基于动态网联信息的混合动力汽车能量-热量一体化优化控制

Incorporating connected traffic information in power management optimization process for hybrid electric vehicles offers unprecedented opportunities for improving fuel economy. Dealing with power and thermal optimal control with an integrated approach allows us to treat the vehicle energy systems with a holistic view to maximize energy efficiency, thereby offering even greater benefits to leverage the energy saving potential while bringing in challenges for control theory and applications. Considering the congested traffic condition in winter, we propose an integrated power and thermal optimal control scheme that aims at achieving the overarching goal of substantial energy savings for hybrid electric vehicles, where the proposed research will focus on solving the general scientific problems in energy efficiency optimization of connected vehicles. To this end, we will first develop a “mechanical-electrical-thermal” coupled model for a hybrid electric vehicle; To provide a bench-mark performance target, the global optimization will be adopted where the energy saving potential and energy saving mechanism will be analyzed; Next, we will exploit a real-time optimization solution for integrated power and thermal control based on multi-source dynamic connected traffic information. Outcomes of the proposed research will extend the energy efficiency optimization strategies for hybrid electric vehicles, continue to dig the energy saving potential and provide key technologies to strengthen the self-design capability for developing intelligent energy saving solutions in our country.

结合网联信息的能量优化控制为混合动力汽车燃油经济性提升带来了新的机遇。在网联环境下，通过集成能量控制与热管理将会给混合动力汽车带来更大的节能空间也对控制理论与方法带来了挑战。本项目考虑寒冷气候条件下的城市拥堵路况，以提高混合动力汽车的燃油经济性为目标，提出基于动态网联信息的能量-热量一体化优化控制方案，提炼网联环境下车辆综合能耗优化控制的共性科学问题并展开深入研究。本项目拟建立“机-电-热”耦合的混合动力系统模型；定量化分析热量与能量一体化优化控制全局节能潜力，揭示多储能元协调控制下系统节能机理；探索能量-热量一体化实时优化方法，提出基于多源动态网联信息的预测节能解决方案。本项目的研究旨在拓展并完善现有的混合动力汽车能效优化控制方案，继续挖掘车辆的节能潜力，为实现我国汽车智能节能产业完全自主化提供重要技术支撑。

本项目考虑寒冷天气城市拥堵路况，以提高混合动力汽车的燃油经济性为目标，聚焦网联环境多尺度车辆能源系统动-热力链协同优化的共性科学问题展开深入研究。首先，建立了数据-机理混合增强的混合动力汽车“机-电-热”多尺度耦合动力学模型，并通过实车数据验证了模型的精度；进而，提出了基于动态网联信息的能量-热量一体化优化控制框架，设计了多动力源系统的多时空尺度能量分层优化控制方法与约束优化问题快速数值求解方法，实现了在dSPACE/MicroAutoBox DS-1401快速成型实验系统上硬件在环实验；最后，提出了网联混合动力汽车经济驾驶节能标准，开源了优化节能benchmark控制器，为电动化汽车控制性能迭代评价提供了新范式。本项目研究结果表明，结合智能网联信息的能量与热量一体化优化控制在寒冷条件下能够有效提升混合动力汽车燃油经济性，在典型城市工况下相较于传统方法节油可达5.46%。依托本项目的研究工作，我们在IFAC E-COSM 2021智能网联混合动力汽车节能benchmark挑战赛中斩获了冠军。