Sensor Fault Estimator and Fault Tolerant Controller for Boost Converter

Zhang, Ping; Zhang, Aihua

doi:10.1109/oncon56984.2022.10126955

Cited by 1 publication

(1 citation statement)

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“…Fault diagnosis is usually divided into hardware and software redundancy methods [ 13 , 14 , 15 ]. Software redundancy (or information redundancy) methods use information recorded in input and output data to achieve monitoring and fault diagnosis [ 16 , 17 , 18 ]. Information redundancy methods are categorized as model-based, signal-based, and knowledge-based approaches, among which model-based methods have been widely used in diagnosing lithium-ion battery sensor faults.…”

Section: Introductionmentioning

confidence: 99%

A Sensor-Fault-Estimation Method for Lithium-Ion Batteries in Electric Vehicles

Lan,

Gao,

Yin

et al. 2023

Sensors

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In recent years, electric vehicles powered by lithium-ion batteries have developed rapidly, and the safety and reliability of lithium-ion batteries have been a paramount issue. Battery management systems are highly dependent on sensor measurements to ensure the proper functioning of lithium-ion batteries. Therefore, it is imperative to develop a suitable fault diagnosis scheme for battery sensors, to realize a diagnosis at an early stage. The main objective of this paper is to establish validated electrical and thermal models for batteries, and address a model-based fault diagnosis scheme for battery sensors. Descriptor proportional and derivate observer systems are applied for sensor diagnosis, based on electrical and thermal models of lithium-ion batteries, which can realize the real-time estimation of voltage sensor fault, current sensor fault, and temperature sensor fault. To verify the estimation effect of the proposed scheme, various types of faults are utilized for simulation experiments. Battery experimental data are used for battery modeling and observer-based fault diagnosis in battery sensors.

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