Abstract: Accurately predicting the remaining useful life (RUL) of lithium batteries plays an important role in understanding their health and managing spare parts resources. Most of the existing lithium battery remaining life prediction methods are limited to the prediction results based on the number of cycles. It is essentially a method oriented to a single time scale, ignoring the practical problem that the health state of lithium batteries is affected by the dual time scales of cycle times and working time. In view of this, this article proposes a lithium battery RUL prediction model based on Transformer under the dual time scales. This method selects the capacity as a key index to characterize its performance degradation. The battery capacity data is processed to obtain training sets and test sets through Kalman filtering and sliding time window. The life information contained in the dual time scales, and fully consider the interrelationship between the life information of different time scale, further, establish a mapping relationship between the capacity and the dual time scales, so as to realize the accurate prediction of the RUL of the lithium battery at the dual time scale. Finally, the effectiveness and potential application value of the proposed method are verified by lithium battery examples.

Key words: deep learning, dual time scale, transformer network, RUL prediction