Battery Based Energy Storage System Research Articles

Comparison of the performances of heuristic optimization algorithms PSO, ABC and GA for parameter estimation in the discharge processes of Li-NMC battery

The effects of the studies performed for the development of cells, which are the fundamental components of electrochemical battery units are felt in many different areas such as electric rail transportation systems, battery-based energy storage systems, battery units in electric vehicles, and energy storage units for individual use. For this goal, studies conducted by other searchers in the similar field have been investigated. In this paper, optimization techniques are used to guess the model parameters with major righteousness using the electrical equivalent circuit model of the battery. The discharge processes of the 18650 cylindrical type 2000 mAh Li-NCM battery cell with 1 A pulsed constant current at 25 ºC have been investigated. The real parameter values obtained have been transferred to the electrical equivalent circuit model. The open circuit voltage is determined as a functional term depending on the state of current supply level by using the curve fitting method in the Matlab. Studies have been carried out on particle swarm optimization algorithm, artificial bee colony algorithm, and genetic algorithm to estimate the battery output terminal voltage by using the open circuit voltage. Comparisons have been made and differences have been analyzed between the technics by using different statistical methods of true error values, the correct prediction ability, and response speed. As a result, the optimization method that makes the most accurate estimation has been determined.

A Balancing Current Ratio Based State-of-Health Estimation Solution for Lithium-Ion Battery Pack

The inevitable battery ageing is a bottleneck that hinders the advancement of battery-based energy storage systems. Developing a feasible health assessment strategy for battery pack is important but challenging due to the joint requirements of the computational burden, modeling cost, estimation accuracy, and battery equalization. This article proposes a balancing current ratio (BCR) based solution to achieve reliable state-of-health (SoH) estimations of all series-connected cells within a pack while significantly reduce the overall reliance on cell-level battery models. Specifically, after employing BCR to describe the properties of the balancing process, the voltage-based active balancing is combined into the SoH estimator design for the first time, leading to a weighted fusion strategy to effectively estimate SoHs of all cells within a pack. Hardware-in-the-loop experiments show that even if a parameter-fixed open-circuit-voltage-resistance model is used for modeling, the typical estimation error of our proposed solution can still be bounded by only 1.5%, which is 70% lower than that of the benchmarking algorithms. Due to the model-free nature of the integrated voltage-based balancing, the robustness and flexibility of the proposed pack SoH estimation solution are also significantly improved.

Techno-socio-economic-environmental estimation of hybrid renewable energy system using two-phase swarm-evolutionary algorithm

A multi-objective optimal sizing model for an off-grid hybrid renewable energy system (HRES) consisting of solar photo-voltaic (PV), wind turbine (WT), diesel generator (DG) and lead-acid battery-based energy storage (ES) system is developed including techno-socio-economic and environmental considerations. The problem is formulated using six objectives; levelized cost of energy (LCOE), renewable fraction (RF), loss of power supply probability (LPSP), human development index (HDI), job creation (JC) and emission curtailment (EMC). To solve this real-world problem with complex practical constraints, a hybrid two-stage PSO-DE optimization (HPSODE) algorithm is constructed by connecting the improved variants of PSO and DE in series. The exploration capability of swarm intelligence is synthesized with the exploitation potential of classical evolutionary concepts, to achieve better performance with least dependence on algorithmic tuning parameters. A fuzzy satisfaction module (FSM) is appended to identify the solution with the highest possible attainment level for all the considered objectives. The selected optimization goals have a significant bearing on the optimal design, some are contradictory while others are similar in nature; therefore, impact analysis is presented. For the minimum cost solution, attainment of LCOE is 100%, HDI attainment is 0.27%, JC is 4.04%, emission curtailment is 6.29%, LPSP attainment is 91.25% and attainment of RF is 92.33%. On the other hand, when all six objectives are optimized, LCOE is attained by 68.61%, HDI by 92.97%, JC by 84.04% and EMC goal is satisfied by 78.98% while attainment of both LPSP and RF is 100%. For the selected location, the best configuration of HRES is PV-WT-ES, with total capacity of 175.44 kW. The levelized cost is 0.0779 $/kWh, human development index (HDI) is 0.6454, 0.2711 jobs are created and annual emission curtailment is 290147.28 kg, whereas unmet power is zero. Well distributed Pareto-optimal solutions are obtained, which offer multiple options to the designer. The results of the proposed HPSODE-FSM approach are compared and corroborated with results from literature and also with other state-of-the-art algorithms.

