Hybrid Energy Storage System Research Articles

Efficiency Maximization of Stand-Alone HRES Based on Tri-Level Economic Predictive Technique

Renewable energy has been widely used in grid-connected and standalone hybrid renewable energy systems. These systems require a hybrid energy storage system due to the unpredictable climate and the inequality between the produced energy and the consumed energy. In this paper, a tri-level optimization method is used to optimize the sizing and the energy management of a standalone HRES, simplify the proposed optimization problem, and speed up the convergence process. Horizon prediction and weighting factor strategies are combined with the tri-level technique to find the most appropriate quantity of each element in the project and find the best energy management strategy. The objective function of the proposed methodology aims to minimize the total cost and improve the efficiency of the whole system. The proposed method was investigated on a standalone PV-WT with battery-hydrogen storage in different scenarios. The simulation results from the Matlab toolbox show that the performance indicators (cost and efficiency) are affected by the combination of the weighting factor and the forecasting index. The total productivity was improved by more than 2.5% in some scenarios while the investment cost and the running cost were reduced by values of 49.3% and 28.6%, respectively.

A New Hybrid Energy Storage System for Electric Vehicle Drive System

In this paper, a new Hybrid Energy Storage System (HESS) for Electric Vehicle (EV) drive systems is proposed to increase their battery lifespan, with the potential to meet peak power demands without heavily straining the batteries. The developed feedback control circuit works as a controller to maintain the voltage of the Supercapacitor (SC) at a value higher than the battery voltage during the high acceleration periods of the driving cycle, creating a relatively constant load profile for the battery. The battery is not used to directly harvest energy from the regenerative braking, being, thus, isolated from frequent charges during high acceleration/deceleration periods, which increases its lifespan. The simulation results demonstrate HESS's effectiveness in significantly reducing battery discharge/charge rates compared to a standalone system.

Integrated optimization for sizing, placement, and energy management of hybrid energy storage systems in renewable power systems

Power systems reliant on renewable energy sources (RES) encounter supply-demand imbalances and stability challenges due to their inherent uncertainties. Hybrid energy storage systems (HESS) have emerged as a flexible and cost-effective solution to address these issues. This paper proposes an integrated optimization method for the capacity, location, and energy management of a HESS in RES-based power systems. The method optimizes battery energy storage system (BESS), electrolyzer (EL), fuel cell (FC), and hydrogen storage tank (HST) to minimize total costs, including power purchase, curtailment compensation, operation and maintenance (O&M), and investment costs, alongside voltage profile deviation index (VPDI) and active power loss index (APLI). A novel hybrid framework is proposed, utilizing mixed-integer linear programming (MILP) optimization at the master level and non-dominated sorting genetic algorithm II (NSGA-II) optimization at the slave level. This framework ensures that MILP provides robust solutions, while NSGA-II derives balanced optimal solutions from the optimal Pareto front. The proposed method was validated using real seasonal data from Korea and tested in IEEE 33 and IEEE 69 bus systems against various benchmark cases. Results indicate significant improvements, with VPDI enhanced by 16.00 % and 10.11 %, APLI by 15.64 % and 12.15 %, and total costs reduced by 58.67 % and 50.56 %, respectively. The sensitivity analysis demonstrated the robustness of the proposed method, showing zero sensitivity to iterations and positive correlations with increased levels of RES. Further verification against other optimization algorithms showed that the proposed method excels in cost efficiency, voltage stability, and line loss reduction, providing highly reliable results.

Improving efficiency of wireless charging system in electric vehicle using a hybrid ultracapacitor-battery energy storage approach

This research aims to enhance the efficiency of wireless charging systems in electric vehicles by integrating a hybrid ultracapacitor-battery energy storage solution. Traditional standalone battery-based energy storage systems in wireless charging often face sub-optimal charging efficiency, resulting in extended charging times and reduced energy transfer efficiency. To address this limitation, we propose a hybrid approach that combines the rapid charging capability of ultracapacitor (supercapacitor) with the long-term storage capacity of batteries. The optimal charging range is 0 cm to 2 cm, and the combined output voltage and current are 5 V to 12 V and 0.63 A, respectively. This hybrid energy storage system will significantly boost electric vehicles (EVs) charging efficiency. Our research involves experimental evaluation and data analysis to assess crucial parameters, including charging efficiency, energy transfer efficiency, and charging time. The experimental results are validated and compared against existing battery-only systems, shedding light on the advantages and limitations of the hybrid approach. This study contributes to the optimization of wireless charging systems, enhancing energy transfer efficiency, and promoting the broader adoption of wireless charging technology in electric vehicles.

