Lithium‑ion batteries remaining useful life prediction using Wiener process and unscented particle filter

Ranran Wang; Hailin Feng

Lithium‑ion batteries remaining useful life prediction using Wiener process and unscented particle filter

Ranran Wang

Hailin Feng

Vol. 20, No. 1, pp. 270-278, Jan. 2020

10.1007/s43236-019-00016-3

Remaining useful life prediction

Lithium-ion batteries

Wiener process

State-space model

Unscented particle filter

Abstract

Remaining useful life (RUL) prediction plays an important role in the prognosis and health management of lithium-ion batteries (LIBs). This paper proposes a new method based on the Wiener process for the RUL prediction of LIBs. Firstly, a state-space model based on the Wiener process is constructed to describe the LIBs degradation process, which considers the four variability sources of the degradation process simultaneously. Then, the model parameters are initialized using maximum likelihood estimation (MLE) and dynamically estimated by an unscented particle filter (UPF) algorithm. Finally, through comparison with other models, the proposed method shows its effectiveness and superiority in describing the degradation process and RUL prediction of LIBs.

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Cite this article

[IEEE Style]

R. Wang and H. Feng, "Lithium‑ion batteries remaining useful life prediction using Wiener process and unscented particle filter," Journal of Power Electronics, vol. 20, no. 1, pp. 270-278, 2020. DOI: 10.1007/s43236-019-00016-3.

[ACM Style]

Ranran Wang and Hailin Feng. 2020. Lithium‑ion batteries remaining useful life prediction using Wiener process and unscented particle filter. Journal of Power Electronics, 20, 1, (2020), 270-278. DOI: 10.1007/s43236-019-00016-3.