Battery Hybrid Energy Storage Research Articles

Rule-Based Dual Planning Strategy of Hybrid Battery Energy Storage System

A reasonable and efficient scheduling strategy does not only help ensure the safe and stable operation of battery energy storage system, but also extend the battery cycling life and reduce the system overall costs. In this paper, a novel rule-based dual planning strategy is proposed to achieve refined management for the hybrid battery energy storage system, including lead-acid battery storage system and lithium iron battery storage system. Aiming at different characteristics and SOC value of different batteries, the upper layer of the proposed scheduling strategy is designed to determine whether the hybrid battery energy storage system is enabled and grid-connected and in the lower layer, the battery scheduling controller will cooperate the operation of different batteries to realize shallow circulation of the lead-acid battery storage. Various simulations are conducted in the environment of Matlab/Simscape, and the simulation results have proved the feasibility and effectiveness of the proposed scheduling strategy.

A comparative study between lithium-ion battery and Pb-acid battery-supercapacitor hybrid energy storage system for frequency control and Energy management of islanded microgrids

Among various storage technologies used for the energy storage systems, the supercapacitors, the Pb-acid-Batteries (PABs) and the lithium-Batteries (LBs) are widely used for microgrid applications. The supercapacitors with high-power density are suitable for fast power regulations; conversely, the PABs have high-energy density, which makes them suitable for long-term energy management. Since the PABs and the supercapacitor can complement each other deficiencies, their combination as a hybrid energy storage system is used. However, the LB has both high-energy and high-power densities. Therefore, an LB Energy Storage System (LBESS) can similarly function like a Pb-acid battery-supercapacitor-hybrid-ESS (PSHESS). This paper tends to determine which one is technically and economically more suitable for applications in islanded microgrids. For this purpose, a frequency control and energy management scheme is proposed. It maintains the balance between demand and supply, and also keeps the microgrid frequency within safe operational limits using the least needed sizes for the energy storage systems. Simulation results reveal the costs of LIBESS and PSHESS would be $140325.93 and $209408.37, respectively, which shows that the cost of the PSHESS is $69082.44 or almost 49.2 % more than the cost of the LIBESS. This indicates that the LBESS is more cost-effective than the PSHESS.

Research on Model Predictive Controlled HESS for Seamless Mode Switching of DC Microgrid

The mode switching of DC microgrids from grid-connected mode to islanded mode can lead to serious dynamic power deficiency and DC bus voltage transient processes. The superconducting magnetic energy storage (SMES)-battery hybrid energy storage system (HESS) with multi-mode model predictive control (MPC) is proposed in this paper. Three cost functions of MPC are designed for different control modes of HESS according to the DC bus signaling. The simulation comparison results show that the proposed control method can improve the dynamic and static control performance, thus stabilize the DC bus voltage level in both grid-connected mode and mode switching process.

Implementation of a predictive energy management strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles1

Hybrid energy storage system supplies a feasible solution to battery peak current reduction by introducing supercapacitor as auxiliary energy source. Energy management control strategy is a key technology for guaranteeing performance. In this paper, we describe a predictive energy management strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles. To utilize the supercapacitor reasonably, Markov chain model is proposed to predict the future load power during a driving cycle. The predictive results are subsequently used by power distribution strategy, which is designed using a low-pass filter and a fuzzy logic controller. The strategy model is developed under MATLAB/Simulink software environment. To validate the performance of the proposed control strategy, a comparison test is implemented based on a 72 V rated voltage hybrid energy storage system experimental platform. The results indicate that the battery peak currents by proposed predictive control strategy are reduced by 26.32%, 28.21% and 27.12% under the UDDS, SC03 and NEDC three driving cycles respectively.

