兼合路径规划的插电式混合动力电动汽车预测性能量管理方法研究

Global optimization is fundamental to achieve optimal energy management of plug-in hybrid electric vehicles(PHEVs). However, most traditional optimization methods suffer from the curse of dimensionality, which limits their practical applications. Furthermore, vehicle routing has great impact on energy consumption of electric vehicles, but there is a lack of consideration in existing energy management. Therefore, according to the trend and actual needs in development of PHEVs, taking the intelligent connected PHEVs as an example, the proposal presents the key scientific issues of how to use the road network to remarkably reduce vehicle energy consumption and realize adaptive online optimal energy management and so on. The predictive energy management with concurrent dynamic routing planning for PHEVs is proposed. The main innovation of the research includes: (1) the multi-scale intelligent prediction of the full-range and short-range driving conditions based on spatial-temporal correlation in road network for vehicle; (2) the dynamic vehicle routing planning based on the comprehensive optimization of the whole-trip energy consumption and running time, which are predicted based on spatial-temporal correlation; (3) and the predictive hierarchical intelligent energy management method realizing adaptive global optimization combined with dynamic vehicle routing planning, and based on full range, short range and instantaneous multi space-time scale synergy. The prototype is developed and the test is carried out. This project aims to explore a novel way to minimize the energy consumption of PHEVs in various practical applications. It will provide new theoretical support and technological foundation for the development and application of vehicle energy management system, and has wide application prospects and great research value.

全局最优化是实现插电式混合动力电动汽车(PHEV)最优能量管理的根本，但传统最优化方法存在“维数灾难”问题限制了其实际应用；行车路径对PHEV能耗有巨大影响，但现有能量管理中都缺乏考虑。因此，本项目结合PHEV发展趋势与实际需要，以智能网联PHEV为对象，围绕利用道路网络大幅降低车辆能耗和自适应最优能量管理等关键科学问题，开展兼合路径规划的预测性能量管理研究。主要创新研究包括：(1)在道路交通动态变化中的车辆行驶工况多尺度、时空分布智能预测研究；(2) 基于时空关联的车辆全程能耗与行车时间预测及综合优化的动态路径规划研究；(3)兼合路径规划、全程-短程-瞬时三时空尺度协同的自适应全局最优、预测性分层智能能量管理方法研究。并研制样机进行试验研究。本项目旨为电动汽车在不同实际应用中的能耗自动最小化探索一条新的途径，为相关系统研制与应用提供新的理论支持和技术基础，具有广泛应用前景，极具研究价值。

发展插电混合动力电动汽车是国家重大战略，降低其能耗是国内外的迫切需要。行车路径对PHEV能耗有巨大作用，但现有能量管理中都缺乏考虑。为此，本项目开展了兼合路径规划的PHEV能量管理控制方法研究。提出了在道路交通动态变化中的车辆行驶工况多尺度、时空分布智能预测方法，提出了基于时空关联的车辆全程能耗与行车时间预测及综合优化的动态路径规划方法，提出了兼合路径规划、综合电池性能管理、多时空尺度协同的自适应全局最优化、预测性分层智能能量管理方法，完成了PHEV能量管理控制系统样机研制和试验研究，解决了利用道路网络大幅降低PHEV能耗和自适应最优能量管理等关键科学问题。经测试与应用表明，可突破现有能量管理控制方法降低车辆能耗的极限，实现大幅降低PHEV能耗达到了20%以上。研究成果已可移植应用于其他各类新能源车辆，实现车辆在不同实际应用中的能耗自动最小化。在本项目资助下，发表与本项目相关论文16篇（均标注基金项目编号），其中SCI一区Top期刊论文8篇；申请国家发明专利5项，其中已获授权发明专利3项；获省部级科学技术奖技术发明一等奖1项。进行了大量的理论和试验研究，获得了具有重要理论和实际价值的结果与数据。