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A prediction-based cycle life test optimization method for cross-formula batteries using instance transfer and variable-length-input deep learning model

  • S.I.: Applications of Machine Learning in Maintenance Engineering and Management (IFAC AMEST 2020)
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cycle life is a key performance indicator in the design and development of lithium-ion power batteries. In order to obtain an appropriate formula, developers need to conduct a large number of cycle life tests (CLTs). However, the high test cost and unbearable time overhead of CLT have seriously hindered the upgrade and development of lithium-ion power batteries. In this paper, a prediction-based CLT optimization method for cross-formula batteries is proposed, which can shorten the number of test cycles by predicting the remaining cycle life of batteries. Specifically, we design an AED-based instance transferability measurement method to select reference battery from the historical database according to curves distance and trend consistent. Then, a highly robust deep learning method named variable-length-input stacked denoising autoencoder (VLI-SDA) is proposed to achieve remaining useful life prediction. The VLI-SDA model adopts a variable-length input strategy to expand the receptive field, fully learn the degradation trend, and ensure an appropriate number of training samples. Combined with the inherent noise reduction capability of the SDA model, the VLI-SDA model can effectively solve the problem of cycle life prediction under high-temperature stress test and small sample conditions. The actual CLT data at three temperatures from a battery company verify the effectiveness of the proposed method. The test temperature, curve shape and other influencing factors are analyzed to help determine optimization strategies.

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Acknowledgements

The authors thank the Contemporary Amperex Technology Co., Limited for providing a large amount of CLT data of Li-ion power battery to support our research activities. Besides, this research is supported by the National Natural Science Foundation of China (Grant Nos. 51605014 and 61803013), the Fundamental Research Funds for the Central Universities (Grant No. YWF-21-BJ-J-517), the National key Laboratory of Science and Technology on Reliability and Environmental Engineering (Grant Nos. 6142004180501), and the Aeronautical Science Foundation of China (Grant No. ASFC-201933051001). In addition, the authors thank the 4th IFAC A-MEST 2020 workshop and its event organizing committee for the collection and recommendation of our previous study.

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Authors and Affiliations

  1. Institute of Reliability Engineering, Beihang University, Beijing, China

    Jian Ma, XinYu Zou, Lulu Sun, Yujie Cheng & Chen Lu

  2. National Key Laboratory of Science and Technology on Reliability and Environmental Engineering, Beijing, China

    Jian Ma, XinYu Zou, Lulu Sun, Yujie Cheng & Chen Lu

  3. School of Reliability and Systems Engineering, Beihang University, Beijing, China

    Jian Ma, XinYu Zou, Lulu Sun, Yujie Cheng & Chen Lu

  4. Contemporary Amperex Technology Co., Limited, Ningde, 352100, Fujian Province, China

    Yuzhuan Su, Jin Chong, Haizu Jin & Yongshou Lin

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  1. Jian Ma
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Correspondence to Yujie Cheng.

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Ma, J., Zou, X., Sun, L. et al. A prediction-based cycle life test optimization method for cross-formula batteries using instance transfer and variable-length-input deep learning model. Neural Comput & Applic 35, 2947–2971 (2023). https://doi.org/10.1007/s00521-022-07322-1

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