Individual Cell Fault Detection for Parallel-Connected Battery Cells Based on the Statistical Model and Analysis

TL;DR

This paper presents a statistical model-based fault detection method for parallel-connected lithium-ion battery cells using high-frequency response analysis, effectively identifying faults with minimal sensors and balancing false alarms and missed detections.

Contribution

It introduces a novel fault diagnosis algorithm leveraging high-frequency response and statistical resistance analysis for parallel battery cells with limited sensing capabilities.

Findings

The proposed algorithm accurately detects faults with low false alarm rates.

Monte Carlo simulations validate robustness against parameter variations.

The method effectively balances detection accuracy and false alarms.

Abstract

Fault diagnosis is extremely important to the safe operation of Lithium-ion batteries. To avoid severe safety issues (e.g., thermal runaway), initial faults should be timely detected and resolved. In this paper, we consider parallel-connected battery cells with only one voltage and one current sensor. The lack of independent current sensors makes it difficult to detect individual cell degradation. To this end, based on the high-frequency response of the battery, a simplified fault detection-oriented model is derived and validated by a physics-informed battery model. The resistance of the battery string, which is significantly influenced by the faulty cell, is estimated and used as the health indicator. The statistical resistance distribution of battery strings is first analyzed considering the distribution of fresh and aged cells. A fault diagnosis algorithm is proposed and the thresholds (i.e., 2 standard deviation interval) are obtained through statistical analysis. Monte Carlo simulation results show that the proposed fault diagnosis algorithm can balance false alarms and missed detections well. In addition, it is verified that the proposed algorithm is robust to the uniform parameter changes of individual battery cells.