Li-ion Battery Energy Storage System Research Articles

Partial-Power Conversion for Increased Energy Storage Capability of Li-Ion Battery Energy Storage System

Full-power converters are used in battery energy storage systems (BESS) because of their simple structure, high efficiency and relatively low cost. However, cell-to-cell variation, including capacity, state of charge (SOC), and internal resistance, will decrease the available capacity of serially connected battery packs, thereby negatively affecting the energy utilization rate (EUTR) of BESS. This article proposes a novel BESS scheme that combines a modular converter with partial power conversion architecture to make a modular partial power converter (MPPC) that addresses the issue. The MPPC consists of input-serial and output-paralleled (ISOP) isolated phase-shift full-bridge (PSFB) sub-modules. It processes only the serial compensation power, about 1/3 of the full power. Consequently, the MPPC shrinks the converter capacity, which can reduce the cost and power loss. Furthermore, this article develops a BESS model considering cell-to-cell variations to analyze the energy storage capability of the MPPC-BESS compared with the existing full-power BESS. To test the model, we run a simulation using parameter values from 100 real retired batteries. Our simulation results show that the MPPC can significantly alleviate the reduction of EUTR as the voltage level increase. Finally, we construct a 36V/720W MPPC-BESS prototype with two battery packs and PSFB sub-modules to verify the bidirectional operating stability and energy storage capability.

Smoothing Intermittent Output Power in Grid-Connected Doubly Fed Induction Generator Wind Turbines with Li-Ion Batteries

Wind energy is an increasingly important renewable resource in today’s global energy landscape. However, it faces challenges due to the unpredictable nature of wind speeds, resulting in intermittent power generation. This intermittency can disrupt power grid stability when integrating doubly fed induction generators (DFIGs). To address this challenge, we propose integrating a Li-ion battery energy storage system (BESS) with the direct current (DC) link of grid-connected DFIGs to mitigate power fluctuations caused by variable wind speed conditions. Our approach entails meticulous battery modeling, sizing, and control methods, all tailored to match the required output power of DFIG wind turbines. To demonstrate how well our Li-ion battery solution works, we have developed a MATLAB/Simulink R2022a version model. This model enables us to compare situations with and without the Li-ion battery in various operating conditions, including steady-state and dynamic transient scenarios. We also designed a buck–boost bidirectional DC-DC converter controlled by a proportional integral controller for battery charging and discharging. The battery actively monitors the DC-link voltage of the DFIG wind turbine and dynamically adjusts its stored energy in response to the voltage level. Thus, DFIG wind turbines consistently generate 1.5 MW of active power, operating with a highly efficient power factor of 1.0, indicating there is no reactive power produced. Our simulation results confirm that Li-ion batteries effectively mitigate power fluctuations in grid-connected DFIG wind turbines. As a result, Li-ion batteries enhance grid power stability and quality by absorbing or releasing power to compensate for variations in wind energy production.

Power quality enhancement for Thailand's wind farm using 5 MWh Li-ion battery energy storage system

Several studies have stated that an increase in wind power plants and unpredictable wind energy generation face several issues during over-voltage. In this study, an operational 8 MW wind farm in Nakhon Ratchasima province faced severe annual feeder trips 146 times, which is not permissible according to the manufacturers. The feeder trips are mainly attributed to high grid voltage caused by low load demand. This study developed a Battery Energy Storage System (BESS) to minimise feeder trips using DigiSILENT and utilized the higher feeder trip period of September to optimise BESS capacity with a 10 % grid load. Notably, a 5 MWh BESS maintained the grid voltage within an allowable range compared to the 1–4 MWh (increment of 1 MWh) throughout the operational period. Furthermore, a 5 MWh BESS was tested during low and high wind periods to assess the stability of over-voltage management. We found that the 5 MWh BESS controlled over-voltage and prevented feeder trips, resulting in enhanced power generation on selective days with high feeder trips, low wind speed, and high wind speed, generating 28.34 MWh, 0.33 MWh, and 76.67 MWh, respectively, compared to conditions without BESS. Additionally, we recommend that the 5 MWh BESS can enhance wind farm power stability and uphold the manufacturer's warranty.

