Electrical Energy Storage Systems Research Articles

Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images

High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery (LIB) applicable to electric vehicle and energy storage system. The artificial intelligence, including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks (CNN), is a powerful tool to explore next-generation electrode materials and functional additives. In this paper, we develop a prediction model that classifies the major composition (e.g., 333, 523, 622, and 811) and different states (e.g., pristine, pre-cycled, and 100 times cycled) of various Li(Ni, Co, Mn)O2 (NCM) cathodes via CNN trained on scanning electron microscopy (SEM) images. Based on those results, our trained CNN model shows a high accuracy of 99.6% where the number of test set is 3840. In addition, the model can be applied to the case of untrained SEM data of NCM cathodes with functional electrolyte additives.

Early warning for thermal runaway in lithium-ion batteries during various charging rates: Insights from expansion force analysis

Thermal runaway introduces a significant challenge in the widespread application of lithium-ion batteries, necessitating advanced early-warning technologies to ensure safety, particularly during charging. Only monitoring the temperature and voltage limit the performance of diagnostic algorithms. The expansion behavior of batteries, which is linked to their operating status, offers a vital indicator of safety. In this study, the evolution of multidimensional signals during overcharging experiments at different current rates is comprehensively investigated. The result shows that the abnormal expansion force can be detected at temperatures as low as 35.4 °C, which achieves an early warning signal 11 min earlier than the onset of battery thermal runaway. The effect of charging rate on battery safety is comprehensively analyzed, showing that the time interval between the warning signal of the expansion force and temperature increases steadily from 151 s to 682 s as the charging rate decreases. However, the charging rate hardly affects the stage of charge boundary of venting, which is around ±118 %. These insights are crucial for understanding early warning mechanisms in overcharged batteries, offering valuable guidance for enhancing the safety of electric vehicles and energy storage systems.

Status and Development Perspectives of the Compressed Air Energy Storage (CAES) Technologies—A Literature Review

The potential energy of compressed air represents a multi-application source of power. Historically employed to drive certain manufacturing or transportation systems, it became a source of vehicle propulsion in the late 19th century. During the second half of the 20th century, significant efforts were directed towards harnessing pressurized air for the storage of electrical energy. Today’s systems, which are based on storing the air at a high pressure, are usually recognized as compressed air energy storage (CAES) installations. This paper aims to provide an overview of different technologies that take advantage of the energy accumulated in the compressed air. Particular attention is paid to the CAES installations that are working as electrical energy storage systems (EESs). These systems, developed originally as large capacity (>100 MWe) and fuel-based installations, may soon become fully scalable, highly efficient, and fuel-free electrical energy storage systems. To present this opportunity, a thorough review encompassing previous and up-to-date advancements in their development was carried out. In particular, CAES concepts, such as diabatic (D-CAES), adiabatic (A-CAES), and isothermal (I-CAES), are described in detail. This review also provides the detailed characteristics of the crucial elements of these configurations, including compressors, expanders, air storage chambers, and thermal storage tanks. Knowledge of these components and their role allows us to understand the main challenges behind the further development of the mentioned CAES setups. Apart from the CAES systems that are designed as EES systems, this paper describes other prospective technologies that utilize the energy of pressurized air. Accordingly, compressed air cars and their key elements are explained in detail. Moreover, the technology renowned as wave-driven compressed air energy storage (W-CAES) is described as well, indicating that the utilization of pressurized air represents a viable option for converting ocean energy into electrical power.

Effect of Chlorinating Agents on Lithium Recovery and Product Purity from Lithium-ion Battery (LIB) Recycling Slags via Chlorination Roasting Process

ABSTRACT With the rapid growth of the electric vehicle (EV) and energy storage system (ESS) markets, the demand for lithium-ion batteries (LIBs) has skyrocketed, highlighting the importance of securing a sustainable supply of lithium. This study explores chlorination roasting as a method for recovering lithium from Li-containing slag, which is a promising alternative lithium source obtained from pyrometallurgical LIB recycling process. We investigate the efficiency of chlorination roasting in producing technical grade (TG) lithium chloride (LiCl) from the slag and quantitatively analyze the impurity behavior during the process. Different chlorinating agents, including CaCl2, NaCl, and PVC, are evaluated for their effectiveness in lithium recovery and purity. The results indicate that CaCl2 is the most effective in achieving high lithium recovery and LiCl purity, at approximately 90%. This offers a promising solution for sustainable lithium production. This research would highlight the importance of developing efficient recycling methods to meet the growing demand for lithium in EV and ESS applications.

