May 2024
·
49 Reads
Electrochimica Acta
This page lists the scientific contributions of an author, who either does not have a ResearchGate profile, or has not yet added these contributions to their profile.
It was automatically created by ResearchGate to create a record of this author's body of work. We create such pages to advance our goal of creating and maintaining the most comprehensive scientific repository possible. In doing so, we process publicly available (personal) data relating to the author as a member of the scientific community.
If you're a ResearchGate member, you can follow this page to keep up with this author's work.
If you are this author, and you don't want us to display this page anymore, please let us know.
May 2024
·
49 Reads
Electrochimica Acta
February 2024
·
27 Reads
Journal of Cleaner Production
January 2024
·
29 Reads
Green Energy and Intelligent Transportation
February 2023
·
85 Reads
·
4 Citations
Batteries
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.
... 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. ...
February 2023
Batteries