A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries. (June 2018)

• Record Type: Journal Article
• Authors: Zhao, Qi; Qin, Xiaoli; Zhao, Hongbo; Feng, Wenquan
• Is Part Of: Microelectronics and reliability. Volume 85(2018)
• Page Start: 99
• Page End: 108
• Publication Date: 2018-06
• Subjects: Lithium-ion battery -- State of health -- Remaining useful life -- Prognostic -- Feature vector selection -- Support vector regression; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2018.04.007
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: Traditional approaches to lithium-ion battery health management mostly focus on the state of charge (SOC) estimation issues, whereas the state of health (SOH) estimation is also critical to lithium-ion batteries for safe operation. For online battery prognostics, it is critical to make timely and accurate response to SOH. The loss of rated capacity of a battery is usually used to determine the battery SOH, whereas the measurement of the capacity of an operating battery is quite challenging. Normally, the rated capacity fading largely relies on laboratory measurements and offline analysis. In this paper, two real-time measurable health indicators (HI) - one is the time interval of an equal charging voltage difference (TIECVD), and the other is the time interval of an equal discharging voltage difference (TIEDVD) - are extracted. A novel method which combines feature vector selection (FVS) with SVR is utilized to model the relationship between these two HIs and capacity, then the online capacity can be evaluated, more accurate prognostics of SOH and remaining useful life (RUL) can be made. Besides, compared to standard SVR, the proposed method takes FVS to cut down the training data size, which improves the efficiency of model training and prediction. In the end, two datasets demonstrated this approach performs both well in accuracy and efficiency. Highlights: Two new health indicators were identified to assess battery capacity during operation. A SVR model was developed to online estimate battery SOH and to predict battery RUL. The proposed method outperformed other state-of-the-art techniques.