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Identification and Quantification of Decomposition Mechanisms in Lithium-Ion Batteries; Input to Heat Flow Simulation for Modeling Thermal Runaway

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Knowledge-data driven sampling diagnosis algorithm for lithium batteries on electric vehicles.

Li Qiangwei¹, Zhou Sida¹, Zhou Xinan¹

¹School of Transportation Science and Engineering, Beihang University, Beijing 100191, China.

ISA Transactions

|January 23, 2025

Summary

This summary is machine-generated.

This study introduces a novel fault diagnosis algorithm for battery voltage sampling. It enhances battery safety by detecting sampling failures using outlier detection and fuzzy entropy, crucial for reliable battery management systems.

Keywords:

Failure modes Fault diagnosis Lithium-ion batteries Sampling

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Area of Science:

Electrical Engineering
Battery Management Systems
Fault Diagnosis

Background:

Voltage is a critical parameter for battery management systems (BMS).
Voltage sampling faults can lead to operational risks and system failures.
Investigating failure modes and developing robust diagnosis algorithms is essential.

Purpose of the Study:

To develop a knowledge-data driven sampling diagnosis algorithm for BMS.
To propose an online intelligent diagnosis algorithm based on fuzzy entropy and outlier detection.
To identify and validate key features for determining voltage sampling failure modes.

Main Methods:

Construction of a knowledge-base of failure modes using equivalent simulating models.
Simulation of six potential BMS voltage sampling failure modes.
Application of outlier detection with fuzzy entropy for intelligent fault diagnosis.
Validation using fault matrix methods and battery-in-loop experiments.
Verification using real-world data from a cloud monitoring platform.

Main Results:

Identified symmetrical voltage distribution and near-zero voltage as key failure features.
Validated the effectiveness of the proposed diagnosis algorithm through simulations and experiments.
Confirmed the algorithm's ability to detect sampling line cuts in real-time.
Demonstrated the practical applicability of the algorithm using cloud monitoring data.

Conclusions:

The developed fault diagnosis algorithm effectively identifies voltage sampling failures in BMS.
The knowledge-data driven approach enhances the reliability and safety of battery operations.
The proposed methodology can be extended to other systems and cloud-based monitoring platforms.