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Automotive Engineering ›› 2024, Vol. 46 ›› Issue (9): 1587-1599.doi: 10.19562/j.chinasae.qcgc.2024.09.006

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Research on Fast Stochastic Model Predictive Control-Based Eco-Driving Strategy for Connected Mixed Platoons

Lijun Qian1(),Jian Chen1,Feng Zhao2,Xinyu Chen1,Liang Xuan1   

  1. 1.Department of Automotive and Traffic Engineering,Hefei University of Technology,Hefei 230009
    2.Unit 32381 of the PLA,Beijing 100070
  • Received:2024-03-16 Revised:2024-04-14 Online:2024-09-25 Published:2024-09-19
  • Contact: Lijun Qian E-mail:qianlijun66@163.com

Abstract:

To address the problem of speed trajectory deviation of connected vehicles (CVs) caused by human driver error, a real-time eco-driving strategy for connected mixed platoons considering human driver error is proposed in this paper. Firstly, real vehicle tests are conducted to collect human driver error data of different drivers to establish the human driver error model based on Markov chain so as to predict the human driver error for a period of time in the future. Then, with the optimization objective of minimizing the fuel consumption of the entire platoon, the platoon speed trajectory optimization problem is formulated as an optimal control problem. Fast stochastic model predictive control (FSMPC) is employed to calculate the optimal speed trajectories of the connected vehicle in the mixed platoon. Both the simulation and intelligent and connected micro-car test results indicate that, compared to the traditional eco-driving strategy based on fast model predictive control (FMPC), the proposed eco-driving strategy can effectively reduce the speed trajectory deviation and fuel consumption of the whole platoon as well as meet the real-time requirements.

Key words: connected vehicle, human driver error, fast stochastic model predictive control, mixed platoon