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    • Adaptive Equivalent Consumption Minimization Energy Management Strategy for Hybrid Heavy Trucks
    DOI：10.13949/j.cnki.nrjgc.2023.01.001
    Key Words:energy management  equivalent consumption minimization strategy（ECMS）  chaos particle swarm optimization（CPSO）  driving cycle recognition
    Author NameAffiliationE-mail
    DU Changqing* School of Automative Engineering， Wuhan University of Technology， Wuhan 430070， China cq_du@whut.edu.cn 
    CHEN Lei School of Automative Engineering， Wuhan University of Technology， Wuhan 430070， China  
    YANG Xiancheng School of Automative Engineering， Wuhan University of Technology， Wuhan 430070， China  
    WU Xianpan School of Automative Engineering， Wuhan University of Technology， Wuhan 430070， China  
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    Abstract:In order to solve the dynamic adjustment of key parameters of equivalent fuel consumption minimization strategy under different working conditions， an adaptive equivalent consumption minimization strategy（A-ECMS） suitable for hybrid heavy vehicles was proposed. Taking six typical driving conditions obtained by hierarchical clustering algorithm as an example， a condition recognition algorithm based on neural network was proposed. The improved chaos particle swarm optimization（CPSO） was applied to optimize three key parameters of equivalent consumption minimization strategy（ECMS）， the scale factor of penalty function and the threshold of engine starting speed under a specific driving cycle. Based on the above two aspects， a new adaptive energy management strategy based on condition identification was proposed to optimize the key parameters. According to the transmission system configuration of the heavy truck， the longitudinal dynamics model of the vehicle was established and verified by simulation. The simulation results show that compared with the traditional equivalent fuel consumption strategy， the fuel consumption of CPSO-ECMS and A-ECMS control strategies under composite driving cycles was reduced by 5.9% and 8.9% respectively.
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