Energy Management System for Hybrid PV/Wind/Battery/Fuel Cell in Microgrid-Based Hydrogen and Economical Hybrid Battery/Super Capacitor Energy Storage

The present work addresses the modelling, control, and simulation of a microgrid integrated wind power system with Doubly Fed Induction Generator (DFIG) using a hybrid energy storage system. In order to improve the quality of the waveforms (voltages and currents) supplied to the grid, instead of a two level-inverter, the rotor of the DFIG is supplied using a three-level inverter. A new adaptive algorithm based on combined Direct Reactive Power Control (DRPC) and fuzzy logic controls techniques is applied to the proposed topology. In this work, two topologies are proposed. In the first one, the active power injected into the grid is smoothened by using an economical hybrid battery and supercapacitor energy storage system. However, in the second one, the excess wind energy is used to produce and store the hydrogen, and then a solid oxide fuel cell system (SOFC) is utilized to regenerate electricity by using the stored hydrogen when there is not enough wind energy. To avoid overcharging, deep discharging of batteries, to mitigate fluctuations due to wind speed variations, and to fulfil the requirement of the load profile, a power management algorithm is implemented. This algorithm ensures smooth output power in the first topology and service continuity in the second. The modelling and simulation results are presented and analysed using Matlab/Simulink.

Optimal voltage of direct current coupling for a fuel cell–battery hybrid energy storage system based on solar energy

A hybrid energy storage system based on a polymer electrolyte membrane fuel cell and a battery is designed and applied using solar energy in this study. The fuel cell and batteries are connected using a direct coupling method. The optimal DC coupling voltage is examined by turning the fuel cell operation on and off. The voltage difference is first evaluated and it is found that a 2 V difference yields the optimal condition with the longest operating time and transition period without system failure. Various on–off couples are then determined. It is found that an on–off​ couple of 49–51 V presents the best transition period, indicating the good harmonized operation between the fuel cell and batteries. Therefore, an on–off couple at 49–51 V for fuel cell operation is the optimal and suitable DC coupling voltage according to this work system.

Simultaneous optimal integration of photovoltaic distributed generation and battery energy storage system in active distribution network using chaotic grey wolf optimization

Goal. The integration of photovoltaic distributed generations in the active distribution network has raised quickly due to their importance in delivering clean energy, hence, participating in solving various problems as climate change and pollution. Adding the battery energy storage systems would be considered as one of the best choices in giving solutions to the mentioned issues due to its characteristics of quick charging and discharging, managing the quality of power, and fulfilling the peak of energy demand. The novelty of the proposed work is the development of new multi-objective functions based on the sum of the three technical parameters of total active power loss, total voltage deviation, and total operation time of the overcurrent protection relay. Purpose. This paper is dedicated for solving the allocation problem of hybrid photovoltaic distributed generation and battery energy storage systems integration in the standard IEEE 33-bus and IEEE 69-bus active distribution networks. Methodology. The optimal integration of the hybrid systems is formulated as minimizing the proposed multi-objective functions by applying a newly developed metaheuristic technique based on various chaotic grey wolf optimization algorithms. The applied optimization algorithms are becoming increasingly popular due to their simplicity, lack of gradient information needed, ability to bypass local optima, and versatility in power system applications. Results. The simulation results of both test systems confirm the robustness and efficiency of the chaotic logistic grey wolf optimization algorithm compared to the rest of the algorithms in terms of convergence to the global optimal solution and in terms of providing the best and minimum multi-objective functions-based power losses, voltage deviation and relay operation time values. Practical significance. Recommendations have been developed for the use of optimal allocation of hybrid systems for practical industrial distribution power systems with the renewable energy sources presence.

Research on the Optimized Operation of Hybrid Wind and Battery Energy Storage System Based on Peak-Valley Electricity Price

