DC Bus Research Articles

A distributed energy management system of autonomous DC microgrid in office buildings based on DC bus signaling

With the development of renewable energy, the character of building energy system gradually transitions from electricity consumer to an integrated role that considers both consume and supply side. Abundant flexible resources within the buildings remain to be exploited and utilized. This paper proposes a distributed energy management system of DC microgrid in office buildings based on DC bus signaling. The system enables to control decentralized terminals in office buildings including photovoltaic (PV), battery, un-shiftable load (UL), thermostatically controlled load (TCL), and electric vehicle (EV). The DC bus voltage is regulated in real-time according to the difference between actual grid power and the grid requirement power, which is arranged as a constant power curve in this paper to grid relief. The viability and effectiveness of the proposed system are validated by the simulation results. The DC bus voltage is varied dynamically and the power of utility grid is almost the same as the demand power. In addition, the impact of terminal capacity and arranged control sequence with the proposed strategy are analyzed and evaluated. For a typical operating condition, the annual adjustment contribution of PV, battery, EV, and TCL among all the terminals take 9.3%, 70.3%, 19.0%, and 1.4%, respectively.

An improved barrier function double integral sliding mode control of SynRM for hybrid energy storage system-based electric vehicle

Electric vehicles (EVs) are acquiring a reputation in the transportation industry as an environment-friendly replacement for traditional vehicles. Three energy sources, comprised of the fuel cell, battery and an ultra-capacitor, are used to form the hybrid energy storage system (HESS) of the EV under consideration. The synchronous reluctance motor (SynRM) is employed as the electric motor to drive the EV, considering its remarkable efficiency, minimal losses, and lack of magnetic material. Due to their nonlinear behaviour, the major components like energy sources and SynRM deviate from the normal behaviour when driving under extreme conditions. The conventional integral sliding mode control (ISMC) has deteriorated performance in the case of disturbances. To tackle these problems, the novel barrier function double integral sliding mode controller (BF-DISMC) is proposed for the control of the HESS and SynRM of the EV. The proposed BF-DISMC provides bounded disturbance rejection. The simulation of designed controllers demonstrates the effectiveness and superior performance over conventional ISMC and double integral sliding mode controller (DISMC). The BF-DISMC successfully eliminates chattering and exhibits a reduction of 2.22% and 3.45% in peak ripple, 35.74% and 66.07% in rise time, 88.90% and 97.49% in root mean square error, along with 37.08% and 65.92% in settling time compared to DISMC and ISMC techniques, respectively, for DC bus voltage tracking. The resilience against disturbances has been validated using the robustness test. Lastly, the experimental results utilizing the hardware-in-loop (HIL) framework are presented to validate the proposed controller.

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

A new optimal space vector modulation with DTC switching strategy for induction motor control

Efforts to achieve swift and precise dynamic torque control have been central in AC drive research. Recent advancements in embedded computer systems have highlighted direct torque control (DTC) and field-oriented control (FOC) as key methods for enhancing torque dynamics, both utilizing space vector modulation (SVM) to optimize voltage source inverter positioning. This study introduces a novel synthesis by integrating DTC with SVM to address limitations in conventional DTC, which suffers from limited voltage vector availability, leading to undesirable torque behavior and significant current fluctuations. The primary goal is to develop an optimal switching modulator for the fastest torque response through the combined application of DTC and SVM. The proposed strategy optimizes DC bus usage, reduces torque fluctuations, minimizes total harmonic distortion in AC motor current, decreases switching losses, and ensures seamless digital system integration. Simulations using MATLAB/SIMULINK demonstrate significant torque, current, and flux linkage ripple reductions, validating the approach's effectiveness. This integration overcomes established limitations, extending the capabilities of motor control methodologies and offering enhanced performance and operational integrity in induction motor drive systems.

