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<p>Online acquisition of polarization kinetics data from impedance spectrum is critical for fault diagnosis and health assessment of fuel cells to overcome lifetime bottleneck, but it is technically challenging due to stringent measurement requirements and complex evolution under different operating conditions. To address this, we propose an impedance spectrum prediction method based on the denoising diffusion implicit model, which requires only collectable short time-domain profiles and can generate high-fidelity impedance spectrum through a stepwise denoising process. To validate our method, we performed extensive comparative impedance tests under various operating conditions for fuel cells with different power levels, constructing four datasets comprising over 5700 samples in total. Through performance evaluation experiments, robustness tests, and transfer learning experiments, our proposed method has demonstrated satisfactory performance and outperforms existing methods by down to 37.3% on median mean absolute percentage error. As a useful resource, we have released the trained model and the largest datasets of impedance spectrum for fuel cells to date.</p>

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Diffusion models enable high-fidelity prediction of fuel cell impedance spectrum from short time-domain profiles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

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  • Hao Yuan,
  • Dayi Tan,
  • Zhihua Zhong,
  • Jiangong Zhu,
  • Pingwen Ming,
  • Xuezhe Wei,
  • Haifeng Dai

摘要

Online acquisition of polarization kinetics data from impedance spectrum is critical for fault diagnosis and health assessment of fuel cells to overcome lifetime bottleneck, but it is technically challenging due to stringent measurement requirements and complex evolution under different operating conditions. To address this, we propose an impedance spectrum prediction method based on the denoising diffusion implicit model, which requires only collectable short time-domain profiles and can generate high-fidelity impedance spectrum through a stepwise denoising process. To validate our method, we performed extensive comparative impedance tests under various operating conditions for fuel cells with different power levels, constructing four datasets comprising over 5700 samples in total. Through performance evaluation experiments, robustness tests, and transfer learning experiments, our proposed method has demonstrated satisfactory performance and outperforms existing methods by down to 37.3% on median mean absolute percentage error. As a useful resource, we have released the trained model and the largest datasets of impedance spectrum for fuel cells to date.