The combined operation of hybrid wind power and a battery energy storage system can be used to convert cheap valley energy to expensive peak energy, thus improving the economic benefits of wind farms. Considering the peak–valley electricity price, an optimization model of the economic benefits of a combined wind–storage system was developed. A charging/discharging strategy of the battery storage system was proposed to maximize the economic benefits of the combined wind–storage system based on the forecast wind power. The maximal economic benefits were obtained based on scenario analysis, taking into account the wind-power forecast error, and costs associated with the loss of battery life, battery operation, and maintenance. Case simulation results highlight the effectiveness of the proposed model. The results show that the hybrid wind–storage system is not only able to convert cheap electricity in the valley period into expensive electricity in the peak period, thus resulting in higher economic benefits, but can also balance the deviation between actual output and plans for the wind power generator to decrease the loss penalty. The analyzed examples show that, following an increase in the deviation of the forecast wind power, the profit of the combined wind–storage system can increase by up to 45% using the charging/discharging strategy, compared with a wind farm that does not utilize energy storage. In addition, the profit of the combined wind–storage system can increase by up to 16% compared with separate systems, following an increase in the deviation penalty deviation coefficient.

Methodology for the Optimisation of Battery Hybrid Energy Storage Systems for Mass and Volume Using a Power-To-Energy Ratio Analysis

Increasingly stringent emission regulations and environmental concerns have propelled the development of electrification technology in the transport industry. Yet, the greatest hurdle to developing fully electric vehicles is electrochemical energy storage, which struggles to achieve profitable specific power, specific energy and cost targets. Hybrid energy storage systems (HESSs), which combine energy- and power-optimised sources, seem to be the most promising solution for improving the overall performance of energy storage. The potential for gravimetric and volumetric reduction is strictly dependent on the overall power-to-energy ratio (PE ratio) of the application, packaging factors, the minimum and maximum PE ratio achievable for the system’s energy- and power-optimised sources and the performance of power electronics. This paper presents a simple optimisation methodology that considers these factors and identifies the optimal HESS requirements that may present new opportunities for a variety of vehicles where low weight and volume are of high importance. The simplicity of the method means that decisions relating to a HESS can be made earlier in the system design process. This method of analysis showed that a battery HESS has the potential to reduce cell mass and volume by over 30% for applications that are well suited to optimal HESS characteristics.

Hybrid improved Sparrow Search Algorithm and sequential quadratic programming for solving the cost minimization of a hybrid photovoltaic, diesel generator, and battery energy storage system

ABSTRACT In this paper, a hybrid photovoltaic/battery/diesel generator power generation system is designed and optimized to power a network of a remote village in China. The purpose of the system designed here is to lessen the fuel costs of the system dependent on the load demand and some constraints. Therefore, the idea comprises an optimization problem that is solved here based on a new optimization policy by an Improved design of Sparrow Search Algorithm (imSSA) and the Sequential Quadratic Programming (SQP). The results of the proposed method are analyzed in different seasons and also on weekdays and weekends to show their effect on the operational cost of the PV/Diesel BESS system. Final results indicate that in both winter and summer, the weekday costs are lesser than weekend fuel costs. Furthermore, the summer fuel cost is lesser than the winter fuel costs which is because of lesser demand in summer and the higher levels of summer radiation imply less usage of auxiliary origins. The results show that at the weekends cost more than the weekday. For instance, for the proposed mode, the winter weekends and the weekdays’ costs are US$12.1 and US$10.2, respectively, or the summer weekends and the weekdays’ costs are US$7.3 andUS$5.7, respectively.

Robust Partial Feedback Linearized Controller Design for Standalone Hybrid PV-BES System

This paper presents a mixed-sensitivity-based robust H∞ loop-shaping partial feedback linearized control scheme to enhance the transient stability of a battery energy storage-associated standalone solar photovoltaic system. The proposed control scheme has been provided independent operating points for a generalized nonlinear dynamical model of DC microgrids connected standalone hybrid solar photovoltaic and battery energy storage system. A parametric uncertainty model is developed for the generalized dynamical model, and the noise disengaging merit of the proposed control technique has been investigated. The designed controller’s performance has been demonstrated under four different scenarios, and it is compared with the conventional PI controller for partial feedback linearized control law.

A data-mining based optimal demand response program for smart home with energy storages and electric vehicles

In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.