Efficient DC–DC power converters for fuel‐cell electric vehicle: A qualitative assessment

AbstractThe general shift in vehicle propulsion systems from internal combustion engine (ICE) power‐train towards electric power‐train has led to the development of energy‐efficient and compact electric drivetrain for next‐generation automobiles such as fuel cell electric vehicles (FCEV). As opposed to ICE, fuel cell vehicles operate with higher powertrain efficiency and are combustion‐less since the only byproduct is water. In light of developmental and environmental goals, fuel cell technology is consequently seen as the evolutionary step in vehicle technology. The electric drivetrain for FCEV consists of power converters, motors, and associated control systems. Direct connection of the fuel cell stack to the DC bus and other system components is inefficient. As a result, it becomes essential to regulate the high‐voltage DC bus connecting the fuel cell stack to other system elements like the motor and energy storage devices. Therefore, DC–DC converters are designed for main power unit to provide the desired and regulated voltage to the DC bus making the system efficient and reliable. High‐voltage step‐up DC–DC converters have undergone extensive research in recent years, which has increased their functionality, performance, and efficiency for FCEV. A survey of these converters, and evaluation of their performance and index parameters, could be very helpful in designing efficient converters and for the development of vehicular electric power‐train. This paper reviews the literature on electric drive‐train, fuel cell systems, and different DC–DC converter topologies for fuel cell electric vehicles. This study aims to conduct a comprehensive assessment of the existing topologies, their applications, and comparative aspects, as well as works that haven't been covered in earlier reviews.

A Hybrid Technique for Bidirectional Smart Charging of EVs Using BLDC Motor and Bidirectional Converter

This paper introduces a novel hybrid approach for bidirectional smart charging of electric vehicles (EVs) powered by brushless DC (BLDC) motors. The proposed technique, named Atomic Orbital Search-Rat Swarm Optimizer (AOS-RSO), leverages the Atomic Orbital Search (AOS) for optimizing energy storage management and the Rat Swarm Optimizer (RSO) for enhancing motor control. The modular hybrid energy storage system (HESS) is designed with five modules, each consisting of a bidirectional DC-DC converter connecting a battery and super capacitor module. A multi-level cascaded converter (MCC) connects the HESS to the BLDC motor inverter of the EV drive, controlling the DC bus voltage. The AOS-RSO approach efficiently manages the state-of-charge (SOC) across the modules, achieving a balanced SOC of 98%. Implemented in MATLAB, the method is evaluated in both open and closed-loop systems and compared with existing techniques. Results show the proposed method achieves 96% efficiency in simulation and 94% in experiments, with a CPU time of 2.84 s. This approach significantly advances smart charging solutions for EVs, offering improved energy management and operational efficiency.

Efficient Hybrid Electric Vehicle Power Management: Dual Battery Energy Storage Empowered by Bidirectional DC–DC Converter

ABSTRACTThis work offers a fuel cell power system with the ability to distribute power to the load from the electrical source and charge an auxiliary battery utilizing regenerative power flows created by the load. The approach is established on a bidirectional closed‐loop DC converter. A bidirectional DC–DC converter is presented as a means of achieving extremely high voltage energy storage systems (ESSs) for a DC bus or supply of electricity in power applications. This paper presents a novel dual‐active‐bridge (DAB) bidirectional DC–DC converter power management system for hybrid electric vehicles (HEVs). The proposed system makes it possible to charge an additional battery with regenerative power flows and distributes power from the electrical source to the load efficiently. The two main stages of the DAB converter, which are the focus of this work, are an interleaved buck/boost converter on the battery and a three‐phase wye‐wye series resonating converter on the DC bus. Each switch's current stress is greatly reduced by this design, which lowers transmission losses and enhances thermal performance. The interleaved buck conversion on the battery allows for lesser current stress in each switch, resulting in lower transmission loss. The increasing complexity and power of automotive embedded electronic systems have made the use of more potent power electronic converters in automobiles necessary. In recent years, many dual volt (42 V/14 V) bidirectional inverter topologies for automotive systems have been presented. However, the majority of them are either inefficient or use a huge number of transistors and magnetic devices in both parallel and series arrangements. As a result, in this study, a bidirectional high‐efficiency inverter with fewer components is provided. The design, modes of operation, and performance metrics of the DAB converter are examined, emphasizing its ability to achieve zero‐voltage switching (ZVS) and zero current switching (ZCS) throughout its operating range. The suggested system seeks to maximize EV power management, guaranteeing high dependability and efficiency. To test all of the aforementioned qualities, an evaluation version was created, with an average efficiency of 97.5%. This research could have a substantial impact on the advancement of power electronic converters for automotive applications, leading to better EV power management, increased system reliability, and increased overall efficiency.

