Battery Inconsistency Detection Using Improved K-means Algorithm

Lithium-ion batteries are widely used in energy storage systems due to their high energy density and long cycle life. However, inconsistencies among individual battery cells in large-scale battery packs can significantly impact performance, safety, and lifespan. Traditional methods for detecting these inconsistencies often rely on manual inspection or threshold based approaches, which are time-consuming and prone to errors. This paper proposes an improved K-means clustering algorithm to identify inconsistencies in battery packs by analyzing voltage data. The improved algorithm optimizes the selection of initial cluster centers and effectively excludes outliers, enhancing clustering accuracy and efficiency. Voltage-based features, such as mean and variance, are extracted from the voltage curves of individual battery cells to characterize their behavior. The proposed method is validated using real-world data from a power station, which has better clustering performance, reducing SSE by 8.9%, DB by 2.5%, and increasing CH by 16.5% compared with the traditional algorithm. This approach provides a robust solution for detecting inconsistencies in battery packs and improving maintenance efficiency and system reliability.