In order to predict the remaining service life of lithium batteries more accurately,a prediction model based on cuckoo algorithm(CS)and deep belief network(DBN)is proposed in this paper.Firstly,16 health indicators(HI)that affect the RUL of lithium batteries are introduced,and nine HIs that are more important for the RUL through random forest(RF)are selected.Then the CS is used to optimize the parameters of the hidden layer in the deep belief network model,and the optimal deep belief network prediction model is established through optimization.Finally,the battery data collected by the University of Maryland(CALCE)is used for the experiment.The results show that the goodness of fit of the CS-DBN model proposed in this paper is up to 98%,and compared with the prediction results of other models,it has smaller error,which verifies the effectiveness of the proposed method.
维普
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