Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms
This study offers a neural network-based deep learning method for energy optimization modeling in electric vehicles (EV). The pre-processed driving cycle is transformed into static maps and fed into a neural network for prototype energy optimization for CAN bus and media control in electric vehicles...
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| Veröffentlicht in: | The Journal of supercomputing 2022-04, Vol.78 (6), p.8493-8508 |
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| container_end_page | 8508 |
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| container_issue | 6 |
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| container_title | The Journal of supercomputing |
| container_volume | 78 |
| creator | Salunkhe, Satish S. Pal, Shelendra Agrawal, Abhishek Rai, Ravi Mole, S. S. Sreeja Jos, Bos Mathew |
| description | This study offers a neural network-based deep learning method for energy optimization modeling in electric vehicles (EV). The pre-processed driving cycle is transformed into static maps and fed into a neural network for prototype energy optimization for CAN bus and media control in electric vehicles. The proposed model includes the prediction of battery state-of-charge as well as the consumption of fuel-at-destination. The controller area network (CAN) bus is the most important element in EV, ensuring its protection is the most difficult task. The abnormal messages of the CAN bus are detected using DNN. The suggested DNN model is an integrated triplet network loss which minimizes the length among the anchor sample as well as the positive sample is comparably minimum than the length measured between anchor sample and negative sample. The proposed DNN model is utilized for CAN bus and various media control in electric vehicles for effective performance. |
| doi_str_mv | 10.1007/s11227-021-04186-5 |
| format | Article |
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The suggested DNN model is an integrated triplet network loss which minimizes the length among the anchor sample as well as the positive sample is comparably minimum than the length measured between anchor sample and negative sample. 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The proposed DNN model is utilized for CAN bus and various media control in electric vehicles for effective performance.</description><subject>Algorithms</subject><subject>Buses (vehicles)</subject><subject>Compilers</subject><subject>Computer Science</subject><subject>Controller area network</subject><subject>Deep learning</subject><subject>Electric vehicles</subject><subject>Interpreters</subject><subject>Machine learning</subject><subject>Machine learning in Intelligent Autonomous Systems</subject><subject>Neural networks</subject><subject>Optimization</subject><subject>Processor Architectures</subject><subject>Programming Languages</subject><issn>0920-8542</issn><issn>1573-0484</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp9kEtLAzEUhYMoWKt_wFXA9ejNazKzLKU-oOhG1yFNk2nKTDImU6H-ekdHcOfqcuB858KH0DWBWwIg7zIhlMoCKCmAk6osxAmaESHZGCt-imZQUygqwek5ush5DwCcSTZDbhVsao449oPv_KcefAzYxYSXi2e8OWSswxZ3dus1NjEMKbYZ-4Bta82QvMEfdudNazM-ZB8avLW2x63VKXwn3TYx-WHX5Ut05nSb7dXvnaO3-9Xr8rFYvzw8LRfrwjBSD0WpJWEEqJYV4YxWojKOgqOkogRkKYQxXEBpqQFTSz5CxArmNGcbAdxRNkc3026f4vvB5kHt4yGF8aWiJS8Jl7KGsUWnlkkx52Sd6pPvdDoqAurbp5p8qtGn-vGpxAixCcpjOTQ2_U3_Q30BvmV3dg</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Salunkhe, Satish S.</creator><creator>Pal, Shelendra</creator><creator>Agrawal, Abhishek</creator><creator>Rai, Ravi</creator><creator>Mole, S. 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| issn | 0920-8542 1573-0484 |
| language | eng |
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| source | SpringerNature Journals |
| subjects | Algorithms Buses (vehicles) Compilers Computer Science Controller area network Deep learning Electric vehicles Interpreters Machine learning Machine learning in Intelligent Autonomous Systems Neural networks Optimization Processor Architectures Programming Languages |
| title | Energy optimization for CAN bus and media controls in electric vehicles using deep learning algorithms |
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