Predictive coordinated control of fuel consumption and emissions for diesel engine vehicles under intelligent network environments
Predictive coordinated control of fuel consumption and emissions for diesel engine vehicles under intelligent network environments
复制标题
智能网联环境下柴油车油耗与排放预测协调控制
DOI：
10.1007/s11432-018-9796-1
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Yunfeng Hu
中科院分区：
文献类型：
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作者：
Di Liu;Hong Chen;Jinwu Gao;Jinghua Zhao;Yunfeng Hu
柴油机因其优良的燃油效率和耐用性，在汽车上得到了广泛的应用。尽管它们只占汽车保有量的一小部分，但它们造成了大多数道路上的排放，如NOx和PM[1]。通过分析柴油发动机的排放特性，发现发动机的瞬时运行，如怠速和启动，与稳态发动机运行相比，在一个行驶周期内引起的总排放量要大得多[2]。因此，现有技术面临的挑战是如何提高柴油机在过渡状态下的排放和油耗性能。随着智能交通和智能汽车的发展，可以直接获得交通信号、道路坡度和车距等交通信息。基于这些信息，它使我们有机会预测未来的车辆状态，并计划所需的车辆扭矩和速度，从而减少发动机的攻击性瞬变。因此，本研究的主要贡献在于提出了一种基于智能网络环境的柴油车油耗和排放预测协调控制策略。该控制方案的创新之处在于：(1)利用智能网络信息来预测车辆的动态特性，并在考虑油耗和排放限制的情况下规划期望扭矩；(2)采用基于数据的建模方法推导出面向控制的模型；(3)采用约束优化控制方法设计扭矩跟踪控制器。
Diesel engines have been widely used in vehicles because of their excellent fuel efficiency and durability. Although they only represent a small percentage of vehicle ownership, they cause the majority of on-road emissions such as NOx and PM [1]. By analyzing the emission characteristics of diesel engines, transient engine operation such as at idle speed and start-up is found to cause substantially more to total emissions over a driving cycle compared to steady-state engine operation [2]. Therefore, the challenge of the existing technology is to improve the emission and fuel consumption performance of diesel engine under transient conditions.With the development of intelligent transportation and intelligent vehicles, traffic information such as traffic signals, road slope, and inter-vehicle distance can be directly obtained. Based on these information, it gives us an opportunity to predict future vehicle states and plan the desired vehicle torque and velocity, such that the aggressive engine transients can be decreased. Therefore, the main contribution of this study is that a predictive coordinated control strategy is proposed for fuel consumption and emission for diesel engine vehicles based on intelligent network environments. The novelty of the proposed control scheme can be summarized as follows:(1) Intelligent network information is used to predict the vehicle dynamics, and plan the desired torque considering fuel consumption and emission limits;(2) a data-based modeling method is applied to deduce the control-oriented model;(3) a constrained optimization control method is applied to design the torque tracking controller.