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Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion Battery Pack

Sanchita Ghosh, Soumyoraj Mallick, Tanushree Roy

TL;DR

The paper tackles internal short circuit (ISC) detection in Li-ion battery packs by introducing a model-free Koopman mode-based framework that uses only module voltage measurements. It employs two parallel Koopman mode generation schemes via the Arnoldi method to build KM distributions and uses kernel density estimation and Kullback-Leibler divergence to identify outliers and locate the shorted module, all within an online learning setup over learning window $\mathcal{L}$ and prediction window $\mathcal{P}$. The approach demonstrates ISC detection within about 30 seconds under resting and charging conditions with a short-circuit resistance of $15\,\Omega$, without requiring pre-trained battery models or historical data, and shows robustness to measurement noise and parameter uncertainties. The method holds promise for fast, scalable ISC detection in diverse pack configurations and chemistries, facilitating safer operation and longer pack life.

Abstract

Monitoring of internal short circuit (ISC) in Lithium-ion battery packs is imperative to safe operations, optimal performance, and extension of pack life. Since ISC in one of the modules inside a battery pack can eventually lead to thermal runaway, it is crucial to detect its early onset. However, the inaccuracy and aging variability of battery models and the unavailability of adequate ISC datasets pose several challenges for both model-based and data-driven approaches. Thus, in this paper, we proposed a model-free Koopman Mode-based module-level ISC detection algorithm for battery packs. The algorithm adopts two parallel Koopman mode generation schemes with the Arnoldi algorithm to capture the Kullback-Leibler divergence-based distributional deviations in Koopman mode statistics in the presence of ISC. Our proposed algorithm utilizes module-level voltage measurements to accurately identify the shorted battery module of the pack without using specific battery models or pre-training with historical battery data. Furthermore, we presented two case studies on shorted battery module detection under both resting and charging conditions. The simulation results illustrated the sensitivity of the proposed algorithm toward ISC and the robustness against measurement noise.

Koopman Mode-Based Detection of Internal Short Circuits in Lithium-ion Battery Pack

TL;DR

The paper tackles internal short circuit (ISC) detection in Li-ion battery packs by introducing a model-free Koopman mode-based framework that uses only module voltage measurements. It employs two parallel Koopman mode generation schemes via the Arnoldi method to build KM distributions and uses kernel density estimation and Kullback-Leibler divergence to identify outliers and locate the shorted module, all within an online learning setup over learning window and prediction window . The approach demonstrates ISC detection within about 30 seconds under resting and charging conditions with a short-circuit resistance of , without requiring pre-trained battery models or historical data, and shows robustness to measurement noise and parameter uncertainties. The method holds promise for fast, scalable ISC detection in diverse pack configurations and chemistries, facilitating safer operation and longer pack life.

Abstract

Monitoring of internal short circuit (ISC) in Lithium-ion battery packs is imperative to safe operations, optimal performance, and extension of pack life. Since ISC in one of the modules inside a battery pack can eventually lead to thermal runaway, it is crucial to detect its early onset. However, the inaccuracy and aging variability of battery models and the unavailability of adequate ISC datasets pose several challenges for both model-based and data-driven approaches. Thus, in this paper, we proposed a model-free Koopman Mode-based module-level ISC detection algorithm for battery packs. The algorithm adopts two parallel Koopman mode generation schemes with the Arnoldi algorithm to capture the Kullback-Leibler divergence-based distributional deviations in Koopman mode statistics in the presence of ISC. Our proposed algorithm utilizes module-level voltage measurements to accurately identify the shorted battery module of the pack without using specific battery models or pre-training with historical battery data. Furthermore, we presented two case studies on shorted battery module detection under both resting and charging conditions. The simulation results illustrated the sensitivity of the proposed algorithm toward ISC and the robustness against measurement noise.

Paper Structure

This paper contains 9 sections, 14 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Koopman operator theory
  4. Arnoldi-based Koopman mode generation
  5. ISC Detection Scheme
  6. Simulation Results
  7. ISC detection under resting condition
  8. ISC detection under charging condition
  9. Conclusions

Figures (4)

  • Figure 1: Block diagram showing the overview of the proposed ISC detection scheme for Lithium-ion battery packs.
  • Figure 2: Flow chart showing the detailed steps for ISC flag generation using two parallel KMD schemes, Arnoldi algorithm, and KL divergence based statistical outlier detection, in case of ISC in battery modules.
  • Figure 3: Plot shows (from top to bottom) the $i$-th module voltage, the pack current, the average distance among the distribution of KMs, and the generated residual under resting condition.
  • Figure 4: Plot shows (from top to bottom) the $i$-th module voltage, the pack current, the average distance among the distribution of KMs, and the generated residual under constant current charging.