Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method

•Test optimization for Li-ion battery formulations reduces the cost of testing.•Deep learning is used to predict battery lifespan in high-temperature testing.•The transfer learning method is based on a stacked denoising autoencoder.•A modified Arrhenius model is used for standard-temperature lifespan estimates.•Prediction accuracy is verified and a time savings of nearly 60% is achieved.