scholarly journals Data-aware monitoring method for fuel economy in ship-based CPS

2020 ◽  
Vol 5 (3) ◽  
pp. 245-252
Author(s):  
Yiran Shi ◽  
Shengjun Xue ◽  
Xing Zhang ◽  
Tao Huang
2021 ◽  
Vol 13 (20) ◽  
pp. 11331
Author(s):  
Kwangho Ko ◽  
Tongwon Lee ◽  
Seunghyun Jeong

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.


Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4471
Author(s):  
Kibok Kim ◽  
Jinil Park ◽  
Jonghwa Lee

Eco-drive is a widely used concept. It can improve fuel economy for different driving behaviors such as vehicle acceleration or accelerator pedal operation, deceleration or coasting while slowing down, and gear shift timing difference. The feasibility of improving the fuel economy of urban buses by applying eco-drive was verified by analyzing data from drivers who achieved high fuel efficiencies in urban buses with a high frequency of acceleration/deceleration and frequent operation. The items that were monitored for eco-drive were: rapid take-off/acceleration/deceleration, accelerator pedal gradient, coasting rate, shift indicator violation, average engine speed, over speed, and gear shifting under low-end engine speed. The monitoring method for each monitored item was set up, and an index was produced using driving data. A fuel economy prediction model was created using machine learning to determine the contribution of each index to the fuel economy. Furthermore, the contribution of each monitoring item was analyzed using the prediction model explainer. Accordingly, points (defined as the eco-drive score) were allocated for each monitoring item. It was verified that this score can represent the eco-drive characteristics based on the relationship between the score and fuel economy. In addition, it resulted in an average annual fuel economy improvement of 12.1%.


Author(s):  
W. T. Donlon ◽  
J. E. Allison ◽  
S. Shinozaki

Light weight materials which possess high strength and durability are being utilized by the automotive industry to increase fuel economy. Rapidly solidified (RS) Al alloys are currently being extensively studied for this purpose. In this investigation the microstructure of an extruded Al-8Fe-2Mo alloy, produced by Pratt & Whitney Aircraft, Goverment Products Div. was examined in a JE0L 2000FX AEM. Both electropolished thin sections, and extraction replicas were examined to characterize this material. The consolidation procedure for producing this material included a 9:1 extrusion at 340°C followed by a 16:1 extrusion at 400°C, utilizing RS powders which have also been characterized utilizing electron microscopy.


1918 ◽  
Vol 86 (2218supp) ◽  
pp. 11-11
Author(s):  
Frank McManamy
Keyword(s):  

1918 ◽  
Vol 86 (2225supp) ◽  
pp. 123-123
Keyword(s):  

2015 ◽  
Vol 43 (2) ◽  
pp. 144-162
Author(s):  
Al Cohn

ABSTRACT Maintaining proper tire inflation is the number one issue facing commercial fleets today. Common, slow-leaking tread area punctures along with leaking valve stems and osmosis through the tire casing lead to tire underinflation with a subsequent loss in fuel economy, reduction in retreadability, tread wear loss, irregular wear, and increase in tire-related roadside service calls. Commercial truck tires are the highest maintenance cost for fleets second only to fuel. This article will examine tire footprint analysis, rolling resistance data, and the effect on vehicle fuel economy from tires run at a variety of underinflated, overinflated, and recommended tire pressures. This analysis will also include the tire footprint impact by running tires on both fully loaded and unloaded trailers. The footprint analysis addresses both standard dual tires (295/75R22.5) along with the newer increasingly popular wide-base tire size 445/50R22.5.


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