车－路环境自适应的重型汽车动力系统控制方法研究

In order to save fuel consumption and reduce emissions of vehicles, many researchers have mainly focused their efforts on the technologies of vehicular powertrain and lightweight. We noticed that, the relationship between vehicle status, road environment and powertrain is a strongly coupled closed-loop system, which has a very important impact on the fuel consumption and emissions. However up to the present, this very important impact has not been effectively involved in the vehicle powertain control system. So, we hope to solve this problem in this project. In his project, the heavy-duty vehicle powertrain will be taken as an example, with a combined method of perception reasoning, sensors monitoring and on-line estimation, the driver behavior, vehicle parameters and road environment signals will be identified and predicted; through the algorithms of samples training and parameter learning, the evaluation and predictive models for fuel consumption and emissions will be established; further, based on the theory and methods from learning control, switch system and predictive control, the engine-power-multi-segment based intelligent control strategy will be studied; the methods for vehicle-road environment self-learning control and control parameters self-learning calibration will be researched according to the demands from the multi-objective optimization for fuel efficiency, emissions as well as drivability coordinated control within a look-ahead time window and driving distance window, as a result, the above mentioned impact from driver behavior, vehicle status and road environment on the fuel consumption and emissions of powertrain can be automatically involved in the powertain control process, and the theoretical basis for this adaptive control of this system can be provided. Meanwhile, the control system prototype will be developed for the hardware-in-loop simulation and experimental research, as a result, the applicative basis for this adaptive control of this system can be provided, too. The vehicle-road environment adaptive control method for automotive powertrain proposed in this project has not been reported so far. Hence, it can be forecasted that the control method worked out from this project will be a kind of new method for automotive powertrain control, can provide a novel way of thinking and new approach for automotive low-cost energy saving and emission reduction with an extremely extensive application prospect in the recent future.

为推动汽车节能减排，国内外主要从动力系统、轻量化等方面着手。车-路环境与动力系统间呈强耦合的闭环系统，对油耗、排放等有巨大反作用力，但在现有控制方法中都没有考虑，属共性关键问题。本项目以重型车动力系统为例，通过感知推理、传感监测和在线估计相结合的方法，进行车-路环境信息识别与预报；基于样本训练和参数学习，建立车辆油耗、排放等性能的评价和预测模型；进而运用学习控制、切换系统和预测控制等理论和方法，进行发动机功率多段智能控制，并在预控制时间/距离窗口内按油耗与排放等多目标优化及与驾驶协调控制的要求，研究车-路环境自适应的动力系统自学习控制及控制参数自学习标定方法，自动考虑该反作用力，为实现系统的自适应控制提供理论基础；并研制控制系统样机进行仿真和试验研究，为成果应用奠定基础。本研究尚未见报道，是汽车动力系统控制新方法、新方向，将为汽车低成本节能减排提供新思路、新途径，应用前景广阔，极具研究价值

为推动汽车节能减排，国内外主要从动力系统、轻量化等方面着手。车-路环 境与动力系统间呈强耦合的闭环系统，对油耗、排放等有巨大反作用力，但在现有控制方法中都没有考虑。为此，本项目开展了车-路环境自适应的重型汽车动力系统控制方法研究。主要研究内容为：（1）车－路环境信息识别与预报方法；（2）车－路环境自适应的汽车动力系统自学习实时控制方法；（3）控制系统研制与系统仿真、试验。经过四年的研究，完成了针对车辆实际行驶环境条件的车-路环境信息识别与预报研究，完成了基于实例学习、工况预测、瞬时和全局能耗预测的动力系统工况自适应在线实时控制方法研究，完成了控制系统的软硬件技术研究、设计和研制，完成了控制方法MIL仿真、HIL仿真、台架测试和整车道路试验研究，实现了车-路环境自适应的、全局近似最优的在线实时控制。经在重型混合动力客车上的应用表明，可较传统系统和方法节油平均约7%、最大近12%，且控制系统成本增加小于1%。在本项目资助下，发表与本项目相关论文17篇（均标注基金项目编号），其中SCI论文5篇，EI论文4篇；申请国家发明专利7项，其中已获授权发明专利5项；获省部级科学技术奖技术发明二等奖1项，中国专利奖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；在项目资助下11人/次参加国际学术会议，扩大了本项目的影响。