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Appl. Sci. 2018, 8(8), 1301; https://doi.org/10.3390/app8081301

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

1
ETEC Department & MOBI Research Group, Vrije Universiteit Brussel (VUB), Pleinlaan 2, 1050 Brussel, Belgium
2
Flanders Make, 3001 Heverlee, Belgium
*
Author to whom correspondence should be addressed.
Received: 3 July 2018 / Revised: 31 July 2018 / Accepted: 2 August 2018 / Published: 4 August 2018
(This article belongs to the Special Issue Plug-in Hybrid Electric Vehicle (PHEV))
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Abstract

In this article, 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. The comparison of the results demonstrates the capability of the proposed techniques for accurate SOH estimation. 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 fifteen rules based on a SOH estimator has better performances over the other technique, with a 1.5% maximum error in comparison to the experimental data. View Full-Text
Keywords: state of health estimation; adaptive neuron-fuzzy inference system (ANFIS); group method of data handling (GMDH); artificial neural network (ANN); electric vehicles (EVs); capacity degradation; lithium-ion battery; time-delay input state of health estimation; adaptive neuron-fuzzy inference system (ANFIS); group method of data handling (GMDH); artificial neural network (ANN); electric vehicles (EVs); capacity degradation; lithium-ion battery; time-delay input
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rahbari, O.; Mayet, C.; Omar, N.; Van Mierlo, J. Battery Aging Prediction Using Input-Time-Delayed Based on an Adaptive Neuro-Fuzzy Inference System and a Group Method of Data Handling Techniques. Appl. Sci. 2018, 8, 1301.

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