Ahmed, Hafiz

Çelik, Doğan

English

With a strong expansion of transportation electrification, electric vehicle charging systems are becoming very important part of the electrified powertrain. This paper proposes a sliding mode based adaptive linear neuron (ADALINE)-proportional resonant (PR) control solution to enhance performance of Vienna rectifier (VR), an AC–DC converter, as a charger for series-linked battery packs of electric vehicles (EVs) operating under unbalanced and distorted grid conditions. A sliding-mode control (SMC) has been utilized for the fast and robust regulation of DC-link voltage while ADALINE-PR control is proposed to regulate the source current errors through the real-time adaptation of the controller gains. Another contribution of this paper is to derive reference current signals without complex positive and negative sequences component separation, coordinatetransformation and phase-locked loop. Besides, constant and pure battery current during charging/discharging is achieved in contrast to the previous studies. The proposed control algorithm achieves superior dynamic andsteady-state performances and eliminate harmonics of source currents and ripples in active power, DC-link voltage and battery current compared to the existing studies. The proposed method has been implemented in a digital signal processor (DSP) TMS320F28335 within a processor in the loop (PIL) quasi-real-time setting. Extensive comparative results demonstrate the effectiveness of proposed control algorithm

Bangor University Research Portal

Sliding mode based adaptive linear neuron proportional resonant control of Vienna rectifier for performance improvement of electric vehicle charging system

https://research.bangor.ac.uk/portal/files/48564866/Unmarked_Manuscript.pdf

Sliding mode based adaptive linear neuron proportional resonant control of Vienna rectifier for performance improvement of electric vehicle charging system

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