Hybrid energy storage system (HESS) in microgrid applications is controlled to balance the power between generation and load sides. However, power loss of converting and model parameter mismatch would affect the control performance. To this end, a deadbeat control algorithm for HESS combined with deep reinforcement learning is proposed in this article. In the proposed method, the variation of optimal HESS current reference caused by nonlinear power loss and model mismatch is regarded as a centralized disturbance that can be compensated by a deep deterministic policy gradient agent, and a deadbeat control generates an optimal duty cycle based on a precise reference current to eliminate system steady-state error and improve dynamic response speed. The effectiveness of the proposed algorithm is verified through simulation and hardware experiments. Results demonstrate that the steady-state error can be maintained within 1%. Compared to conventional deadbeat control methods, the proposed method reduces the bus voltage spike and settling time by 34.24%–44.44% and 16.66%–40.00%, respectively.

With electric vehicles (EVs) and charging facilities as a bridge, the coupling of transportation network (TN) and distribution network (DN) is getting closer, and EV charging navigation considering TN-DN convergence is a current research hotspot. In order to reduce the total cost of EV charging navigation and the impact of charging load on the grid, this paper proposes a novel bi-layer coordinated charging navigation model (Bi-CCNM). The upper layer model is to minimize the total cost of EV charging navigation. To precisely estimate travel costs, a dynamic spatio-temporal energy consumption estimation model is established, which considers the impact of dynamic traffic flow on EV travel resistance. The lower layer model aims to reduce energy exchange between the charging station (CS) and the main grid, and maximize the utilization of local renewable energy sources. To cope with the intermittent nature of renewable energy generation, this paper utilizes Vehicle-to-Grid (V2G) technology to effectively mitigate the impact of EV charging loads on the grid. To efficiently tackle the Bi-CCNM, the Joint Optimization algorithm combining Generalized Benders Decomposition and Logarithmic Barrier Function Method (JO-GBLB) is developed. Ultimately, an optimal solution can be obtained through the interaction of information between the two layers. Real-world case validates the effectiveness of the proposed the Bi-CCNM, energy consumption estimation model, and JO-GBLB algorithm. The results indicate which it provides a low-cost charging navigation solution while significantly reducing the power exchange between the CS and the main grid, effectively preventing safety issues caused by load fluctuations. Besides, the accuracy of energy consumption estimation of EVs increases by 5.1% - 6.7%.

In this paper, an MPC-based faster joint control method is proposed for hybrid energy storage system (HESS), which consists of battery and supercapacitor in photovoltaic dc-microgrid. The proposed method utilizes the uncompensated power from the battery to improve the dc-link restoration and decrease overshoot. Simulations are conducted to validate the robustness and rapidity of the proposed method, and the results are compared with traditional double-loop PI controllers. The comparison results demonstrate that the new controller has higher dynamic performance and better robustness.

Hybrid energy storage systems (HESS) are used to satisfy the power demand in microgrids. The supercapacitor (SC) is responsible for the high-frequency charge and discharge behaviors. For model predictive control (MPC) methods, the inaccurate modeling of the supercapacitor and control delay may cause the fluctuation of bus voltage. This paper proposes a fractional-order model predictive control (FOMPC), which provides more adjustable parameters, so it can optimize the control effect through parameter tuning. The method is validated in the simulation results.

Batteries of electronic quantities detection and state of health have always been the core of the battery management system of electric vehicles, which is capable of estimating SOC accurately and quickly and ensuring the safe operation of electric vehicles. Aiming at the problem of large estimation deviation of SOC and SOH in the whole life cycle of lithium battery, this paper proposes a multi-innovation dual Unscented Kalman Filter based on fractional-order model. Firstly, the fractional-order model of lithium battery is established and the parameters of the model are identified by a genetic algorithm. Secondly, the fractional-order multi-innovation Unscented Kalman Filter is proposed to estimate SOC, and the ohmic resistance and SOH are estimated by Unscented Kalman Filter to improve the SOC estimation accuracy in the whole life cycle. Finally, the proposed algorithm is verified by Urban Dynamometer Driving Schedule(UDDS) dynamic condition data.

In this research paper, the Multi Agent System (MAS) is introduced to Multi Micro- Grid (MMG) mesh system with the consensus control to share the power between arbitrary inverters to meet the load demand. Each microgrid consists with the Hybrid Energy Storage (HESS) which include the battery and Supercapacitor (SC) to supply/absorb the energy according to the load demand. The consensus based droop characteristics are used with MAS topology to share the power between different microgrids. The overall system consists with five microgrids and they interconnected as meshed network. The implemented control architecture achieve the DC voltage stability among all the microgrids.The system's stability being analysed mathematically using graph theory. MATLAB/Simulink virtual environment is used to simulate the overall system. The Java Agent Development Framework (JADE) is used as the platform to see the status of the agents. The overall simulation results substantiate in different modes of operation and compared with the conventional control method.

This paper investigates some issues associated with the power sharing in the Hybrid Energy Storage System (HESS) in the Multi Micro- Grid System (MMGS) to meet the load demands. To address this problem in isolated microgrids, which often arises in emergency situations, the present study proposes an efficient energy management system (EMS) which operates the battery and supercapacitor (SC) based on their State of Charge (SOC) level using fuzzy logic based control algorithm. The optimal amount of charging/discharging of the battery and SC is decided by the fuzzy inference system (FIS) . Simulations are carried out by creating a microgrid test bench for fuzzy logic system (FLS) in MATLAB/Simulink environment. The performance of the proposed approach is validated considering different modes of operation and loading conditions and found to be satisfactory.

Although the capability of digital microprocessors has been developed rapidly, the heavy computation burden is a challenge for the model predictive control on the applications of power electronics. This paper presents a zero iteration model predictive control (MPC) for a hybrid energy storage system (HESS) with dual batteries. A PI controller is used to predicting the closed-form battery current and control signals, which can be used for relax the constraints and find the active constants in order to obtain the solutions without iteration. The dual battery can share the power to ensure the charging/discharging behaviours are always limited to the specified active constraint.

With the rapid demand in the power system, the renewable energy sources (RES) have led to the higher penetration in the existing main grid. The proper control strategy for energy storage systems (ESS) have received a huge attention. The ESS is critical to maintain the correct power balance between the RESs and load demand. In this research, fuzzy logic control algorithm is implemented among other existing control algorithms to operate the battery and supercapacitor (SC) based hybrid energy storage system (HESS) in an optimal way. The battery is used to compensate the energy requirement during long duration, while the supercapacitor handles mainly the transient power fluctuations. This increases the battery life cycle due to limited stress on the battery. The proposed fuzzy inference system decides the optimal amount of charging/discharging of the HESS based on the state of charge (SOC) constraints of the battery and supercapacitor. MATLAB/Simulink is used to create and implement the microgrid test bench and fuzzy logic system (FLS). The simulation results substantiate the potential of FLS in the microgrid energy management in different modes of operation and load conditions.

The energy management system (EMS) of microgrid deals with lots of complex issues, both from generation and demand side. The intermittent nature of renewable energy sources and constantly fluctuating load demands require robust energy management. This paper introduces the structure of hierarchical control strategies and common control approaches in the microgrid. In this paper, a novel enhanced EMS in islanding mode is presented for the hybrid energy storage system (HESS), comprising of two batteries and supercapacitors. A coordination control scheme is introduced at the primary level for the HESS. This will reduce the power stress on batteries, while improving the power quality.