Impact of initial open-circuited potential on the consistency of lithium ion battery

Wang, Hongwei; Tao, Ziqiang; Ma, Qiang; Fu, Yanling; Bai, Hua; Zhu, Yusong; Xiao, Haiqing; Bai, Hua

doi:10.1088/1755-1315/153/2/022023

Cited by 2 publications

(1 citation statement)

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“…Therefore, the consistency of the battery system is not an absolute concept but a relative concept to be compared. The evaluation of the actual inconsistency fault of different units is often based on the empirical value [5] , but changes in actual working conditions or parameters often lead to subjective errors in the results. Therefore, finding a method that can accurately and effectively predict single-cell inconsistency fault is important [6] .…”

Section: Introductionmentioning

confidence: 99%

Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

Chang,

Gu,

et al. 2024

Big Data Min. Anal.

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Unsupervised learning algorithms can effectively solve sample imbalance. To address battery consistency anomalies in new energy vehicles, we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three vehicles using charging fragment data from actual operating conditions. We extract battery-related features, such as the mean of maximum difference, standard deviation, and entropy of batteries and then apply principal component analysis to reduce the dimensionality and record the amount of preserved information. We then build models through a collection of unsupervised learning algorithms for the anomaly detection of cell consistency faults. We also determine whether unsupervised and supervised learning algorithms can address the battery consistency problem and document the parameter tuning process. In addition, we compare the prediction effectiveness of charging and discharging features modeled individually and in combination, determine the choice of charging and discharging features to be modeled in combination, and visualize the multidimensional data for fault detection. Experimental results show that the unsupervised learning algorithm is effective in visualizing and predicting vehicle core conformance faults, and can accurately predict faults in real time. The "distance+boxplot" algorithm shows the best performance with a prediction accuracy of 80%, a recall rate of 100%, and an F1 of 0.89. The proposed approach can be applied to monitor battery consistency faults in real time and reduce the possibility of disasters arising from consistency faults.

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Section: Introductionmentioning

confidence: 99%