Optimal Sensor Placement in Lithium-Ion Battery Pack for Fault Detection and Isolation

TL;DR

This paper develops a systematic methodology for optimal sensor placement in large lithium-ion battery packs to enhance fault detection and isolation capabilities, crucial for reliable energy storage systems.

Contribution

It introduces a novel approach combining analytical redundancy and structural analysis to determine minimal sensor sets for effective fault diagnosis in battery packs.

Findings

Identifies the intrinsic analytical redundancy in battery systems.

Analyzes sensor placement strategies for fault diagnosis.

Demonstrates how to achieve fault detection with minimal sensors.

Abstract

Energy storage systems for transportation and grid applications, and in the future for aeronautical applications, require the ability of providing accurate diagnosis to insure system availability and reliability. In such applications, battery packs may consist of hundreds or thousands of interconnected cells, and of the associated electrical/electronic hardware. This paper presents a systematic methodology for approaching some aspects of the design of battery packs, and in particular understanding the degree of analytical redundancy (AR) in the system that can be used for diagnostic strategies. First, the degree of AR that is intrinsic in the battery system is determined. Then, structural analysis tools are used to study how different measurements (current, voltage, and temperature) may improve the ability of monitoring and diagnosis of a battery system. Possible sensor placement strategies that would enable the diagnosis of individual sensor faults and individual cell faults for different battery pack topologies are analyzed as well. The work presented in this paper illustrates how to achieve the required fault detection and isolation (FDI) capabilities using a minimal or optimal sensor set, which is a critical step in the design of a large battery pack.