Advanced Fault Diagnosis for Lithium-Ion Battery Systems

Lithium-ion batteries have become the mainstream energy storage solution for many applications, such as electric vehicles and smart grids. However, various faults in a lithium-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 paper provides a comprehensive review of fault mechanisms, fault features, and fault diagnosis of various faults in LIBS, 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.

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

IEEE Industrial Electronics Magazine ◽

10.1109/mie.2020.2964814 ◽

2020 ◽

Vol 14 (3) ◽

pp. 65-91 ◽

Cited By ~ 1

Author(s):

Xiaosong Hu ◽

Kai Zhang ◽

Kailong Liu ◽

Xianke Lin ◽

Satadru Dey ◽

...

Keyword(s):

Fault Diagnosis ◽

Lithium Ion Battery ◽

Lithium Ion ◽

Fault Features ◽

Battery Systems

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A novel Sensor Fault Diagnosis method for Lithium-ion Battery System Using Hybrid System Modeling

2018 Condition Monitoring and Diagnosis (CMD) ◽

10.1109/cmd.2018.8535711 ◽

2018 ◽

Author(s):

Chanzwen Zhen ◽

Ziqiang Chen ◽

Deyana Huanz

Keyword(s):

Fault Diagnosis ◽

Lithium Ion Battery ◽

Hybrid System ◽

System Modeling ◽

Lithium Ion ◽

Sensor Fault ◽

Battery System ◽

Diagnosis Method

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Design and Implementation of a Smart Lithium-Ion Battery System with Real-Time Fault Diagnosis Capability for Electric Vehicles

Energies ◽

10.3390/en10101503 ◽

2017 ◽

Vol 10 (10) ◽

pp. 1503 ◽

Cited By ~ 21

Author(s):

Zuchang Gao ◽

Cheng Chin ◽

Joel Chiew ◽

Junbo Jia ◽

Caizhi Zhang

Keyword(s):

Fault Diagnosis ◽

Lithium Ion Battery ◽

Real Time ◽

Electric Vehicles ◽

Lithium Ion ◽

Battery System ◽

Design And Implementation

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Genetic Algorithm based Back-Propagation Neural Network approach for fault diagnosis in lithium-ion battery system

2015 6th International Conference on Power Electronics Systems and Applications (PESA) ◽

10.1109/pesa.2015.7398911 ◽

2015 ◽

Cited By ~ 2

Author(s):

Zuchang Gao ◽

Cheng Siong Chin ◽

Wai Lok Woo ◽

Junbo Jia ◽

Wei Da Toh

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Lithium Ion Battery ◽

Back Propagation ◽

Lithium Ion ◽

Back Propagation Neural Network ◽

Network Approach ◽

Neural Network Approach ◽

Battery System

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Fault Diagnosis of Lithium-ion Battery System Based on Hybrid System and Recursive Least Squares-Extended Kalman Filter

DEStech Transactions on Environment Energy and Earth Science ◽

10.12783/dteees/iceee2019/31786 ◽

2019 ◽

Author(s):

Tiantian Lin ◽

Ziqiang Chen ◽

Changwen Zheng

Keyword(s):

Kalman Filter ◽

Fault Diagnosis ◽

Least Squares ◽

Lithium Ion Battery ◽

Extended Kalman Filter ◽

Hybrid System ◽

Lithium Ion ◽

Recursive Least Squares ◽

Battery System

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High-energy-density lithium-ion battery using a carbon-nanotube–Si composite anode and a compositionally graded Li[Ni0.85Co0.05Mn0.10]O2 cathode

Energy & Environmental Science ◽

10.1039/c6ee01134a ◽

2016 ◽

Vol 9 (6) ◽

pp. 2152-2158 ◽

Cited By ~ 183

Author(s):

Joo Hyeong Lee ◽

Chong S. Yoon ◽

Jang-Yeon Hwang ◽

Sung-Jin Kim ◽

Filippo Maglia ◽

...

Keyword(s):

Carbon Nanotube ◽

Energy Density ◽

Lithium Ion Battery ◽

State Of The Art ◽

Lithium Ion ◽

High Energy ◽

High Energy Density ◽

Rechargeable Battery ◽

Composite Anode ◽

Battery System

A Li-rechargeable battery system based on state-of-the-art cathode and anode technologies demonstrated high energy density, meeting demands for vehicle application.

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Robust actuator and sensor fault reconstruction of wind turbine using modified sliding mode observer

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331218754620 ◽

2018 ◽

Vol 41 (6) ◽

pp. 1504-1518 ◽

Cited By ~ 10

Author(s):

Mostafa Rahnavard ◽

Moosa Ayati ◽

Mohammad Reza Hairi Yazdi

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Rotational Speed ◽

Sliding Mode ◽

Sliding Mode Observer ◽

Sensor Faults ◽

Actuator Faults ◽

Fault Reconstruction ◽

Different Order ◽

Aerodynamic Torque

This paper proposes a robust fault diagnosis scheme based on modified sliding mode observer, which reconstructs wind turbine hydraulic pitch actuator faults as well as simultaneous sensor faults. The wind turbine under consideration is a 4.8 MW benchmark model developed by Aalborg University and kk-electronic a/s. Rotor rotational speed, generator rotational speed, blade pitch angle and generator torque have different order of magnitudes. Since the dedicated sensors experience faults with quite different values, simultaneous fault reconstruction of these sensors is a challenging task. To address this challenge, some modifications are applied to the classic sliding mode observer to realize simultaneous fault estimation. The modifications are mainly suggested to the discontinuous injection switching term as the nonlinear part of observer. The proposed fault diagnosis scheme does not require know the exact value of nonlinear aerodynamic torque and is robust to disturbance/modelling uncertainties. The aerodynamic torque mapping, represented as a two-dimensional look up table in the benchmark model, is estimated by an analytical expression. The pitch actuator low pressure faults are identified using some fault indicators. By filtering the outputs and defining an augmented state vector, the sensor faults are converted to actuator faults. Several fault scenarios, including the pitch actuator low pressure faults and simultaneous sensor faults, are simulated in the wind turbine benchmark in the presence of measurement noises. Simulation results show that the modified observer immediately and faithfully estimates the actuator faults as well as simultaneous sensor faults with different order of magnitudes.

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