Capacities prediction and correlation analysis for lithium-ion battery-based energy storage system

Lithium-ion battery-based energy storage system plays a pivotal role in many low-carbon applications such as transportation electrification and smart grid. The performance of battery significantly depends on its capacities under different operational current cases, which would be affected and determined by its component parameters interacting with one another. Due to the complex interdependency of electrical, chemical, and mechanical dynamics within a battery, it is a key but challenging issue to predict battery capacities under various current cases and analyze correlations of key parameters within a battery. This paper proposes an XGBoost-based interpretable machine learning framework, which fills the gap of predicting and analyzing how battery capacities under different current rates with respect to battery component parameters of interest. The parameter importance ranking is obtained by using the Gini index within the XGBoost model, while the correlations of all parameter pairs are quantified by using the predictive measure of association. The proposed framework is tested in two popular lithium-ion battery types with three various current levels. Illustrative results show that the proposed XGBoost-based framework is able to not only produce satisfactory capacity prediction performance but also provide reliable importance as well as correlation quantifications of involved battery component parameters. These could promote the prediction and analysis of battery capacities under different current rates, further benefitting the monitoring and optimization of battery management for wider low-carbon applications.

Feature-based lithium-ion battery state of health estimation with artificial neural networks

Precise online lithium-ion battery state of health estimation is critical for the correct operation and management of battery-based energy storage systems such as microgrids and electric vehicles. However, in such applications it is necessary to maintain standard operation and therefore difficult to experimentally determine. Advancements in machine learning techniques and capabilities allow for precise and efficient data-driven predictions. In this paper we propose a simple, yet effective state of health estimation model based on the extraction of features observed from patterns in the voltage, current and temperature profiles during the charging process, which then through artificial neural networks allow for per cycle estimations. We then apply this model to two groups of batteries from the NASA Ames PCoE Battery data set. Results show that the proposed model is capable of estimating the state of health of batteries discharged under varied conditions with resulting coefficients of determination between 0.896 and 0.992 while also employing significantly less input data than other works.

A bottom-up approach for techno-economic analysis of battery energy storage system for Irish grid DS3 service provision

This paper presents a bottom-up approach for techno-economic analysis of a Li-ion battery based Energy Storage System (BESS) to provide grid ancillary services under the “Delivering a Secure, Sustainable Electricity System” (DS3) programme in the Irish market. There are two types of service procurement mechanisms that exist in the DS3 programme: volume capped and volume uncapped. A design methodology of the storage system is investigated to optimise the installed capacity and minimize the initial cost for volume capped DS3 services. Based on the real-life project assessment, three different cost models are considered to evaluate different investment indices. The goal is to assess the economic performance of BESS for different contracted capacity systems under the different operating and financial conditions. Extensive simulation and sensitivity analysis based on the Monte Carlo method have been carried out to compare the cost model outputs. The findings are expected to give a clear idea to the investors and decision-makers about the Irish DS3 ancillary service market and clarify specific concerns on the economic feasibility of BESS for DS3 services. Moreover, it discusses the risks and opportunities related to the high volatility of the system component costs. • A bottom-up approach for Techno-economic analysis of ESS to provide DS3 grid services. • A simplified optimal sizing method for a Volume Capped ESS-based DS3 providing unit. • Clarify specific concerns on the economic feasibility of ESS for DS3 Services. • Analyse the solution economic sensitivity towards equipment cost variations. • Provide a comprehensive perception to stakeholders into the Irish DS3 service market.

Comparative Analysis of Hybrid Electric Architectures for Specialized Agricultural Tractors

In this work, a comparative numerical analysis between the performance of a conventional specialized orchard tractor and those of three different hybrid electric tractor configurations is presented. The aim was to compare several powertrain configurations in the same working scenarios derived from field measurements. Peak power capabilities and endurance were numerically tested with specific load scenarios involving both transportation mission profiles and field activities with external implements powered through the power take off of the tractor. The proposed hybrid architectures were configured with the same battery-based energy storage system to perform the comparison with the same energy storage capabilities. Two parallel, two series and one electro-hydraulic hybrid configuration were modeled and tested through simulations. The parallel ones excelled in peak power performance, whereas the series configurations had the highest fuel savings. The electro-hydraulic configuration was proposed as an alternative able to allow for a downsized engine but also for the introduction of the Continuously Variable Transmission (CVT) functionality, which is always an interesting feature for such working machines.

