Two Statistical Degradation Models of Batteries Under Different Operating Conditions

Jin-Zhen Kong () and Dong Wang ()
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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

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 269-282 from Springer

Abstract: 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.

Keywords: Statistical degradation model; Remaining useful life; Batteries; Data science (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_11

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DOI: 10.1007/978-3-031-07155-3_11

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