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Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

Author

Listed:
  • Ma, Jian
  • Xu, Shu
  • Shang, Pengchao
  • ding, Yu
  • Qin, Weili
  • Cheng, Yujie
  • Lu, Chen
  • Su, Yuzhuan
  • Chong, Jin
  • Jin, Haizu
  • Lin, Yongshou

Abstract

Cycle life testing in battery development is crucial for the selection of a formulation, but it is time-consuming and costly for battery enterprises. A test optimization approach for different Li-ion power battery formulations is designed based on a hybrid remaining-useful-life prediction method to reduce the high cost of constant temperature–stress testing. The test life is replaced by an accurately predicted lifespan to end the testing early and shorten the cycle number. Batteries having the same formulation and tested at different temperatures are integrally optimized for more test savings. Firstly, high-temperature stress testing is stopped early at a preset threshold, and an instance-based transfer learning method is used to predict the battery lifespan by transferring similar historical test samples of different battery formulations to train a highly robust deep learning prediction model. Standard-temperature testing is completely eliminated by utilizing a modified Arrhenius model to estimate the battery lifespan. The model improvements include replacing the high-temperature stress test lifespan with the abovementioned prediction and introducing a prediction error correction coefficient to increase prediction accuracy. The accuracy of the prediction is verified using actual test data from a battery company, resulting in a time savings of nearly 60%. The optimization strategy has extensive application prospects for other constant-stress tests for batteries and other products.

Suggested Citation

  • Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:appene:v:262:y:2020:i:c:s0306261920300027
    DOI: 10.1016/j.apenergy.2020.114490
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    5. Song, Dengwei & Cheng, Yujie & Zhou, An & Lu, Chen & Chong, Jin & Ma, Jian, 2024. "Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
    6. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    7. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    8. Ganna Kostenko & Artur Zaporozhets, 2024. "Transition from Electric Vehicles to Energy Storage: Review on Targeted Lithium-Ion Battery Diagnostics," Energies, MDPI, vol. 17(20), pages 1-17, October.
    9. Nisitha Padmawansa & Kosala Gunawardane & Samaneh Madanian & Amanullah Maung Than Oo, 2023. "Battery Energy Storage Capacity Estimation for Microgrids Using Digital Twin Concept," Energies, MDPI, vol. 16(12), pages 1-18, June.
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