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Estimating the state of health(SOH) of lithium battery is one of the most important key techniques of lithium electric vehicle battery management system. The traditional error back propagation(BP) neural network is easy to bring the weight fall into local optimal solutions, which can lead to inaccurate prediction results. Combined with the cuckoo search algorithm(CS) which has global optimization ability. A method based on cuckoo search algorithm optimized BP neural network model for predicting the SOH of lithium ion battery is proposed, the core of the method is optimizing the BP neural network's initial weights and thresholds. This method can reduce the dependence of the algorithm on the initial value. At the same time, in order to verify the generalization performance of the algorithm, use the NASA open source lithium battery data set No. 6 battery and No. 7 battery for simulation experiments, and the CS-BP algorithm is simulated to predict the root mean square error (RMSE) of SOH. They are 0.2658 and 0.2620, and the mean absolute percentage error (MAPE) is 0.3319% and 0.2605%, respectively. Compared with BP algorithm, particle swarm optimization BP neural network (PSO-BP), genetic algorithm optimized BP neural network (GA-BP), cuckoo search algorithm optimized BP neural network(CS-BP) has smaller prediction error.