Grid-forming Control for Power Converters Based on Hybrid Energy Storage Systems During Islanding Operation

Grid-forming Control for Power Converters Based on Hybrid Energy Storage Systems During Islanding Operation

Integrated electric vehicle design and simulation: Combining a mechanical and electrical model using Simscape/Multibody

This paper presents a novel methodology for modeling and implementing an electric vehicle (EV) simulation model using the Multibody approach. The proposed method provides a robust solution for studying EV performance, taking into consideration the dynamic behavior of its complex mechanical system. The mechanical design was carried out using SOLIDWORKS software and then exported to the Simscape/Multibody platform. This process was performed to combine the obtained mechanical design with an electrical model, developed using the Simscape and MATLAB/Simulink toolbox, in the same simulation platform. The proposed EV is powered by an electromechanical PowerDrive based on a permanent magnet synchronous machine (PMSM), supplied by a hybrid energy storage system (HESS). The implemented HESS, operating in a fully active topology, is controlled by a deterministic rule-based energy management system (EMS). To evaluate the proposed model performance, simulations were conducted under ECE-15.

Internet of things important roles in hybrid photovoltaic and energy storage system: a review

Internet of things important roles in hybrid photovoltaic and energy storage system: a review

Neural network controller for hybrid energy management system applied to electric vehicles

Hybrid energy storage systems based on batteries and supercapacitors can mitigate the aging of electric vehicle batteries aging by avoiding high currents and rapid discharge cycles. This system requires energy management systems that efficiently split the power during real driving cycles. This paper outlines a design methodology for creating a Multilayer Perceptron neural controller that governs the power distribution between the storage system components. In parallel, a Proportional-Integral-Derivative controller was implemented to ensure voltage regulation in the primary DC bus, ensuring stable operation of the entire system and providing flexibility to the neural design. The controller was tuned using particle swarm optimization, hippopotamus optimization, and differential evolution algorithms, designed to minimize the battery root-mean-square current in a comprehensive vehicle simulation. The training was structured into layers to approach the physical problem and controller optimization facets. The controller’s primary goal is to minimize the battery strain, mitigating stress events and prolonging its lifespan. The energy management neural controller was designed using a simple drive cycle and later validated with a realistic cycle. The findings demonstrate the feasibility of achieving a significant reduction in current, both in peak value and on average, especially when compared to basic energy management strategies. The process uncovered a strategy that prioritizes the use of the supercapacitor in power balance, with this effect being more pronounced during critical load events. The main bus voltage remained stable, with no deviations exceeding 5%, owing to the voltage regulator’s stability. Additionally, the particle swarm optimization and the hippopotamus optimization techniques exhibited notable performance compared to the differential evolution algorithm for this problem. Subsequently, the design was evaluated in a more complex cycle, revealing a slight decrease in average battery current performance. However, it still maintained a significant reduction in peak battery current compared to traditional controllers. Nevertheless, this methodology demonstrated power management optimization efficacy and can be extended to more complex scenarios.

A multi-objective optimization algorithm-based capacity scheduling method for photovoltaic power hybrid energy storage systems

In this study, the combination of crossover algorithm and particle swarm optimization—crossover algorithm-particle swarm optimization (CS-PSO) algorithm—to optimize photovoltaic hybrid energy storage scheduling, improving global search and convergence speed, is discussed. The new method reduces energy storage costs and energy losses, ensures supply–demand balance and interaction power constraints, and maintains population diversity through cross-search. The CS-PSO algorithm introduces battery state of charge optimization for energy storage scheduling, improving global search and convergence speed, and obtaining accurate multi-objective scheduling results. The experiment shows that the optimal configuration for photovoltaic energy storage is 10 045 batteries + 687 244 supercapacitors, with a cost of 3.452 × 105 yuan and an energy loss of less than 5%. CS-PSO has similar costs but lower losses and faster convergence compared to traditional methods.

