A Sensor Fault Diagnosis Scheme for a DC/DC Converter used in Hybrid Electric Vehicles

Al-Sheikh, Hiba; Hoblos, Ghaleb; Moubayed, Nazih; Karami, Nabil

doi:10.1016/j.ifacol.2015.09.611

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…7 and 8 show the corresponding detection functions generated by the GLR test. The optimal decision threshold value, γ l , and the optimal sliding window width, W, of the statistical test can be achieved using ROC analysis [16]. …”

Section: Simulation Resultsmentioning

confidence: 99%

“…At every measurement time step t k , the GLR test is applied on the recent W residual values to test for faults occurring in the last W time steps with W being the sliding window width of the statistical test. Hypothesis testing is carried by generating a detection function g l (t k ) for each residual where GLR l (τ) is the natural logarithm of the statistic in (16) …”

Section: B Residual Evaluation Using Glr Testmentioning

confidence: 99%

“…On the other hand, the main disadvantage lies in the sensitivity of the choices of the detection threshold and sliding window width on the test power. The authors propose a way to determine the optimal values of γ l and W in their work in [16].…”

Section: B Residual Evaluation Using Glr Testmentioning

confidence: 99%

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Piecewise switched Kalman filtering for sensor fault diagnosis in a DC/DC power converter

Al-Sheikh

Hoblos

Moubayed

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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With the extensive use of power electronics in modern HEVs comes the need for efficient methods of condition monitoring and fault diagnosis to ensure the reliability of the electrical power system. This paper addresses sensor faults in a DC/DC power converter system interfacing the main energy storage unit and the AC drive in a hybrid electric vehicle. A residual-based fault diagnosis system that detects and localizes sensor faults in the examined system is designed. The proposed scheme extends the classic Kalman-based detection filter commonly used in linear time-invariant systems to a piecewise switched filter suitable for switched linear systems as those encountered in power electronics. Residuals of measured signals are generated by employing a bank of switched Kalman filters on a stochastic model of the converter. The generalized likelihood ratio test is then used as a statistical change detection method to evaluate the residuals and generate a detection function which is compared with a decision threshold to detect the occurrence of a sensor fault. Keywords-sensor fault diagnosis; DC/DC converter; piecewise switched state-space model; swicthed linear Kalman filter; GLR testI.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: B Residual Evaluation Using Glr Testmentioning

confidence: 99%

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Piecewise switched Kalman filtering for sensor fault diagnosis in a DC/DC power converter

Al-Sheikh

Hoblos

Moubayed

2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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“…Lu et al [17] conducted the survey and highlighted the key issues in the battery management system of the electric vehicles such as the cell voltage measurement, battery states estimation, battery uniformity and equalization [24] and battery fault diagnosis methods [25] based on adaptive feedback control [26]. A detailed focus was given to the SOC and SOH estimation, and the methods were classified based on the given input conditions (remaining charge, capacity, current, selfdischarge rate, initial SOC value, etc).…”

Section: Survey On Battery Modelling Methodsmentioning

confidence: 99%

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Garg

Vijayaraghavan

Zhang

et al. 2017

Int. J. Energy Res.

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Summary The improvement in the operating range of electric vehicles can be accomplished by robust modelling of the design and optimization of the energy storage capacity of the battery pack system. In this work, the authors have conducted a comprehensive survey on battery modelling methods and identified critical areas of improvement vital for estimating the battery capacity. This work proposes the artificial intelligence approach of automated neural networks search (ANS) in development of the robust battery capacity models for the lithium ion batteries based on the inputs (temperature and discharge rates). The robustness in the models is introduced by incorporating uncertainties in the inputs (the temperature and discharge rates, the architecture of algorithm and the models). The statistical analysis and validation of the models reveal that the models formulated using an ANS approach outperform the response surface regression models with correlation coefficient achieved as high as 0.97. The uncertainty analysis based on normal distribution of the inputs suggests that the models formulated from ANS are least sensitive to change in the input conditions when compared to response surface regression models. The global sensitivity analysis reveals that the temperature is a dominant factor for accurate battery capacity estimation. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Young et al reviewed the recent trends in battery technology development and classified the battery models into electrochemical and equivalent circuit models. To understand the physics and the fundamental aspects of battery mechanisms, the electrochemical models based on finite element analysis/differential equations are important but their use is restricted due to its computational complexity . On the other hand, the equivalent circuit models are lumped‐parameter models and can be used for long‐term simulation studies.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of battery modules state for electric vehicle using artificial neural network and experimental validation

Liang

Bao

Zhang

et al. 2018

Energy Science & Engineering

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This work undertakes research problem on prediction of state of battery modules used in electric vehicle. Past studies focussed extensively on the estimation of state of charge and state of health of a single cell/battery. However, the focus on the estimation of state of entire battery module is hardly studied. During the actual operation of electric vehicle, the environmental conditions (road slope and climate) and factors such as abnormal voltage and temperature conditions causes the deviations among the battery modules from its equilibrium state. As a result, its power efficiency and the life‐cycle decreases. One of the reasons of deviation is the defects in manufacturing of a battery module. For this purpose, the evaluation and estimation of state of battery modules is a priority to have information on which module among the given ones should be discharged first. Therefore, this study proposes the methodology for the evaluation of state of battery modules based on its current and temperature. Firstly, the rules are defined based on experiments to determine the priority for the discharge of each of the six modules. Dataset of 6000 samples obtained comprises of the 12 inputs (temperature and current) and 6 outputs (on‐off state of switch). Each output represents the priority of module to be discharged. To analyze this multi‐input multi‐output dataset, the popular artificial intelligence algorithm namely, artificial neural network with three training algorithms (Levenberg, Scaled conjugate and Bayesian regularization), using a number of neurons from 2 to 20 in the hidden layer is used. It was found that the model obtained using Levenberg algorithm performs the best.

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A Sensor Fault Diagnosis Scheme for a DC/DC Converter used in Hybrid Electric Vehicles

Cited by 11 publications

References 20 publications

Piecewise switched Kalman filtering for sensor fault diagnosis in a DC/DC power converter

Piecewise switched Kalman filtering for sensor fault diagnosis in a DC/DC power converter

Design of robust battery capacity model for electric vehicle by incorporation of uncertainties

Evaluation of battery modules state for electric vehicle using artificial neural network and experimental validation

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