Kuo, T.J.T.J.KuoKUNG-YEN LEEMAO-HSIUNG CHIANG2020-01-172020-01-172019-03-28https://scholars.lib.ntu.edu.tw/handle/123456789/451532https://www.scopus.com/record/display.uri?eid=2-s2.0-85070327906&origin=resultslistLiFePO4 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.[SDGs]SDG7conference paper10.1088/1757-899X/486/1/0120832-s2.0-85070327906https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070327906&doi=10.1088%2f1757-899X%2f486%2f1%2f012083&partnerID=40&md5=68a0f28abc9aeb6e2561d976efba920c