Kalman filter optimize Transformer method for state of health prediction on lithium-ion battery
Reviewing methods for predicting the lithium-ion battery state of health(SOH),we propose a battery SOH prediction method,which uses Transformer network based on Kalman Filter.Firstly,the battery data is preprocessed by adding Gaussian noise and auto-encoder reconstrution to remove the original noise from the data and strengthen the data features.Secondly,the battery data is extracted using the proposed(Kalman Filter-Transformer,KF-Transformer)model to extract the features of battery SOH changes,so that the Transformer network can better capture the nonlinear changes of battery SOH.Finally,the mapping from the features to the prediction of battery SOH is accomplished through the linear layer to obtain the prediction results of lithium-ion battery SOH.In this paper,three datasets(NASA,CALCE CS2 and CALCE CX2)are used for training and testing,and the two datasets use different temperatures and different batteries.The R2 value of the predicted result in this paper is 0.987 and the mean absolute percentage error(MAPE)value is 1.8%,which is better than Multilayer Perceptron(MLP),Recurrent Neural Network(RNN),Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU).These results demonstrate the accuracy and stability of the proposed method for SOH prediction.
lithium-ion batterybattery state of healthauto-encodertransformer networkKalman Filter
万方数据
维普
