摘要
选取不同的温度作为加速因子,通过容量衰减数据推导其循环寿命衰减的内在联系,建立一种电池容量衰减的加速测试与评估方法。具体过程为:1)应用机器学习岭回归方法对获得的数据进行三次多项式、二次多项式、一次多项式及不同正则化力度的拟合,确定适于当前数据的拟合方程;2)基于阿伦尼斯(Arrhenius)模型,建立了以电池500次循环、1 000次循环对应容量保持率为寿命特征的加速寿命模型预测单点寿命,建立了以二次多项式系数为寿命特征的加速循环寿命模型预测曲线任一点寿命,并进行验证。试验结果表明所建立的加速寿命模型能较准确地预测三元锂电池寿命情况,具有一定的工程应用意义。
Different temperatures were selected as acceleration factors,and the inherent relationship between the cycle life decay was derived through capacity decay data.A method for accelerating test-ing and evaluating battery capacity decay was established.The specific process is as follows:1)Apply machine learning ridge regression method to fit the obtained data with cubic polynomial,quadratic poly-nomial,first-order polynomial,and different regularization forces,and determine the fitting equation suitable for the current data;2)Based on the Arrhenius model,an accelerated lifespan model was es-tablished to predict single point lifespan with the capacity retention rate corresponding to 500 and 1000 cycles of the battery as the lifespan feature.An accelerated lifespan model with quadratic polynomial coefficients as the lifespan feature was established to predict the lifespan at any point on the curve and validated.The experimental results indicate that the established accelerated life model can accurately predict the life.of ternary lithium batteries,and has certain engineering application significance.
作者
钱凯程
谢欢
高怡晨
沈驰
QIAN Kaicheng;XIE Huan;GAO Yichen;SHEN Chi(Shanghai Motor Vehicle Testing and Certification Technology Research Center Co.,Ltd.)
出处
《上海节能》
2024年第4期673-679,共7页
Shanghai Energy Saving
关键词
三元锂电池
循环寿命
岭回归
阿伦尼斯模型
加速因子
Ternary Lithium Battery
Cycle Life
Ridge Regression
Arrhenius Model
Acceleration Factor