Lithium-ion Battery Energy Storage Research Articles

Multi-objective electricity cost and indirect CO2 emissions minimization in commercial and industrial buildings utilizing stand-alone battery energy storage systems

A large portion of global carbon emissions are attributable to electricity generation. Several previous studies indicate that both electricity cost and carbon emission reductions are not attainable with stand-alone battery energy storage systems for residential buildings. However, in this study, lithium-ion battery energy storage dispatch (charging and discharging) is optimized as a multi-objective decarbonization and cost-saving strategy in ten commercial and industrial facilities. The analysis tests 100 energy storage capacities, 5 discharge times, and 2 control strategies with and without enrollment in event-based demand response. Unlike smaller energy consumers, the results show significant indirect CO2 emissions reductions (>31%) paired with significant electricity cost reductions (>10%) are possible from stand-alone battery energy storage systems in a large commercial facility. Additionally, the results indicate that enrollment in the event-based demand response program and dispatch under the load shifting control strategy are always optimal to minimize both the discounted payback period and the indirect CO2 emissions.

Improved Feature Decoupling Transfer Network Modeling based on Singular Value Decomposition for SOC Estimation in Energy-Storage Lithium-ion Batteries

Deep learning (DL) methods is applied extensively in the field of state of charge (SOC) estimation, which require training data and test data to have similar distribution. Discrepancies in data distribution arising from the complexity and diversity of lithium-ion batteries under operational conditions in practice, as well as the difficulty in obtaining data labels, make it enormously challenging to access sufficient battery data to train a specific deep estimator. Aiming to improve the performance of cross-domain SOC estimation for lithium-ion batteries, a model for SOC estimation which combines transfer learning with singular value decomposition (SVD) is proposed. To begin with, a gated recurrent unit (GRU) network is employed to avail the nonlinear dynamic characteristics of the battery from the source and target domains. Then, the features are decoupled by using SVD method to extract task-relevant, important and minor information in the network. Further, the amount of transferred information over the source network to the target network is automatically tuned by the maximum mean discrepancy (MMD) to determine the different degrees of similarity in domain, and the cosine discrepancy to measure the discrepancy on the same domain, which achieves the optimized performance of the target network.

Accurate and Efficient Energy Management System of Fuel Cell/Battery/Supercapacitor/AC and DC Generators Hybrid Electric Vehicles

The development of hybrid electric vehicles (HEVs) is rapidly gaining traction as a viable solution for reducing carbon emissions and improving fuel efficiency. One type of HEV that is gaining significant interest is the fuel cell/battery/supercapacitor HEV (FC/Bat/SC HEV), which combines fuel cell, battery, supercapacitor, AC, and DC generators. These FC/B/SC HEVs are particularly appealing because they excel at efficiently managing energy and cater to a wide range of driving requirements. This study presents a novel approach for exploiting the kinetic energy of a sensorless HEV. The vehicle has a primary fuel cell resource, a supercapacitor, and lithium-ion battery energy storage banks, where each source is connected to a special converter. The obtained hybrid system allows the vehicle to enhance autonomy, support the fuel cell during low production moments, and improve transient and steady-state load requirements. The exploitation of kinetic energy is performed by the DC and AC generators that are linked to the electric vehicle front wheels to transfer the HEV’s wheel rotation into power, contributing to the overall power balance of the vehicle. The energy management system for electric vehicles determines the FC setpoint power through the classical state machine method. At the same time, a robust speed controller-based artificial intelligence algorithm reduces power losses and enhances the supply efficiency for the vehicle. Furthermore, we evaluate the performance of a robust controller with a speed estimator, specifically using the adaptive neuro-fuzzy inference system (ANFIS) and the model reference adaptive system (MRAS) estimator in conjunction with the direct torque control-support vector machine (DTC-SVM), to enhance the torque and speed performance of HEVs. The results demonstrate the feasibility and reliability of the vehicle while utilizing the additional DC and AC generators to extract free kinetic energy, both of which contributed to 28% and 24% of the total power for the vehicle, respectively. This approach leads to a vehicle supply efficiency exceeding 96%, reducing the burden on fuel cells and batteries and resulting in a significant reduction in fuel consumption, which is estimated to range from 25% to 35%.

