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Efficient Fault Diagnosis in Lithium-Ion Battery Packs: A Structural Approach with Moving Horizon Estimation

Amir Farakhor, Di Wu, Yebin Wang, Huazhen Fang

TL;DR

The paper tackles fault diagnosis in lithium-ion battery packs under limited sensing and embedded-processing constraints by exploiting structural properties such as cell uniformity and fault sparsity within a Moving Horizon Estimation (MHE) framework. It introduces a hierarchical MHE approach that first performs inter-module diagnosis on lumped module models and then conducts intra-module diagnosis within identified modules, significantly reducing computation. A mixed $L_{2,1}$ norm relaxation replaces the nonconvex $L_0$ penalty on fault increments, and uniformity constraints $|q_{ij}-q_{i'j'}|\le \Delta q$, $|T_{ij}-T_{i'j'}|\le \Delta T$ help distinguish faults from normal variation. Simulations on $3$P$2$S and $3$S$2$P configurations with ISC, ESC, and sensor faults demonstrate accurate fault localization and estimation while reducing sensor counts and enabling parallel computation, highlighting practical impact for battery management systems.

Abstract

Safe and reliable operation of lithium-ion battery packs depends on effective fault diagnosis. However, model-based approaches often encounter two major challenges: high computational complexity and extensive sensor requirements. To address these bottlenecks, this paper introduces a novel approach that harnesses the structural properties of battery packs, including cell uniformity and the sparsity of fault occurrences. We integrate this approach into a Moving Horizon Estimation (MHE) framework and estimate fault signals such as internal and external short circuits and faults in voltage and current sensors. To mitigate computational demands, we propose a hierarchical solution to the MHE problem. The proposed solution breaks up the pack-level MHE problem into smaller problems and solves them efficiently. Finally, we perform extensive simulations across various battery pack configurations and fault types to demonstrate the effectiveness of the proposed approach. The results highlight that the proposed approach simultaneously reduces the computational demands and sensor requirements of fault diagnosis.

Efficient Fault Diagnosis in Lithium-Ion Battery Packs: A Structural Approach with Moving Horizon Estimation

TL;DR

The paper tackles fault diagnosis in lithium-ion battery packs under limited sensing and embedded-processing constraints by exploiting structural properties such as cell uniformity and fault sparsity within a Moving Horizon Estimation (MHE) framework. It introduces a hierarchical MHE approach that first performs inter-module diagnosis on lumped module models and then conducts intra-module diagnosis within identified modules, significantly reducing computation. A mixed norm relaxation replaces the nonconvex penalty on fault increments, and uniformity constraints , help distinguish faults from normal variation. Simulations on PS and SP configurations with ISC, ESC, and sensor faults demonstrate accurate fault localization and estimation while reducing sensor counts and enabling parallel computation, highlighting practical impact for battery management systems.

Abstract

Safe and reliable operation of lithium-ion battery packs depends on effective fault diagnosis. However, model-based approaches often encounter two major challenges: high computational complexity and extensive sensor requirements. To address these bottlenecks, this paper introduces a novel approach that harnesses the structural properties of battery packs, including cell uniformity and the sparsity of fault occurrences. We integrate this approach into a Moving Horizon Estimation (MHE) framework and estimate fault signals such as internal and external short circuits and faults in voltage and current sensors. To mitigate computational demands, we propose a hierarchical solution to the MHE problem. The proposed solution breaks up the pack-level MHE problem into smaller problems and solves them efficiently. Finally, we perform extensive simulations across various battery pack configurations and fault types to demonstrate the effectiveness of the proposed approach. The results highlight that the proposed approach simultaneously reduces the computational demands and sensor requirements of fault diagnosis.

Paper Structure

This paper contains 15 sections, 22 equations, 5 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Literature Review
  3. Statement of Contributions
  4. PROBLEM FORMULATION
  5. Battery Pack Structure
  6. Electro-thermal Modeling
  7. ISC fault modeling
  8. ESC fault modeling
  9. Sensor fault modeling
  10. MHE Formulation
  11. THE PROPOSED HIERARCHICAL APPROACH
  12. SIMULATION RESULTS
  13. $3$P$2$S battery pack
  14. $3$S$2$P battery pack
  15. CONCLUSIONS

Figures (5)

  • Figure 1: The considered battery pack configurations. (a) Series-parallel ($n$S$m$P). (b) Parallel-series ($m$P$n$S).
  • Figure 2: The cell-level electro-thermal model, adapted from ICPS-FA-2024. (a) The electrical model of the cell $ij$. (b) The thermal model of the cell $ij$.
  • Figure 3: The lumped module-level electro-thermal model. (a) Lumped model for module $P_i$. (b) Lumped model for module $S_i$.
  • Figure 4: Simulation results of the inter-module diagnosis for the $3$P$2$S configuration. (a) The modules' SoC under ESC. (b) The modules' temperature under ESC. (c) The estimated faults under ESC. (d) The modules' SoC under voltage sensor fault. (e) The modules' temperature under voltage sensor fault. (f) The estimated faults under voltage sensor fault.
  • Figure 5: Simulation results of the intra-module diagnosis for the $3$S$2$P configuration. (a) The cell's SoC under ISC. (b) The cells' temperature under ISC. (c) The estimated faults under ISC. (d) The cells' SoC under current sensor fault. (e) The cells' temperature under current sensor fault. (f) The estimated faults under current sensor fault.