A robust control strategy for power management of a DC microgrid based on EVs in presence of energy storage battery

One of the dual active bridge (DAB) converters problems is to have smooth coordinated power sharing operation in a DC-microgrid in presence of the inductance mismatch and load variations. This paper deals with this problem by proposing a robust control strategy for an EVs-based DC microgrid relaying on three-phase DAB converter (DAB3) with a comprehensive stability, tracking, and sensitivity analysis which totally could not be seen in existing works. Using the proposed controller, the DAB3 is interfaced to connect EVs and battery unit with DC link for feeding the DC loads. Firstly, so as to obtain the phase-shift angle (PSA) needed for managing the transmitted power of DAB3, an initial closed-loop control technique is developed including integral-sliding control, feedforward coefficient and Low Pass Filter (LPF). Then, dynamical assessment of PSA error is executed using Lyapunov theory leading to new dynamic components for the PSA. These new components which are organized based on the boundaries defined for the transmitted power error, are generated through passing the error under boundaries from the compensators achieved from the Lyapunov theory. To further evaluation of proposed PSA, the new closed-loop description of proposed control technique attains two transfer functions for assessing the effects of the controller and compensators coefficients on the stability margin and tracking ability. Moreover, a comprehensive sensitivity analysis is scheduled to understand which factors can provide an affir-mative impact on the uncertainty capability of proposed PSA under various operating conditions. It is worth mentioning that the battery-connected DAB3 will balance the power injected into the DC-link by generating the power difference between the sum EVs power and sum loads power. Simulation results affirm that the proposed controller compared with a conventional PI controller is able to provide more robustness feature and accurate performance for power management in the EVs-based DC microgrid.