A Preliminary Study of Virtual Humidity Sensors for Vehicle Systems 2014-01-1156
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.
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. Download Citation
Author(s):
Chen Fang, Xipeng Wang, Qi Dai, Yi Murphey, David Weber, Perry MacNeille
Affiliated:
Univ. of Michigan, Ford Motor Co.
Pages: 7
Event:
SAE 2014 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Neural networks
Pressure
Humidity
Fuel economy
Sensors and actuators
Manifolds
Emissions
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