Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques

Rahbari, Omid; Mayet, Clément; Omar, Noshin; Mierlo, Joeri Van

doi:10.3390/app8081301

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…Moreover, the estimated results are compared with the direct actual measured SOH indicators using standard tests. The results indicate that the adaptive neuron-fuzzy inference system with 15 rules based on an SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data [10].…”

Section: Energy Storage Systems For Electric and Hybrid Vehiclesmentioning

confidence: 90%

“…In the paper of Omid Rahbari et al [10], two techniques that are congruous with the principle of control theory are utilized to estimate the state of health (SOH) of real-life plug-in hybrid electric vehicles (PHEVs) accurately, which is of vital importance to battery management systems. The relation between the battery terminal voltage curve properties and the battery state of health is modelled via an adaptive neuron-fuzzy inference system and a group method of data handling.…”

Section: Energy Storage Systems For Electric and Hybrid Vehiclesmentioning

confidence: 99%

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Special Issue “Plug-In Hybrid Electric Vehicle (PHEV)”

Mierlo

2019

Applied Sciences

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Climate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy-efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy-storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This Special Issue is focused on the recent advances in electric vehicles and (plug-in) hybrid vehicles that address the new powertrain developments and go beyond the state-of-the-art (SOTA).

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Section: Energy Storage Systems For Electric and Hybrid Vehiclesmentioning

confidence: 90%

Section: Energy Storage Systems For Electric and Hybrid Vehiclesmentioning

confidence: 99%

Special Issue “Plug-In Hybrid Electric Vehicle (PHEV)”

Mierlo

2019

Applied Sciences

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“…As the effect of capacity loss is of greater interest than the aging mechanism at the system level, the focus of the authors is mainly on capacity loss instead of aging mechanism. Nevertheless, further details and information on aging mechanisms and modeling can be found in our previous articles [32,35,36].…”

Section: Battery Pack Modelmentioning

confidence: 99%

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

et al. 2019

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Electric vehicles (EVs) are recognized as promising options, not only for the decarbonization of urban areas and greening of the transportation sector, but also for increasing power system flexibility through demand-side management. Large-scale uncoordinated charging of EVs can impose negative impacts on the existing power system infrastructure regarding stability and security of power system operation. One solution to the severe grid overload issues derived from high penetration of EVs is to integrate local renewable power generation units as distributed generation units to the power system or to the charging infrastructure. To reduce the uncertainties associated with renewable power generation and load as well as to improve the process of tracking Pareto front in each time sequence, a predictive double-layer optimal power flow based on support vector regression and one-step prediction is presented in this study. The results demonstrate that, through the proposed control approach, the rate of battery degradation is reduced by lowering the number of cycles in which EVs contribute to the services that can be offered to the grid via EVs. Moreover, vehicle to grid services are found to be profitable for electricity providers but not for plug-in electric vehicle owners, with the existing battery technology and its normal degradation.

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“…ANFIS is a hybridization of the neural and fuzzy system. Takagi-Sugeno type fuzzy inference system (FIS) is used in ANFIS [27,28]. ANFIS uses the artificial neural network (ANN) to learn knowledge from the feature set, and the learning is imposed into the FIS.…”

Section: Training Of the Anfis Classifiermentioning

confidence: 99%

Design of ANFIS for Hydrophobicity Classification of Polymeric Insulators with Two-Stage Feature Reduction Technique and Its Field Deployment

2018

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Hydrophobicity of polymeric insulator plays a vital role in determining the insulation quality in outdoor overhead electrical transmission and distribution lines. Loss of hydrophobicity increases the leakage current and leads to flashover. Monitoring hydrophobicity becomes a fundamental requirement to ensure continuity of power line operations. Hydrophobicity of polymeric insulator is classified according to STRI (Swedish Transmission Research Institute) guidelines. This paper proposes an intelligent ANFIS (Adaptive Neuro-Fuzzy Inference System) based classifier to determine the hydrophobicity quality using the digital image of the insulator. Ten statistical features are extracted from the digital images. Two stages of feature reduction are employed to reduce the number of features. Pre-design stage uses PCA (Principal Component Analysis) and reduces the number of features to six from ten and the post-design stage analyzes the accumulation effect to reduce the number of features to four. Various ANFIS classifiers are trained using these reduced features extracted from the image. The performance of these ANFIS classifiers is evaluated in both field and laboratory specimens. Results indicate classification accuracy of 96.4% and 93.3% during the training and testing phase when triangular membership function with linear output function is employed in ANFIS. A GUI (Graphical User Interface) has also been designed to facilitate the use of the proposed system by field operators.

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Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques

Cited by 10 publications

References 34 publications

Special Issue “Plug-In Hybrid Electric Vehicle (PHEV)”

Special Issue “Plug-In Hybrid Electric Vehicle (PHEV)”

Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme

Design of ANFIS for Hydrophobicity Classification of Polymeric Insulators with Two-Stage Feature Reduction Technique and Its Field Deployment

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