考虑驾驶风格不确定性的插电式混合动力汽车个性化生态自适应巡航协同优化方法

doi:10.3901/JME.2025.18.214

摘要/Abstract

摘要： 为了满足不同状态下驾乘人员的个性化需求，降低智能插电式混合动力汽车在跟驰场景中的能耗和排放，基于模型预测控制原理提出一种个性化生态自适应巡航控制协同优化方法。基于车辆动力传动系统特性和跟驰场景下的车间运动关系，综合考虑受控车辆的跟踪性能和节能减排诉求，确定巡航系统的性能指标和目标函数；引入多维度车辆状态和道路环境特征，根据实车道路试验采集的自然驾驶数据，通过双向长短期记忆网络对前车运动状态进行短期预测；考虑驾驶风格的不确定性，基于贝叶斯定理，采用马尔可夫链蒙特卡洛方法建立自车行为状态转移模型，并由辛钦大数定律获取自车未来平稳车速；通过城市、城郊和高速等典型行驶场景验证方法的有效性和优越性。研究结果表明，所提方法能有效模拟驾驶员在不同工况下的驾驶风格，同时相比于基于车辆运动学和智能驾驶员模型的分层优化方法的能耗成本降低了4.77%～23.97%，并且取得了10.35%～37.47%的减排效果，有效实现了车辆跟踪性和节能减排的协同优化。

关键词: 自适应巡航控制, 智能汽车, 驾驶风格, 节能减排, 模型预测控制

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

中图分类号:

U461

朱鹏兴, 胡建军, 李嘉佳, 彭航, 王鑫. 考虑驾驶风格不确定性的插电式混合动力汽车个性化生态自适应巡航协同优化方法[J]. 机械工程学报, 2025, 61(18): 214-229.

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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