A hierarchical enhanced data-driven battery pack capacity estimation framework for real-world operating conditions with fewer labeled data

Abstract

Battery pack capacity estimation under real-world operating conditions is important for battery performance optimization and health management, contributing to the reliability and longevity of battery-powered systems. However, complex operating conditions, coupling cell-to-cell inconsistency, and limited labeled data pose great challenges to accurate and robust battery pack capacity estimation. To address these issues, this paper proposes a hierarchical data-driven framework aimed at enhancing the training of machine learning models with fewer labeled data. Unlike traditional data-driven methods that lack interpretability, the hierarchical data-driven framework unveils the “mechanism” of the black box inside the data-driven framework by splitting the final estimation target into cell-level and pack-level intermediate targets. A generalized feature matrix is devised without requiring all cell voltages, significantly reducing the computational cost and memory resources. The generated intermediate target labels and the corresponding features are hierarchically employed to enhance the training of two machine learning models, effectively alleviating the difficulty of learning the relationship from all features due to fewer labeled data and addressing the dilemma of requiring extensive labeled data for accurate estimation. Using only 10% of degradation data, the proposed framework outperforms the state-of-the-art battery pack capacity estimation methods, achieving mean absolute percentage errors of 0.608%, 0.601%, and 1.128% for three battery packs whose degradation load profiles represent real-world operating conditions. Its high accuracy, adaptability, and robustness indicate the potential in different application scenarios, which is promising for reducing laborious and expensive aging experiments at the pack level and facilitating the development of battery technology.