State of health estimation of lithium-ion battery considering sensor uncertainty

The state of health (SOH) is closely related to the stable operation of lithium-ion (Li-ion) battery energy storage systems. Existing methods for SOH estimation assume a reliable multi-source sensor in the battery management system, yet the sensors are prone to failures leading to abnormal measurement data in practical applications. To alleviate the above issue, this paper proposes a detection before fusion multi-model framework (DFMF) considering the uncertainties of multi-source sensor data. Firstly, a density-based spatial clustering of applications with noise is adopted to identify the working states of sensors via detecting the faults from their raw data. Then, the working states of sensors are fed to a state machine to realize the fault isolation. Finally, a data-driven SOH estimation based on multi-source sensors is developed, which includes both the single-sensor mode and the multi-sensor joint mode. The proposed DFMF is validated on two degradation datasets from different aging conditions and chemistries. The experimental results demonstrate the proposed DFMF has a high accuracy and strong robustness in Li-ion SOH estimation. It is noted that the DFMF can provide SOH estimation results with the root mean squared error within 1.68 % and 1.17 % on the two datasets, respectively.

Guidelines for designing a digital twin for Li-ion battery: A reference methodology

The integration of digital technologies is causing a significant change in the energy sector. These innovations have transformed traditional energy grids into intelligent grids. As a result, the digital replica of Battery Energy Storage Systems (BESS) has become one of the most crucial components in the energy sector. Digital twin technology enables the seamless integration of BESS into intelligent grids and offers numerous benefits, such as easy identification and prediction of faults, real-time system monitoring, optimization, temperature regulation, and estimation of parameters. As a result, the overall performance of BESS is improved by the digital twin technology. Consequently, this paper discusses the general guidelines that must be followed to develop a digital twin for a Li-ion BESS successfully. The main function is to define how to design a digital twin able to optimize the system and facilitate early and predictive fault detection and diagnosis.

A comparison of optimal peak clipping and load shifting energy storage dispatch control strategies for event-based demand response

Buildings, specifically large commercial buildings, are key contributors to the increasing electrical energy demand that is taxing the reliability of an ageing U.S. power grid. Through utility sponsored demand response programs and electrical energy storage systems, large buildings can simultaneously save money on their electricity bill and improve power grid reliability – with little to no change in their operations. Few studies have explored demand response benefits and appropriate control strategies for large commercial buildings. In this study, optimal peak clipping and load shifting control strategies of a Li-ion battery energy storage system are formulated and analyzed over 2 years of 15-minute interval demand data for a large commercial building in the Southwest United States. Furthermore, this analysis assesses the discounted payback period of a Li-ion battery energy storage system while considering cases with and without enrollment in the local utility’s event-based demand response program. Degradation in the Li-ion battery energy storage system’s rated power and capacity are considered throughout this analysis. Key findings in this study show that enrollment in event-based demand response can provide a reasonable (<10 years) discounted payback period of Li-ion battery energy storage systems.

Energy Management Strategy Considering Energy Storage System Degradation for Hydrogen Fuel Cell Ship

Abstract A hybrid energy system (HES) including hydrogen fuel cell systems (FCS) and a lithium-ion (Li-ion) battery energy storage system (ESS) is established for hydrogen fuel cell ships to follow fast load transients. An energy management strategy (EMS) with hierarchical control is presented to achieve proper distribution of load power and enhance system stability. In the high-control loop, a power distribution mechanism based on a particle swarm optimization algorithm (PSO) with an equivalent consumption minimization strategy (ECMS) is proposed. In the low-level control loop, an adaptive fuzzy PID controller is developed, which can quickly restore the system to a stable state by adjusting the PID parameters in real time. Compared with the rule-based EMS, hydrogen consumption is reduced by 5.319%, and the stability of the power system is significantly improved. In addition, the ESS degradation model is developed to assess its state of health (SOH). The ESS capacity loss is reduced by 2% and the daily operating cost of the ship is reduced by 1.7% compared with the PSO-ECMS without considering the ESS degradation.

Internal short circuit fault diagnosis based on 1DVCNN in lithium battery storage system

Because LiFePO4 batteries are widely used in the energy storage system, their safety has received a great deal of research. If an internal short circuit (ISC) in a Li-ion battery energy storage system leads to thermal runaway, it will pose an uncontrollable hazard. Therefore, there is a proposed method in this study that is a one-dimensional voltage-correlated convolutional neural network (1DVCNN) to detect and localize ISC faults in time, which is a combination of feature extraction, correlation analysis, and convolutional neural networks. First, the voltage signal of the LiFePO4 battery collected in the energy storage system is then converted into a Pearson Correlation Coefficient (PCC) by correlation analysis of the data. Specific PCCs are then selected as features of a one-dimensional convolutional neural network (1DCNN) to diagnose faults. Finally, the fault dataset is used to train the model and the results are then validated against the untrained fault data. In this paper, the ISC faults are modeled by both building an actual training platform for lithium batteries and MATLAB simulations to obtain the dataset used to train the proposed model. By comparing the accuracy of the training results of the models using different features, it is found that 1DVCNN is able to detect the ISC fault in the energy storage system in a more timely and effective manner than in other studies.