Energy and Economic Analysis of Renewable Energy-Based Isolated Microgrids with AGM and Lithium Battery Energy Storage: Case Study Bigene, Guinea-Bissau

By the year 2020, 90% of the population with access to electricity worldwide was surpassed. However, the reality is very different for many countries, especially for those on the African continent that had more than 572 million people without electricity service at the end of 2019. This work studies the implementation of an isolated microgrid activated with photovoltaic energy and energy storage in batteries under the case study of the community of Bigene, located in the African country of Guinea-Bissau. This type of project is a potential solution to the problem of access to energy, but as the cost of the energy storage system is typically very high, this work technically and economically addresses the effect of using absorbed glass material (AGM) and lithium batteries. A simulator was developed using TRNSYS software to analyze the operation of the microgrid under a defined annual demand profile for different types of users, and economic analysis was conducted considering a project lifetime of 25 years. The results showed no significant differences in the solar fraction of both types of batteries when the photovoltaic power was less than 600 kW, regardless of the capacity of the storage bank. The analysis of auxiliary power requirements showed that lithium technology leads to a lower consumption from 800 kW of PV capacity, and utilizing less than this capacity did not have a significant difference with AGM batteries. In this microgrid with a photovoltaic capacity of less than 700 kW and an energy storage of less than 2580 kWh, the type of storage technology, AGM or lithium, did not represent a considerable difference in the levelized cost of energy, indicating that AGM technology could be selected considering its low initial investment cost compared to lithium batteries.

Mesopore-dominant defective nitrogen-doped tubular porous carbon for electrochemical energy storage

Biomass-derived carbon materials have grasped tremendous interests owing to the abundance, affordability, environmental friendliness, and potential for efficient energy storage. Herein, mesopore-dominant nitrogen-doped tubular porous carbon (NTPC) possessing abundant defects from cattails was rational synthesized through one-step activation and doping approach. The profound roles of pore structure and N heteroatoms were explored by physical characterization and electrochemical tests. Importantly, the dominated mesopores accelerate free access of electrolytes to the interior and build rapid ions transfer channels, while the incorporation of nitrogen heteroatoms modifications gives to more active sites for ions storage via arising of surface defects. The NTPC materials exhibit excellent energy storage and rate capabilities in lithium-ion batteries and supercapacitors applications. Serving as the anode of lithium-ion batteries, the reversible specific capacity 810 mAh g−1 could be achieved at 100 mA g−1, and the capacity retention rate remains 97.4 % after 500 cycles at 500 mA g−1. Meanwhile, NTPC delivered an impressive specific capacitance of 271 F g−1 at 1 A g−1 as electrode material in electrochemical supercapacitors, where only 0.5 % loss of capacitance in a symmetric supercapacitor was obtained even after 10,000 cycles at 10 A g−1, indicating the remarkable cycling durability and promising potential in electrochemical energy storage.

Operational risk analysis of a containerized lithium-ion battery energy storage system based on STPA and fuzzy evaluation

Lithium-ion battery energy storage system (BESS) has rapidly developed and widely applied due to its high energy density and high flexibility. However, the frequent occurrence of fire and explosion accidents has raised significant concerns about the safety of these systems. To evaluate the safety of such systems scientifically and comprehensively, this work focuses on a MW-level containerized lithium-ion BESS with the system-theoretic process analysis (STPA) method. The work identified 53 unsafe control actions and corresponding loss scenarios. By combining loss scenarios and expert evaluation opinions, the key risk factors for system operation are obtained. Finally, this work proposed corresponding countermeasures and suggestions to address the key risk factors and improve the safety and reliability of the entire system operation. This work discusses the operational risks of MW-class containerized lithium-ion BESS and provides technical guidance for engineers in system designs, safe operations, and engineering applications.

An analysis of li-ion induced potential incidents in battery electrical energy storage system by use of computational fluid dynamics modeling and simulations: The Beijing April 2021 case study

To further grasp the failure process and explosion hazard of battery thermal runaway gas, numerical modeling and investigation were carried out based on a severe battery fire and explosion accident in a lithium-ion battery energy storage system (LIBESS) in China. The composition and transport law of gas caused by large-scale LIB failure were theoretically analyzed, and the explosion risk of thermal runaway gas mixture in complex space after accidental ignition were systematically discussed by the computational fluid dynamics (CFD) technology. After the investigation, the underground cable trench is the key channel that causes the thermal runaway gas of lithium iron phosphate batteries to be transported to the building 20 m away and induces the explosion. After about 8500 s of battery burning, the average concentration of the thermal runaway mixed gas diffused from the cable trench is 16.4%. The arrangement of obstacles in the accident building is the main factor determining the explosion overpressure of thermal runaway gas, and the overpressure generated at the end of the obstacle path exceeds 70 kPa. Multiple windows in the battery room play an effective explosion-venting effect, but increase the damage range of outdoor high-temperature flame. In addition, the System-Theoretical Accident Model and Processes (STAMP) was used to analyze the causes of the accident, and the safety constraints that should be imposed by the three control levels of the government, functional departments and energy storage power stations were introduced to prevent battery failure and fire accidents in the BESS. The research method and analysis results will provide important ideas and reference for the investigation of fire and explosion accidents in BESS.

