Abstract
To advance the lithium-ion battery (LIB) technology more quickly, its lifetime should be predicted accurately. The precise prediction of LIB lifetime can help in producing new batteries, better use and operation of batteries. It is worthy for noting here that the LIB is a heavy nonlinear system suffering from battery fading, degradation, uncertainty and variability of operating conditions. Therefore, this article presents a hybrid extended Kalman filter with Newton Raphson method for lifetime prediction of lithium-ion batteries. The data analyses are based on commercial lithium iron phosphate/graphite cells cycled at fast charge. The cycle life expectancy is in the range of 150 to 2,300 cycles. The discharge voltage characteristics are used to present capacity degradation. The battery datasets are used with a hybrid Extended Kalman Filter (EKF) and Newton Raphson method to match the predicted cycle life and the actual cycle life of the battery. The effectiveness of the proposed method is verified by making a fair comparison with the linear regression-based machine-learning method. In the testing of 100 lifecycles, the test error and root mean square error record 3.26% and 10.93 compared with the linear regression that achieves 9.1% and 211, respectively. With the proposed hybrid approach, the lifetime prediction of LIBs can be further enhanced.