Estimation of Cell-to-Cell Variation and State of Health for Battery Modules with Parallel-Connected Cells

TL;DR

This paper introduces a unified framework for accurately estimating cell-to-cell variation and state of health in battery modules with parallel-connected cells using only module-level signals, enhancing flexibility and efficiency.

Contribution

It presents a novel, versatile framework that decouples CtCV and SoH estimation tasks, enabling accurate, low-complexity, and onboard-implementable assessments from module-level data.

Findings

Accurately estimates CtCV and SoH using module-level signals

Decouples CtCV and SoH estimation for greater flexibility

Effective across different C-rates and suitable for onboard use

Abstract

Estimating cell-to-cell variation (CtCV) and state of health (SoH) for battery modules composed of parallel-connected cells is challenging when only module-level signals are measurable and individual cell behaviors remain unobserved. Although progress has been made in SoH estimation, CtCV estimation remains unresolved in the literature. This paper proposes a unified framework that accurately estimates both CtCV and SoH for modules using only module-level information extracted from incremental capacity analysis (ICA) and differential voltage analysis (DVA). With the proposed framework, CtCV and SoH estimations can be decoupled into two separate tasks, allowing each to be solved with dedicated algorithms without mutual interference and providing greater design flexibility. The framework also exhibits strong versatility in accommodating different CtCV metrics, highlighting its general-purpose nature. Experimental validation on modules with three parallel-connected cells demonstrates that the proposed framework can systematically select optimal module-level features for CtCV and SoH estimations, deliver accurate CtCV and SoH estimates with high confidence and low computational complexity, remain effective across different C-rates, and be suitable for onboard implementation.