Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods

Lithium (Li)-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles (EVs) and smart grids. However, various faults in a Li-ion battery system (LIBS) can potentially cause performance degradation and severe safety issues. Developing advanced fault diagnosis technologies is becoming increasingly critical for the safe operation of LIBS. This article provides a comprehensive review of the mechanisms, features, and diagnosis of various faults in LIBSs, including internal battery faults, sensor faults, and actuator faults. Future trends in the development of fault diagnosis technologies for a safer battery system are presented and discussed.

Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

https://www.zenodo.org/record/2642457/files/A%20review%20on%20various%20temperature-indication%20methods%20for%20Li-ion%20batteries.pdf

A review on various temperature-indication methods for Li-ion batteries

Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement

A novel fault diagnosis method for lithium-Ion battery packs of electric vehicles

Model-based real-time thermal fault diagnosis of Lithium-ion batteries

On-board Thermal Fault Diagnosis of Lithium-ion Batteries For Hybrid Electric Vehicle Application

Since its introduction in 2011, the ISO 26262 standard has provided the state-of-the-art methodology for achieving functional safety of automotive electrical and electronic systems. Among other requirements, the standard requires estimation of quantified metrics such as the Probabilistic Metric for Hardware Failure (PMHF) using quantitative failure analysis techniques. While the standard provides some brief guidance, a complete methodology to calculate the PMHF in detail has not been well described in literature. This paper will draw out several key frameworks for successfully calculating the probabilistic metric for hardware failure using Fault Tree Analysis (FTA). At the top levels of the analysis, methods drawn from previous literature can be used to organize potential failures within a complex multifunctional system. At the lower levels of the FTA, the effects of all fault categories, including dual-point latent and detected faults, can be accounted for using appropriate diagnostic coverage and proof-test interval times. A simple example is developed throughout the paper to demonstrate the methods. Some simplifications are proposed to estimate an upper bound on the PMHF. Conclusions are drawn related to the steps and methods employed, and the nature of PMHF calculation in practical real-world systems.

Nabarun Das

Papers

Model-based real-time thermal fault diagnosis of Lithium-ion batteries

On-board Thermal Fault Diagnosis of Lithium-ion Batteries For Hybrid Electric Vehicle Application

Quantified fault tree techniques for calculating hardware fault metrics according to ISO 26262