Online fusion estimation method for state of charge and state of health in lithium battery storage systems

To ensure the safe and reliable operation of Li-ion battery energy storage systems, it is important to diagnose the operational status and aging degree of the batteries. In this study, an online fusion estimation method based on back propagation neural network and genetic algorithm (BP-GA) is used for estimating the state of charge (SoC) and state of health (SoH) of Li-ion batteries. First, the effective features of SoC and SoH of Li-ion batteries during charging and discharging are analyzed, and the relevant features are extracted. Subsequently, a conventional back propagation neural network (BPNN) for SoC and SoH estimation is described. The extracted feature quantities are then used as inputs to the neural network for the estimation of the SoC and SoH of Li-ion batteries. A comparison of the SoC and SoH estimation results using, respectively, a single BPNN and a fusion algorithm using a genetic algorithm (GA) to optimize the BPNN shows that the accuracy and efficiency of the estimation results using BP-GA are significantly higher than that of the BPNN. The experimental component of this paper is a comparison experiment using the Panasonic 18650PF and the real-world data from NASA. The results show that the feature extraction operation is relatively easy to perform and combined with the neural network method used in this paper can have good accuracy, reduce the complexity of the algorithm, and improve the detection efficiency.

Private and External Costs and Benefits of Replacing High-Emitting Peaker Plants with Batteries

Falling costs of lithium-ion (Li-ion) batteries have made them attractive for grid-scale energy storage applications. Energy storage will become increasingly important as intermittent renewable generation and more frequent extreme weather events put stress on the electricity grid. Environmental groups across the United States are advocating for the replacement of the highest-emitting power plants, which run only at times of peak demand, with Li-ion battery systems. We analyze the life-cycle cost, climate, and human health impacts of replacing the 19 highest-emitting peaker plants in California with Li-ion battery energy storage systems (BESS). Our results show that designing Li-ion BESS to replace peaker plants puts them at an economic disadvantage, even if facilities are only sized to meet 95% of the original plants' load events and are free to engage in arbitrage. However, five of 19 potential replacements do achieve a positive net present value after including monetized climate and human health impacts. These BESS cycle far less than typical front-of-the-meter batteries and rely on the frequency regulation market for most of their revenue. All projects offer net air pollution benefits but increase net greenhouse gas emissions due to electricity demand during charging and upstream emissions from battery manufacturing.

Aging aware adaptive control of Li-ion battery energy storage system for flexibility services provision

Battery energy storage systems (BESSs) play a major role as flexible energy resource (FER) in active network management (ANM) schemes by bridging gaps between non-concurrent renewable energy sources (RES)-based power generation and demand in the medium-voltage (MV) and low-voltage (LV) electricity distribution networks. However, Lithium-ion battery energy storage systems (Li-ion BESS) are prone to aging resulting in decreasing performance, particularly its reduced peak power output and capacity. BESS controllers when employed for providing technical ancillary i.e. flexibility services to distribution (e.g. through ANM) or transmission networks must be aware of changing battery characteristics due to aging. Particularly of importance is BESSs' peak power changes aiding in protection of the Li-ion BESS by restricting its operation limits of it for safety reasons and improving its lifetime in the long run. In this paper, firstly an architecture for ANM scheme is designed considering Li-ion BESSs as one of the FERs in an existing smart grid pilot (Sundom Smart Grid, SSG) in Vaasa, Finland. Further, Li-ion BESS controllers are designed to be adaptive in nature to include its aging characteristics, i.e. tracking the changing peak power as the aging parameter, when utilised for ANM operation in the power grid. Peak power capability of the Li-ion nickel‑manganese‑cobalt (NMC) chemistry-based battery cell has been calculated with the experimental data gathered from accelerated aging tests performed in the laboratory. Impact of such aging aware and adaptive Li-ion BESS controllers on the flexibility services provision for power system operators needs will be analysed by means of real-time simulation studies in an existing SSG pilot.

