A control benchmark on the energy management of a plug-in hybrid electric vehicle

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• 作者：A. Sciarretta^a ; ^{antonio.sciarretta@ifp.fr" class="auth_mail} ; L. Serrao^c ; ¹ ; P.C. Dewangan^a ; ^e ; P. Tona^a ; E.N.D. Bergshoeff^h ; ⁵ ; C. Bordonsⁱ ; L. Charmpa^e ; ⁵ ; Ph. Elbert^d ; L. Eriksson^f ; T. Hofman^h ; M. Hubacher^h ; P. Isenegger^h ; F. Lacandia^g ; ⁶ ; A. Laveau^e ; ⁴ ; H. Li^e ; ³ ; D. Marcosⁱ ; T. Nü ; esch^d ; S. Onori^g ; ⁷ ; P. Pisu^b ; J. Rios^b ; E. Silvas^h ; M. Sivertsson^f ; L. Tribioli^g ; ² ; A.-J. van der Hoeven^h ; M. Wu^e ; ³
• 关键词：Supervisory control ; Plug-in hybrid electric vehicles ; Energy management ; Optimal control ; Rule-based control
• 刊名：Control Engineering Practice
• 出版年：August 2014
• 年：2014
• 卷：29
• 期：Complete
• 页码：287-298
• 全文大小：1921 K

文摘

A benchmark control problem was developed for a special session of the IFAC Workshop on Engine and Powertrain Control, Simulation and Modeling (E-COSM 12), held in Rueil-Malmaison, France, in October 2012. The online energy management of a plug-in hybrid-electric vehicle was to be developed by the benchmark participants. The simulator, provided by the benchmark organizers, implements a model of the GM Voltec powertrain. Each solution was evaluated according to several metrics, comprising of energy and fuel economy on two driving profiles unknown to the participants, acceleration and braking performance, computational performance. The nine solutions received are analyzed in terms of the control technique adopted (heuristic rule-based energy management vs. equivalent consumption minimization strategies, ECMS), battery discharge strategy (charge depleting–charge sustaining vs. blended mode), ECMS implementation (vector-based vs. map-based), ways to improve the implementation and improve the computational performance. The solution having achieved the best combined score is compared with a global optimal solution calculated offline using the Pontryagin's minimum principle-derived optimization tool HOT.