通过基于排序的可行集限制减少可重构电池混合整数模型预测控制中的组合冗余
Reducing Combinatorial Redundancy in Mixed-Integer MPC via Ranking-Based Feasible-Set Restriction for Reconfigurable Batteries
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中文总结 AI 辅助
针对混合整数模型预测控制中相似单元选择的组合爆炸问题，提出基于排序的可行集限制，使选择数量线性增长，在20单元可重构电池仿真中实现100%最优性证明，求解时间降低13.4倍，荷电状态均衡度提升8.7倍。
中文摘要 AI 辅助
当混合整数模型预测控制必须从多个相似单元中选择激活哪些单元时，许多相同规模的选项可能产生非常接近的预测结果，而分支定界法可能无法在一个采样周期内证明最优性。我们针对这类决策提出了一种基于排序的可行集限制：单元根据每个采样时刻计算的适用性得分进行排序，并施加单调约束，仅允许排名靠前的单元被激活。优化器随后只需选择激活多少个单元，因此每个预测阶段的可行选择数量呈线性增长而非组合增长，同时保留模型、成本函数和其余约束。我们将该方法应用于可重构电池组，其中每个串联电池单元可被旁路，使用归一化健康状态、荷电状态和电压裕度的固定权重对单元进行评分。在一个包含20个电池单元的仿真中，采用动态驾驶循环，每次在线求解限时1秒，所有活跃的在线受限求解均在根节点终止并证明最优性，相比之下，无限制子集选择的证明率为77.83%，且第95百分位求解器时间降低了13.4倍。循环平均的电池间荷电状态标准差下降了8.7倍，且无功率削减，代价是电池切换次数增加了3.45倍。从相同状态沿两条轨迹求解两个问题表明，在两次求解均被证明且未削减功率的状态下，该限制使最优成本平均提高0.26%和0.14%。
英文摘要
When mixed-integer model predictive control must select which of several similar units to activate, many same-size selections can yield closely spaced predicted outcomes, and branch-and-bound may fail to certify optimality within one sampling period. We propose a ranking-based feasible-set restriction for this class of decisions: the units are ordered by a suitability score computed at each sampling instant, and a monotone constraint permits only the top-ranked units to be active. The optimizer then chooses only how many units to activate, so the admissible selections per prediction stage grow linearly instead of combinatorially, while the model, cost function, and remaining constraints are retained. We instantiate the method for reconfigurable battery packs in which each series-connected cell can be bypassed, scoring cells with fixed weights on normalized state of health, state of charge, and voltage headroom. In a 20-cell simulation over a dynamic drive cycle with a 1 s limit per online solve, all active online restricted solves terminate at the root node with certified optimality, compared with 77.83% certification for unrestricted subset selection, and the 95th-percentile solver time is lower by a factor of 13.4. The cycle-mean cell-to-cell standard deviation in state of charge falls 8.7-fold without power curtailment, at the expense of 3.45 times as much cell switching. Solving both problems from identical states along two trajectories shows that the restriction raises the optimal cost by 0.26% and 0.14% on average at states where both solves are certified and uncurtailed.
发表机构
- Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
