论文标题
使用凸优化的电池/超级电容器电动汽车中的最佳功率分配
Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles using Convex Optimization
论文作者
论文摘要
本文介绍了一个框架，以优化电动汽车存储系统中电池和超级电容器之间的功率分配。提出了凸的最佳控制配方，该配方可最大程度地减少总能量消耗，同时对电池和超级电容器中存储的功率输出和总能量进行硬性约束。提出了一种交替的乘数方法（ADMM）算法的方法，为此，计算和内存需求与预测范围的长度线性缩放（可以使用并行处理来减少）。将最佳控制器与低通滤波器相比，在数值模拟中与全电池基线进行了比较，在数值模拟中，它可提供电池降解的显着改善（通过峰值电池电量的降低71.4％，在峰值电池电量中降低了71.4％，根均值电池电量电池电量为21.0％，电池通过电池通过13.7％的能量消耗量为13.7％），以及5.7％的能量消耗量。5.7％。还表明，ADMM算法可以在一秒钟内以15分钟以上的预测范围解决优化问题，因此是在线退缩Horizon Control的有前途的候选人。
This paper presents a framework for optimizing the power allocation between a battery and supercapacitor in an electric vehicle energy storage system. A convex optimal control formulation is proposed that minimizes total energy consumption whilst enforcing hard constraints on power output and total energy stored in the battery and supercapacitor. An alternating direction method of multipliers (ADMM) algorithm is proposed, for which the computational and memory requirements scale linearly with the length of the prediction horizon (and can be reduced using parallel processing). The optimal controller is compared with a low-pass filter against an all-battery baseline in numerical simulations, where it is shown to provide significant improvement in battery degradation (inferred through reductions of 71.4% in peak battery power, 21.0% in root-mean-squared battery power, and 13.7% in battery throughput), and a reduction of 5.7% in energy consumption. It is also shown that the ADMM algorithm can solve the optimization problem in a fraction of a second for prediction horizons of more than 15 minutes, and is therefore a promising candidate for online receding-horizon control.