An Energy Management System for Distributed Energy Storage System Considering Time-Varying Linear Resistance

As the proportion of renewable energy in energy use continues to increase, to solve the problem of line impedance mismatch leading to the difference in the state of charge (SOC) of each distributed energy storage unit (DESU) and the DC bus voltage drop, a distributed energy storage system control strategy considering the time-varying line impedance is proposed in this paper. By analyzing the fundamental frequency harmonic components of the pulse width modulation (PWM) signal carrier of the converter output voltage and output current, we can obtain the impedance information and, thus, compensate for the bus voltage drop. Then, a novel, droop-free cooperative controller is constructed to achieve SOC equalization, current sharing, and voltage regulation. Finally, the validity of the system is verified by a hardware-in-the-loop experimental platform.

Virtual Inertia Extraction from a DC Bus Capacitor in a Three−Phase DC/AC Inverter-Based Microgrid with Seamless Synchronisation Operation Modes

It is crucial to employ power electronics converters for energy transfer in distributed energy resources such as photovoltaics, and wind energy system. Thus, parameters such as voltage, current, active and reactive powers, and frequency referred to the AC side need to be controlled. The local load could be operated either from converter itself or grid, hence the system could be called microgrid. In this paper, the active and reactive powers would be controlled separately when the inverter is connected to the grid. While in the islanded or autonomous mode, the proposed control would support the voltage and frequency. The proposed controller would be designed and function as the synchronous machine’s voltage regulator and governor. The virtual synchronous machine is adopted to obtain the inertia and the concept is that the frequency of the grid is linked to the virtual frequency or so called the virtual speed of the rotor. The virtual frequency is obtained directly from the DC bus voltage of the inverter and this is achieved by allowing the DC link capacitor voltage to swing boarder than the grid frequency by making the capacitor voltage imitating frequency of grid. Where the frequency of grid is associated to the virtual frequency which is derived directly from DC capacitor voltage, thus, large level of inertia is extracted. The basic formulation, supervisory control, extraction the inertia from DC capacitor voltage, coordination transition, and evaluation the stability is presented in this paper. The mimic operation of the inverter as Synchronous Machine SM can make the distributed energy resources to be operated in either grid connected or islanded modes without the need to adopt control structure for the required mode. In addition, it provides robust performance in comparison with the traditional current, voltage and frequency control approaches. Moreover, the controller would be implemented for inverter having any type of filters and it is resilient to any variations in the filter and grid parameters. Furthermore, there is no concerning regarding instability problems, where it achieves the synchronisation smoothly and swiftly, and it is appropriate for implemented digitally.

A new DC bus voltage control strategy for energy routers based on distributed coordination optimization

Abstract The types of power ports of energy routers at the side of the distribution network platform are complex and have different characteristics, which makes the operation conditions of energy routers complex and changeable. Although many pieces of literature have proposed more relevant control strategies to effectively realize the efficient and stable operation of energy routers under the two basic working conditions of grid-connected and isolated islands. However, when considering various complex operating conditions such as start-up, overload/overcharge, sudden failure of distribution network, grid-connected, and inter-island switching, the reliable operation of the existing energy routers is still faced with great challenges. In this paper, a DC bus voltage control strategy based on distributed coordination optimization is proposed for typical operating conditions of energy routers. Simulation results show that this strategy can effectively achieve the DC bus voltage control performance of energy routers under various burst operating conditions and strengthen the robustness of output voltage of energy routers under isolated islands.

Voltage control strategy of output bus of grid-connected port of energy router based on energy feedback

Abstract The remaining power conversion ports are equivalent to constant power loads for the grid-connected port of the voltage source. The negative impedance characteristics of the constant power loads will worsen the voltage source characteristics of the grid-connected port and even cause system instability. According to the influence mechanism of constant power load on the DC bus voltage control performance during grid-connection, a new voltage control strategy based on energy feedback is proposed in this paper. The simulation results show that the proposed control strategy can more effectively reduce the negative impedance characteristics of the energy router’s constant power load and improve the bus voltage’s control performance compared with the existing control strategies.

