Thermal Fault Detection and Localization Framework for Large Format Batteries

TL;DR

This paper presents a framework for detecting and localizing thermal faults in large format batteries using optimized sensor placement and a 2D thermal model, enhancing safety and diagnostics.

Contribution

It introduces a novel framework combining sensor placement optimization with a 2D thermal model for improved fault detection in large format batteries.

Findings

Optimized sensor placement improves fault detectability.

The 2D thermal model enhances localization accuracy.

Experimental validation confirms effectiveness.

Abstract

Safety against thermal failures is crucial in battery systems. Real-time thermal diagnostics can be a key enabler of such safer batteries. Thermal fault diagnostics in large format pouch or prismatic cells pose additional challenges compared to cylindrical cells. These challenges arise from the fact that the temperature distribution in large format cells is at least two-dimensional in nature (along length and breadth) while such distribution can be reasonably approximated in one dimension (along radial direction) in cylindrical cells. This difference makes the placement of temperature sensor(s) non-trivial and the design of detection algorithm challenging. In this work, we address these issues by proposing a framework that (i) optimizes the sensor locations to improve detectability and isolability of thermal faults, and (ii) designs a filtering scheme for fault detection and localization based on a two-dimensional thermal model. The proposed framework is illustrated by experimental and simulation studies on a commercial battery cell.