Implementation of a Fuel Estimation Algorithm on SoC FPGA

Dhaou, Imed Ben; Mahroogi, Faisal O.; Tenhunen, Hannu

doi:10.1109/iscas.2019.8702420

Cited by 1 publication

(3 citation statements)

References 12 publications

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“…The comparison presented in Table 10 favors the IP over the floating-point package. The critical delay for the data path is 4 ns, which is nearly 17.8 times less than the one proposed in [13]. The proposed architecture can be used for eco-routing.…”

Section: Feature Approximation Binary Search Directmentioning

confidence: 87%

“…Real-time implementation of fuel estimation algorithms has received scant attention. This work is an extension of previously published work [9,13]. The contributions reported in this work are the following:…”

Section: Introductionmentioning

confidence: 83%

“…The engine RPM was fixed to 2800 for a passenger vehicle and 2400 for a truck. The authors of [13] envisioned an RPM algorithm and designed a hardware architecture using floating point arithmetic for the implementation of the fuel estimation algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Implementation of a Fuel Estimation Algorithm Using Approximated Computing

Dhaou

2022

JLPEA

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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.

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Section: Feature Approximation Binary Search Directmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 83%