Energy Management in Plug-In Hybrid Electric Vehicles: Convex Optimization Algorithms for Model Predictive Control
Energy Management in Plug-In Hybrid Electric Vehicles: Convex Optimization Algorithms for Model Predictive Control
复制标题
DOI：
10.1109/tcst.2019.2933793
复制
发表时间：
2019-02
影响因子：
4.8
通讯作者：
Sebastian East;M. Cannon
中科院分区：
文献类型：
--
作者：
Sebastian East;M. Cannon
本文详细介绍了用于解决与模型预测控制的非线性损失的混合动力电动汽车的能量管理的优化问题的凸制剂的算法的计算性能的调查。提出了一种投影邻域点法，通过将不等式约束作为投影施加到控制输入上，减小了牛顿步矩阵求逆的规模和复杂度，并通过与交替方向乘子法（ADMM）和通用凸优化软件CVX的仿真比较，验证了投影邻域点法的性能.结果发现，ADMM算法具有良好的性能时，需要一个解决方案，具有适度的精度，而投影的邻域点的方法是有利的，当需要高精度，并且都是显着快于CVX。
This article details an investigation into the computational performance of algorithms used for solving a convex formulation of the optimization problem associated with model predictive control for energy management in hybrid electric vehicles with nonlinear losses. A projected interior-point method is proposed, where the size and complexity of the Newton step matrix inversion is reduced by applying inequality constraints on the control input as a projection, and its properties are demonstrated through simulation in comparison with an alternating direction method of multipliers (ADMM) algorithm and a general purpose convex optimization software CVX. It is found that the ADMM algorithm has favorable properties when a solution with modest accuracy is required, whereas the projected interior-point method is favorable when high accuracy is required, and that both are significantly faster than CVX.