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Two Statistical Degradation Models of Batteries Under Different Operating Conditions

In: Artificial Intelligence, Big Data and Data Science in Statistics

Author

Listed:
  • Jin-Zhen Kong

    (Shanghai Jiao Tong University, The State Key Laboratory of Mechanical Systems and Vibration)

  • Dong Wang

    (Shanghai Jiao Tong University, The State Key Laboratory of Mechanical Systems and Vibration)

Abstract

The commercialization of electric vehicles (EVs) demands higher performances of rechargeable batteries. Accurate assessments of state of health (SOH) and remaining useful life (RUL) of batteries are important to indicate battery status and ensure EVs safety. However, the accuracies of existing battery capacity degradation models are not sufficient to describe battery states under the complicated impacts of usage environments. Various operating conditions will make degradation modeling more challenging and difficult, for instance, different discharge rates and discontinuous charge and discharge can influence the capacity degradation tendencies of batteries. To address the above issues, two statistical degradation models are respectively proposed to implement battery prognostics in different usage conditions based on the knowledge of big data and data science. Results show that the proposed methods outperform many existing works.

Suggested Citation

  • Jin-Zhen Kong & Dong Wang, 2022. "Two Statistical Degradation Models of Batteries Under Different Operating Conditions," Springer Books, in: Ansgar Steland & Kwok-Leung Tsui (ed.), Artificial Intelligence, Big Data and Data Science in Statistics, pages 269-282, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-07155-3_11
    DOI: 10.1007/978-3-031-07155-3_11
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