Equivalent Emission Minimization Strategy of Intelligent Connected HEV Based on Multi-step Prediction of Driving Intention

doi:10.3901/JME.2023.18.271

Abstract

Abstract: Vehicle "carbon compliance" and "carbon neutralization" are important components to achieve the carbon compliance of the whole society. To reduce the total emissions of Intelligent Connected HEV(ICHEV), an Equivalent Emissions Minimization Strategy(EEMS) of ICHEV based on multi-step prediction of driving intention(MPDI) is proposed. First, the online prediction process of driving intention under V2X is presented, a deep learning model of CNN-LSTM is constructed to make single-step prediction of driving intention, and a multi-step recursive prediction method of driving intention is proposed. Second, considering the inconsistency between the maximum efficiency zone and the minimum tailpipe emission zone of engine, an equivalent emission model of ICHEV energy consumption is established, and an energy online management strategy based on EEMS is proposed. Third, the disturbance effect of frequent change of driving intention on EEMS is analyzed, the algorithm for solving the whole vehicle demand power based on multi-step prediction of driving intention is given, and the Hamiltonian function solution method of EEMS is given. The experimental results show that the EEMS based on multi-step prediction of driving intention can effectively reduce ICHEV exhaust emissions by 25% on average compared with the traditional energy management strategy.

Key words: intelligent connected HEV, energy management strategy, driving intention prediction, equivalent exhaust emission

CLC Number:

TN915

WANG Yuefei, WANG Zhi, SUN Rui, WANG Chao, XIAO Kai, PAN Bin. Equivalent Emission Minimization Strategy of Intelligent Connected HEV Based on Multi-step Prediction of Driving Intention[J]. Journal of Mechanical Engineering, 2023, 59(18): 271-282.

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