面向无人驾驶车辆节能的迭代优化预测控制方法

doi:10.3969/j.issn.1674-8484.2023.01.010

汽车安全与节能学报 ›› 2023, Vol. 14 ›› Issue (1): 80-88.DOI: 10.3969/j.issn.1674-8484.2023.01.010

面向无人驾驶车辆节能的迭代优化预测控制方法

刘熠¹(), 宫新乐²^,^*(), 唐云³, 胡嫚¹, 马捷³, 秦毅³, 吴飞³^,^*(), 蒲华燕³, 罗均³

1.西南大学工程技术学院，重庆 400715，中国
2.清华大学，汽车安全与节能国家重点实验室，北京 100084，中国
3.重庆大学机械与运载工程学院，重庆 400044，中国

收稿日期:2022-04-18 修回日期:2022-11-24 出版日期:2023-02-28 发布日期:2023-03-07
通讯作者: 宫新乐,吴飞
作者简介:吴飞 (1987—)，男(汉)，湖南，副教授。E-mail：wufeifrank@cqu.edu.cn。
*宫新乐(1990—)，男(汉)，黑龙江，博士后。E-mail：xinlegong@gmail.com。
刘熠(1998—)，男(汉)，贵州，硕士研究生。E-mail：liuyiswu@126.com。
基金资助:
国家自然科学基金(52102438);中国博士后科学基金(2022M710523);中央高校基本科研业务费项目(2022CDJXY-006)

An iterative optimization-based predictive control method for eco-driving of unmanned vehicles

LIU Yi¹(), GONG Xinle²^,^*(), TANG Yun³, HU Man¹, MA Jie³, QIN Yi³, WU Fei³^,^*(), PU Huayan³, LUO Jun³

1. College of Engineering and Technology, Southwest University, Chongqing 400716, China
2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
3. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044，China

Received:2022-04-18 Revised:2022-11-24 Online:2023-02-28 Published:2023-03-07
Contact: GONG Xinle,WU Fei

摘要/Abstract

摘要：

该文提出一种数据驱动的无人驾驶重型车辆节能驾驶迭代优化预测控制（IOBPC）方法。基于历史数据构造并迭代更新终端状态约束集和终端油耗函数，并对约束集和终端油耗函数进行近似处理，提高算法计算效率；通过学习车辆状态轨迹和燃油消耗的关联机理，保证优化算法代价函数在迭代过程中单调递减并最终收敛，从而实现车辆燃油经济性的显著提升。结果表明：迭代优化预测控制器在多次迭代后使车辆轨迹收敛，燃油消耗减少了约10.2%，相比基于动态规划（DP）的节能驾驶策略，节能效果得到了进一步的提升，且调节参数较少，支持实时求解，更利于实际应用。

关键词: 无人驾驶重型车辆, 节能驾驶, 迭代优化, 预测控制

Abstract:

A data-driven Iterative Optimization-Based Predictive Control (IOBPC) method for energy saving of unmanned heavy vehicles was proposed. Based on the historical data, the terminal state constraint set and terminal cost function were constructed and updated iteratively. And the approximate treatment of the constraint set and terminal cost function improved the computational efficiency of the algorithm. By learning the correlation between vehicle state trajectory and fuel consumption, the cost function of the optimization algorithm was guaranteed to decrease monotonically and converge in the iterative process, so as to realize the significant improvement of vehicle fuel economy. The results show that the iterative optimization predictive controller makes the vehicle trajectory converged and reduces fuel consumption by about 10.2% after several iterations. Compared with the energy-saving driving strategy based on dynamic programming (DP), the energy-saving effect is further improved. Moreover, it has fewer adjustment parameters and supports real-time solution, which is more conducive to practical application.

Key words: unmanned heavy vehicles, eco-driving, iterative optimization, predictive control

中图分类号:

U463.61

刘熠, 宫新乐, 唐云, 胡嫚, 马捷, 秦毅, 吴飞, 蒲华燕, 罗均. 面向无人驾驶车辆节能的迭代优化预测控制方法[J]. 汽车安全与节能学报, 2023, 14(1): 80-88.

LIU Yi, GONG Xinle, TANG Yun, HU Man, MA Jie, QIN Yi, WU Fei, PU Huayan, LUO Jun. An iterative optimization-based predictive control method for eco-driving of unmanned vehicles[J]. Journal of Automotive Safety and Energy, 2023, 14(1): 80-88.

图/表 11

图1 车辆纵向受力图

图2 迭代优化预测控制方法架构

图3 迭代优化预测控制算法原理架构

图4 仿真平台示意图

整车质量，m	30 t
滚动阻力因数，C_r	0.005 5
空气阻力因数，C_D	0.69
车辆横截面积，A	10 m²
车身长度，l	8 m

表1 车辆参数

离散时间，t_s	0.1 s
预测步长，N	30
约束集步长，N_f	10

表2 典型坡道仿真参数

图5 不同典型坡道仿真结果

图6 典型坡道迭代油耗

离散时间，t_s	0.2 s
预测步长， N	30
约束集步长， N_f	10

表3 实际道路仿真参数

图7 实际道路仿真结果

图8 实际道路迭代油耗

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面向无人驾驶车辆节能的迭代优化预测控制方法

An iterative optimization-based predictive control method for eco-driving of unmanned vehicles

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