Unifying Model-Based Prognosis With Learning-Based Time-Series Prediction Methods: Application to Li-Ion Battery

• Published in: IEEE systems journal Vol. 15; no. 4; pp. 5245 - 5254
• Main Authors: Aggab, Toufik, Avila, Manuel, Vrignat, Pascal, Kratz, Frederic
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive neuro-fuzzy inference system (ANFIS); Adaptive systems; Artificial neural networks; Automatic Control Engineering; Battery charge measurement; Computer Science; Degradation; Fuzzy logic; Integrated circuit modeling; Learning; Li-ion battery; Lithium-ion batteries; Mathematical models; Maximum likelihood estimation; Maximum likelihood method; Measuring instruments; observer; Parameter identification; Predictive models; Prognosis; Prognostics and health management; Rechargeable batteries; remaining useful life (RUL); Support vector machines; support vector regression (SVR)
• ISSN: 1932-8184; 1937-9234; 1937-9234; 1932-8184
• DOI: 10.1109/JSYST.2021.3080125

In this article, we propose a practical prognosis approach. It estimates online the remaining duration of the system before the system performance requirements are no longer met to fulfill a specific mission. The systems targeted by this approach have the particularity of a lack of measuring instruments capable of providing indications of potential degradation. The approach is based on the estimated model of system behavior. The approach is in two phases. In the first phase, an observer is used to estimate unmeasured states and relevant parameters that can characterize system performance. In the second phase, in the beginning, the historical parameters obtained are used to identify models describing their dynamics using learning time-series prediction methods. In this article, the support vector regression and the adaptive neuro-fuzzy inference system are illustrated. Then, the model is simulated to estimate the future performance of the system and compare it to the desired performance. A comparison of the results obtained using learning time-series prediction methods with those obtained using the maximum likelihood method is carried out. The results show that the proposed approach makes it possible to estimate the remaining lifetime with particularly good quality. To illustrate the proposed failure prognosis approach, a Li-ion battery was used.