Identification of Internal Short-Circuit Faults in Lithium-Ion Batteries Based on a Multi-Machine Learning Fusion

Abstract

Internal short-circuit (ISC) faults are a common cause of thermal runaway in lithium-ion batteries (LIBs), which greatly endangers the safety of LIBs. Different LIBs have common features related to ISC faults. Due to the insufficient volume of acquired ISC fault data, conventional machine learning models could not effectively identify ISC faults. To compensate for the above deficiencies, this paper proposes a multi-machine learning fusion method to predict ISC faults and to perform faults warning classification under multiple operating conditions using the input of voltage normalization. Firstly, learning data acquisition is captured by experiments and simulation. Secondly, the simulation data are inputted into the ResNet-convolutional neural network (CNN) for pretraining, followed by the transfer learning method to freeze parts of the model layers in the CNN, and part of the experimental data are also inputted into the CNN model for parameter fine-tuning to build a multi-machine learning model. Finally, the degree of ISC faults within the laboratory battery is predicted based on the multi-machine learning model. The results show that the CNN model had a 99.9% prediction accuracy on the simulated dataset, and the multi-machine learning fusion model after transfer learning had a 96.67% prediction accuracy on the laboratory battery dataset, which can accurately identify different levels of ISC faults in batteries and realize the graded warning of ISC faults.

Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review

“…Therefore, seeking suitable algorithms to solve this problem is a challenge. In [70], in response to the insufficient amount of collected internal short-circuit (ISC) fault data, the author proposed a multi-ML fusion method. This method uses voltage normalization to input the ISC faults into prediction, classifies fault warnings, trains simulation data through CNN, and then uses TL to build a multi-ML model.…”

Displacement Values Calculation Method for Ship Multi-Support Shafting Based on Transfer Learning

“…Fundamentally, this method involves initializing model parameters based on acquired knowledge rather than relying solely on random initialization [31]. It effectively combines static experimental simulation data with real-time information, enabling model adaptation to dynamic environments and the construction of accurate models [32,33].…”

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

“…Unlike modelbased methods, data-driven battery FDD methods do not rely on an accurate battery model, while FDD is performed by real-time data such as voltage, current, and temperature generated during battery operation. Commonly employed data-driven diagnostic techniques encompass entropy analysis (EA) [28], statistical analysis (SA) [29], and machine learning (ML) [30]. Xia et al [31] introduced a fault detection method for lithium batteries based on the voltage profile correlation coefficient.…”

“…Motivated by the literature [31,32,34], this paper proposes a method for ISC fault localization and detection of lithium batteries based on curvilinear Manhattan distance and voltage variance analysis. Different from the FDD methods proposed in the studies [25,30], the proposed method in this paper is accurate and efficient, which can realize the online monitoring and localization of lithium battery ISC faults, and thus, it is more in line with practical industrial application scenarios. Specifically, the curvilinear Manhattan distance between the cell voltages of a lithium battery pack is calculated to rapidly and accurately locate the position of the faulty cell.…”