| 引用本文: | 徐福国,申铁龙.基于交通信息预测的智能网联混合动力汽车能源优化控制[J].控制理论与应用,2025,42(8):1534~1542.[点击复制] |
| XU Fu-guo,SHEN Tie-long.Look-ahead horizon-based energy optimization with traffic prediction for connected HEVs[J].Control Theory & Applications,2025,42(8):1534~1542.[点击复制] |
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| 基于交通信息预测的智能网联混合动力汽车能源优化控制 |
| Look-ahead horizon-based energy optimization with traffic prediction for connected HEVs |
| 摘要点击 2599 全文点击 176 投稿时间：2024-01-31 修订日期：2025-07-30 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2024.40088 |
| 2025,42(8):1534-1542 |
| 中文关键词 可视域 网联汽车 混合动力汽车 能源效率优化 交通预测 |
| 英文关键词 look-ahead horizon connected and automated vehicle (CAV) hybrid electric vehicle (HEV) energy effi ciency optimization traffic prediction |
| 基金项目 |
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| 中文摘要 |
| 车间通信和智能驾驶的快速发展为进一步提高混合动力汽车的能源效率提供了可能.另外,路线的地理信息对
混合动力汽车的能源效率优化有很大的影响.本文考虑行驶路线的坡度对能源优化的影响,提出一种基于交通信息预测
的实时能源管理策略,本策略同时对动力链和汽车的动态进行优化.首先,为了保证在红绿灯交通场景中的安全通过,
本文提出了基于逻辑控制策略.其次,考虑与前车车间距的安全性,以实现能源消耗最小的目标,本文形成了一个带有
约束的最优化问题,并利用极小值原理进行求解.最优控制问题中优化区间内的前车车速是系统的外部干扰信号,实现
其精准的预测对能量优化性能的提高有着重要的作用,本文提出一种利用实时的网联信息的基于极限学习机的预测算
法. 最后,本文建立了一个交通在环的动力链仿真平台.所提出的算法在这一平台上得到验证,在不同交通密度的情景
中, 可以得出带有预测的能量管理策略可以平均提高17%的汽车能源效率. |
| 英文摘要 |
| With the development of fast communication technology between ego vehicle and other traffic participants,
and automated driving technology, there is a big potential in the improvement of energy efficiency of hybrid electric vehicles
(HEVs). Moreover, the terrain along the driving route is a non-ignorable factor for energy efficiency of HEV running on
the hilly streets. This paper proposes a look-ahead horizon-based optimal energy management strategy to jointly improve
the efficiencies of powertrain and vehicle for connected and automated HEVs on the road with slope. Firstly, a rule-based
framework is developed to guarantee the success of automated driving in the traffic scenario. Then a constrained optimal
control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction
of inter-vehicular distance constraint between ego vehicle and preceding vehicle. Both speed planning and torque split of
hybrid powertrain are provided by the proposed approach. Moreover, the preceding vehicle speed in the look-ahead horizon
is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-everything. The
optimal solution is derived through the Pontryagin’s maximum principle. Finally, to verify the effectiveness of the proposed
algorithm, a traffic-in-the-loop powertrain platform with data from real world traffic environment is built. It is found that
the fuel economy for the proposed energy management strategy improves in average 17.0 % in scenarios of different traffic
densities, compared to the energy management strategy without prediction of preceding vehicle speed. |
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