发表机构
College of Artificial Intelligence, Nankai University; Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation, Nankai University（南开大学人工智能学院; 南开大学天津介入式脑机接口与智能康复重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对电池储能系统在动态负载下的功率分配问题，提出基于预设性能控制的两个分布式估计器，实现SoC均衡与功率共享的实时估计，并验证了其有效性与可扩展性。
AI 中文摘要
电池储能系统（BESS）被集成到智能电网中，以增强可扩展性、经济性和绿色性。而荷电状态（SoC）均衡是BESS的基本问题之一，它可以最大化容量的利用率。具有统一SoC相对变化率的BESS可以同时充满或放空，而在此功率分配方案中需要实时估计SoC均衡和功率共享状态。因此，本文采用预设性能控制（PPC）方法，基于多智能体系统（MASs）设计了两个分布式估计器，以在动态负载驱动下实时估计功率共享和SoC均衡状态。通过这种方式，这两个平均值最终可以以几乎零误差的性能被很好地估计，并且两个估计器的动态性能和稳态性能可以通过不同参数进行调整。类似地，一致性性能和动态跟踪性能被解耦。这些结果为增益的选择提供了更广泛的范围。为了验证所设计估计器的有效性、鲁棒性和先进性，设计并讨论了一些包含4个BESS作为负载分布的电阻网络案例。此外，为了测试可扩展性，将包含12个BESS的大规模系统应用于所设计的方案。
英文摘要
Battery energy storage system (BESS) is integrated into the smart grid to enhance scalability, economy, and greenery. And the State-of-Charge (SoC) balance is one of the basic problems of BESSs, which can maximize the utilization of capacity. BESSs with a unified relative variation rate for SoC can be simultaneously filled or empty, while real-time estimation schemes of SoC balance and power sharing states are required in this power allocation scheme. Therefore, the prescribed performance control (PPC) method is applied in this paper to design two distributed estimators based on multi-agent systems (MASs), in order to estimate the power sharing and SoC balance states in real-time under dynamic load driving. In this way, these two average values can ultimately be well estimated with almost zero error performance, and dynamic performance and steady-state performance of the two estimators can be adjusted by different parameters. Similarly, consensus performance and dynamic tracking performance are decoupled. These results provide a broader range for the selection of gains. To verify the effectiveness, robustness and progressiveness of the designed estimators, some cases with a resistance network containing 4 BESSs as load distribution are designed and discussed. Further more, to test scalability, a large-scale system containing 12 BESSs is conducted to the designed scheme.
DOI：10.1080/00207721.2025.2534901
