发表机构
Chalmers University of Technology; Nanjing University of Aeronautics and Astronautics（查尔姆斯理工大学; 南京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于排序前缀的可行集限制方法，将可重构电池组混合整数模型预测控制的每步参与模式从组合数降为线性，显著提升实时性并保持控制性能。
AI 中文摘要
针对具有逐电池旁路开关的串联可重构电池组，提出了一种基于排序前缀的可行集限制方法，用于实时混合整数模型预测控制。在每个采样时刻，根据单步目标信息驱动的适宜性评分对电池进行排序，并将参与变量限制为该排序的前缀。该限制将每步可采用的参与模式数量从组合数减少为与电池数量成线性关系，同时保持模型、目标和物理约束不变。在20节电池组上，采用全球统一轻型车辆测试循环进行驱动，所提出的控制器对每个活动步骤均给出认证，所有实例均无需分支即可求解，并将第95百分位的求解器时间相对于完整子集选择控制器降低了6.8倍。循环平均的电池间荷电状态标准差降低了3.4倍，且未发生功率削减。相同状态参考求解显示，在联合认证步骤上平均限制差距低于0.11%，并且计算优势在测试的初始条件、电池异质性水平、电池组规模和预测时域上均持续存在，无需重新调整参数。
英文摘要
A ranked-prefix feasible-set restriction is proposed for real-time mixed-integer model predictive control of series-connected reconfigurable battery packs with per-cell bypass switching. Cells are ordered at each sampling instant by a one-step objective-informed suitability score, and the engagement variables are constrained to prefixes of that ordering. The restriction reduces the number of admissible per-step engagement patterns from combinatorial to linear in the number of cells while leaving the model, objective, and physical constraints unchanged. For a 20-cell pack driven over a worldwide harmonized light vehicles test cycle, the proposed controller certifies every active step, solves every instance without branching, and reduces the 95th-percentile solver time by a factor of 6.8 relative to the full subset-selection controller. The cycle-mean cell-to-cell state-of-charge standard deviation decreases by a factor of 3.4, and no power curtailment occurs. Same-state reference solves show a mean restriction gap below 0.11% on jointly certified steps, and the computational gains persist across the tested initial conditions, cell heterogeneity levels, pack sizes, and prediction horizons without retuning.
CommentsSubmitted to IEEE Transactions on Control Systems Technology. 15 pages, 4 figures. Code: https://doi.org/10.5281/zenodo.22136253; data: https://doi.org/10.5281/zenodo.22134357
