控制理论(社会学)
燃料效率
弹道
能源管理
最优控制
电池(电)
流量(计算机网络)
计算机科学
模型预测控制
荷电状态
控制器(灌溉)
汽车工程
PID控制器
动态规划
能源消耗
车辆动力学
工程类
模拟
能量(信号处理)
数学优化
控制工程
控制(管理)
数学
算法
电气工程
温度控制
人工智能
计算机安全
生物
功率(物理)
农学
统计
天文
量子力学
物理
作者
Chao Sun,Jiaqi Wang,Chao Sun,Bo Liu,Weiwei Huo,Fuchun Sun
出处
期刊:Energy
[Elsevier]
日期:2023-03-01
卷期号:267: 126469-126469
被引量:12
标识
DOI:10.1016/j.energy.2022.126469
摘要
A guided proportional integral controller-based Pontryagin’s minimum principle (PI-PMP) energy management control strategy based on dynamic traffic information for plug-in fuel cell vehicles is proposed in this paper. Combined with the real-world traffic flow data of high-way driving scenarios, an improved equivalent consumption minimization strategy (ECMS) based on dichotomy is used to quickly search for the optimal initial costate value and reference battery state of charge (SOC) trajectory. A horizon velocity predictor based on artificial neural networks (ANNs) is used to achieve short-term velocity prediction, and a PI-PMP control strategy is adopted to realize SOC trajectory following. Simultaneously, the sensitivity of the costate in ECMS and PI-PMP is analyzed in depth. The simulation results of three scenarios with no/static/dynamic traffic flow information show that the guided PI-PMP energy management strategy based on dynamic traffic flow information has a significant energy-saving effect and real-time optimization potential.
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