Estimation of Cell-to-Cell Variation and State of Health for Battery Modules with Parallel-Connected Cells
This work addresses a critical problem in battery management systems for electric vehicles or energy storage, enabling better monitoring and safety, though it is incremental as it builds on existing SoH estimation methods.
The paper tackled the challenge of estimating cell-to-cell variation and state of health for battery modules with parallel-connected cells using only module-level signals, achieving accurate estimates with high confidence and low computational complexity in experimental validation.
Estimating cell-to-cell variation (CtCV) and state of health (SoH) for battery modules with 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.