Modular multilevel converter-based hybrid energy storage system for electric vehicles: Design, simulation, and performance evaluation

ABSTRACT Electric vehicles (EVs) are critical to reducing greenhouse gas emissions and advancing sustainable transportation. This study develops a Modular Multilevel Converter-based Hybrid Energy Storage System (HESS) integrating lithium-ion batteries (BT) and supercapacitors (SC) to enhance energy management and EV performance. A control strategy equalizes voltage across SC modules, achieving deviations within 1% of reference values, while optimizing energy transfer for peak demands. Simulations using PSIM software demonstrate a 12% improvement in energy efficiency and a 15% enhancement in dynamic response compared to battery-only systems. The optimized energy flow extends battery life by 20%, reducing overall ownership costs. Evaluations under the New European Driving Cycle (NEDC) validate the system’s performance, with recorded energy efficiencies of 87% for urban driving, 89% for suburban conditions, and 92% for highways. Supercapacitor modules respond to peak demands within 0.8 seconds, delivering a maximum current of 170 A during acceleration. This innovative approach ensures balanced energy distribution, reduces voltage fluctuations, and increases overall system robustness. Future work will focus on experimental validation under real-world conditions and integrating advanced SC materials to enhance performance. This work bridges a critical gap in energy storage systems for EVs, contributing to cleaner transportation solutions and aligning with global sustainability goals.

Multi-timescale hierarchical dispatch strategy of hybrid energy storage for multiple auxiliary service markets

As a flexible regulatory resource, hybrid energy storage system (HESS) is capable of providing multiple reliable ancillary services, which improves the adaptability of the distribution system to large-scale grid connection of the distributed generation (DG) and alleviate the pressure of peak load and frequency response. In this context, this paper proposes an optimal dispatch strategy of a HESS for DG electricity production and multiple auxiliary service markets to create stackable benefits for HESS operators. Firstly, different types of energy storage system (ESS) (energy-based and power-based) are unified to the joint optimal framework of peak shaving (PS), frequency containment reserves (FCR), and secondary frequency regulation (SFR). By constructing a virtual ESS model based on the idle-time reuse response, an optimal bidding strategy for HESS under this revenue combination is proposed to achieve the optimal utilization of resource allocation. Furthermore, an “hourly–minute–secondly” progressive time series is introduced, and a multi-timescale hierarchical dispatch model named “daily baseline–regulation basepoint–real-time regulation” is constructed. Specifically, the PS capacity is allocated day-ahead and a two-stage capacity allocation method for FCR and SFR is proposed in the intraday, which realizes the parallel optimal of HESS at the scale of full clearance in the auxiliary service markets. Results show that compared to the combined benefits of two types of ESS, the proposed method achieved the comprehensive income increased by 4.87 % and the auxiliary services income increased by 15.2 %. Under this revenue combination, the SFR market can create an additional 60 %–90 % economic value for HESS operators. The proposed hierarchical optimal model can better adapt to the trading rules of different auxiliary service markets and provide guidance for HESS and DG to further participate in the electricity market.

Capacity configuration optimization of regenerative braking energy utilization system for electrified railways based on power sharing and energy storage

The full utilization of regenerative braking energy (RBE) within the railway traction power supply system (TPSS) is of great significance for railway energy conservation and power grid safety. The traditional railway traction power supply system adopts a segmented structure, and achieving efficient utilization of RBE between adjacent traction substation (TS) is a challenge. This paper proposes an RBE utilization system (RBEUS) based on railway power regulator (RPC) and hybrid energy storage system (HESS), which achieves efficient utilization of RBE between adjacent TS and maximum demand reduction of TPSS through power sharing and energy storage. A power flow control strategy based on state machine logic is proposed, and the energy scheduling reference values of RPC and HESS under different typical operating conditions are calculated. By analyzing the mathematical relationship between the costs and benefits of RBEUS, a multi constraint RBEUS lifecycle net profit optimization model is established. The optimal capacity configuration of RBEUS is obtained by using an improved beluga whale optimization algorithm with the goal of maximizing the total net income of two TSs. The advantages of the proposed method in terms of RBE utilization and economic benefits are verified through engineering case analysis and scheme comparison.

Transient biomass-SOFC-energy storage hybrid system for microgrids peak shaving based on optimized regulation strategy

To address the issues of energy supply instability and peak-shaving in remote microgrids, this paper proposes a biomass-SOFC (Solid Oxide Fuel Cell) -energy storage hybrid system to meet the power demands of the microgrids. Additionally, it integrates the long short-term memory (LSTM) prediction algorithm for peak shaving in the microgrids. Firstly, transient models of biomass gasifier and SOFC and the vanadium redox flow battery (VRFB) model are established, and the experimental results are used to verify the models. Subsequently, the dynamic responses and sensitivity analysis of the biomass gasification process and the internal temperature field of the SOFC are investigated. Next, an analysis is conducted of the differences in VRFB parameters obtained based on the forecasted load and the real load. Finally, the ultimate peak regulation strategy is determined, and the VRFB parameters are reasonably designed. The research results indicate that, following optimization, the designed capacity and maximum power of the VRFB are 6.7 MWh and 1.5 MW, respectively. This result represents a reduction of 23 % and 11.76 % compared to the values before optimization, leading to a total investment decrease of 26.21 % for the energy storage system. In addition, the annual profit from the peak shaving operation of the system is 189,879 dollars.

