基于主成分分析的液流电池状态估计策略研究

Research on State Estimation Strategy for Flow Batteries Based on PCA

为克服放电后期电压拐点对锌溴液流电池(ZBFB)荷电状态(SOC)辨识精度的影响,研究基于主成分分析(PCA)的锌溴液流电池荷电状态估计方法。以提高锌溴液流电池SOC参数辨识精度为目标,特别是解决锌溴液流电池在放电后期特有的电压拐点非线性特性,在兼顾实际算力限制与运行速率要求的条件下,对锌溴液流电池SOC参数辨识算法进行分析。研究结果表明,所提方法兼顾空间维度特征提取以及系统降维建模,可有效改善电压拐点处的电池SOC的辨识精度,为锌溴液流电池的长时储能应用提供理论与算法分析基础。

To overcome the influence of the voltage inflection point in the later stage of discharge on the accuracy of State of Charge(SOC)identification in Zinc Bromine Flow Batteries(ZBFB),it proposes a SOC estimation method based on Principal Compo-nent Analysis(PCA).With the aim of improving the accuracy of SOC parameter identification in ZBFB,particularly addressing the nonlinear characteristics of voltage inflection points unique to ZBFB during late discharge,and considering computational limitations and operational speed requirements,an analysis of SOC parameter identification algorithms in ZBFB is conducted.The research results indicate that by considering spatial dimension feature extraction and system dimension reduction model-ing,the proposed method can effectively enhance the accuracy of SOC parameter identification in ZBFB,which lays a theoret-ical and algorithmic foundation for the long-term energy storage applications of ZBFB.