Integrated Battery Energy Storage into an Optimal Low Voltage Distribution System with PV Production for an Urban Village

The feed-in tariff for rooftop photovoltaic (PV) systems has not yet been adopted in Cambodia, the utility thus does not purchase excess power by reverse power flows from PV production, and only unidirectional energy meters are used even in the capital. The power distribution systems are expanded in recent years due to load demand increase, and this issue makes the distribution system designers perform the planning. The design of a low voltage distribution system and suitable solutions are proposed to handle these issues. This paper purposes of designing an optimal low voltage distribution topology with the integration of PV and three different proposed solutions of reverse power relay (RPR), solar hybrid inverter (SHI) and battery energy storage (BES) in an urban village, which consists of improving a balanced load and existing PV production usage. Firstly, the repeated phase ABC is applied to minimize the power loss and unbalanced load in the three-phase distribution system. Next, the integration of RPR, SHI, and BES into household grid-connected PV production systems have been proposed as solutions. To confirm a proposed method, the urban village in Phnom Penh, Borey Mungkul Phnom Penh, Sangkat Veal Spov, is chosen and modelled for a case study. Simulation results allow concluding that the integrated battery energy storage into the optimal low voltage distribution topology provides the best solution to design the grid connection PV system.

Decentralised control strategy for hybrid battery energy storage system with considering dynamical state‐of‐charge regulation

Hybrid battery energy storage system (HBESS) consists of high power density battery and high energy density battery will have a bright future in special isolated DC microgrid conditions such as the all-electric ships and all-electric airplanes, which have strict limitation on storage capacity and size. In this study, a new decentralised control strategy based on mixed droop is proposed to HBESSs with considering the batteries. In decentralised control strategy, conventional V–I droop controller is utilised to high energy density battery to mainly supply the steady power, I–V droop controller is utilised to high power density battery to respond to power change and supply a few steady power. In addition, dynamical state-of-charge (SoC) regulation algorithm is utilised to reassign the battery power according to their own SoC. The power coordination of the high energy density batteries and high discharge rate batteries is achieved by adjusting the values virtual impedance and reference input voltage. Case study shows that the proposed control strategy is flexible and efficient.

A Novel Weighted Superposition Attraction Algorithm-based Optimization Approach for State of Charge and Power Management of an Islanded System with Battery and SuperCapacitor-based Hybrid Energy Storage System

Nowadays the Renewable Energy Sources (RESs) are being endorsed by the population extensively. So, to exploit the RESs effectively, the concept of Energy Storage System (ESS) has been described in the literature. An ESS can balance the instability and the power demanded by the load, continuing its study and reliable nature. So far, Battery has been proved to be a leading energy storage device as compared to other devices and can be used as an ESS. But a solitary ESS fails to satisfy the assets in the matter of lifespan, cost, durability, dynamic response, and capability. So, to overcome these problems, a Hybrid Energy Storage System (HESS) using a Battery and a SuperCapacitor (SC) has been proposed in this framework. A SC can diminish the strain of the Battery and function as a buffer to counter the oscillations in power. The system is designed using MATLAB/Simulink domain and the simulation output shows an effective dynamic response of the HESS than that shown by an ESS alone. A PID controller is included that operates the parameters such that the system can yield quick feedback. However, the demerit of the PID controller is that it cannot respond to nonlinearity in a system. So, to dynamically tune the PID controller parameters for optimal operation, a robust Weighted Superposition Attraction Algorithm (WSAA) has been implemented in this work. For justifying the enhancement in systems transient stability, Total Harmonic Distortion (THD) analysis has been done and the values obtained are well within the IEEE-517 limits..

Research and implementation of new-type supercapacitor and battery hybrid energy storage system

When a dump truck brakes, it is difficult to effectively absorb the braking energy due to the transient mutation of braking energy. At the same time, braking energy production is too high to store easily. Focusing on these problems, this paper proposes a new type of two-stage series supercapacitor and battery (SP&B) hybrid energy storage system (ESS). Using the characteristics of supercapacitors, which can charge and discharge quickly with large currents, the system combines the supercapacitors with a battery with a high energy storage capacity. This allows sudden large currents to be absorbed and released rapidly, while large amounts of energy can be stored. This system can also reduce the charge and discharge times of the SP&B to meet the working requirements of dump truck braking while improving the service life of the system. After completion of the research and implementation of the proposed SP&B hybrid ESS, a laboratory dynamic model was set up as an experimental platform and vehicle operation experiments were performed. The obtained results show that the energy recovery efficiency of the hybrid ESS can reach 53.5%, which can realize both security and stability for dump truck braking energy recovery and utilization (BER&U) systems.

