Modeling of discharge voltage for lithium-ion batteries through orthogonal experiments at subzero environment
Modeling of discharge voltage for lithium-ion batteries through orthogonal experiments at subzero environment
复制标题
通过零度以下环境下的正交实验对锂离子电池放电电压进行建模
DOI：
10.1016/j.est.2022.105058
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发表时间：
2022-08
影响因子：
9.4
通讯作者：
Chen Zhang
中科院分区：
文献类型：
--
作者：
Huixing Meng;Yan-Fu Li;Chen Zhang
锂离子电池已广泛应用于越来越多的工业和家庭领域。锂离子电池放电过程中端电压估算的准确性对于确保电池供电设施的可用性和安全性至关重要。以往的研究并未充分考虑放电过程影响因素的优先顺序以及运行参数与模型参数之间的相关性。在本文中，我们在零下环境下对锂离子电池进行了正交实验。基于实验数据，我们提出了三次多项式来估计锂离子电池在零下环境下的放电电压。在我们的电池实验中，影响因素是充电电流、充电终止电压、放电电流和放电终止电压。我们在正交实验中确定关键的操作参数。我们提出了经验方程来描述操作参数和曲线拟合参数之间的关系。结果表明，三次多项式可以用于明确地估计和预测放电电压。我们还将三次多项式与几种简化的回归和机器学习方法进行了比较。事实证明，前者可以比后者用更少的计算资源获得可比的结果。此外，三次多项式也比后者具有更好的可解释性。 • 进行了锂离子电池在零下环境下的正交实验。 • 提取放电过程的关键影响因素。 • 提出了估计锂离子电池放电电压的三次多项式。 • 生成操作参数和曲线拟合参数之间的方程。
Lithium-ion batteries have been widely utilized in increasing number of industrial and household domains. The accuracy of the terminal voltage estimation in the discharge processes of lithium-ion batteries is crucial to ensure the availability and safety of battery-powered facilities. In prior studies, the priority of influencing factors of discharging processes, as well as the correlations between operational parameters and model parameters have not been thoroughly considered. In this paper, we conduct an orthogonal experiment of lithium-ion batteries at subzero environment. Based on experiment data, we propose the cubic polynomial to estimate the discharge voltage for lithium-ion batteries at the subzero environment. In our battery experiment, the influencing factors are the charge current, end-of-charge voltage, discharge current, and end-of-discharge voltage. We determine crucial operational parameters in the orthogonal experiment. We propose the empirical equations to depict the relationships between operational parameters and curve-fitting parameters. The results show that the cubic polynomial can be utilized for estimating and predicting the discharge voltage explicitly. We also compare the cubic polynomial with several simplified regression and machine learning methods. It is demonstrated that the former can obtain comparable results, with fewer computation resources, than the laters. In addition, the cubic polynomial also gains better explainability than the laters. • An orthogonal experiment of li-ion batteries at subzero environment is conducted. • The crucial influencing factors in discharge processes are extracted. • A cubic polynomial to estimate discharge voltage of li-ion batteries is proposed. • Equations between operational parameters and curve fitting parameters are generated.
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DOI：
10.1016/j.asoc.2017.12.041
发表时间：
2018-04
期刊：
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影响因子：
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发表时间：
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