Implementation of a Fuel Estimation Algorithm Using Approximated Computing
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 consum...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Journal of Low Power Electronics and Applications |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9268/12/1/17 |
| _version_ | 1797470411370266624 |
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| author | Imed Ben Dhaou |
| author_facet | Imed Ben Dhaou |
| author_sort | Imed Ben Dhaou |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:36:01Z |
| format | Article |
| id | doaj.art-e0e4773ab77e4828b5e95beed87a2230 |
| institution | Directory Open Access Journal |
| issn | 2079-9268 |
| language | English |
| last_indexed | 2024-03-09T19:36:01Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Low Power Electronics and Applications |
| spelling | doaj.art-e0e4773ab77e4828b5e95beed87a22302023-11-24T01:56:04ZengMDPI AGJournal of Low Power Electronics and Applications2079-92682022-03-011211710.3390/jlpea12010017Implementation of a Fuel Estimation Algorithm Using Approximated ComputingImed Ben Dhaou0Department of Computer Science, Hekma School of Engineering, Computing, and Informatics, Dar Al-Hekma University, Jeddah 22246-4872, Saudi ArabiaThe 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.https://www.mdpi.com/2079-9268/12/1/17FPGAeco-drivingfloating-point arithmetic |
| spellingShingle | Imed Ben Dhaou Implementation of a Fuel Estimation Algorithm Using Approximated Computing Journal of Low Power Electronics and Applications FPGA eco-driving floating-point arithmetic |
| title | Implementation of a Fuel Estimation Algorithm Using Approximated Computing |
| title_full | Implementation of a Fuel Estimation Algorithm Using Approximated Computing |
| title_fullStr | Implementation of a Fuel Estimation Algorithm Using Approximated Computing |
| title_full_unstemmed | Implementation of a Fuel Estimation Algorithm Using Approximated Computing |
| title_short | Implementation of a Fuel Estimation Algorithm Using Approximated Computing |
| title_sort | implementation of a fuel estimation algorithm using approximated computing |
| topic | FPGA eco-driving floating-point arithmetic |
| url | https://www.mdpi.com/2079-9268/12/1/17 |
| work_keys_str_mv | AT imedbendhaou implementationofafuelestimationalgorithmusingapproximatedcomputing |