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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2019> 28(1): 162-170
Kongjian Qin, Yu Liu, Xi Hu. Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(1): 162-170. doi: 10.15918/j.jbit1004-0579.17156
Citation: Kongjian Qin, Yu Liu, Xi Hu. Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(1): 162-170.doi:10.15918/j.jbit1004-0579.17156
shu

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

doi:10.15918/j.jbit1004-0579.17156
  • 1.

    Department of Automobile, Tsinghua University, Beijing 100084, China;China Automotive Technology and Research Center, Tianjin 300300, China

  • 2.

    China Automotive Technology and Research Center, Tianjin 300300, China

  • Received Date:2017-11-09
    Abstract
  • A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus (PHEB). Firstly, the principal component analysis (PCA) and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle, congestion driving cycle, urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly, an improved particle swarm optimization (IPSO) algorithm is proposed, and is used to optimize the control parameters of PHEB under different driving cycles, respectively. Then, the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally, for an actual running vehicle, the driving condition is identified by relevance vector machine (RVM), and the corresponding control parameters are selected to control the vehicle. The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy, and the feasibility of the variable parameter self-adaptive control strategy is verified.
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