Battery Hybrid Energy Storage System Research Articles

A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology

This paper develops a multi-objective co-design optimization framework for the optimal sizing and selection of battery and power electronics in hybrid battery energy storage systems (HBESSs) connected to the grid. The co-design optimization approach is crucial for such a complex system with coupled subcomponents. To this end, a nondominated sorting genetic algorithm (NSGA-II) is used for optimal sizing and selection of technologies in the design of the HBESS, considering design parameters such as cost, efficiency, and lifetime. The interoperable framework is applied considering three first-life battery cells and one second-life battery cell for forming two independent battery packs as a hybrid battery unit and considers two power conversion architectures for interfacing the hybrid battery unit to the grid with different power stages and levels of modularity. Finally, the globally best HBESS system obtained as the output of the framework is made up of LTO first-life and LFP second-life cells and enables a total cost of ownership (TCO) reduction of 29.6% compared to the baseline.

Non-droop-control-based cascaded superconducting magnetic energy storage/battery hybrid energy storage system

Existing parallel-structured superconducting magnetic energy storage (SMES)/battery hybrid energy storage systems (HESSs) expose shortcomings, including transient switching instability, weak ability of continuous fault compensation, etc. Under continuous faults and long-term power fluctuations, SMES part in existing SMES/battery HESSs will run out its energy and fail to quickly respond to subsequent disturbances. This paper proposes a new cascaded topology of SMES/battery HESS. It remains the superiorities of conventional HESS schemes from aspects of fast response (~5 ms) and large energy capacity. Moreover, it avoids the use of droop control and then resolves the transient dynamic problem between the switchover of the battery and SMES. In the cascaded HESS, both the energy storages simultaneously activate when power variation is detected, where the SMES is just for compensating the initial power insufficiency before the full-power start of the battery. The stored energy in SMES can be sustainably utilized and available for multiple repeated power compensations. A simulation is established to discuss the performance of the HESS as an uninterruptable power source with comparisons of the other three cases in aspects of response time, energy capacity, and capital cost. Finally, a scaled-down experiment is tested, and performance comparisons are discussed to verify the technical advancement of cascaded HESS.

Integrated Fuzzy-Logic and Triple-Loop PI-Based Management Strategy for a Lead-Acid/Lithium-Ion Hybrid Battery Energy Storage System

The huge success of electric vehicles across the world is challenged by a lack of infrastructure and a major increase in battery material prices. This challenge positions internal combustion engine vehicles (ICEVs) to remain a vehicle of choice. The majority of these vehicles use a lead-acid battery (LAB) for starting, lighting, and ignition (SLI) functions. However, these LABs are faced with challenges of short lifespan and low storage capacity because of improved electronic systems in modern ICEVs. In this manuscript, we propose an extension application of a hybrid LAB and lithium-ion energy storage system (ESS) for a vehicle using a single source of 70 Ah and 90 Ah capacity. Whereas previously, a hybrid energy storage system (HESS) for use in a vehicle using a source of 50 Ah battery capacity was proposed. Hence, the unique contribution of the study is using an integrated fuzzy-logic and triple-loop-proportional-integral-based battery management strategy (BMS) to improve LAB performance in a wide range of vehicles with different battery capacities sizes. The results show that the proposed BMS can help increase LAB lifespan and improve the storage capacity of the system, thus ensuring reliability. Additionally, compared to a single use of LAB, the combined energy storage system shows superior performance.

Performance of a hybrid battery energy storage system

The use of energy storage systems is inevitable in a power grid dominated by renewable generators. This paper presents a performance overview of a 100 kW/270 kWh, grid-connected, hybrid battery energy storage system. The hybrid system uses two types of battery chemistries, li-ion and lead–acid connected directly at the DC bus — without power electronic converters. After a brief introduction and a short technical description of the project, the paper presents a three year, 2019 to 2021, operational data set. The battery data is later split into individual charge/discharge cycles and analyzed in terms of power and strings current sharing, energy, round-trip efficiency and energy transfer between the strings. The analysis shows that the average round-trip energy efficiency of the system is 90% and depends on the depth of discharge. The energy transfer between the strings can happen during charge or discharge and the average values are 5.5% (during charge) and 2.47% (during discharge) of the total discharged energy. Minimum capacity loss was recorded for the lead–acid cells and practically no capacity degradation for the li-ion cells.

Autonomous Multiport Solar Power Plant with Lithium Ion Battery Storage Using a Voltage Source Inverter for Automotive Applications

Electrical power systems are accessible in renewable energy systems, and hybrid battery systems or energy storage systems (ESS) are capable of delivering uninterruptible power to the demand even if faults occur. Additionally, the energy storage device increases system dynamics during power fluctuations. A photovoltaic (PV) battery hybrid system with an ESS link is considered, and an impact leveling management system is planned to transfer the ability to load as well as the battery. Electricity generation is vital, and also the method is fairly complicated. The goal of lowering production prices, maintaining instrumentation, and its fuels, all play necessary roles within the method. As a result, producers should import fuel to satisfy the electrical demand, the assembly value rises. We are considering using renewable energy sources to avert a harmful economic imbalance. We tend to introduce a replacement system to complete the load of alternative energy. To boost inertia and improve frequency responsiveness, load side inverters linking PV to grid and energy storage systems are used on a utility-scale (ESS). Furthermore, with growing PV penetration, independent development of PV and ESS linking to the load and the battery is one of the alternatives for steady operation of the standalone system. The associate integrated the idea for PV and ESS integration to AC load and ESS and it is used for automotive applications.

