Development of a neural network model for soh of LiFePO4 batteries under different aging conditions
Journal
IOP Conference Series: Materials Science and Engineering
Journal Volume
486
Journal Volume
486
Journal Issue
1
Journal Issue
1
Start Page
012083
Date Issued
2019-03-28
Author(s)
Abstract
LiFePO4 batteries have a variety of superior properties, such as higher power densities, higher capacities, longer lifetimes and better safety. For these reasons, LiFePO4 batteries are used extensively in electric vehicles and energy storage devices. However, there is an issue with the battery capacity in that it begins to rapidly fade after a certain number of charge and discharge cycles under compound influence of temperature and discharging current, which may lead to safety concerns. Therefore, it is very important to investigate the characteristics (voltage, current and capacity) of LiFePO4 batteries in relationship to the number of cycles and environmental temperature. In this paper, for the sake of high efficiency and safe operation of LiFePO4 batteries, we propose a Back Propagation neural network (BPNN) model which estimates the state of health (SoH) of the battery, so that the accumulated error of the capacities under different operating environments can be corrected. The accuracy of the model was verified in an electric vehicle with an average error of only 1.56%. The results show that the proposed model is satisfactory.
Event(s)
2019 4th Asia Conference on Power and Electrical Engineering, ACPEE 2019, Hangzhou, 28 March 2019 through 31 March 2019. Code 149895
SDGs
Type
conference paper