基于模型-数据融合方法的锂离子电池储能系统惯性支持持续功率边界在线估计

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy Pub Date : 2025-05-16 DOI:10.1016/j.apenergy.2025.126064

Shaoxin Shi , Qiao Peng , Tianqi Liu , Yunteng Dai , Jinhao Meng

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引用次数: 0

摘要

锂离子电池储能系统（BESS）在为电网提供惯性支撑方面显示出巨大的潜力。电池的有效惯性支撑和安全运行之间的平衡是一个挑战，这需要准确估计电池的输出边界，特别是在在线工作条件下。然而，现有的电池输出功率边界评估方法往往忽略了特殊的支持惯性的输出轮廓和在线应用的要求，限制了评估的准确性和效率。提出了一种基于模型-数据融合法（MDFM）的BESS惯性支撑持续功率边界在线估计方法。首先，通过一系列实验研究了惯性支撑条件下电池的阻抗特性，在此基础上建立了考虑电池非线性固相扩散效应的负电阻等效电路模型（ECM）。考虑到荷电状态（SOC）和放电电流对负阻抗的非线性影响，采用支持向量机（SVM）对负阻抗进行建模，将实验结果作为训练数据输入。在此基础上，提出了一种基于mdfm的改进ECM参数在线估计方法，其中支持向量机实时估计负阻抗。在此基础上，采用基于多约束的方法，在截止电压、SOC和最大电流阈值的约束下，在线估计了BESS的惯性支撑SPB。最后，通过实验验证了基于mdfm的ECM估计方法和基于多约束的在线SPB估计方法。与传统的峰值功率估计方法相比，该方法显著提高了BESS输出边界在线评估的准确性。

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Online estimation of inertia-supporting sustaining power boundary of lithium-ion battery energy storage systems based on model-data fusion method

Lithium-ion battery energy storage system (BESS) demonstrates great potential to provide inertia support to the power grid. The balance between the efficient inertia support and secure operation of battery is challenging, which requires accurate estimation of battery output boundary, especially in online working conditions. However, the existing methods for assessing the output power boundary of battery usually ignore the special inertia-supporting output profile and the requirement for online application, limiting the accuracy and efficiency. This paper proposes a novel online estimation method of inertia-supporting sustaining power boundary (SPB) of BESS based on model-data fusion method (MDFM). First, a series of experiments are conducted to investigate the impedance characteristics of battery under inertia-supporting condition, based on which a negative resistor-based equivalent circuit model (ECM) is developed to involve the nonlinear solid-phase diffusion effects of battery. Recognizing the nonlinear impact of state of charge (SOC) and discharge current rate on the negative impedance, a support vector machine (SVM) is applied to model the negative impedance, where the experimental results are input as the training data. Then, an MDFM-based method is proposed for online parameter estimation of the improved ECM, where the negative impedance is estimated by the SVM in real-time. Based on the ECM, the inertia-supporting SPB of BESS, constrained by the cut-off voltage, SOC and maximum current thresholds, is estimated online by a multi-constraint-based method. Finally, experiments are conducted to validate the MDFM-based ECM estimation method and the multi-constraint-based online SPB estimation method. Compared to conventional peak power estimation methods, the proposed method significantly improves the accuracy of BESS's output boundary assessment in an online manner.

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来源期刊

Applied Energy 工程技术-工程：化工

CiteScore

21.20

自引率

10.70%

发文量

1830

审稿时长

41 days

期刊介绍： Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.