A1 Refereed original research article in a scientific journal
Implementation of a Fuel Estimation Algorithm Using Approximated Computing
Authors: Ben Dhaou Imed
Publisher: MDPI
Publication year: 2022
Journal: Journal of Low Power Electronics and Applications
Journal name in source: JOURNAL OF LOW POWER ELECTRONICS AND APPLICATIONS
Journal acronym: J LOW POWER ELECT AP
Article number: 17
Volume: 12
Issue: 1
Number of pages: 13
eISSN: 2079-9268
DOI: https://doi.org/10.3390/jlpea12010017
Web address : https://www.mdpi.com/2079-9268/12/1/17
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175278121
The rising concerns about global warming have motivated the international community to take remedial actions to lower greenhouse gas emissions. The transportation sector is believed to be one of the largest air polluters. The quantity of greenhouse gas emissions is directly linked to the fuel consumption of vehicles. Eco-driving is an emergent driving style that aims at improving gas mileage. Real-time fuel estimation is a critical feature of eco-driving and eco-routing. There are numerous approaches to fuel estimation. The first approach uses instantaneous values of speed and acceleration. This can be accomplished using either GPS data or direct reading through the OBDII interface. The second approach uses the average value of the speed and acceleration that can be measured using historical data or through web mapping. The former cannot be used for route planning. The latter can be used for eco-routing. This paper elaborates on a highly pipelined VLSI architecture for the fuel estimation algorithm. Several high-level transformation techniques have been exercised to reduce the complexity of the algorithm. Three competing architectures have been implemented on FPGA and compared. The first one uses a binary search algorithm, the second architecture employs a direct address table, and the last one uses approximation techniques. The complexity of the algorithm is further reduced by combining both approximated computing and precalculation. This approach helped reduce the floating-point operations by 30% compared with the state-of-the-art implementation.
Downloadable publication This is an electronic reprint of the original article. |  |