Battery energy storage integration in wind farms: Economic viability in the Spanish market

This paper proposes an economic assessment tool that determines the viability of a battery energy storage system (BESS) integrated within renewable power plants for different market applications such as day-ahead price arbitrage, participation in the balancing market and schedule tracking by reducing wind deviations. In particular, maximum BESS investment prices are derived to make BESS potentially viable for each functionality. A case study of an actual 30 MW wind farm participating in the Spanish electricity market is used to test the economic viability of different sizes of Li-ion and vanadium-redox BESS. Results show that with the actual structure and level prices in Spain, the participation in the balancing market could achieve positive internal rates of return, and the combination of additional market functionalities does not improve substantially BESS viability. • Formulation of an optimization model for economic assessment and sizing of hybrid operation of battery energy storage systems and renewable power plants. • Single and combined market functionalities considered: price arbitrage, reserve provision, and wind schedule tracking. • Application to the Spanish power system: Li-ion and Va-Re integrated in a real Spanish 30 MW wind farm are assessed. • Results have shown that in Spain, only the possibility of balancing market participation can make the BESS potentially viable depending on the size of the device.

Explosion protection for prompt and delayed deflagrations in containerized lithium-ion battery energy storage systems

Explosion hazards can develop when gases evolved during lithium-ion battery energy system thermal runaways accumulate within the confined space of an energy storage system installation. Tests were conducted at the cell, module, unit, and installation scale to characterize these hazards. Three installation level tests show that explosion scenarios can occur as prompt ignitions within seconds of cell gas venting or delayed ignitions where gases ignite after a longer duration of accumulation, especially when fire protection systems are actuated. Deflagration venting and exhaust ventilation system design approaches that can be implemented at the installation level are evaluated using a dataset generated from cell, module, and unit level tests. Data from the installation level tests demonstrate the use and effectiveness of deflagration venting for containerized li-ion battery energy storage systems.

Large scale energy storage in Uppsala, Sweden: an analysis of voltage fluctuations and a service stacked portfolio

Extensive electrification has created an increased demand of electric energy and power. Congestion issues are expected to become more common as transmission and distribution grids reach their capacity limits. One of the regions in Sweden that has experienced this already is Uppsala. The distribution system operator, Vattenfall Distribution has initiated an R&amp;D project that connects a 5 MW/20 MWh Li-ion battery energy storage system to temporarily ease the grid congestion. This study covers an analysis of grid voltage fluctuations using a largescale battery in a distribution grid. The power quality is examined at the 10 kV connection point of the storage, where voltage and power flow are presented. Also, an example of how value stacking may be implemented is illustrated by adding a secondary service during the year. The test results indicate no significant distortion of the voltage level at the measurement point during smooth ramping. It can be concluded that smooth ramping of the BESS has very low or no impact on the local power quality, and additional services should be provided during the year to create several revenue streams. Future studies could focus on fast ramping of the BESS and stacking services in an intra-season perspective.

Li-ion battery technology for grid application

Battery energy storage systems (BESS) are forecasted to play a vital role in the future grid system, which is complex but incredibly important for energy supply in the modern era. Currently, Li-ion batteries are the most widely deployed BESS for a wide range of grid services but need substantial understanding and improvement for effective market creation. Hence, the main purpose of this review is to provide a comprehensive overview of the current status and challenges of Li-ion battery energy storage systems for grid application from various aspects based on real-world projects.

Intelligent control of battery energy storage for microgrid energy management using ANN

&lt;span&gt;In this paper, an intelligent control strategy for a microgrid system consisting of Photovoltaic panels, grid-connected, and li-ion battery energy storage systems proposed. The energy management based on the managing of battery charging and discharging by integration of a smart controller for DC/DC bidirectional converter. The main novelty of this solution are the integration of artificial neural network (ANN) for the estimation of the battery state of charge (SOC) and for the control of bidirectional converter. The simulation results obtained in the MATLAB/Simulink environment explain the performance and the robust of the proposed control technique.&lt;/span&gt;

Modeling of Li-ion battery energy storage systems (BESSs) for grid fault analysis

