Degradation path prediction of lithium-ion batteries under dynamic operating sequences

Abstract

Reliable battery management requires the degradation of lithium-ion batteries (LIBs) under variable usage patterns to be accurately and continuously monitored and predicted. However, the chemically entangled internal states and the nonlinear accumulation of degradation mechanisms pose challenges to establishing these management processes. Here we present our comprehensive analysis of the degradation path for different operating sequences. The analysis is based on a dataset we constructed using measurements from 72 commercial battery cells operated according to 24 dynamic operating sequences and by employing a periodic diagnostic protocol to quantify the kinetic degradation at various states of charge. By incorporating the path-dependent characteristics of battery degradation into deep learning approaches, we developed a framework capable of predicting future health states from the state at a single time-point without historical information. Our predictive framework achieves test average percent errors of 0.76% and 0.81% for the degradation paths and capacity trajectories, respectively. The proposed battery management schemes offer high prediction reliability and accuracy for dynamic operation and are anticipated to be useful for extending the operational lifetime of LIBs.