Battery state of health estimation across electrochemistry and working conditions based on domain adaptation

Accurately assessing the health status of lithium-ion batteries is essential to ensure their safe and efficient application in electric vehicles and energy storage systems. Although various methods have been proposed to achieve battery state of health (SOH) estimation, most of them are only applicable to certain battery types or operating conditions. To address this issue, a novel method is proposed in this study, which leverages data-driven techniques and domain adaptation to cater to different battery electrochemistry and operating conditions. First, the evolution of battery aging is investigated and the incremental capacity sequence with ample aging information is extracted to indicate battery health. Then, the convolutional neural network and bidirectional long-short term memory network are combined to capture the nonlinear relationship between the input sequence and battery SOH. Next, a domain adaptation (DA) based on adversarial training is employed to enhance model adaptability by realizing domain-invariant features extraction. Furthermore, data augmentation is utilized to address data imbalance caused by significant lifespan disparity among different batteries. Finally, datasets with different battery types and aging conditions are used to verify the proposed method. The average estimation error of battery SOH can be controlled within 4.77 %, with over 30 % reduction contributed by the DA.

Optimal sizing and power losses reduction of photovoltaic systems using PSO and LCL filters.

The integration of renewable energy systems into electricity grids is a solution for strengthening electricity distribution networks (SEDNs). Renewable energies such as solar photovoltaics are suitable for reinforcing a low-voltage line by offering an electrical energy storage system. However, the integration of photovoltaic systems can lead to problems of harmonic distortion due to the presence of direct current or non-linear feedback in networks from other sources. Therefore, connection standards exist to ensure the quality of the energy before injection at a point of common coupling (PCC). In this work, particle swarm optimization (PSO) is used to control a boost converter and to evaluate the power losses and the harmonic distortion rate. The test on the IEEE 14 bus standard makes it possible to determine the allocation or integration nodes for other sources such as biomass, wind or hydrogen generators, in order to limit the impact of harmonic disturbances (LIHs). The evaluation of the harmonic distortion rate, the power losses as well as the determination of the system size is done using an objective function defined based on the integration and optimization constraints of the system. The proposed model performs better since the grid current and voltage are stabilized in phase after the photovoltaic source is injected.

Обзор методов контроля и диагностики систем накопления электрической энергии

Goal: review of the current situation and perspective directions for the development of technology for testing electrical energy storage systems based on lithium-ion batteries. Methods: using literature analysis, a review and systematization of the history of lithium-ion battery system monitoring technology development was conducted and the advantages and disadvantages of the most common state of charge (SOC) methods were compared. Results: lithium-ion battery systems represent an inevitable trend in the future of electrochemical energy storage. To increase their stability and economic efficiency, it is necessary that diagnostic systems allow the internal parameters of the battery to be determined with sufficient accuracy and the types of faults to be quickly determined. In the future, research in this area will continue to improve the accuracy of data acquisition systems and create accurate digital battery models. Practical significance: a review and systematization of the history of development of technology for monitoring the condition of lithium-ion battery systems was carried out in order to identify current problems and promising directions for future research in this area.

Design of passive voltage balancer system for lead acid battery

The use of rechargeable batteries for electrical energy storage requires a voltage balancing system. The voltage balancing system requires monitoring in the use of rechargeable batteries so that they can be utilized properly and can improve the electrical energy storage system. In this research, a monitored lead acid battery voltage balancing system was designed so that the management of the battery voltage balance level and the storage of electrical energy in rechargeable batteries can be stored and used optimally. In this research a series of lead acid battery voltage detection and power dissipation circuits were designed. The power dissipation circuit uses the controlled shunt resistor method which is used when the voltage of the lead acid battery being charged exceeds the maximum voltage. This method is easy to implement and can display the value of the lead acid battery voltage and other parameters, so that it can be monitored by the user. The results obtained show that the average voltage error for batteries 1 and 2 is 0.09% and 0.3% respectively. The power dissipation circuit can dissipate lead acid battery power above the reference voltage VREF=±7 V, with a balanced voltage of 6.8 V at 140 minutes and 160 minutes.

Review of Mechanical, Electrochemical, Electrical, and Hybrid Energy Storage Systems Used for Electric Vehicles

The population rate in the world is increasing rapidly. Depending on the population, the need for transportation increases at the same rate. Traditional vehicles, which provide great convenience in transportation, bring with them some disadvantages. For example, the fossil fuel used in conventional vehicles creates greenhouse gases such as CO2 and N2O. This has a negative impact on global warming. To eliminate these negativities, interest in electric vehicle (EV) and hybrid electric vehicles (HEV) technology studies has increased recently. Some problems have arisen with these technological studies. The range problem in vehicles is the biggest of these problems. Therefore, various solutions are sought for energy storage problems in vehicles. In this article, studies on HEV and energy storage in EVs are examined. According to the data obtained because of this examination, the performance analysis of the Energy Storage Systems (ESS) was made. The performances of the electrochemical batteries used in HEVs and EVs were compared. In addition to these, flywheel energy storage system was also investigated in HEVs and EVs to recover the energy lost because of braking.