Direct Damping Feedback Control Using Power Electronics-Interfaced Resources

This paper proposes a direct damping feedback control method against power system oscillations under small or large disturbances. For a targeted oscillation mode, this control method continuously minimizes the difference between the real-time estimated damping ratio and a desired value by changing power outputs of selected inverter-based resources. The method adopts a proportional-integral controller whose parameters are tuned using a nonlinear single-input-single-output model on damping estimation and control with a single-oscillator equivalent regarding the targeted power system mode. Also, optimization of these controller parameters considers both robustness and time performance in damping control. The paper also proposes utilizing a “zero-th order” parametric resonance phenomenon to simplify the controller design. Tests on a small power system and a 140-bus 48-machine Northeast Power Coordinating Council system validate the effectiveness of the proposed damping controller utilizing battery-based energy storage systems.

Battery-based storage systems in high voltage-DC bus microgrids. A real-time charging algorithm to improve the microgrid performance

Battery-based energy storage systems (BESS) play a crucial role on renewable energy sources-based microgrids (RES-based microgrids) since they are responsible for lightening the difference between generation and consumption. For this purpose, a BESS is usually the only one device in RES-based microgrids that has two functions: (1) to supply energy when there is energy deficit between renewable sources and load demand, and (2) to absorb energy when there is energy excess. Then, the BESS operation and therefore its lifetime is closely related to the energy management strategy (EMS). However, most solutions found in the scientific literature are based exclusively on power balance and battery state of charge (SOC) as main decision parameters; but by contrast, in case of BESS, it is critical a suitable charge control to ensure the maximum efficiency and minimum degradation, increasing BESS lifespan as much as possible, especially for those RES-based microgrids where the BESS is responsible for supporting the DC bus. This paper presents a new charging algorithm designed to prevent and mitigate the BESS degradation, assuring high charging efficiency when it is integrated into the microgrid and directly connected to high voltage-DC bus connection. Additionally, the proposed algorithm contemplates the possibility of taking advantage of all available renewable energy sources, assuming an increase in BESS performance. Simulation and experimental results will show that the proposed algorithm can improve the BESS and microgrid operation with respect to solutions proposed in the market and in the scientific literature.

A Designed Energy Management System (EMS) for an Off- Grid Residential Microgrid

This paper proposes an Energy Management System (EMS) of an off-grid residential microgrid comprised of a solar photovoltaic array, wind turbine, and a battery-based energy storage system for a residential building in a remote area. The aim is to design a less complex energy management system that addresses energy resources dispatch challenge in a hybrid residential microgrid. It equitably dispatches resources and manages the generated energy to meet the residential load. The management control system algorithm is implemented using the state machine and simulated in MATLAB/Simulink environment for validation. Three scenarios are simulated, they emulate various contingencies scenarios that might rise due to the intermittency nature of the renewable resources. Simulation results show that the proposed energy management system achieves its design objectives. It maintains the power supply and demand balance, through an appropriate scheduling of resources even during contingency due to the intermittence nature of the renewable resources

Proposed method for evaluating controllers of battery-based storage system in maximum demand reductions

Monthly maximum power demand is the highest power demand that ever happen in a month. Governments or utility companies plan the additional generation capacity over a projected period to meet the monthly maximum power demands. However, battery-based energy storage systems (BESS) can be used to reduce the maximum power demands, hence deferring the additional generation capacity, and providing substantial financial savings to customers and utility companies. Controllers are the central intelligence of BESS for performing the peak demand reductions. This paper begins with a literature review of the existing controllers for BESS to perform peak reductions. Several shortcomings are then highlighted with regards to the existing controllers. Firstly, there is no well-thought-out method for evaluating the true performances of the controllers in peak reductions. Hence, researchers are not able to show the true abilities of their controllers accompanied by new features such as forecasting techniques and optimization algorithms. Secondly, researchers use the daily peak reductions as the prime target of their controllers instead of the monthly maximum demand reductions. Also, many fails to recognize the importance of monitoring the other technical parameters such as the voltage magnitudes, power factors, and system frequency during peak reductions. This paper proposes a new method by the means of two indices for measuring the true performances of BESS controllers. These indices are applied to juxtapose the true abilities of the existing controllers. The monthly maximum demand reductions are proposed as the prime target of the controllers. Other essential parameters such as voltage magnitude are proposed to be monitored and presented in this paper.

