Estimation of engine torque based on improved BP neural network
Aiming at the mass-energy power assembly control system in HEVs, a method is designed to estimate the engine torque, which is based on improved BP neural network. Based on the experiment results in engine dynamometer, and strong nonlinear characteristic of the engine is taken into account, tradition...
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| creator | Xudong Wang Xiaogang Wu Jimin Jing Tengwei Yu |
| description | Aiming at the mass-energy power assembly control system in HEVs, a method is designed to estimate the engine torque, which is based on improved BP neural network. Based on the experiment results in engine dynamometer, and strong nonlinear characteristic of the engine is taken into account, traditional BP neural network error function is improved, and it is trained by optimal stopping, as a result over-fitting will be avoided. The engine torque output model is established with MATLAB, and it has high estimated accuracy and nice generalization ability. After all, validity of the algorithm mentioned above is verified by experiments. |
| doi_str_mv | 10.1109/VPPC.2009.5289684 |
| format | Conference Proceeding |
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Based on the experiment results in engine dynamometer, and strong nonlinear characteristic of the engine is taken into account, traditional BP neural network error function is improved, and it is trained by optimal stopping, as a result over-fitting will be avoided. The engine torque output model is established with MATLAB, and it has high estimated accuracy and nice generalization ability. 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Based on the experiment results in engine dynamometer, and strong nonlinear characteristic of the engine is taken into account, traditional BP neural network error function is improved, and it is trained by optimal stopping, as a result over-fitting will be avoided. The engine torque output model is established with MATLAB, and it has high estimated accuracy and nice generalization ability. After all, validity of the algorithm mentioned above is verified by experiments.</description><subject>Control systems</subject><subject>Design engineering</subject><subject>Electronic mail</subject><subject>Engines</subject><subject>Equations</subject><subject>estimation</subject><subject>hybrid electric vehicle</subject><subject>Mathematical model</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>optimal stopping rule</subject><subject>Power engineering and energy</subject><subject>torque</subject><subject>Torque control</subject><issn>1938-8756</issn><isbn>9781424426003</isbn><isbn>1424426006</isbn><isbn>9781424426010</isbn><isbn>1424426014</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVUMtOwzAQNIJKlJIPQFz8Awl-ru0Tgqg8pErkUHGtbMdBhjYpTgri77FEL-xldnY1o9EgdEVJRSkxN69NU1eMEFNJpg1ocYIKozQVTAgGhJLTf5zwMzSnhutSKwkzdJGl2hChQZyjYhzfSR6RrSSfo9vlOMWdneLQ46HDoX-LfcDTkD4PATs7hhbnT9zt0_CV9_sG9-GQ7DbD9D2kj0s06-x2DMURF2j9sFzXT-Xq5fG5vluV0ZCpZC2zXjhQorVgmPPOgRBgvVWd8kC1k14SKVutQuc4SOMIM-ADhBCsAL5A13-2MR82-5Qjp5_NsQ7-C5PsTyA</recordid><startdate>200909</startdate><enddate>200909</enddate><creator>Xudong Wang</creator><creator>Xiaogang Wu</creator><creator>Jimin Jing</creator><creator>Tengwei Yu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200909</creationdate><title>Estimation of engine torque based on improved BP neural network</title><author>Xudong Wang ; Xiaogang Wu ; Jimin Jing ; Tengwei Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-2d2ac4b674da692bcbb6446aca7f7c618b5c5055d87efb3659b0296ce6eeea463</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Control systems</topic><topic>Design engineering</topic><topic>Electronic mail</topic><topic>Engines</topic><topic>Equations</topic><topic>estimation</topic><topic>hybrid electric vehicle</topic><topic>Mathematical model</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>optimal stopping rule</topic><topic>Power engineering and energy</topic><topic>torque</topic><topic>Torque control</topic><toplevel>online_resources</toplevel><creatorcontrib>Xudong Wang</creatorcontrib><creatorcontrib>Xiaogang Wu</creatorcontrib><creatorcontrib>Jimin Jing</creatorcontrib><creatorcontrib>Tengwei Yu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Xudong Wang</au><au>Xiaogang Wu</au><au>Jimin Jing</au><au>Tengwei Yu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Estimation of engine torque based on improved BP neural network</atitle><btitle>2009 IEEE Vehicle Power and Propulsion Conference</btitle><stitle>VPPC</stitle><date>2009-09</date><risdate>2009</risdate><spage>1679</spage><epage>1683</epage><pages>1679-1683</pages><issn>1938-8756</issn><isbn>9781424426003</isbn><isbn>1424426006</isbn><eisbn>9781424426010</eisbn><eisbn>1424426014</eisbn><abstract>Aiming at the mass-energy power assembly control system in HEVs, a method is designed to estimate the engine torque, which is based on improved BP neural network. Based on the experiment results in engine dynamometer, and strong nonlinear characteristic of the engine is taken into account, traditional BP neural network error function is improved, and it is trained by optimal stopping, as a result over-fitting will be avoided. The engine torque output model is established with MATLAB, and it has high estimated accuracy and nice generalization ability. After all, validity of the algorithm mentioned above is verified by experiments.</abstract><pub>IEEE</pub><doi>10.1109/VPPC.2009.5289684</doi><tpages>5</tpages></addata></record> |
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| ispartof | 2009 IEEE Vehicle Power and Propulsion Conference, 2009, p.1679-1683 |
| issn | 1938-8756 |
| language | eng |
| recordid | cdi_ieee_primary_5289684 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Control systems Design engineering Electronic mail Engines Equations estimation hybrid electric vehicle Mathematical model neural network Neural networks Neurons optimal stopping rule Power engineering and energy torque Torque control |
| title | Estimation of engine torque based on improved BP neural network |
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