Hybrid AC/DC microgrids have been planned for the better interconnection of different distributed generation systems (DG) to the power grid, and exploiting the prominent features of both ac and dc microgrids. Connecting these microgrids requires an interlinking AC/DC converter (IC) with a proper power management and control strategy. During the islanding operation of the hybrid AC/DC microgrid, the IC is intended to take the role of supplier to one microgrid and at the same time acts as a load to the other microgrid and the power management system should be able to share the power demand between the existing AC and dc sources in both microgrids. This paper considers the power flow control and management issues amongst multiple sources dispersed throughout both ac and dc microgrids. The paper proposes a decentralized power sharing method in order to eliminate the need for any communication between DGs or microgrids. This hybrid microgrid architecture allows different ac or dc loads and sources to be flexibly located in order to decrease the required power conversions stages and hence the system cost and efficiency. The performance of the proposed power control strategy is validated for different operating conditions, using simulation studies in the PSCAD/EMTDC software environment.

This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.

Complementary features of batteries and supercapacitors can be effectively used in a Hybrid Energy Storage System (HESS). The utilization of the HESS in Electric Vehicles (EVs) offers many advantages such as efficient regenerative braking, battery safety, and improved vehicle acceleration. In this paper, a new Regenerative Braking System (RBS) is proposed for EVs with HESS and driven by Brushless DC (BLDC) motor. During regenerative braking, the BLDC acts as a generator. Hence, using appropriate switching algorithm, the DC-link voltage is boosted and the energy is transferred to the supercapacitor or the battery through the inverter. The harvested energy can be utilized to improve the vehicle acceleration and/or keep the battery pack from deep discharging during driving uphill. In order to provide a reliable and smooth brake, braking force distribution is realized through an Artificial Neural Network (ANN). Simultaneously, the braking current is adjusted by a PI controller for constant torque braking. In order to evaluate the performance of the proposed RBS, different simulations and experiments are carried out. The results confirm high capability of the proposed RBS.

Plug-in electric vehicles in the future will possibly emerge widely in city areas. Fleets of such vehicles in large numbers could be regarded as considerable stochastic loads in view of the electrical grid. Moreover, they are not stabled in unique positions to define their impact on the grid. Municipal parking lots could be considered as important aggregators letting these vehicles interact with the utility grid in certain positions. A bidirectional power interface in a parking lot could link electric vehicles with the utility grid or any storage and dispersed generation. Such vehicles, depending on their need, could transact power with parking lots. Considering parking lots equipped with power interfaces, in more general terms, parking-to-vehicle and vehicle-to-parking are propose here instead of conventional grid-to-vehicle and vehicle-to-grid concepts. Based on statistical data and adopting general regulations on vehicles (dis)charging, a novel stochastic methodology is presented to estimate total daily impact of vehicles aggregated in parking lots on the grid. Different scenarios of plug-in vehicles' penetration are suggested in this paper and finally, the scenarios are simulated on standard grids that include several parking lots. The results show acceptable penetration level margins in terms of bus voltages and grid power loss.

This paper proposes a control strategy for the stable operation of the micro‐grid dluring different operating modes while providing the DC voltage control and well quality DC‐Ioads supply. The proposed method adapts the battery energy storage system (BESS) to employ the same control architecture for grid‐connected mode as well as the islanded operation with no need for knowing the micro‐grid operating mode or switching between the corresponding control architectures. Furthermore, the control system presents effective charging of the battery in the micro‐grid. When the system is grid connected and during normal operation, AC grid converter balances active power to ensure a constant DC voltage while the battery has the option to store energy for necessary usage. In order to achieve the system operation under islanding conditions, a coordinated strategy for the BESS, RES and load management including load shedding and considering battery state‐of‐charge (SoC) and battery power limitation is proposed. Seamless transition of the battery converter between charging and discharging, and that of grid side converter between rectification and inversion are ensured for different grid operating modes by the proposed control method. MATLAB/SIMULINK simulations and experimental results are provided to validate the effectiveness of the proposed battery control system.

High-voltage battery pack and charger structure are the main concerns of the electric vehicle (EV) developments. To achieve required voltage and power in propulsion systems, cascading battery stacks is essential. However, due to small discrepancies between different cells, charge imbalance occurs. Charge imbalance reduces battery cells life cycle, accessible energy, and system performance. Hence, charge equalization among battery stacks is necessary. Furthermore, EVs should have some essential requirements to provide desired contributions in future networks. Therefore, bidirectional power flow and reactive power control are other important capabilities of the battery charger system. In this paper, a bidirectional structure with two operation modes for a modular battery stack is developed. The developed structure has active and reactive control capabilities during grid to vehicle and vehicle to grid operations. To improve the battery system performance, a modular charge equalizer circuit (CEC) for both operation modes is developed. The modular configuration of the battery stack improves the system reliability and flexibility. Moreover, the overall system performance for the CEC and converter is improved by utilizing the integrated control strategy. Performance of the proposed structure is evaluated using simulations and laboratory experiments. The results confirm the capability of the proposed structure.