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Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery

Author

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  • Dong, Chenchen
  • Sun, Dashuai

Abstract

Data-driven thermal runaway diagnosis based on small amounts of thermal runaway data often struggles to produce satisfactory accuracy. However, in actual application scenarios, obtaining real thermal runaway data has a high cost. To this end, we propose a diagnostic method for multi-source domain transfer learning with few-shot learning (MDTL-FSL), which combines the ideas of small sample learning and adversarial learning. Use multiple different but related thermal runaway case data to obtain common diagnostic knowledge to achieve thermal runaway diagnosis. First of all, in order to avoid negative migration of the algorithm model caused by large differences in data distribution between multi-source domains, a data distribution difference measurement method under segmented pressure differences is proposed. This measurement method is simple and effective from multi-source domains. Select the source domain with a small difference in data distribution from the target domain. Secondly, the adversarial learning idea is integrated into multi-source domain transfer learning to learn temporal invariant features. Then, due to the small number of samples in each source domain in the multi-source domain under the thermal runaway scenario, a source domain reorganization mechanism was designed to find the decision boundary based on meta-learning ideas to achieve small-sample learning in the multi-source domain. Finally, we used cells produced by different battery manufacturers to trigger thermal runaway. By comparison and verification with other data-driven algorithms, the results show that the MDTL-FSL model proposed in this article has higher accuracy. At the same time, we use batteries of different types and capacities to trigger thermal runaway under different working conditions. The MDTL-FSL algorithm can issue early warnings before thermal runaway occurs, thereby effectively ensuring the safe operation of the energy storage system.

Suggested Citation

  • Dong, Chenchen & Sun, Dashuai, 2024. "Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    • Handle: RePEc:eee:appene:v:365:y:2024:i:c:s0306261924006317
    DOI: 10.1016/j.apenergy.2024.123248
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    References listed on IDEAS

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