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

Format:

Book

Conference/Event

Author/Creator:

Tan, Piqiang, author.

Contributor:

Liu, Xiang

Yang, Aoji

Yao, Chaojie

Conference Name:

SAE 2024 Vehicle Powertrain Diversification Technology Forum (2024-12-06 : Xi'An, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

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

Notes:

Vendor supplied data

Publisher Number:

2025-01-7009

Access Restriction:

Restricted for use by site license