基于凸优化的纯电动汽车功率优化分配
Optimal Power Allocation in Battery/Supercapacitor Electric Vehicles using Convex Optimization
摘要：
本文提出了一种电动汽车储能系统中电池和超级电容器之间功率分配的优化框架。提出了一种凸最优控制公式，其最小化总能量消耗，同时对电池和超级电容器中存储的功率输出和总能量实施硬约束。提出了一种乘子交替方向法（ADMM）算法，该算法的计算量和内存需求与预测时域长度成线性关系（并可通过并行处理来降低）。在数值模拟中，将最优控制器与低通滤波器针对全电池基线进行比较，其中示出了在电池退化方面的显著改善（通过峰值电池功率降低71.4%、均方根电池功率降低21.0%和电池吞吐量降低13.7%来推断），以及能耗降低5.7%。它还表明，ADMM算法可以解决最优化问题在几分之一秒的预测范围超过15分钟，因此是一个很有前途的候选人在线滚动时域控制。
英文摘要：
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.
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