An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network. (3rd May 2019)

• Record Type: Journal Article
• Authors: Li, Wenhua; Jiao, Zhipeng; Du, Le; Fan, Wenyi; Zhu, Yazun
• Is Part Of: International journal of hydrogen energy. Volume 44:Number 23(2019)
• Page Start: 12270
• Page End: 12276
• Publication Date: 2019-05-03
• Subjects: Lithium-ion battery -- Vibration stress -- Remaining useful life (RUL) -- Health indicator (HI) -- Long short-term memory (LSTM) -- Elman neural network; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2019.03.101
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: Remaining useful life (RUL) prognosis of lithium-ion battery can appraise the battery reliability to determine the advent of failure and mitigate risk. To acquire measurement data at similar working conditions as electrical vehicles (EVs), this paper mainly conducted the experiment about battery charging and discharging under vibration stress. Indirect health indicator (HI) was extracted from the time of equal discharge voltage from the upper to the lower, and the battery capacity proved to be estimated by the adopted indirect HI through grey relational analysis. Then, the RUL prognosis model based on Elman neural network was established. Finally, the feasibility of this RUL prognosis model based on Elman neural networks in an application in predicting RUL of battery under vibration stress was verified. Highlights: The experiments under vibration stress was conducted. Indirect HI was extracted from voltage features of battery under vibration stress. The indirect RUL prognosis based on Elman neural network was established. The feasibility of this prognosis model in an application was verified.