Improved sparrow algorithm to optimize kernel extreme learning machines for lithium battery status of health estimation on incremental capacity curve

Abstract The advancement of new energy vehicles and energy storage devices has increasingly demanded a more accurate state of health estimation for lithium batteries. Data-driven models are gradually widely used but still have the problems of long iteration time, low accuracy, and unreliable selection of feature factors. Thus, to promote the application of data-driven models in estimating the health state of lithium batteries, this paper proposes a multi-method improved sparrow algorithm optimized kernel extreme learning machine framework for lithium battery health state estimation. To improve the speed and accuracy issues of the data-driven model, this paper selects the kernel extreme learning machine as the network model and the improved sparrow algorithm using chaotic mapping and self-designed exponential weight factors as the optimization algorithm for automatic parameter search to achieve high accuracy and speed. Secondly, to strengthen the reliability of data feature factor selection, multiple feature factors are selected in the incremental capacity curve to improve the stability of feature factors. Finally, the rapidity and reliability of the proposed model on the SOH prediction of lithium batteries are verified by experiments, which provides an effective way to manage lithium batteries healthily.