网联环境下基于分层式模型预测控制的车队能量控制策略研究

doi:10.3901/JME.2020.14.119

机械工程学报 ›› 2020, Vol. 56 ›› Issue (14): 119-128.doi: 10.3901/JME.2020.14.119

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网联环境下基于分层式模型预测控制的车队能量控制策略研究

唐小林¹, 李珊珊¹, 王红², 段紫文¹, 李以农¹, 郑玲¹

1. 重庆大学汽车工程学院重庆 400044;
2. 清华大学车辆与运载学院北京 100084

收稿日期:2019-11-17 修回日期:2020-03-20 出版日期:2020-07-20 发布日期:2020-08-12
通讯作者: 唐小林(通信作者),男,1984年出生,博士,副教授,博士研究生导师。主要研究方向为混合动力汽车NVH与能量管理。E-mail:tangxiaolin6@126.com
作者简介:李珊珊,女,1995年出生,硕士研究生。主要研究方向为混合动力汽车系统控制。E-mail:shanLee206@163.com
基金资助:
国家自然科学基金（51705044）、国家重点研发计划（2017YFB0102603-3）和机械系统与振动国家重点实验室开放基金课题（MSV202016）资助项目。

Research on Energy Control Strategy Based on Hierarchical Model Predictive Control in Connected Environment

TANG Xiaolin¹, LI Shanshan¹, WANG Hong², DUAN Ziwen¹, LI Yinong¹, ZHENG Ling¹

1. School of Automotive Engineering, Chongqing University, Chongqing 400044;
2. School of Vehicle and Mobility, Tsinghua University, Beijing 100084

Received:2019-11-17 Revised:2020-03-20 Online:2020-07-20 Published:2020-08-12

摘要/Abstract

摘要： 网联环境下如何同时兼顾车间协同控制与车辆燃油经济性是提高交通效率与发挥节能潜力的关键技术之一。首先，为解决车队协同控制，同时减少车辆燃油消耗，以功率分流式混合动力汽车车队为研究对象，以城市道路为背景，建立多车速度规划与跟驰模型，基于该模型设计了模型预测控制（Model predictive control，MPC）算法，规划汽车队列的未来车速，以提高混合动力汽车车队的跟车稳定性；其次，利用分层式模型预测控制设计出一种实时能量管理策略，上层从优化速度及频繁加减速等为目标获取最优经济车速，下层控制系统根据该最优车速对混合动力汽车实施能量管理；最后该实时能量管理策略在保证跟车稳定的前提下，优化车辆燃油经济性；将该算法与动态规划（Dynamic programming，DP）进行结果对比。结果表明，该方法能够实现整个车队的协同跟车控制，获得较好的燃油经济性。

关键词: 混合动力汽车, 智能网联, 模型预测控制, 能量管理策略

Abstract: How to consider both the cooperative control of the vehicle platoon and fuel economy is one of the key technologies to improve the traffic efficiency and give full play to energy saving potential in connected environment. Firstly, in order to solve the collaborative control of the vehicle platoon and reduce fuel consumption, a multi-vehicle speed planning and following model is established for hybrid electric vehicle platoon in urban roads. Based on the model, the model predictive control (MPC) is designed to plan the future speed and improve the stability of hybrid electric vehicle platoon. Secondly, a layered model predictive control is used to design a real-time energy management strategy. The upper layer obtains the optimal economic vehicle velocity with the goal of optimizing the speed and frequent acceleration. The lower layer control system implements energy management for the hybrid electric vehicle based on the optimized vehicle speed. This real-time energy management strategy optimizes vehicle fuel economy on the premise of ensuring stable follow-up. Finally, the algorithm is compared with the results of dynamic programming(DP). The results show that the method can realize the coordinated follow-up control of the entire fleet and obtain better fuel economy.

Key words: hybrid electric vehicle, network environment, model predictive control, energy management strategy

中图分类号:

U461

唐小林, 李珊珊, 王红, 段紫文, 李以农, 郑玲. 网联环境下基于分层式模型预测控制的车队能量控制策略研究[J]. 机械工程学报, 2020, 56(14): 119-128.

TANG Xiaolin, LI Shanshan, WANG Hong, DUAN Ziwen, LI Yinong, ZHENG Ling. Research on Energy Control Strategy Based on Hierarchical Model Predictive Control in Connected Environment[J]. Journal of Mechanical Engineering, 2020, 56(14): 119-128.

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网联环境下基于分层式模型预测控制的车队能量控制策略研究

Research on Energy Control Strategy Based on Hierarchical Model Predictive Control in Connected Environment

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