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.
Highlights
Notwithstanding the Paris Agreement, a technological transient from a hydrocarbon-based economy to the post-petroleum era, there is less tangible projective evidence of declining fossil-fueled based economies all over the world
Two data-driven techniques are developed for the state of health estimation
The developed techniques utilize an adaptive neuro-fuzzy inference system and group method of data handling to train the relation of the battery terminal voltage and state of health, enjoying the advantage over existing methods, as mentioned previously, with no need for computing power and a complex battery model
Summary
Notwithstanding the Paris Agreement, a technological transient from a hydrocarbon-based economy to the post-petroleum era, there is less tangible projective evidence of declining fossil-fueled based economies all over the world. The electrification of the transportation sector with existing electrical infrastructure leads the power system to collapse. It can be prevented if electric vehicles are coordinated and scheduled for a proper charging time-period and rate. The majority of scientists have reached a consensus on viable alternatives for fossil fuels, mainly wind and solar energy, which have relatively low generation costs as well as high generation potential, respectively. Their fluctuations in output are a serious problem [3].
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