Prediction of remaining useful life for lithium-ion battery with multiple health indicators

• Autorzy: Su Chun , Chen Hongjing , Wen Zejun

Identyfikatory

DOI

10.17531/ein.2021.1.18

• Języki publikacji: EN

Abstrakty

EN

Lithium-ion (Li-ion) battery has become a primary energy form for a variety of engineering equipments. To ensure the equipments’ reliability, it is crucial to accurately predict Liion battery’s remaining capacity as well as its remaining useful life (RUL). In this study, we propose a novel method for Li-ion battery’s online RUL prediction, which is based on multiple health indicators (HIs) and can be derived from the battery’s historical operation data. Firstly, four types of indirect HIs are built according to the battery’s operation current, voltage and temperature data respectively. On this basis, a generalized regression neural network (GRNN) is presented to estimate the battery’s remaining capacity, and the nonlinear autoregressive approach (NAR) is applied to predict the battery’s RUL based on the estimated capacity value. Furthermore, to reduce the interference, twice wavelet denoising are performed with different thresholds. A case study is conducted with a NASA battery dataset to demonstrate the effectiveness of the method. The result shows that the proposed method can obtain Li-ion batteries’ RUL effectively.

Słowa kluczowe

EN

lithium-ion (Li-ion) battery; remaining useful life; RUL; health indicator; HI; generalized regression neural network (GRNN); non-linear autoregressive (NAR)

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 1
• Strony: 176--183
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Su Chun

• School of Mechanical Engineering, Southeast University, Nanjing 211189, China
• Hunan Provincial Key Lab of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China

autor

Chen Hongjing

• School of Mechanical Engineering, Southeast University, Nanjing 211189, China

autor

Wen Zejun

• Hunan Provincial Key Lab of Health Maintenance for Mechanical Equipment, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).