Sliding mode based adaptive linear neuron proportional resonant control of Vienna rectifier for performance improvement of electric vehicle charging system
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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Yn: Journal of Power Sources, Cyfrol 542, Rhif 15 September 2022, 231788, 15.09.2022.
Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid
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TY - JOUR
T1 - Sliding mode based adaptive linear neuron proportional resonant control of Vienna rectifier for performance improvement of electric vehicle charging system
AU - Ahmed, Hafiz
AU - Çelik, Doğan
PY - 2022/9/15
Y1 - 2022/9/15
N2 - 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.
AB - 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.
U2 - 10.1016/j.jpowsour.2022.231788
DO - 10.1016/j.jpowsour.2022.231788
M3 - Article
VL - 542
JO - Journal of Power Sources
JF - Journal of Power Sources
SN - 0378-7753
IS - 15 September 2022
M1 - 231788
ER -