Physics-Informed Temporal Generative Adversarial Networks: A Potent Tool for Lithium Battery Data Generation

Accurately predicting the aging process of lithium batteries is essential for battery health management and predictive maintenance. However, the scarcity of real aging data limits the reliability of existing models. To address this issue, this paper proposes a Physics-Informed Temporal Generative Adversarial Network (PITGAN)-based framework for generating multi-dimensional time-series battery data to support health assessment. The framework integrates temperature-aware feature embedding to dynamically incorporate thermal environment characteristics and utilizes a hybrid GRU-convolutional architecture to capture long-range temporal dependencies and local electrochemical features. Additionally, an improved GAN method is proposed, combining the conditional control capability of CGAN with the smooth optimization characteristics of WGAN-GP to enhance training stability and improve the authenticity of the generated data. The feature extraction module preserves the physical constraints of voltage-capacity curves, while the generator-discriminator structure enables joint modeling of multi-dimensional data distributions. The analysis of experimental results shows that the generated data closely resembles actual data in terms of distribution, with a voltage-capacity curve error of less than 1.8 %. Furthermore, state-of-charge and state-of-health estimation models trained on the generated data achieve a mean absolute error below 3.2 %.

• Publication year

  2025

• Venue

  International Symposium on Computer Science and Intelligent Control

• Publication date

  2025-09-26

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ISCSIC67494.2025.11351943
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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