Fast Dual-Loop Nonlinear Receding Horizon Control for Energy Management in Hybrid Electric Vehicles
Fast Dual-Loop Nonlinear Receding Horizon Control for Energy Management in Hybrid Electric Vehicles
复制标题
DOI：
10.1109/tcst.2018.2797058
复制
发表时间：
2019-05
影响因子：
4.8
通讯作者：
Johannes Buerger;Sebastian East;M. Cannon
中科院分区：
文献类型：
--
作者：
Johannes Buerger;Sebastian East;M. Cannon
本文针对插电式混合动力汽车的能量管理问题提出了一种后退优化策略。该方法采用双环模型预测控制策略。内部反馈回路解决了使用预测驾驶周期的知识在未来较短的时间内最佳地跟踪电池能量状态的给定参考轨迹的问题。外部反馈回路通过近似解决整个驾驶周期的最优能量管理问题来生成电池状态能量参考轨迹。与内环和外环相关的后退地平线优化问题可以使用专门的投影牛顿法来解决。该控制器与基于庞特里亚金最小原理的现有方法进行了比较，并讨论了对未来驾驶周期的不精确了解的影响。本文包含详细的模拟研究：首先，评估相关的无不确定性方法的最优性及其计算负载。其次，说明了对未来驾驶周期的不精确了解的影响。
This paper proposes a receding horizon optimization strategy for the problem of energy management in plug-in hybrid electric vehicles. The approach employs a dual-loop model predictive control strategy. An inner feedback loop addresses the problem of optimally tracking a given reference trajectory for the battery state of energy over a short future horizon using knowledge of the predicted driving cycle. An outer feedback loop generates the battery state of energy reference trajectory by solving approximately the optimal energy management problem for the entire driving cycle. The receding horizon optimization problems associated with both inner and outer loops are solved using a specialized projected Newton method. The controller is compared with existing approaches based on Pontryagin’s minimum principle and the effects of imprecise knowledge of the future driving cycle are discussed. This paper contains a detailed simulation study: first, this assesses the optimality of the associated uncertainty-free approach and its computational load. Second, the effects of imprecise knowledge of the future driving cycle are illustrated.