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Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus
Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, g...
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Published in: | Mechanical systems and signal processing 2015-08, Vol.60-61, p.785-798 |
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creator | Zeng, Xiaohua Yang, Nannan Wang, Junnian Song, Dafeng Zhang, Nong Shang, Mingli Liu, Jianxin |
description | Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
•This paper proposes a predictive model based dynamic coordination control strategy.•The dynamic model of the power-split hybrid electric bus is built.•An engine estimation algorithm is designed for the control strategy.•Co-simulation results verify that smooth mode shifting is realized. |
doi_str_mv | 10.1016/j.ymssp.2014.12.016 |
format | article |
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•This paper proposes a predictive model based dynamic coordination control strategy.•The dynamic model of the power-split hybrid electric bus is built.•An engine estimation algorithm is designed for the control strategy.•Co-simulation results verify that smooth mode shifting is realized.</description><identifier>ISSN: 0888-3270</identifier><identifier>EISSN: 1096-1216</identifier><identifier>DOI: 10.1016/j.ymssp.2014.12.016</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Algorithms ; Buses (vehicles) ; Comfort ; Dynamic coordination ; Dynamics ; Engine torque estimation ; Feedback control ; Hybrid electric bus ; Hybrid vehicles ; Mathematical models ; Predictive model ; Riding ; Strategy</subject><ispartof>Mechanical systems and signal processing, 2015-08, Vol.60-61, p.785-798</ispartof><rights>2015 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c406t-3742e640a15fea68cd0d968494f77c62c1f253749464d62c8589753dd27c1dec3</citedby><cites>FETCH-LOGICAL-c406t-3742e640a15fea68cd0d968494f77c62c1f253749464d62c8589753dd27c1dec3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zeng, Xiaohua</creatorcontrib><creatorcontrib>Yang, Nannan</creatorcontrib><creatorcontrib>Wang, Junnian</creatorcontrib><creatorcontrib>Song, Dafeng</creatorcontrib><creatorcontrib>Zhang, Nong</creatorcontrib><creatorcontrib>Shang, Mingli</creatorcontrib><creatorcontrib>Liu, Jianxin</creatorcontrib><title>Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus</title><title>Mechanical systems and signal processing</title><description>Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
•This paper proposes a predictive model based dynamic coordination control strategy.•The dynamic model of the power-split hybrid electric bus is built.•An engine estimation algorithm is designed for the control strategy.•Co-simulation results verify that smooth mode shifting is realized.</description><subject>Algorithms</subject><subject>Buses (vehicles)</subject><subject>Comfort</subject><subject>Dynamic coordination</subject><subject>Dynamics</subject><subject>Engine torque estimation</subject><subject>Feedback control</subject><subject>Hybrid electric bus</subject><subject>Hybrid vehicles</subject><subject>Mathematical models</subject><subject>Predictive model</subject><subject>Riding</subject><subject>Strategy</subject><issn>0888-3270</issn><issn>1096-1216</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNp9kD9PwzAQxS0EEqXwCVgysjjYjuMkAwOq-CdVggFm49oXcJXEwXaK8u1xKTPT6Z3e73TvIXRJSU4JFdfbfO5DGHNGKM8py9PuCC0oaQSmjIpjtCB1XeOCVeQUnYWwJYQ0nIgFen_xYKyOdge4dwY6vFEBTGbmQfVWZ9o5b-ygonVDEkP0rstC9CrCx5y1zmej-waPw9jZmH3OG29NBh3o6BO9mcI5OmlVF-Diby7R2_3d6-oRr58fnla3a6zTHxEXFWcgOFG0bEGJWhtiGlHzhrdVpQXTtGVlMjVccJNkXdZNVRbGsEpTA7pYoqvD3dG7rwlClL0NGrpODeCmIGlFaApdkDJZi4NVexeCh1aO3vbKz5ISue9TbuVvn3Lfp6RMpl2ibg4UpBQ7C14GbWHQqT6f4krj7L_8Dx1tgRw</recordid><startdate>20150801</startdate><enddate>20150801</enddate><creator>Zeng, Xiaohua</creator><creator>Yang, Nannan</creator><creator>Wang, Junnian</creator><creator>Song, Dafeng</creator><creator>Zhang, Nong</creator><creator>Shang, Mingli</creator><creator>Liu, Jianxin</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20150801</creationdate><title>Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus</title><author>Zeng, Xiaohua ; Yang, Nannan ; Wang, Junnian ; Song, Dafeng ; Zhang, Nong ; Shang, Mingli ; Liu, Jianxin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c406t-3742e640a15fea68cd0d968494f77c62c1f253749464d62c8589753dd27c1dec3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Buses (vehicles)</topic><topic>Comfort</topic><topic>Dynamic coordination</topic><topic>Dynamics</topic><topic>Engine torque estimation</topic><topic>Feedback control</topic><topic>Hybrid electric bus</topic><topic>Hybrid vehicles</topic><topic>Mathematical models</topic><topic>Predictive model</topic><topic>Riding</topic><topic>Strategy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zeng, Xiaohua</creatorcontrib><creatorcontrib>Yang, Nannan</creatorcontrib><creatorcontrib>Wang, Junnian</creatorcontrib><creatorcontrib>Song, Dafeng</creatorcontrib><creatorcontrib>Zhang, Nong</creatorcontrib><creatorcontrib>Shang, Mingli</creatorcontrib><creatorcontrib>Liu, Jianxin</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Mechanical systems and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zeng, Xiaohua</au><au>Yang, Nannan</au><au>Wang, Junnian</au><au>Song, Dafeng</au><au>Zhang, Nong</au><au>Shang, Mingli</au><au>Liu, Jianxin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus</atitle><jtitle>Mechanical systems and signal processing</jtitle><date>2015-08-01</date><risdate>2015</risdate><volume>60-61</volume><spage>785</spage><epage>798</epage><pages>785-798</pages><issn>0888-3270</issn><eissn>1096-1216</eissn><abstract>Parameter-matching methods and optimal control strategies of the top-selling hybrid electric vehicle (HEV), namely, power-split HEV, are widely studied. In particular, extant research on control strategy focuses on the steady-state energy management strategy to obtain better fuel economy. However, given that multi-power sources are highly coupled in power-split HEVs and influence one another during mode shifting, conducting research on dynamic coordination control strategy (DCCS) to achieve riding comfort is also important. This paper proposes a predictive-model-based DCCS. First, the dynamic model of the objective power-split HEV is built and the mode shifting process is analyzed based on the developed model to determine the reason for the system shock generated. Engine torque estimation algorithm is then designed according to the principle of the nonlinear observer, and the prediction model of the degree of shock is established based on the theory of model predictive control. Finally, the DCCS with adaptation for a complex driving cycle is realized by combining the feedback control and the predictive model. The presented DCCS is validated on the co-simulation platform of AMESim and Simulink. Results show that the shock during mode shifting is well controlled, thereby improving riding comfort.
•This paper proposes a predictive model based dynamic coordination control strategy.•The dynamic model of the power-split hybrid electric bus is built.•An engine estimation algorithm is designed for the control strategy.•Co-simulation results verify that smooth mode shifting is realized.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.ymssp.2014.12.016</doi><tpages>14</tpages></addata></record> |
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subjects | Algorithms Buses (vehicles) Comfort Dynamic coordination Dynamics Engine torque estimation Feedback control Hybrid electric bus Hybrid vehicles Mathematical models Predictive model Riding Strategy |
title | Predictive-model-based dynamic coordination control strategy for power-split hybrid electric bus |
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