Internet of Things Oriented Battery SOH Predicting Based on WOA_1DCNN_Bi-LSTM Model

Abstract

Using battery power is an effective way to improve the efficiency of IOT system construction. However, the existing battery State of Health (SOH) prediction methods can't achieve the timeliness of prediction under accuracy ensuring. It brings difficulties to the maintenance of IOT devices. This paper focuses the SOH prediction of batteries in IOT devices. This paper proposes an prediction approach based on the combination of one dimension convolution neural network (1DCNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) - the 1DCNN_Bi-LSTM model. The parameters in 1DCNN_Bi-LSTM model are optimized with Whale Optimization Algorithm (WOA). The high-dimensional features are derived by 1DCNN. Then, these features are transferred to Bi-LSTM to explore the effective memory information. Finally, the prediction results of battery SOH are output from the fully connected layer. In the training process, parameters, such as the number of nodes of each layer and the number of epochs, are optimized with WOA. The WOA_1DCNN_Bi-LSTM model is tested with NASA battery circulation dataset, and the results show that the model has accurate and stable prediction effect on battery SOH. The mean absolute error (MAE) and mean square error (MSE) of the model are 2.1% and 0.624% respectively.