Time-Variant Online Auto-Tuned PI Controller Using PSO Algorithm for High Accuracy Dual Active Bridge DC-DC Converter

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Abstract
The proliferation of clean energy and environmentally friendly transportation has contributed to the development of electric vehicles (EVs) including the EV DC charger system. A dual active bridge (DAB) is a DC-DC converter that has the required features for an EV DC charger. A proportional-integral (PI) controller is a common method in power electronics applications, including DAB. However, the manual tuning of PI parameters using Ziegler-Nichols (ZN) needs a lengthy time and the tuning values are practical and well-functioning at the tuning point only. Moreover, the fixed gains in offline tuning cannot fully control the system output as needed and do not guarantee the robustness of the system. This paper proposes a time-variant online auto-tuned PI controller using a particle swarm optimization (PSO) algorithm for the 200 kW DAB system. The DAB performance with the proposed controller is evaluated in terms of steady-state error, eSS and dynamic performance under various reference voltages at different loads and load step changes. Comparative analysis between the proposed method and manual tuning performance are presented. A hardware-in-the-loop (HIL) experimental circuit is built to validate the simulation results. The DAB with the proposed method produces 64% higher accuracy and 40% faster response compared to manual tuning. tuning.

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