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

Considerations on the impact of battery ageing estimation in the optimal sizing of solar home battery systems

The exponential reduction in cost of photovoltaic panels and the forecasts projecting a similar behaviour for battery-based energy storage systems are driving increasing attention on residential energy systems based on photovoltaic (PV) panels and batteries. Such systems, if combined with demand electrification, could help decarbonising the sector and foster the adoption of distributed energy systems. In this work, a modular tool for the techno economic analysis of smart energy systems is applied to a case study in the residential sector. The considered system is composed by PV panels, battery, heat pump and air conditioning system. The tool integrates a method for the estimation of cyclic and calendric ageing of the battery. The effects of varying battery capacity and its maximum depth of discharge on its lifetime have been investigated. An optimization algorithm has been employed to find the battery capacity that maximizes net present value (NPV) for given battery prices and PV panels areas. Results have highlighted the impact of using ageing models on the economics of residential energy systems, suggesting that discontinuities might arise in the functions to be optimized, hence requiring more robust optimization algorithms. Results also suggest that not accounting for battery aging could lead to incorrect predictions regarding the economics of the system. The breakeven price for the introduction of batteries in residential energy systems is lower when using ageing algorithms, while, at the same time, higher optimal capacities have been obtained for lower battery prices.

Comprehensive study and improvement of experimental methods for obtaining referenced battery state-of-power

As a soft sensor, the state-of-power (SoP) estimator reveals critical information on battery-based energy storage systems. A set of reliable ‘referenced values’ is the key to evaluate the precision of such soft sensors at their designing stage and could influence the overall reliability of the battery systems. However, experimentally obtaining the ‘referenced SoP’ is non-trivial since high-current pulse tests (>10C) are required to charge/discharge the batteries to their cut-off conditions. The associated high-power experimental platforms could be expensive, while frequently applying large current at boundary conditions may leave potential safety issues. Aiming at these problems, this paper focuses on obtaining referenced SoP, rather than onboard SoP estimations. A novel equivalent discharging test is designed to accurately recover the voltage response of high-current pulses from a set of low-current tests, resulting in a 33% reduction of the peak discharging current. In addition, a flexible softmax neural network is further proposed to generate SoP values for the intervals between pulse tests. With these tools, reliable SoP values with errors lower than 0.5% can be readily obtained. The SoP obtained from our approach can be further utilised as a highly accurate benchmark to evaluate the accuracy of other onboard battery SoP estimators.

Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems

With the rapid development of renewable energy, the lithium-ion battery has become one of the most important sources to store energy for many applications such as electrical vehicles and smart grids. As battery performance would be highly and directly affected by its electrode manufacturing process, it is vital to design an effective solution for achieving accurate battery electrode mass loading prognostics at early manufacturing stages and analyzing the effects of manufacturing parameters of interest. To achieve this, this study proposes a hybrid data analysis solution, which integrates the kernel-based support vector machine (SVM) regression model and the linear model–based local interpretable model-agnostic explanation (LIME), to predict battery electrode mass loading and quantify the effects of four manufacturing parameters from mixing and coating stages of the battery manufacturing chain. Illustrative results demonstrate that the derived hybrid data analysis solution is capable of not only providing satisfactory battery electrode mass loading prognostics with over a 0.98 R-squared value but also effectively quantifying the effects of four key parameters (active material mass content, solid-to-liquid ratio, viscosity, and comma-gap) on determining battery electrode properties. Due to the merits of explainability and data-driven nature, the design data–driven solution could assist engineers to obtain battery electrode information at early production cases and understand strongly coupled parameters for producing batteries, further benefiting the improvement of battery performance for wider energy storage applications.

