Transfer learning based remaining useful life prediction of lithium-ion battery considering capacity regeneration phenomenon

Abstract

Lithium-ion battery (LIB) has been widely used in various energy storage systems, and the accurate remaining useful life (RUL) prediction for LIB is critical to ensure the normal operation of system. However, the capacity regeneration (CR) phenomenon caused by the non-working state of LIB will seriously affect the capacity degradation trajectory of LIB, thus leading to an inaccurate RUL prediction result. To solve this problem, this paper proposes a non-parametric CR detection algorithm to detect the CR phenomenon online and quantitatively correct the prediction errors caused by different CR phenomena. In addition, a data reconstruction algorithm and a parameter update scheme are proposed to solve the problem of unit-to-unit difference among LIBs. Finally, the long and short term memory neural network-based transfer learning model is applied to predict the RUL of target LIB. The effectiveness of the proposed model is verified by the real LIB dataset of NASA. Compared with some existing RUL prediction models considering CR phenomenon, the proposed model exhibits higher accuracy and better universality.