Research on application technology of lithium battery assessment technology in energy storage system

Due to the complexity of the state change mechanism of lithium batteries, there are problems such as difficulties in aging characterization. Establishing a state assessment model for lithium batteries can reduce its safety risk in energy storage power station applications. Therefore, this paper proposes a method for establishing a lithium battery model including aging resistance under the combination of digital and analog, and uses the time–frequency domain test analysis method to establish the aging impedance expression to realize the parameterization of the aging process and state estimation of lithium batteries. A battery model that can characterize the battery aging process is established by combining digital and simulation methods, and the equivalent representation of the lithium battery aging process is realized. Finally, a simulation example is designed to verify its effectiveness, which provides important reference value for the practical engineering application of lithium battery energy storage system.

Hybrid lithium-ion battery and hydrogen energy storage systems for a wind-supplied microgrid

Microgrids with high shares of variable renewable energy resources, such as wind, experience intermittent and variable electricity generation that causes supply–demand mismatches over multiple timescales. Lithium-ion batteries (LIBs) and hydrogen (H2) are promising technologies for short- and long-duration energy storage, respectively. A hybrid LIB-H2 energy storage system could thus offer a more cost-effective and reliable solution to balancing demand in renewable microgrids. Recent literature has modeled these hybrid storage systems; however, it remains unknown how anticipated, but uncertain, cost reductions and performance improvements will impact overall system cost and composition in the long term. Here, we developed a mixed integer linear programming (MILP) model for sizing the components (wind turbine, electrolyser, fuel cell, hydrogen storage, and lithium-ion battery) of a 100% wind-supplied microgrid in Canada. Compared to using just LIB or H2 alone for energy storage, the hybrid storage system was found to provide significant cost reductions. A sensitivity analysis showed that components of the H2 subsystem meaningfully impact the total microgrid cost, while the impact of the LIB subsystem is dominated by its energy storage capacity costs. Regarding efficiency, decreased electrolyzer efficiency causes the greatest increase in total system cost, whereas increased fuel cell efficiency has the greatest potential to reduce total system cost. As technologies evolve, the H2 subsystem assumes a greater role (i.e., it is larger and receives/supplies more energy over more hours) compared to the LIB subsystem, but LIB continues to provide frequent intra-day balancing in the microgrid.

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.

Performance Assessment of a Grid-Connected Two-Stage Bidirectional Converter for a Combined PV–Battery Energy Storage System

This paper presents a comprehensive performance assessment of a two-stage power electronic (PE) converter for interfacing the grid of a lithium-ion battery energy storage system (Li-BESS) for building-integrated PV (BIPV) applications. A performance assessment of the control system was conducted for the two-stage PE interface with a common DC-link, which consisted of a bi-directional boost converter with a cascaded PI controller and an AC/DC converter with proportional-integral (PI) and proportional-resonant (PR) controllers. The assessment covered loss analysis and useful lifetime estimation for the 10 kW PE interface with a wide-bandgap SiC power MOSFET at different loads for both the charging and discharging modes of a 50 kWh lithium-ion battery system. Additionally, a performance comparison of various switching frequencies was performed. It was observed that the system was stable up to a switching frequency of 30 kHz, and that increasing the switching frequency improved the responsiveness of the converter by decreasing the settling time; however, there were diminishing returns at higher switching frequencies. To obtain a proper balance between responsiveness and lower loss, a switching frequency of 10 kHz was selected.

Development and prospect of flywheel energy storage technology: A citespace-based visual analysis

With the rise of new energy power generation, various energy storage methods have emerged, such as lithium battery energy storage, flywheel energy storage (FESS), supercapacitor, superconducting magnetic energy storage, etc. FESS has attracted worldwide attention due to its advantages of high energy storage density, fast charging and discharging speed, high energy conversion rate, easy maintenance, and no environmental pollution, and has been applied in aerospace, military, electric power, and transportation fields. This article uses the citespace review tool to intrinsically analyze and summarize the papers published from 2010 to 2022 in the field of FESS. Relevant knowledge maps such as keywords and research hotspots that carry out FESS research were obtained. Since this technology is developing gradually ►The historical development of FESS is summarized in this article for new/existing researchers. Finally ►This paper presents the future development trend based on reviewed literatures.

