Data Generation and Preliminary Fault Detection of Power Battery Based on GAN Algorithm

2025-01-7009

01/31/2025

Features
Event
SAE 2024 Vehicle Powertrain Diversification Technology Forum
Authors Abstract
Content
The safety of power batteries is an important issue that has attracted widespread attention in new energy vehicle technology. In this paper, Generative Adversarial Networks (GAN) are introduced, and the data generation and fault diagnosis of power battery life-cycle data are carried out. GAN is composed of a pair of generators and discriminators, combining signal processing with neural networks, using the discriminator architecture based on Fourier transform and the generator architecture based on wavelet transform, so that the neural network can learn the characteristics of power battery life-cycle data from the perspective of time and frequency domain, and use the good performance of wavelet transform in data denoising and repair to generate high-quality and low-noise data, and use Fourier transform to target the characteristics of periodicity. Identify and distinguish the periodic characteristics and time-frequency domain data characteristics in the generated data and laboratory data. The results show that the GAN architecture adopted in this paper can generate high-quality power battery charge and discharge cycle data, and can observe the location of power battery fault data.
Meta TagsDetails
DOI
https://doi.org/10.4271/2025-01-7009
Pages
8
Citation
Tan, P., Yang, A., Liu, X., and Yao, C., "Data Generation and Preliminary Fault Detection of Power Battery Based on GAN Algorithm," SAE Technical Paper 2025-01-7009, 2025, https://doi.org/10.4271/2025-01-7009.
Additional Details
Publisher
Published
Jan 31
Product Code
2025-01-7009
Content Type
Technical Paper
Language
English