Parameter Identification and Maximum Power Estimation of Battery/Supercapacitor Hybrid Energy Storage System Based on Cramer–Rao Bound Analysis
Parameter Identification and Maximum Power Estimation of Battery/Supercapacitor Hybrid Energy Storage System Based on Cramer–Rao Bound Analysis
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DOI：
10.1109/tpel.2018.2859317
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发表时间：
2019-05
影响因子：
6.7
通讯作者：
Ziyou Song;Jun Hou;H. Hofmann;Xinfan Lin;Jing Sun
中科院分区：
文献类型：
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作者：
Ziyou Song;Jun Hou;H. Hofmann;Xinfan Lin;Jing Sun
本文介绍了分析，设计和实验验证的参数识别的电池/超级电容器（SC）混合储能系统（HESS）的状态监测和最大功率估计的目的。基于Fisher信息矩阵和Cramer-Rao界分析，得到了考虑电压测量噪声的蓄电池和SC参数辨识误差的解析界。不同参数的识别需要不同的信号模式来确保高准确度，从而在多参数识别过程中进行权衡。通过适当设计的电流分布，使用具有遗忘因子的递归最小二乘法来识别HESS参数。然后使用所识别的参数来估计HESS的最大功率容量。电池和SC的最大功率能力估计为1和30 s的时间范围。当可以注入最佳励磁电流时，参数识别算法可以应用于包括电池或SC的系统。在HESS实验台上进行了实验验证，结果表明，该算法能够有效地估计HESS最大功率。
This paper presents the analysis, design, and experimental validation of parameter identification of battery/supercapacitor (SC) hybrid energy storage system (HESS) for the purpose of condition monitoring and maximum power estimation. The analytic bounds on the error of battery and SC parameter identification, considering voltage measurement noise, are obtained based on the Fisher information matrix and Cramer–Rao bound analysis. The identification of different parameters requires different signal patterns to ensure high accuracy, rendering tradeoffs in the multiparameter identification process. With an appropriately designed current profile, HESS parameters are identified using recursive least squares with a forgetting factor. The identified parameters are then used to estimate the maximum power capability of the HESS. The maximum power capabilities of the battery and SC are estimated for both 1 and 30 s time horizons. The parameter identification algorithm can be applied to systems including either batteries or SCs when the optimal excitation current can be injected. Experimental validation is conducted on an HESS test-bed, which shows that the proposed algorithm is effective in estimating the HESS maximum power based on appropriate current excitation.