Three-Level Double LLC Resonant Converter with Ripple-Free Input Current for DC Microgrid Application

DC distribution systems have garnered interest in recent years due to their advantages over AC distribution systems, such as their high power conversion efficiency and lack of harmonic issues. An isolated DC-DC converter with low-input noise, high efficiency, and high-power density is required for DC microgrids within DC distribution systems. Although existing DC to DC converters have high efficiency and high power density, they still have input noise problems due to pulsating input currents. Thus, a large input filter should be inserted, which increases the cost and degrades the power density. In this study, a novel three-level double LLC resonant converter with zero-input-current ripple is presented for DC microgrid application with a high-voltage DC bus. The input-current ripple of the proposed converter theoretically decreases to zero without adding large input filters. Moreover, the voltage stress on each main switch is only half of the input voltage when using the modified three-level structure, which enables the use of low-voltage-rated power switches for high-voltage input applications. In addition, all the primary switches and secondary diodes are softly switched over a wide input voltage range. The experimental results of the prototype are presented to verify the performance of the proposed converter.

Data-based power management control for battery supercapacitor hybrid energy storage system in solar DC-microgrid.

This paper addresses the energy management control problem of solar power generation system by using the data-driven method. The battery-supercapacitor hybrid energy storage system is considered to smooth the power fluctuation. A new model-free control method is utilized in the stand-alone photovoltaic DC-microgrid to provide the power to meet the demand load, while guaranteeing the DC bus voltage is stable. Furthermore, the proposed data-based power management control strategy only needs I/O data. Numerical simulations with real data verify the effectiveness of the proposed method.

Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell

Fuel cell electric vehicles (FCEVs) are among the devices that have emerged in recent years. To provide electricity to the electric motors, they use a proton-exchange membrane fuel cell (PEMFC) as the primary energy source and a secondary source consisting of an energy storage system (battery or supercapacitors). The addition of these sources to the motors and accessories of a vehicle requires the association of static converters to condition the different power sources. In addition, a high-efficiency and enhanced-reliability power converter is essential to connect the PEMFC to the vehicle’s DC bus. This paper proposes a robust feedback controller for a four-phase interleaved boost converter used with PEMFC. The proposed controller has double loops based on a state-feedback controller, and an inner loop which translates the differential equation of the system into a state representation by linearization around its operation points. The reference current is generated by state feedback in the outer loop; the state variable is defined by using a change variable. The strong robustness and highly dynamic characteristics of the proposed controller are demonstrated through its performance in terms of output voltage, source current, and settling time. The findings indicate that the proposed controller achieves a response time of 20 ms, resulting in an over 50% improvement compared to the controllers referenced in the literature. Additionally, it reduces both current and voltage ripple, keeping them each below 10%. Further, the controller gains synthesis is validated using the linear quadratic regulator (LQR) technique as well as boundary conditions, and its robustness is verified, taking into account the uncertainty of various operating conditions and discrepancies in circuit components. A double-loop super-twisting sliding mode controller, a backstepping control algorithm, and a PI controller are selected for comparison and discussion. Subsequently, the effectiveness of the proposed controller is evaluated through simulation with the parameters of a 500 W fuel cell system.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system’s reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor’s increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array’s operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.

Fixed-Time Backstepping Sliding-Mode Control for Interleaved Boost Converter in DC Microgrids

Interleaved boost converters (IBCs) are commonly used as interface converters for DC microgrids (MGs) due to their high efficiency and low output ripple. However, the MGs system can easily become unstable due to the negative impedance characteristics of constant power load (CPL) and rapid power fluctuations. This paper proposes a fixed-time backstepping sliding-mode controller (FTBSMC) aimed at stabilizing the MGs system and achieving fixed-time tracking of the DC bus voltage. Firstly, the fixed-time disturbance observer (FxTDO) estimates the load disturbance at a fixed time, which improves the fast disturbance resistance of the system. Then, based on the dis-turbance estimation, the FTBSMC is designed, which combines the fast dynamic response of the sliding-mode control with the global stability of the backstepping control, avoiding the singularity problem of the conventional sliding-mode control. In addition, a first-order nonlinear filter is employed to avoid the direct differentiation of conventional backstepping control and at the same time to ensure global fixed-time stability. The fixed-time convergence of the proposed FTBSMC is rigorously demonstrated by using Lyapunov stability analysis. Finally, the FTBSMC proposed is verified by simulation and experiment in terms of faster dynamic response and stronger robustness.