Battery energy storage systems (BESSs) are expected to play a key role in enabling high integration levels of intermittent resources in power systems. Like wind turbine generators (WTG) and solar photovoltaic (PV) systems, BESSs are required to meet grid code requirements during grid disturbances. However, BESSs fundamentally differ from WTG and PV systems because they are not only required to operate as a source (discharging) but also as a load (charging). This paper investigates the system-level behavior of BESSs under grid fault conditions. It presents first a generic electromagnetic transient (EMT)-type model of a two-stage Li-ion BESS that can be configured to comply with grid codes. Compared to previous studies, the proposed model introduces a key step in the characterization of BESSs considering decoupled sequence control (DSC) and the non-linear impact of current limiters under stringent unbalanced faults. The model is used to demonstrate that BESSs behave differently in charging mode, a factor that needs to be accounted in protective relaying practices. The conducted simulations show that the charging mode is more stringent on both dc-link voltage regulation and grid voltage support, and that this is aggravated by the use of DSC scheme compliant with VDE-AR-N 4120 grid code.

Design improvement of thermal management for Li-ion battery energy storage systems

Thermal management of lithium-ion battery cells provides several advantages to reach high performance electric-vehicles and hybrid-electric-gadgets. Geometrical features and patterns of internal channels of liquid cooling plate (LCP) are key factors for the battery cell cooling efficiency. This paper concerns a new design of battery thermal management and the effect of ribbed channels with double inlets and outlets on the reduction of mass flowrate in the coolant channels of LCP, the hydrothermal performance factor of the battery, the friction coefficient, and the reduction of pumping power by utilizing comprehensive 3D simulations. The Conjugate Heat Transfer approach is employed for the fluid flow and the heat transfer calculations. To enhance the heat transfer surface and cooling efficiency, longitudinal ribs are considered in four different configurations inside the cooling channels. Furthermore, the effects of ribs on the aforementioned parameters in comparison with the non-rib channels are investigated. The results demonstrated that the optimized ribbed configuration increases the total amount of heat transfer by up to 68%, reduces the mass flowrate to 16%, and lowers the pumping power by 20% in comparison with rib-less channels. Meanwhile, the hydrothermal cooling performance factor of enhanced up to 58% and the maximum temperature approximately remains constant.

A full-scale electrical-thermal-fluidic coupling model for li-ion battery energy storage systems

Nowadays, lithium-ion battery (LIB) technology provides one of the most important approaches for large-scale electricity storage. In this work, an electrical-thermal-fluidic coupled model is proposed for practical LIB-based energy storage systems (ESSs). The coupled model is established based on the equivalent circuit model (ECM) which describes electrical behavior of LIBs, the airflow turbulent model, and the “single domain of multiple sub-regions” thermal model. Currents and voltages of LIB cells, airflow velocity field and pressure field, and temperature distribution in the whole ESS, can be simulated by the developed full-scale model. Simulation results of a practical commercial LIB ESS (1 MW/2 MWh) in typical frequency regulation operation, including electrical-thermal characteristics represented by currents and heat generation rates of LIBs in the ESS, flow characteristics represented by airflow field and mass flow rate distribution, ambient temperature sensitivity analysis represented by temperature distributions of the ESS initially under 27℃ and 38℃ ambient respectively, are displayed and discussed, exhibiting the capability of the proposed model. The proposed model has been validated by comparing the simulated results with the actual measured data; the reasonably good agreement demonstrates the effectiveness of the proposed model. Therefore, the established model has the potential to provide a feasible and powerful approach or tool to perform multi-physics simulations of practical large-scale LIB ESSs for different functions, including but not limited to detailed temperature evaluations, cooling structure optimization, materials selection, and thermal-safety status evaluating and monitoring.

DREMUS: A Data-Restricted Multi-Physics Simulation Model for Lithium-Ion Battery Storage

This paper presents a modelling approach to support the techno-economic analysis of Li-Ion battery energy storage systems (BESS) for third party organisations considering the purchase or use of BESS but lacking the detailed knowledge of battery operation and degradation. It takes into account the severe data-limitations and provides the best possible approximation for its long-term electrical, thermal and ageing performance. This is achieved by constructing flexible and scalable ageing models from experimental data based on manufacturer's datasheets, warranties and manuals as key inputs. The precision of the individual models has been determined using experimental data and has been found with <8 % normalised root-mean-square deviation (NRMSD) in all cases to be sufficiently accurate. Through linearization methods, this model is able to compare the long-term performance of BESS and quantify the degradative impact of specific charge/discharge mission profiles, which improves the tangibility of BESS as value generating asset.