Sliding Mode Based Adaptive Linear Neuron Proportional Resonant Control of Vienna Rectifier for Performance Improvement of Electric Vehicle Charging System

Abstract

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, coordinate transformation 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 and steady-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.