Abstract:

Accurate prediction of lithium-ion battery capacity degradation trajectories enhances the efficiency of battery materials research. Aiming to resolve the challenges associated with the Transformer network in the prediction of lithium-ion battery capacity degradation trajectory, this study adopts the sliding window strategy and constructs a lithium-ion battery capacity degradation trajectory prediction method based on Informer, a time series forecasting model. First, the sliding window is used to divide and re-splice the dataset; this facilitates the neural network to exploit the correlation within the dataset; subsequently, the global timestamp applicable to lithium-ion battery data is designed according to the periodic time series capturing ability of Informer; finally, the model output is realized through the multistep rolling prediction method by using the first 10% of the battery capacity data to alleviate the error accumulation in the prediction, subsequently obtaining the complete prediction trajectory. The accuracy and training efficiency of the established model are verified using the lithium-ion battery dataset provided by the University of Maryland. Different error evaluation and time overhead metrics are selected in the training process; additionally, the generalizability of the model is verified using the lithium-ion battery dataset provided by NASA. Comparing the prediction results of the model in this study with that of the multilayer perceptron neural network, recurrent neural network, and Transformer network model, the following is observed: the degraded trajectories obtained in this study are best fitted to the real trajectories; the training time overhead is small; and, the average absolute and root mean square errors of the prediction results are controlled at 2.57% and 3.5%, thus verifying the validity of the proposed prediction method.

Key words: lithium-ion battery, capacity degradation trajectory, long-term time series forcasting, sliding window strategy, Informer network