Remaining useful life prediction and cycle life test optimization for multiple-formula battery: A method based on multi-source transfer learning

Abstract

Achieving highly accurate predictions based on less data with multiple formulations has become a significant challenge. Unlike the traditional prediction model that ignores the similarities and differences between multi-formula batteries, the proposed multi-source transfer RUL prediction is an effective way to jointly transfer several samples and merge degradation information from multi-source domains, thereby taking full advantage of the information. Aiming at the problem of negative transfer and insufficient transfer when multi-source domains are available, dynamic time warping is employed to adaptively select transferable samples, ignoring the local individual differences. The least absolute shrinkage and selection operator are utilized to generate an optimal subset and allocate personalized weights. Then, an optimization strategy for the cycle life tests is proposed by automatically giving a reasonable threshold of stopping the test. The proposed method is verified based on an actual dataset with multiple formulations collected under different test conditions from a battery manufacturer. Experimental results show that the proposed method has high accuracy based on even a small amount of data. The systematic optimization strategy can significantly reduce the time and cost of the test.