Health status prediction of lithium ion batteries based on zero-shot learning

In this paper, we propose an adaptive transfer learning method for predicting battery health status. We construct a multi-task transfer learning framework to address the problem of transferring battery health status predictions across different usage scenarios. To overcome the challenge of determining the optimal weight for different task loss functions, we introduce an adaptive optimization method that automatically assigns the optimal weight to the multi-task objective function. Our experiments on lithium-ion battery data from Huazhong University of Science and Technology show that our proposed method outperforms other typical prediction methods.