Since lithium-ion batteries have been widely applied in energy storage systems and electric vehicles,the accurate estimation of their state-of-health(SOH) is a necessary condition for ensuring the reliable and safe operation of the system. SOH is analyzed from the perspective of capacity,with seven health indicators which are extracted from the constant current-constant voltage charging voltage and temperature curves as input. Based on the data-driven method,a sparrow search algorithm-back propagation neural network(SSA-BPNN) SOH estimation method for lithium-ion batteries is proposed,and data enhancement is applied to further improve the model's robustness. Finally,this method is verified on the NASA Randomized Battery Usage Dataset. Compared with the traditional BP neural network without data enhancement,the SOH estimation accuracy of the proposed method is significantly improved. The maximum absolute error and root mean square error of SOH estimation on the test set are less than 3％and 1.32％,respectively. Experimental results show that this method has advantages of small error,fast convergence,global search capability and adaptation to different characteristics of battery aging.
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