论文标题
连接电动车辆的综合纵向速度决策和能源效率控制
Integrated Longitudinal Speed Decision-Making and Energy Efficiency Control for Connected Electrified Vehicles
论文作者
论文摘要
为了提高连接自动型电动车辆的驾驶活动和能源效率，本文介绍了综合的纵向速度决策和能源效率控制策略。所提出的方法是层次控制体系结构，假定该结构由高级和低级控制组成。作为这项研究的核心，将模型的预测控制和增强学习结合在一起，以改善一组自动化车辆的动力总成机动性和燃油经济性。高层利用了通过车辆进行连接的自动驾驶汽车的信号阶段，时机和状态信息，以减少在红灯处停车的车辆通信。高级使用模型预测控制技术输出最佳车辆速度，并从低级con-con-tolller接收功率拆分控制。这两个级别通过真实车辆中的控制器区域网络相互通信。低级利用一种无模型的加固学习方法来改善每辆连接的自动驾驶汽车的燃油经济性。数值测试表明，通过减少红光空转，可以明显改善车辆的迁移率（缩短了30％）。通过不同的能效体控制（燃料经济性促进13％）之间的比较分析，通过比较分析来验证所提出方法的有效性和性能。
To improve the driving mobility and energy efficiency of connected autonomous electrified vehicles, this paper presents an integrated longitudinal speed decision-making and energy efficiency control strategy. The proposed approach is a hierarchical control architecture, which is assumed to consist of higher-level and lower-level controls. As the core of this study, model predictive control and reinforcement learning are combined to improve the powertrain mobility and fuel economy for a group of automated vehicles. The higher-level exploits the signal phase and timing and state information of connected autonomous vehicles via vehicle to infrastructure and vehicle to vehicle communication to reduce stopping at red lights. The higher-level outputs the optimal vehicle velocity using model predictive control technique and receives the power split control from the lower-level con-troller. These two levels communicate with each other via a controller area network in the real vehicle. The lower-level utilizes a model-free reinforcement learning method to improve the fuel economy for each connected autonomous vehicle. Numerical tests illustrate that vehicle mobility can be noticeably improved (traveling time reduced by 30%) by reducing red-light idling. The effectiveness and performance of the proposed method are validated via comparison analysis among different energy efficiency controls (fuel economy promoted by 13%).
