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

Qinan Zhou, Jing Sun

TL;DR

This paper tackles estimating cell-to-cell variation (CtCV) and state of health (SoH) at the module level for parallel-connected cells using only module-level measurements. It introduces a unified ICA/DVA-based framework that decouples CtCV and module-level SoH (M-SoH) estimation and employs information-theoretic feature selection with relevance vector regression to produce sparse, probabilistic estimators. The approach is validated on an experimental dataset of three-parallel NCA modules across multiple charging C-rates, demonstrating accurate CtCV and M-SoH estimation with low onboard computational burden and robustness to different CtCV metrics. The work enables practical, onboard degradation monitoring by separating feature selection offboard from lightweight, in-situ estimation and supports extension to different CtCV definitions and chemistries.

Abstract

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.

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

TL;DR

This paper tackles estimating cell-to-cell variation (CtCV) and state of health (SoH) at the module level for parallel-connected cells using only module-level measurements. It introduces a unified ICA/DVA-based framework that decouples CtCV and module-level SoH (M-SoH) estimation and employs information-theoretic feature selection with relevance vector regression to produce sparse, probabilistic estimators. The approach is validated on an experimental dataset of three-parallel NCA modules across multiple charging C-rates, demonstrating accurate CtCV and M-SoH estimation with low onboard computational burden and robustness to different CtCV metrics. The work enables practical, onboard degradation monitoring by separating feature selection offboard from lightweight, in-situ estimation and supports extension to different CtCV definitions and chemistries.

Abstract

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.
Paper Structure (18 sections, 19 equations, 12 figures, 5 tables)

This paper contains 18 sections, 19 equations, 12 figures, 5 tables.

Table of Contents

  1. INTRODUCTION
  2. FOUNDATIONAL CONCEPTS
  3. Quantification of Cell-to-Cell Variation and Module-Level SoH
  4. Definitions of Module-Level IC/DV Curves and Features
  5. Distortions of Module-Level IC/DV Curves due to Cell-to-Cell Variations
  6. THE PROPOSED METHOD FOR ESTIMATION OF CELL-TO-CELL VARIATION AND MODULE-LEVEL STATE OF HEALTH
  7. Key Information-Theoretic Concepts for Feature Selection
  8. Information Theory-Based Feature Selection
  9. Relevance Vector Regression (RVR)
  10. BATTERY MODULE DATASET
  11. Dataset Overview
  12. Experiment Protocols
  13. Module-Level IC/DV Curves and Features
  14. PERFORMANCE OF THE PROPOSED FRAMEWORK
  15. Feature Ranking and Selection Results
  16. ...and 3 more sections

Figures (12)

  • Figure 1: Overview of Proposed Framework for Estimation of Cell-to-Cell Variation and Module-Level State of Health
  • Figure 2: Definitions of Incremental Capacity (IC) and Differential Voltage (DV) Features
  • Figure 3: IC Curve Distortions Caused by CtCVs. Note that, under the same C-rate and similar M-SoH, no discernible trend between CtCVs and the resulting IC curve distortions can be observed from the plots.
  • Figure 4: Distribution of Cell-to-Cell Variations and Module-Level SoHs inside Dataset
  • Figure 5: Example M-IC/DV Curves and Related Features for the Dataset
  • ...and 7 more figures

Theorems & Definitions (3)

  • Remark 1
  • Remark 2
  • Remark 3