A Battery Management Strategy in a Lead-Acid and Lithium-Ion Hybrid Battery Energy Storage System for Conventional Transport Vehicles

Conventional vehicles, having internal combustion engines, use lead-acid batteries (LABs) for starting, lighting, and ignition purposes. However, because of new additional features (i.e., enhanced electronics and start/stop functionalities) in these vehicles, LABs undergo deep discharges due to frequent engine cranking, which in turn affect their lifespan. Therefore, this research study seeks to improve LABs’ performance in terms of meeting the required vehicle cold cranking current (CCC) and long lifespan. The performance improvement is achieved by hybridizing a lead-acid with a lithium-ion battery at a pack level using a fully active topology approach. This topology approach connects the individual energy storage systems to their bidirectional DC-DC converter for ease of control. Besides, a battery management strategy based on fuzzy logic and a triple-loop proportional-integral (PI) controller is implemented for these conversion systems to ensure effective current sharing between lead-acid and lithium-ion batteries. A fuzzy logic controller provides a percentage reference current needed from the battery and regulates the batteries’ state-of-charge (SoC) within the desired limits. A triple-loop controller monitors and limits the hybridized system’s current sharing and voltage within the required range during cycling. The hybridized system is developed and validated using Matlab/Simulink. The battery packs are developed using the battery manufacturers’ data sheets. The results of the research, compared with a single LAB, show that by controlling the current flow and maintaining the SoC within the desired limits, the hybrid energy storage system can meet the desired vehicle cold cranking current at a reduced weight. Furthermore, the lead-acid battery lifespan based on a fatigue cycle-model is improved from two years to 8.5 years, thus improving its performance in terms of long lifespan.

Retraction Note: Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems

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Energy management of stationary hybrid battery energy storage systems using the example of a real-world 5 MW hybrid battery storage project in Germany

Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important for the transition to large shares of renewable energy sources in the electricity grid. Hybrid battery storage systems are an interesting option to increase the profitability of BESS by combining low-cost battery technologies with more expensive, but also more efficient and robust ones. In theory, such a hybrid system can be cheaper than a single-technology system by leveraging synergy effects while still satisfying the requirements of a given application. An energy management system, which allocates the setpoint power of the BESS to the individual battery technologies, is crucial for taking advantage of the hybrid system layout. This paper details an analysis of different energy management algorithms for hybrid BESS using the example of a real-world project called M5BAT and compares the latter to alternative BESS layouts. Besides two heuristic algorithms, a non-predictive optimization and a predictive optimization are developed. Models comprising the electrical, thermal and aging behavior of the considered BESS components are introduced. Simulations of the operation of the BESS providing frequency containment reserve are conducted for determining the operating costs. The performance of the algorithms is evaluated based on the simulation results. Comparing the results shows significant differences in the operating costs between the algorithms, especially when optimized to reduce operating costs. The economic advantage of hybrid BESS is validated by additional simulations of a virtual hybrid BESS and a virtual single-technology BESS. Although the layout has not been optimized in terms of individual sizing of the different battery technologies, the hybrid BESS show a considerable advantage over the single-technology BESS.Highlights-Comparative study of energy management algorithms for power allocation in hybrid battery energy storage systems.-Development of heuristic power allocation algorithms and optimization approaches for cost-optimal operation.-Modeling of all relevant components and effects that contribute to the operating costs.-Simulations based on frequency-containment reserve operation of a real-world system.-Evaluation of the advantage of hybrid over mono-technology battery energy storage.

Design and Performance Analysis of Hybrid Battery and Ultracapacitor Energy Storage System for Electrical Vehicle Active Power Management

The electrical energy storage system faces numerous obstacles as green energy usage rises. The demand for electric vehicles (EVs) is growing in tandem with the technological advance of EV range on a single charge. To tackle the low-range EV problem, an effective electrical energy storage device is necessary. Traditionally, electric vehicles have been powered by a single source of power, which is insufficient to handle the EV’s dynamic demand. As a result, a unique storage medium is necessary to meet the EV load characteristics of high-energy density and high-power density. This EV storage system is made up of two complementing sources: chemical batteries and ultracapacitors/supercapacitors. The benefits of using ultracapacitors in a hybrid energy storage system (HESS) to meet the low-power electric car dynamic load are explored in this study. In this paper, a HESS technique for regulating the active power of low-powered EV simulations was tested in a MATLAB/Simulink environment with various dynamic loading situations. The feature of this design, as noted from the simulation results, is that it efficiently regulates the DC link voltage of an EV with a hybrid source while putting minimal load stress on the battery, resulting in longer battery life, lower costs, and increased vehicle range.

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.

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.

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.

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.