Battery internal resistance prediction and rapid sorting method based on production line big data

doi:10.19799/j.cnki.2095-4239.2025.0221

Abstract

Abstract:

Based on a production line big data platform, rapid sorting of outgoing batteries using capacity data can improve the consistency of battery packs and extend their service life; however, this method alone does not ensure dynamic consistency. In this study, a novel approach is proposed to predict the internal resistance of batteries during the capacity grading stage using production line big data, followed by rapid sorting that combines predicted internal resistance with capacity data. Specifically, production line data are collected, and relevant features are screened using the Pearson correlation coefficient (PCC). A neural network model based on a multilayer perceptron (MLP) is constructed to predict battery internal resistance. The predicted internal resistance values are then integrated with capacity data, and the Fuzzy C-Means (FCM) clustering algorithm is employed to classify the batteries into four grades. The effectiveness of the proposed sorting method is evaluated using charge-discharge voltage curve characteristics as assessment criteria and compared with the traditional capacity-based sorting method. The results demonstrate that the mean absolute percentage error (MAPE) of internal resistance prediction is 1.2%, and the overall optimization rate of the sorting process reaches 14.9%, representing a significant improvement over traditional capacity sorting. This study offers a new method for the rapid sorting of production line batteries, supporting the enhancement of battery pack consistency and performance.

Key words: production line big data, lithium-ion battery, internal resistance prediction, clustering analysis, battery sorting

CLC Number:

TM 912

Xinyu BAO, Xiangdong KONG, Taolin LV, Zhicheng ZHU, Xuebing HAN, Xin LAI, Yuejiu ZHENG, Tao SUN. Battery internal resistance prediction and rapid sorting method based on production line big data[J]. Energy Storage Science and Technology, 2025, 14(9): 3541-3551.

Figures/Tables 14

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参数名称	参数选择
激活函数(activation)	Relu
优化器(solver)	Adam
隐藏层(hidden layer)	(16，8)
最大迭代次数(max_iter)	1000
收敛容差(tol)	1×10^-4
学习率(learning rate)	0.005

指标	综合优化率/%
充电起始电压V_{Ci_std}	27.1
充电结束电压V_{Ce_std}	15.2
放电起始电压V_{Di_std}	16.6
放电结束电压V_{De_std}	7.9
充电电压曲线距离d_{C_mean}	9.3
放电电压曲线距离d_{D_mean}	13.5
综合一致性K_a	14.9