| 庞哲远,杨騉,宋政湘,孟锦豪.结合电化学特征的变电站后备电源电池SOH估计[J].电子测量与仪器学报,2026,40(1):33-42 |
| 结合电化学特征的变电站后备电源电池SOH估计 |
| Estimating SOH of substation battery backup power basedon electrochemical characteristics |
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| DOI： |
| 中文关键词: 电化学阻抗谱 健康状态 铅酸电池 电化学特征 |
| 英文关键词:electrochemical impedance spectroscopy state of health lead acid battery electrochemical features |
| 基金项目:新疆维吾尔自治区重点研发计划(2022B01019-2)项目资助 |
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| Author | Institution |
| Pang Zheyuan | School of Electrical Engineering, Xi′an Jiaotong University, Xi′an 710049, China |
| Yang Kun | School of Electrical Engineering, Xi′an Jiaotong University, Xi′an 710049, China |
| Song Zhengxiang | School of Electrical Engineering, Xi′an Jiaotong University, Xi′an 710049, China |
| Meng Jinhao | School of Electrical Engineering, Xi′an Jiaotong University, Xi′an 710049, China |
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| 中文摘要: |
| 为迅速且高效地评估变电站后备电源电池的健康状态（state of health，SOH），解决缺乏实际运行数据导致数据驱动方法估计准确度低的问题，提出了一种结合电化学特征和高斯回归（Gaussian process regression，GPR）的变电站电池SOH估计方法。传统研究采用单一老化实验获得的特征参数难以准确反映变电站后备电源铅酸电池实际老化状况。从电池电化学本质出发，设计浮充和循环老化实验，采集了不同老化机制下的电化学阻抗（electrochemical impedance spectroscopy，EIS）数据。随后，分别利用皮尔逊相关性分析和灰色关联度分析提取具有高度代表性的电化学特征参数，结合两种实验老化特征更加接近电池实际老化特征，有效提升了训练数据的质量和效率，减少了所需训练数据的规模。最后，采用这些提取的特征参数训练GPR模型，以实现实际变电站电池SOH准确估计。结果表明，方法的估计随机变电站获取的电池SOH的绝对误差（absolute error，AE）<0.08，平均绝对误差（mean absolute error，MAE）为0.033 0，均方根误差（root mean squared error，RMSE）为0.038 6，方法无需采集实际数据，仅利用少量实验数据即可有效估计变电站电池SOH。 |
| 英文摘要: |
| To rapidly and efficiently evaluate the state of health (SOH) of substation backup power batteries and address the issue of low estimation accuracy in data-driven methods due to the lack of actual operational data, this paper proposes a SOH estimation method for substation batteries that combines electrochemical characteristics and Gaussian process regression (GPR). Traditional studies that use characteristic parameters obtained from single aging experiments struggle to accurately reflect the actual aging conditions of lead-acid batteries used in substation backup power. Starting from the electrochemical essence of the battery, this method designs float charging and cyclic aging experiments to collect electrochemical impedance spectroscopy (EIS) data under different aging mechanisms. Subsequently, highly representative electrochemical characteristic parameters are extracted using Pearson correlation analysis and grey relational analysis. The combination of these two experimental aging characteristics more closely approximates the actual aging characteristics of the battery, effectively improving the quality and efficiency of the training data and reducing the amount of training data required. Finally, these extracted characteristic parameters are used to train the GPR model to achieve accurate SOH estimation for actual substation batteries. The results show that the absolute error (AE) in estimating the SOH of randomly selected substation batteries is less than 0.08, with an average absolute error (MAE) of 0.033 0 and a root mean square error (RMSE) of 0.038 6. This method does not require the collection of actual data and can effectively estimate the SOH of substation batteries with a small amount of experimental data. |
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