Development and Demonstration of Microgrid System Utilizing Second-Life Electric Vehicle Batteries

As microgrids grow in popularity, the cost of energy storage becomes a more and more pressing issue for the industry to solve. One potential solution to this problem is the development of second-life battery-based energy storage systems (ESSs). This paper discusses the design, construction, and operation of a commercial-scale microgrid consisting of 164.5 kW of solar photovoltaics (PV), 262 kWh of energy storage, 2 buildings with a total area of 1550 m2, and an average power demand of 85 kW. The ESS was built using second-life Nissan Leaf battery modules to demonstrate the performance potential of retired electric vehicle (EV) batteries for stationary energy storage. Prior to assembling the ESS, each Nissan Leaf module was tested to characterize its state of health (SoH). The average SoH of the modules before use was 71%, however evidence gathered suggested this is near the upper limit of initial second-life battery health. This challenges the conventional wisdom in the literature that EV batteries are retired soon after they reach 80% SoH. Data from the first year of microgrid operation were presented, demonstrating that the second-life batteries performed as designed. Analysis revealed that the microgrid achieved an average reduction in maximum peak-time demand of 60% and peak-time energy use of 39%. Results support the case that second-life batteries are well-suited for commercial-scale energy storage.

Analytical studies of three different datasets for peak reductions with battery-based storage system

Two basic controllers, namely threshold- and schedule-based controllers, have been used with different advanced features for battery-based energy storage system to perform peak reductions. However, the peak reductions are only influenced by the abilities of the newly featured controllers but also by the types of datasets being used to calculate the peak demands. There are three different datasets, namely instantaneous, average and energy load data. The main contribution of this paper is to present how different datasets can affect the peak reductions achieved by the two basic controllers without using any new features. The studies are extended to analyse how different datasets can influence the peak reductions by the controllers under different forecasting errors. The results show that threshold-based controller can achieve the highest peak reductions when the instantaneous load data is used. However, schedule-based controller is better than threshold-based controller when the levels of peak demands are highly unpredictable. These results are important because they can affect the prospective of energy storage system in the future.

Energy Management Electronic Device for Islanded Microgrids Based on Renewable Energy Sources and Battery-based Energy Storage

Energy management systems are one of the most important components in the operation of an electric microgrid. They are responsible for ensuring the supervision of the electrical system, as well as the coordination and reliability of all loads and distributed energy resources in order for the microgrid to be operated as a unified entity. Because of that, an energy management system should be fast enough at processing data and defining control action to guarantee the correct performance of the microgrid. This paper explores the design and implementation of an energy management system deployed over a dedicated electronic device. The proposed energy management device coordinates the distributed energy resources and loads in a residential-scale islanded microgrid, in accordance with a rule-based energy management strategy that ensures reliable and safe operation of the battery-based energy storage system. A hardware-int-he-loop test was performed with a real-time simulation platform to show the operation of the electronic device

Суперконденсаторні енергонакопичувачі для освітлювальних систем з комбінованим електроживленням

Modern continuous lighting systems use powerful high-performance LEDs as light sources and an important task is to begin using alternative renewable energy sources for their power supply (including during the day). The simplest of the renewable energy sources is photovoltaic solar energy converter. However, solar photovoltaic generation depends significantly on many factors - geographical location, time of day, state of the atmosphere, time of year and the like. In addition, photovoltaic generation depends on the weather conditions and cloudiness, which makes it unstable and prone to change drastically (by an order of magnitude) during daylight hours. Therefore, an important element of the power system based on renewable energy sources is the system of accumulation of generated energy. The method of power stabilization using supercapacitors for systems with a significant change in power generation in the electrical power system is analyzed. The paper offers design principles of the power supply systems for powerful LEDs with supercapacitor energy storage devices intended to make the use of energy from sources with variable generation more efficient. The systems with supercapacitor-based drives, which allow to ensure stable operation of the lighting system when the power supply from an alternative source is absent or reduced, provide high safety and reliability, and have a significantly longer service life than battery-based energy storage systems.

Power electronic transformers: A review

The medium frequency transformer is a key component for the design of input–output isolated converter design when the isolation and/or voltage matching is needed. These kinds of converters are used in different applications such as battery based energy storage systems, the high voltage DC conversion, grid interfaces of renewable energy sources, etc. Design at the high frequency significantly decreases the size and improves the efficiency of the power transformer. With the recent advancements of soft magnetic core materials and switching devices, the high frequency transformers become more interesting not only as a part of power converters but also as a replacement of conventional line frequency transformers. In this detailed review study, studies on the design of the power transformers used in power electronic converters are examined and their application areas, operating frequency values, core material types are investigated and classified. In addition, the design methodology is proposed with Finite Element Analysis (FEA) software and a power electronic transformer is designed with the different core materials.