Comparison of Multi-step Prediction Models for Voltage Difference of Energy Storage Battery Pack Based on Unified Computing Operation Platform (Supporting Information)

The voltage difference of battery pack is a very important index for the state evaluation of energy storage battery. When the voltage difference is too large inside the battery pack, it may cause a series of safety problems. By predicting the voltage difference of battery pack, potential dangerous situations can be detected as early as possible, and necessary measures can be taken to ensure the safety of the energy storage battery, so as to realize the reliability improvement, efficiency improvement, and safety guarantee of the energy storage system. Through the multi-step prediction for the voltage difference of the energy storage battery pack, the variation trend of the voltage difference can be predicted in advance, so as to warn the possible voltage difference over-limit fault. At present, there are many methods for multi-step prediction of time series data, but which one is most suitable for predicting the voltage difference of the energy storage battery pack is still lack of research. In this paper, the stationarity and correlation of energy storage battery pack’s voltage difference data are analyzed and processed, and different multi-step prediction algorithms are used to predict the voltage difference of energy storage battery pack. The prediction results generated by different models are compared and analyzed, and the most suitable model selection for predicting the voltage difference of energy storage battery pack is discussed.

电池组电压差是评估储能电池状态的关键指标。当电池组内部电压差过大时，可能导致一系列安全问题的发生。通过预测电池组电压差，可以尽早发现潜在的危险状况，并采取相应措施以确保储能电池的安全，从而实现储能系统的可靠性提升、效率优化及安全保障。通过对储能电池组电压差的多步预测，可以预先预测电压差的变动趋势，以便提前预警可能出现的电压差超限故障。目前，关于时间序列数据的多步预测方法众多，但针对储能电池组电压差预测的最适宜方法仍需深入研究。在本文中，对储能电池组电压差数据的平稳性和相关性进行了分析与处理，并运用不同的多步预测算法对电池组电压差进行预测。对不同模型生成的预测结果进行了比较与分析，并讨论了预测储能电池组电压差的最适宜模型选择。