State of Charge and State of Health Estimation of Lithium-Ion Battery Packs With Inconsistent Internal Parameters Using Dual Extended Kalman Filter

Abstract The internal battery parameters of the lithium-ion battery energy storage system may be inconsistent due to different aging degrees during the operation, and the thermal effect can also threaten the safety of the system. In this paper, based on the second-order resistor–capacitor equivalent circuit model and the dual extended Kalman filter (DEKF) algorithm, an electrical simulation model of a LIB pack with inconsistent parameters considering the thermal effect is established, in which state of charge (SOC) and state of health (SOH) are estimated using DEKF, while the temperature is calculated by a thermal module. The simulation results show that the DEKF algorithm has a good effect on battery state and parameter estimation, with the root-mean-square error of voltage is lower than 0.01 V and SOC mean absolute error (MAE) is below 1.50%, while SOH error is 3.37%. In addition, the thermal module can provide an accurate estimation of the inconsistent temperature rise of the battery pack, and the MAE between the model-calculated temperature and the experiment is no more than 6.60%. The results provide the basic data for the scale-up of the electrothermal co-simulation model of the LIB energy storage system.

Research on the adaptability of proton exchange membrane electrolysis in green hydrogen–electric coupling system under multi-operating conditions

The green hydrogen–electric coupling system can consume locally generated renewable energy, thereby improving energy utilization and enabling zero-carbon power supply within a certain range. This study focuses on a green hydrogen–electric coupling system that integrates photovoltaic, energy storage, and proton exchange membrane electrolysis (PEME). Firstly, the impact of operating temperature, power quality, and grid auxiliary services on the characteristics of the electrolysis cell is analyzed, and a voltage model and energy model for the cell are established. Secondly, a multi-operating conditions adaptability experiment for PEME grid-connected operation is designed. A test platform consisting of a grid simulator, simulated photovoltaic power generation system, lithium battery energy storage system, PEME, and measurement and acquisition device is then built. Finally, experiments are conducted to simulate multi-operating conditions such as temperature changes, voltage fluctuations, frequency offsets, harmonic pollution, and current adjustment speed. The energy efficiency and consumption is calculated based on the recorded data, and the results are helpful to guide the operation of the system.

Ageing and energy performance analysis of a utility-scale lithium-ion battery for power grid applications through a data-driven empirical modelling approach

Energy storage systems are becoming one of the most relevant technologies to effectively support renewable energy source (RES) deployment at large. The present work proposes a detailed ageing and energy analysis based on a data-driven empirical approach of a real utility-scale grid-connected lithium-ion battery energy storage system (LIBESS) for providing power grid services. The system under investigation is an operative utility-scale LIBESS integrated with a multi-MW PV plant and connected to the medium voltage network, located in Southern-Italy. The ageing and energy analysis has been performed using data measured by the supervisory control and data acquisition (SCADA) system directly connected with the LIBESS. This large amount of data is collected in a cloud database and then elaborated using proper software tools. This experimental campaign applied on a commercial LIBESS covers the impact of degradation mechanisms, such as cycle and calendar ageing, the battery and global system efficiency as well as the role of auxiliaries' power consumption under normal and power grid services operations. Thanks to the low complexity of the proposed data-driven model, it could be easily replicated in any other LIBESS facility, both operative in real-world framework and in laboratory context. The state-of-health (SOH) estimated by the degradation model implemented in this work after 3 years of operations and after 356 full-cycles equivalents is equal to 95.88 %, with an average capacity loss compared to the nominal capacity of about 1.37 % per year. Energy analysis revealed an average system global efficiency of 85 % for operations close to the nominal power that decreases to 65 % for operations at low power rates. While considering the grid network services operations, the power grid applications investigated are primary frequency regulation, secondary voltage regulation and PV unbalances reduction. Our results show that primary frequency regulation is the most efficient service in terms of ageing and energy performances. It has been evaluated a capacity loss of 0.03 kWh per each full-cycle equivalent performed during primary frequency regulation. Differently, secondary voltage regulation and PV unbalances reduction present a capacity loss of 0.1 kWh and of 0.05 kWh per each full-cycle equivalent, respectively. At the same time, the global efficiency of primary regulation (around 83 %) is remarkably higher compared to secondary voltage regulation (about 47 %) and to PV unbalances reduction (about 55 %). Results support the relevance to identify the most impacting key performance indicators for effective exploitation of LIBESS in power system and network services applications.

