Adaptive online auto-tuning using Particle Swarm optimized PI controller with time-variant approach for high accuracy and speed in Dual Active Bridge converter

Abstract

<abstract> <p>Electric vehicles (EVs) are an emerging technology that contribute to reducing air pollution. This paper presents the development of a 200 kW DC charger for the vehicle-to-grid (V2G) application. The bidirectional dual active bridge (DAB) converter was the preferred fit for a high-power DC-DC conversion due its attractive features such as high power density and bidirectional power flow. A particle swarm optimization (PSO) algorithm was used to online auto-tune the optimal proportional gain (<italic>K<sub>P</sub></italic>) and integral gain (<italic>K<sub>I</sub></italic>) value with minimized error voltage. Then, knowing that the controller with fixed gains have limitation in its response during dynamic change, the PSO was improved to allow re-tuning and update the new <italic>K<sub>P</sub></italic> and <italic>K<sub>I</sub></italic> upon step changes or disturbances through a time-variant approach. The proposed controller, online auto-tuned PI using PSO with re-tuning (OPSO-PI-RT) and one-time (OPSO-PI-OT) execution were compared under desired output voltage step changes and load step changes in terms of steady-state error and dynamic performance. The OPSO-PI-RT method was a superior controller with 98.16% accuracy and faster controller with 85.28 s<sup>-1</sup> average speed compared to OPSO-PI-OT using controller hardware-in-the-loop (CHIL) approach.</p> </abstract>