Systems
Technical Paper
2014-01-1156
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
New vehicle control algorithms are needed to meet future emissions and fuel economy mandates that are quite likely to require a measurement of ambient specific humidity (SH). Current practice is to obtain the SH by measurement of relative humidity (RH), temperature and barometric pressure with physical sensors, and then to estimate the SH using a fit equation. In this paper a novel approach is described: a system of neural networks trained to estimate the SH using data that already exists on the vehicle bus. The neural network system, which is referred to as a virtual SH sensor, incorporates information from the global navigation satellite system such as longitude, latitude, time and date, and from the vehicle climate control system such as temperature and barometric pressure, and outputs an estimate of SH. The conclusion of this preliminary study is that neural networks have the potential of being used as a virtual sensor for estimating ambient and intake manifold's SH.
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Authors
Citation
Fang, C., Wang, X., Dai, Q., Murphey, Y. et al., "A Preliminary Study of Virtual Humidity Sensors for Vehicle Systems," SAE Technical Paper 2014-01-1156, 2014, https://doi.org/10.4271/2014-01-1156.Also In
References
- Rakopoulos , C. Influence of ambient temperature and humidity on the performance and emissions of nitric oxide and smoke of high speed diesel engines in the Athens/Greece region Energy Conversion and Management 31 5 447 458 1991
- Liang , C. , Srinivasan , S. and Jacobson , E. NOx Emission Control System Using a Virtual Sensor United States Patent, No. US6882929B2 2005
- Kubesh , J. , Podnar , D. , Latusek , J. and McCaw , D. Engine Control to Reduce the Emission Variability United States Patent, No. US6581571B2 2003
- Niimi , N. , Yoshida , T. , and Isogai , T. Capacitive Humidity Sensors Using Highly Durable Polyimide Membrane SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 6 1 328 334 2013 10.4271/2013-01-1337
- Wu , X. , Johnson , P. and Akbarzadeh , A. Application of heat pipe heat exchangers to humidity control in air conditioning systems Applied Thermal Engineering 17 6 561 568 1997
- Cheng et al. Virtual Vehicle Sensors Based On Neural Networks Trained Using Data Generated By Simulation Models U.S. Patent 6,236,908 May 22 2001
- MacNeille et al. Virtual ambient weather condition sensing U.S. Patent Application US20120158207 June 21 2012
- Rehman , S. and Mohandes , M. Artificial neural network estimation of global solar radiation using air temperature and relative humidity Energy Policy 36 2008 571 576 2008
- Sloane , S. and Wolff , T. Prediction of ambient light scattering using a physical model responsive to relative humidity: validation with measurements from Detroit Atmospheric Environment 19 4 669480 1985
- Mustafaraj , G. , Lowry , G. and Chen , J. Prediction of room temperature and relative humidity by autoregressive linear and non-linear neural network models for an open office Energy and Buildings 43 2011 1452 1460 2011
- Yigit , K. and Ertunc H. Prediction of the air temperature and humidity at the outlet of a cooling coil using neural networks International Communications in Heat and Mass Transfer 33 2006 898 907 2006
- Sigumonrong , A. , Bong , T. , Fok , S. and Wong , Y. Self-learning Neurocontroller for Maintaining Indoor Relative Humidity International Symposium on Neural Networks 2 1297 1301 2 2001
- Thornton Peter E , Hasenauer Hubert , White Michael A Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria Agricultural and Forest Meteorology 104 4 15 September 2000 255 271 0168-1923 10.1016/S0168-1923(00)00170-2 http://www.sciencedirect.com/science/article/pii/S0168192300001702
- Lu , T. and Viljanen , M. Prediction of indoor temperature and relative humidity using neural network models: model comparison Neural Comput & Applic 2009 18 345 357 2009 10.1007/s00521-008-0185-3
- Teodosiu , C. , Hohota , R , Rusaouen , G. and Woloszyn , M. Numerical prediction of indoor air humidity and its effects on indoor environment Building and Environment 38 2003 655 664 2003
- http://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule
- http://www.ncdc.noaa.gov/
- http://en.wikipedia.org/wiki/METAR#US_METAR_abbreviations