A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid

ABSTRACTThe intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS.

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.

State of Charge Balancing Control Strategy for Wind Power Hybrid Energy Storage Based on Successive Variational Mode Decomposition and Multi-Fuzzy Control

To address the instability of wind power caused by the randomness and intermittency of wind generation, as well as the challenges in power compensation by hybrid energy storage systems (HESSs), this paper proposes a state of charge (SOC) balancing control strategy based on Successive Variational Mode Decomposition and multi-fuzzy control. First, a consensus algorithm is used to enable communication between energy storage units to obtain the global average SOC. Then, the Secretary Bird Optimization Algorithm (SBOA) is applied to optimize the Successive Variational Mode Decomposition (SVMD) and Variational Mode Decomposition (VMD) for the initial allocation of wind power, resulting in the smoothing power for hybrid energy storage and the grid integration power. Finally, considering the deviation between the current SOC of the storage units and the global average SOC, dynamic partitioning is used for multi-fuzzy control to adjust the initial power allocation, achieving SOC balancing control. Simulations of the control strategy were conducted using Matlab/Simulink, and the results indicate that the proposed approach effectively smooths wind power fluctuations, achieving stable grid integration power. It enables the SOC of the HESS to quickly align with the global average SOC, preventing the HESS from entering unhealthy SOC regions.

Design of a Supercapacitor Module and Control Algorithm for Practical Verification of a Hybrid Energy Storage System

This paper presents an approach to designing a supercapacitor (SC) module according to defined power profiles and providing a control algorithm for sharing the energy from the SC module and accumulator in a hybrid energy storage system (HESS). This paper also presents a view of a printed circuit board (PCB) of the SC module and an interconnection board between the bidirectional converter, accumulator, and SC module. The practical part of the paper presents the measurement of the voltages and currents on the SC module, accumulator, and output of the DC/DC converter to visualize the energy flow between them.

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.

Study on adaptive VSG parameters and SOC control strategy for PV-HESS primary frequency regulation

Hybrid energy storage plays a critical role in primary frequency regulation during large-scale renewable energy integration. Rational power distribution between multiple types of energy storage, as well as the use of a VSG control technique, are effective approaches to improving primary frequency regulation capability. To that end, this paper presents an adaptive Virtual Synchronous Generator (VSG) characteristics and state of charge (SOC) management technique for photovoltaic (PV) - hybrid energy storage system (HESS) primary frequency regulation. First, a power distribution mechanism for HESS on the direct current side is implemented. Second, a primary frequency control strategy is proposed based on adaptive rotational inertia and damping coefficient of VSG and SOC regulation of energy storage. Finally, a simulation analysis confirms the feasibility of the proposed primary frequency control strategy under transient conditions. The relevant results are presented as follows. Compared to traditional control strategies, the improved adaptive VSG parameter and energy storage SOC control strategy reduces the overshoot and adjustment time of VSG active power and frequency response by 68.57%, 23.94%, 19.05% and 9.80%, respectively. The decline in SOC of battery energy storage is decreased by 3.18%. The proposed control strategy also limits grid frequency fluctuation within ±0.2 Hz, with a recovery time of less than 0.5 seconds. The overall root mean square error of frequency fRMSE is a maximum of 0.02 Hz. The primary frequency regulation capability of the PV-HESS is enhanced, ensuring the operational safety of energy storage systems and effectively improving the stability while also improving grid stability and security.

Maximum power point tracking control and power distribution of marine current power system based on hybrid energy storage

Abstract With the development of industry in modern society leading to the energy crisis and the problem of the earth’s environmental pollution, it is especially important to generate electricity from renewable and clean energy sources. Marine current power generation, as a form of marine energy use, is also the object of widespread concern at home and abroad. A system consisting of supercapacitors and batteries can smooth out the fluctuations of the generating power. Power distribution is a necessary step to achieve smooth grid-connected power fluctuation by using the hybrid energy storage system (HESS). In the article, low-pass filter (LPF) and empirical mode decomposition (EMD) algorithms are used to distribute the marine current power generation, and the simulation experimental model of the present system is constructed in the Matlab/Simulink environment. Results of the simulation experiments show that the power curve of the generator side is obtained, and the generation power distribution is realized. Power signals of different frequencies are separated through the power distribution, so as to obtain the reference power of supercapacitors and the batteries.