A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles

Abstract Hybrid energy storage systems have attracted more and more interests due to their improved performances compared with sole energy source in system efficiency and battery lifetime. This study aims to propose a real-time energy management control strategy for achieving these goals. The strategy is based on a combination of wavelet transform, neural network and fuzzy logic. Wavelet transform is an effective tool in extracting different frequency components of load power demand to match the characteristics of battery and supercapacitor. However, it is hard to be directly applied in a real-time system. For this, a neural network model, which is offline trained using the dataset obtained from the wavelet transform decomposition, is developed to online predict the low frequency power demand for the battery. Accordingly, the high frequency power demand is online calculated and distributed to the supercapacitor. In addition, a fuzzy logic based supervisory controller is further developed for controlling the supercapacitor voltage within a certain suitable range. Finally, a 72 V battery and 96 V supercapacitor hybrid energy storage system real-time hardware platform has been developed to validate the effectiveness of the proposed energy management control strategy.

A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems

Abstract The hybrid energy storage system is a kind of complex system including state coupling, input coupling, environmental sensitivity, life degradation, and other characteristics. How to accurately estimate the internal state of the system, delay the battery life degradation, realize the coordinated and optimized control of power and energy have become the focus and difficulty of the hybrid energy storage system. With the application and popularization of hybrid energy storage systems in electric vehicles and smart grids, relevant theoretical and technological breakthroughs become more and more urgent. Many new achievements, new theories, new methods and new technologies from the fields of materials, information, energy, control and artificial intelligence have been put into this field. This paper comprehensively reviewed the key issues for control and management in hybrid energy storage systems from the aspects of multi-scale state estimation, aging mechanism investigation, life prediction, and energy optimization control of the hybrid energy storage system. Through the in-depth review of key scientific issues, the latest theoretical techniques and application results are presented. Finally some future research challenges and outlooks are presented, in hopes to provide some new ideas and inspirations for the future investigation of the hybrid energy storage systems.

Supercapacitor/battery hybrid energy storage unit for brushless DC motor operation

In this study, a supercapacitor (SC)/battery hybrid energy storage unit (HESU) is designed with battery, SC and metal–oxide–semiconductor field-effect transistors. Combined with the operation of brushless DC motor (BLDCM) and the output mode of the proposed HESU, the vector combinations that are suitable for different operation states of the BLDCM are analysed. By using reasonable vector combinations, the HESU can utilise the SC to store the regenerative energy from BLDCM during the braking process and assist the battery to power the motor during the acceleration process. Also, the designed HESU can achieve high-voltage output by connecting the SC and battery in series, to suppress the commutation torque ripple of BLDCM in the motoring state. Finally, the correctness of the theoretical analysis and the feasibility of the proposed method are verified by the experiment results.

Artificial neural network-based dynamic power management of a DC microgrid: a hardware-in-loop real-time verification

In this paper, the performance of a DC microgrid has been examined among photovoltaics modules, loads and hybrid energy storage system (HESS). The primary goal of this article is to construct and verify the proposed artificial neural network (ANN)-based control system for a DC microgrid (DCMG). To exploit the energy source maximum and to regulate the DC bus voltage of HESS, an ANN-based dynamic power management control system for a DCMG is proposed and implemented to manage power-sharing among photovoltaics, loads, hybrid battery and supercapacitor energy storage to address the demand–generation disparity. The proposed control strategy employed on DC microgrid with HESS has been simulated and compared with the existing techniques in Matlab® environment. Furthermore, the results have been experimentally verified in hardware-in-loop (HIL) on OPAL-RT real-time simulator.