Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 69, Issue 11, Pages 11665-11674Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2021.3127035
Keywords
Batteries; Degradation; Predictive models; Stress; Adaptation models; Prognostics and health management; Mathematical models; Failure prognostics; lithium batteries; remaining useful life (RUL)
Categories
Funding
- National Natural Science Foundation of China [51975355]
Ask authors/readers for more resources
In this article, an accelerated stress factors-based nonlinear Wiener process model is proposed to improve battery prognostics under different working conditions. The method enables online individual battery prognostics by updating model parameters and designing accelerated stress-relevant drift functions. RUL predictions are conducted at different discharge rates and temperatures to demonstrate the accuracy and robustness of the proposed method.
Accurate remaining useful life (RUL) of batteries plays an imperative role in ensuring safe operations and avoiding catastrophic accidents. However, in practice, complicated working conditions bring challenges to accurate battery prognostics. In this article, an accelerated stress factors-based nonlinear Wiener process model is proposed to enrich inadequate battery prognostic works at various operating conditions. To realize online individual battery prognostics, once a new measurement is available, the parameters of a state-space model constructed by the proposed model are posteriorly updated. Then, based on the Peukert law and the Arrhenius equation, two specific accelerated stress-relevant drift functions and their associated degradation models at different discharge rates and temperatures are respectively designed. Subsequently, RUL predictions are conducted using the proposed method. RUL predictions at different discharge rates and different temperatures demonstrate the accuracy and robustness of the proposed prognostic models. According to some general prognostic metrics, the proposed method is proved to be superior to four existing RUL prediction approaches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available