An asymmetric encoder–decoder model for Zn-ion battery lifetime prediction

Abstract

As the battery cycles between charging and discharging, the working conditions or improper operations such as overcharge and over discharge will aggravate the negative reaction inside the battery, generate irreversible chemical substances, and reduce the number of active substances involved in the electrochemical reaction, resulting in a decrease in battery capacity. Batteries that lose 20% of their capacity can be considered to have failed. A failed battery shows that the battery capacity and power decay faster, and the electrical characteristics, stability, and safety of the battery will drop significantly. As a means of improving the machine learning model’s accuracy and generalization for RUL prediction of zinc-ion batteries, this paper mainly discusses about the design of the encoder–decoder model structure and the application of optimization methods. Then, the method of neural network hyperparameter optimization is studied. Finally, the validity of the research work done in this paper is verified by a series of comparative experiments.