Degradation-Conscious Multiobjective Optimal Control of Reconfigurable Li-Ion Battery Energy Storage Systems

Lithium-ion battery energy storage systems are made from sets of battery packs that are connected in series and parallel combinations depending on the application’s needs for power. To achieve optimal control, advanced battery management systems (ABMSs) with health-conscious optimal control are required for highly dynamic applications where safe operation, extended battery life, and maximum performance are critical requirements. The majority of earlier research assumed that the battery cells in these energy storage systems were identical and would vary uniformly over time in terms of cell characteristics. However, in real-world situations, the battery cells might behave differently for a number of reasons. Overcharging and over-discharging are caused by an electrical imbalance that results from the cells’ differences in properties and capacity. Therefore in this study, a stratified real-time control scheme was developed for the dual purposes of minimizing the capacity fade and the energy losses of a battery pack. Each of the cells in the pack is represented by a degradation-conscious physics-based reduced-order equivalent circuit model. In view of the inconsistencies between cells, the proposed control scheme uses a state estimator such that the parametric values of the circuit elements in the cell model are determined and updated in a decentralized manner. The minimization of the capacity fade and energy losses is then formulated as a multiobjective optimization problem, from which the resulting optimal control strategy is realized through the switching actions of a modular multilevel series-parallel converter which interconnects the battery pack to an external AC system. A centralized controller ensures optimal switching sequence of the converter leading to the maximum utilization of the capacity of the battery pack. Both simulation and experimental results are used to verify the proposed methodologies which aim at minimizing the battery degradation by reconfiguring the battery cells dynamically in accordance with the state of health (SOH) of the pack.

Comprehensive Reliability Assessment Method for Lithium Battery Energy Storage Systems

Electrochemical energy storage systems have the advantages of fast power response, intensive energy storage, flexible and convenient deployment, but the output characteristics of the battery system directly affect the service effect of the energy storage power plant. Therefore, it is quite necessary to analyze the reliability of battery energy storage and scientifically assess the system’s performance. This paper considers the aging state of the battery storage system as well as sudden failures and establishes a comprehensive reliability assessment method for battery energy storage systems that take into account the battery health index and the impact of thermal runaway failures, which makes up for the shortcomings of existing reliability assessment methods.

An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems

Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively.

Economic Controls Co-Design of Hybrid Microgrids with Tidal/PV Generation and Lithium-Ion/Flow Battery Storage

Due to the uncontrollable generators, islanded microgrids powered only by renewable energy require costly energy storage systems. Energy storage needs are amplified when load and generation are misaligned on hourly, monthly, or seasonal timescales. Diversification of both loads and generation can smooth out such mismatches. However, the ideal type of battery to smooth out remaining generation deficits will depend on the duration(s) that energy is stored. This study presents a controls co-design approach to design an islanded microgrid, showing the benefit of hybridizing tidal and solar generation and hybridizing lithium-ion and flow battery energy storage. The optimization of the microgrid’s levelized cost of energy is initially studied in grid-search slices to understand convexity and smoothness. Then, a particle swarm optimization is proposed and used to study the sensitivity of the hybrid system configuration to variations in component costs. The study highlights the benefits of controls co-design, the need to model premature battery failure, and the importance of using battery cost models that are applicable across orders of magnitude variations in energy storage durations. The results indicate that such a hybrid microgrid would currently produce energy at five times the cost of diesel generation, but flow battery innovations could bring this closer to only twice the cost while using 100% renewable energy.

Research on modeling and control strategy of lithium battery energy storage system in new energy consumption

Energy storage technology is one of the effective means to promote the consumption of new energy. It has the advantages of improving the flexibility and stability of power grid. Energy storage plays an important role in improving the peaking and valley filling function of the load side of the power grid. Based on the two-stage topology of the energy storage system, this paper establishes the mirror model of the practical application engineering of the energy storage system, and uses the data-driven method to establish the energy storage battery model. On this basis, the multi-objective control strategy is adopted for the peak regulating power of the energy storage system and the load state balance of the battery. The support vector machine algorithm is used to predict the daily load data of the power grid, and the constant power algorithm is proposed to control the battery control node signal. Finally, taking the battery compartment of the energy storage system as the simulation object, the effectiveness of the proposed control strategy is verified, which provides a theoretical basis for the topic research.