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A Novel Autoregressive Rainflow—Integrated Moving Average Modeling Method for the Accurate State of Health Prediction of Lithium-Ion Batteries
Processes ( IF 3.5 ) Pub Date : 2021-04-30 , DOI: 10.3390/pr9050795
Junhan Huang , Shunli Wang , Wenhua Xu , Weihao Shi , Carlos Fernandez

The accurate estimation and prediction of lithium-ion battery state of health are one of the important core technologies of the battery management system, and are also the key to extending battery life. However, it is difficult to track state of health in real-time to predict and improve accuracy. This article selects the ternary lithium-ion battery as the research object. Based on the cycle method and data-driven idea, the improved rain flow counting algorithm is combined with the autoregressive integrated moving average model prediction model to propose a new prediction for the battery state of health method. Experiments are carried out with dynamic stress test and cycle conditions, and a confidence interval method is proposed to fit the error range. Compared with the actual value, the method proposed in this paper has a maximum error of 5.3160% under dynamic stress test conditions, a maximum error of 5.4517% when the state of charge of the cyclic conditions is used as a sample, and a maximum error of 0.7949% when the state of health under cyclic conditions is used as a sample.

中文翻译:

一种新颖的自回归雨流—集成移动平均建模方法,可准确预测锂离子电池的健康状态

准确估算和预测锂离子电池的健康状态是电池管理系统的重要核心技术之一,也是延长电池寿命的关键。但是,很难实时跟踪健康状况以预测和提高准确性。本文选择三元锂离子电池作为研究对象。基于循环法和数据驱动思想,将改进的雨流计数算法与自回归综合移动平均模型预测模型相结合,为电池健康状态预测方法提供了新的方法。在动态应力测试和循环条件下进行了实验,并提出了一种置信区间法来拟合误差范围。与实际值相比,本文提出的方法的最大误差为5。
更新日期:2021-04-30
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