Dynamic control of grid-following inverters using DC bus controller and power-sharing participating factors for improved stability

Integrating Grid-Following Inverters (GFLs) into power systems presents significant stability challenges, particularly in systems with reduced strength and high renewable energy penetration. This study delves into the dynamic interactions between parallel synchronous generators (SGs) and GFLs, highlighting the impact of reduced system inertia and transient stability margins. A novel DC bus controller is proposed to enhance the inertia and stability of GFLs during grid disturbances by dynamically adjusting power references based on load demand. Through mathematical modelling, small signal analysis, and MATLAB simulations, the study evaluates the effects of inverter-based resources (IBRs) on power system stability. The proposed GFL configuration demonstrates improved stability control, achieving an 80 % increase in stability under weak grid conditions. This paper provides insights into optimized control strategies, emphasizing the importance of power-sharing participation factors (PSPF) for effective GFL integration in modern power systems with high renewable energy penetration.

Unit prediction horizon [formula omitted] based model predictive control for the fuel cell based plug-in hybrid electric vehicle with rule-based energy management system

Plug-in hybrid electric vehicles (PHEVs) provide a good alternative in achieving better performance and in the reduction of harmful gas emissions. The hybrid energy storage system (HESS) and the integrated charging unit constitute the PHEV under consideration. To meet the load demands, the proposed HESS is a coupled system comprising a fuel cell, high energy density battery, and high-power density super-capacitor. On-board charging involves the utilization of a DC–DC buck converter and an uncontrolled rectifier and two buck-boost converters are utilized to facilitate a smooth transfer of energy. A rule-based supervisory controller has been implemented for different load conditions which takes into account the state of charge of energy sources and also the total power inflow of the power sources. A model predictive control (MPC) technique is implemented to ensure that PHEVs function smoothly in terms of regulation of DC bus voltage and tracking of current. Unit prediction horizon i.e. only one step ahead looking into the future is considered for the MPC to make it computationally less expensive. MPC is designed in such a way that its performance is close to our favorite linear controller which in our case is the H∞ controller. The inverse optimal control technique is used for determining the weight matrices of the cost function. The proposed controller has been simulated using MATLAB. Also, the performance comparison is made between the designed MPC and the non-linear control approach i.e. integral sliding mode control and integral backstepping-based control to show that it achieves comparable or superior performance to the non-linear controller. The real-time performance of H∞ based MPC is verified using controller hardware in the loop experimental setup.

A robust sliding mode control strategy for DC voltage stabilization in solar powered electric vehicle charging station

ABSTRACT Integrating DC power sources and AC grid in an electric vehicle charging station through converters can introduce oscillations, potentially leading to system instability. This paper explored the utilization of a sliding mode controller in a bidirectional DC-DC buck-boost converter for DC bus voltage control within the charging station. This controller aims to promptly respond to power fluctuations, offering robustness against variations in system parameters, external disturbances, and uncertainties. Control strategies play a critical role in mitigating DC link voltage fluctuations and ensuring power stability. The system entails a photovoltaic array employing maximum power point tracking for optimal energy harnessing. In addition, a bidirectional buck-boost converter is employed to effectively manage battery charging in buck mode and discharging in boost mode. A comparative analysis between Proportional-Integral control and sliding mode control incorporating a washout filter was conducted. This assessment is simulated and validated using MATLAB/Simulink, and the Sim Power System toolbox. The proposed model offers a comprehensive solution for integrating solar power, battery storage, and grid-based charging by enhancing overall system efficiency and reliability.