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Dynamic Load Identification for Battery Pack Bolt Based on Machine Learning
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Batteries are exposed to dynamic load during vehicle driving. It is significant to clarify the load input of the battery system during vehicle driving for battery pack structural design and optimization. Currently, bolt connection is mostly applied for battery pack constraint to vehicle, as well as for module assembly inside the pack. However, accurate bolt load is always difficult to obtain, while directly force measurement is expensive and time consuming in engineering. In this paper, a precise data driven model based on Elman neural network is established to identify the dynamic bolt loads of the battery pack, using tested acceleration data near bolts. The dynamic bolt force data is measured at the same time with the acceleration data during vehicle running in different driving conditions, utilizing customized bolt force sensors. A data preprocessing method synthesizing Wavelet denoising method and machine learning algorithm is designed to improve model precision under dynamic condition. Parts of the pretreated acceleration and force data that obtained in various driving conditions are employed for model training, while the rest for model validation. Meanwhile, an index is introduced to quantitatively assess the identification accuracy against the measured force data. The identified loads show good consistency with the tested data. The error of the estimated bolt force result is within 20%. Finally, the reliability and generalization of the method are discussed. This method in this paper does not rely on prior known structural characteristics, and may be further developed for mechanical monitoring and diagnosis in battery modules in the future.
CitationLiu, R., Hou, Z., Wang, S., Sheng, D. et al., "Dynamic Load Identification for Battery Pack Bolt Based on Machine Learning," SAE Technical Paper 2020-01-0865, 2020, https://doi.org/10.4271/2020-01-0865.
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