State-of-health estimation for lithium-ion batteries using relaxation voltage under dynamic conditions

Abstract

The data-driven approach accurately estimates the state-of-health of lithium-ion batteries using online data, aiding consumers in operational and maintenance decisions. However, the stochastic charging and discharging behavior in realistic scenarios leads to continuous transient processes that render conventional features undetectable or exacerbate fluctuations. Here, we use domain knowledge and equivalent circuit modeling to investigate the extraction of physical features of aging through a relatively stable relaxation process under dynamic conditions. Our study uses 16-cell data from the National Aeronautics and Space Administration randomized dataset and compares four basic data-driven models for validation. The results show that incorporating a limited set of previous discharge step information significantly enhances model robustness and accuracy. The best-performing model, auto-relevance determination gaussian process regression, achieves a low root mean square error of 1.94 %. Physically interpretable features do not rely on historical data, require a smaller sample size, and exhibit greater generalizability across different current scenarios. This method does not depend on a specific charging method, making it practical and adaptable. Therefore, the data-driven approach utilizing relaxation voltages and correlation features offers a viable solution for accurately estimating the health state of lithium-ion batteries under dynamic conditions.