Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model

Abstract

Lithium-ion batteries (LIBs) are widely used in the assembly of battery packs for electric vehicles and energy storage grids due to their high power density, low self-discharge rate and reasonable costs. Accurate estimation of state of health (SOH) and remaining useful life (RUL) are crucial challenges in developing battery management systems (BMS). In this paper, differential thermal voltammetry (DTV) signal analysis methods and recursive neural networks data-driven methods are combined to approach battery degradation tracking. Firstly, with the Savitzky-Golay (SG) method and Pearson correlation analysis, the DTV curve is smoothed, and three useful feature variables are extracted from different dimensions, bridging signaling characteristics and phase transition characteristics. Then four recursive neural networks are constructed and compared based on NASA databases. The Bayesian optimization method is applied to improve hyperparameter values and the Monte Carlo (MC) simulation is used to quantify uncertainties. The proposed data-driven method can predict the RUL and estimate the SOH of battery accurately. The root mean square error (RMSE) for prediction results could reach below 1% and the capacity rebound phenomenon could be captured as well. The proposed integrated degradation model can contribute to the real-time prediction and optimization of battery health conditions based on cloud computing platform, promoting the continuous development of cloud battery management systems in framework of Cyber Hierarchy and Interactional Network (CHAIN).