{"title":"Stochastic model predictive energy management for series hydraulic hybrid vehicle","authors":"Daiwei Feng, Dagui Huang, Dinggen Li","doi":"10.1109/ICMA.2011.5986284","DOIUrl":null,"url":null,"abstract":"This paper investigates the application of stochastic model predictive control (SMPC) methodology for developing power management strategies tailored for the serial hydraulic hybrid vehicle (SHHV). The velocity demand from the driver is expressed as a random Markov process. A forward-facing closed-loop model of the SHHV powertrain is built and simulated in MATLAB/SIMULINK. The predictive model in SMPC is formulated by successive on-line linearization. The simulation results over a standard driving cycle are presented to show the improved performance of SMPC over other deterministic approaches.","PeriodicalId":317730,"journal":{"name":"2011 IEEE International Conference on Mechatronics and Automation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Mechatronics and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMA.2011.5986284","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
This paper investigates the application of stochastic model predictive control (SMPC) methodology for developing power management strategies tailored for the serial hydraulic hybrid vehicle (SHHV). The velocity demand from the driver is expressed as a random Markov process. A forward-facing closed-loop model of the SHHV powertrain is built and simulated in MATLAB/SIMULINK. The predictive model in SMPC is formulated by successive on-line linearization. The simulation results over a standard driving cycle are presented to show the improved performance of SMPC over other deterministic approaches.
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