Co-optimization Method for Personalized Eco-adaptive Cruise Control of Plug-in Hybrid Electric Vehicles Considering Uncertainties in Driving Style

doi:10.3901/JME.2025.18.214

Abstract

Abstract: A co-optimization method for personalized eco-adaptive cruise control is proposed based on the model predictive control, with the aim to meet the individual needs of drivers and passengers, and reduce energy consumption and emissions in car-following scenarios for intelligent plug-in hybrid electric vehicles. The performance metrics and objective function for the cruise control system take into account the tracking performance and the requirements for energy efficiency and emission reduction of the vehicle under control. These factors are integrated, considering the powertrain characteristics of the vehicle and the relationships between vehicles in car-following scenarios. Multi-dimensional vehicle states and features of the road environment are incorporated, and the short-term prediction of the preceding vehicle's motion state is carried out through a bidirectional long short-term memory network based on the natural driving data collected from actual vehicle road tests. To take into account the uncertainty of driving style, a self-vehicle behavior state transition model is established using the Markov chain Monte Carlo method based on the Bayesian theorem. The future steady-state speed of the self-vehicle is then obtained through the law of large numbers. The effectiveness and superiority of the proposed method are validated through typical driving scenarios including city, suburban, and highway. The research results indicate that the proposed method effectively imitates the driving style of drivers under different conditions. Meanwhile, compared with the hierarchical optimization methods based on vehicle kinematics and intelligent driver model, the energy consumption cost is reduced by 4.77%-23.97%, and an emission reduction of 10.35%-37.47% is achieved, which effectively realizes the co-optimization of vehicle tracking performance and capabilities of energy saving and emission reduction.

Key words: adaptive cruise control, intelligent vehicle, driving style, energy conservation and emission reduction, model predictive control

CLC Number:

U461

ZHU Pengxing, HU Jianjun, LI Jiajia, PENG Hang, WANG Xin. Co-optimization Method for Personalized Eco-adaptive Cruise Control of Plug-in Hybrid Electric Vehicles Considering Uncertainties in Driving Style[J]. Journal of Mechanical Engineering, 2025, 61(18): 214-229.

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