An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network

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. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.