The deep concern towards efficient and green energy has brought to the development of intermediate energy storage system. Electric vehicles (EVs), for example, is an application where the energy storage devices are vital. In EV, the bidirectional DC-DC converter is high in demand due to its nature EV of having two or more separate DC sources. A recent technology called the dual active bridge (DAB) converter has quickly become popular in DC-DC converter thanks to its inherent galvanic isolation via a high-frequency transformer. Furthermore, DAB topology allows for high-efficiency conversion and soft switching adaption. This paper presents the optimization of the phase shift angle controller of DAB using Particle Swarm Optimization (PSO). From the preliminary study, it was found out that the optimization performs well until there are abrupt changes in the system. The system is not able to respond to the reference voltage step change as the traditional one-time execution PSO have local optima entrapped in react to the changes. The paper proposes a reset function to allow PSO to identify that its current optimized value is not optimal anymore. This drives PSO to look for a new optimal value to improve DAB's overall dynamic performance. Simulation of the modified PSO is done on a 200 kW DAB system with 20 kHz switching frequency is developed in MATLAB/Simulink environment. Simulation has been carried out with the objective to minimize the steady-state error as well as to improve the dynamic performance of the DAB. The DAB performance with the proposed solution is evaluated in terms of steady-state error and transient response by testing the system under various reference voltage and step-change input voltage. The self-excited re-exploration PSO also have been presented as to respond to the reference voltage step change as the basic PSO have local optima entrapped in react to the changes. In order to validate the simulation results, a hardware-in-the-loop (HIL) experimental circuit is built in Typhoon HIL-402 to verify the dynamic response and the steady state performance of the system.

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