Estimation of Driving Resistance Coefficients with Neural Network and Its Low-Power Implementation using FPGA
It is important to acquire the states of a car not only on test environments but also on real roads to realize a carbon-neutral society. However, it is labor- and cost-inefficient due to the need of many in-car sensors. To alleviate this issue, this research acquires driving resistance coefficients by not directly measuring them but by predicting them using a neural network. In addition, the authors implement the proposed neural network model on an FPGA to enable its execution within the spare power of a car.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/02878321
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Authors:
- Hanamata, Shinichi
- Akiyama, Soramichi
- Hirata, Mitsuo
- Publication Date: 2022-5
Language
- English
- Japanese
Media Info
- Media Type: Digital/other
- Features: Figures; Photos; References; Tables;
- Pagination: pp 605-610
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Serial:
- Transactions of Society of Automotive Engineers of Japan
- Volume: 53
- Issue Number: 3
- Publisher: Society of Automotive Engineers of Japan
- ISSN: 0287-8321
- EISSN: 1883-0811
- Serial URL: https://www.jstage.jst.go.jp/browse/jsaeronbun
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Automobile driving; Electric vehicles; Energy consumption; Neural networks; Predictive models; Resistance (Mechanics)
- Subject Areas: Data and Information Technology; Energy; Highways; Vehicles and Equipment;
Filing Info
- Accession Number: 01847715
- Record Type: Publication
- Source Agency: Japan Science and Technology Agency (JST)
- Files: TRIS, JSTAGE
- Created Date: May 31 2022 3:36PM