[ "丁海丽(1988),女,高级工程师,从事电力营销管理、计量技术、大数据分析工作。" ]
网络出版日期:2025-06-27,
纸质出版日期:2025
移动端阅览
丁海丽, 严绍奎. Li/SOCl电池可靠性预测方法综述[J]. 宁夏电力, 2025,(3).
DING Haili, YAN Shaokui. Review on reliability prediction method of Li/SOClbattery[J]. Ningxia electric power, 2025, (3).
丁海丽, 严绍奎. Li/SOCl电池可靠性预测方法综述[J]. 宁夏电力, 2025,(3). DOI: 10.3969/j.issn.1672-3643.2025.03.004.
DING Haili, YAN Shaokui. Review on reliability prediction method of Li/SOClbattery[J]. Ningxia electric power, 2025, (3). DOI: 10.3969/j.issn.1672-3643.2025.03.004.
锂亚硫酰氯(Li/SOCl2)电池是智能电能表的核心备用电源,其运行可靠性是电费计算和分时阶梯电量准确计量的保证,但因Li/SOCl2电池复杂的结构特性和硬件环境因素的影响,实现可靠性的精准预测面临挑战。针对Li/SOCl2电池可靠性预测方法进行综述,从传统实验方法、基于模型的预测方法和基于数据驱动的预测方法三方面系统分析了现有预测方法和技术,归纳了各种电池剩余寿命预测方法的优点和存在的问题。未来需融合多方法优势,优化实验技术、增强模型耦合性,改进数据驱动模型的可解释性,以提升预测精度,进而为智能电能表长期稳定运行提供理论支撑。
Lithium thionyl chloride (Li/SOCl2) batteries are the core backup power source for smart energy meters
and their operational reliability is the guarantee of accurate metering for tariff calculation and time-stepped power metering
but due to the complex structural characteristics of Li/SOCl2 batteries and the influence of hardware environmental factors
it is a challenge to realize accurate prediction of their reliability.In this paper
we review the methods of Li/SOCl2 battery reliability prediction
systematically analyze the existing prediction methods and technologies from three aspects:traditional experimental methods
model-based prediction methods and data-driven prediction methods
summarize the advantages of various battery residual life prediction methods
the remaining problems
and the need to integrate the advantages of multi-methods in the future to optimize experimental techniques
enhance the coupling of the model
and improve the interpretability of the data-driven model
in order to ensure the accuracy of time-sharing ladder metering.In the future
it is necessary to integrate the advantages of multiple methods
optimize the experimental technology
enhance the model coupling
so as to improve the prediction accuracy
and then provide theoretical support for the long-term and stable operation of smart meters.
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