Thermal Management for a Stadium Power Supply Container Using a Rack-Level Air Cooling Strategy

This study investigates the airflow and thermal management of a compact electric energy storage system by using computational fluid dynamic (CFD) simulation. A porous medium model for predicting the flow resistance performance of the battery modules in a battery cabinet is developed. By studying the influence of rack shapes, the effects of heat exchanger arrangements and other parameters on the airflow and battery thermal distribution are analyzed. When applying a larger bottom air channel, the inlet flow uniformity of each battery cabin in the cabinet increases by 5%. Meanwhile, temperature standard deviation decreases by 0.18 while raising the flow rate from 3 m/s to 8 m/s, indicating better temperature uniformity in the battery cabin. When the charge–discharge ratio reaches 0.5 C, the temperature deviation of the entire cabinet significantly increases, reaching 8 K. Furthermore, a rack-level thermal management scheme is proposed to effectively reduce the thermal deviation of the container electric energy storage system and improve the overall temperature uniformity. Results reveal that the rack-level thermal management of the wavy cabinet in the electric storage container can effectively improve the thermal uniformity of the distributed battery cabin, and the overall thermal deviation is controlled within 1.0 K.

Experimental demonstration of a dispatchable power-to-power high temperature latent heat storage system

The development of electricity storage solutions is crucial to support the integration of variable renewable electricity sources in electricity systems. This study experimentally characterizes a state-of-the-art full-scale electrical thermal energy storage (ETES) system in outdoor conditions. The system integrates a 600 kWhth high-temperature latent heat storage module and a 13 kWe Stirling engine. The heat storage module uses an 88Al12Si metallic alloy as the phase change material to store electricity converted to thermal energy by a resistive heater in charging mode. The Stirling engine uses hydrogen as the working fluid for re-electrification (discharging). The system uses liquid sodium as the heat transfer fluid between the latent heat storage module and both the charging (electrical heater) and discharging (engine) sub-systems. Over the characterization period, the ETES prototype produced stable electricity at an average rate of 10.5 ± 1 kW for 13 consecutive hours daily with overall and power block first-law efficiencies of 23 % and 25 %, respectively. The effective storage utilization ratio varied between 0.58 and 0.94 depending on the discharge parameters. The response time was <5 s for output power regulation. The results highlight the potential of this latent heat thermal energy storage system to satisfy long-duration electricity storage applications and therefore contribute to the stabilization of electricity grids.

Designing the next generation of symmetrical organic redox flow batteries using helical carbocations

In recent years, non-aqueous fully organic Redox Flow Batteries (RFBs) have displayed potential in broadening the electrochemical window and enhancing energy density in RFBs by relying on redox-active organic molecules to provide improved sustainability in comparison to metal-based charge carriers. Of particular interest, systems that rely on a single bipolar redox molecule (BRM) for their operation, known as symmetrical organic RFBs, have gained momentum as the utilization of a BRM eliminates membrane crossover issues, thus extending the lifespan of electrical energy storage systems while reducing their cost. In this manuscript, we will present our contribution to this field through the design of tunable bipolar molecules within the helicene carbocation class. This particular type of BRM is synthetically very affordable and has proven to be highly modifiable and robust. Through the examination of 11 examples, we will demonstrate how an approach based on readily available electrochemical tools can be efficiently employed to generate and assess a library of compounds for future full flow RFB applications.

An applied deep reinforcement learning approach to control active networked microgrids in smart cities with multi-level participation of battery energy storage system and electric vehicles

This study proposed an intelligent energy management strategy for islanded networked microgrids (NMGs) in smart cities considering the renewable energy sources uncertainties and power fluctuations. Energy management of active power and frequency control approach is based on the intelligent probabilistic wavelet fuzzy neural network-deep reinforcement learning algorithm (IPWFNN-DRLA). The control strategy is formulated with deep reinforcement learning approach based on the Markov decision process and solved by the soft actor-critic algorithm. The NMG local controller (NMGLC) provides information such as the frequency, active power, power generation data, and status of the electric vehicle's battery energy storage system to the NMG central controller (NMGCC). Then the NMGCC calculates active power and frequency support based on the IPWFNN-DRLA approach and sends the results to the NMGLC. The proposed model is developed in a continuous problem-solving space with two structures of offline training and decentralized distributed operation. For this purpose, each NMG has a control agent (NMGCA) based on the IPWFNN algorithm, and the NMGCA learning model is formulated based the online back-propagation learning algorithm. The proposed approach demonstrates a computation accuracy exceeding 98%, along with a 7.82% reduction in computational burden and a 61.1% reduction in computation time compared to alternative methods.

Effect of Heteroatom Doping on Electrochemical Properties of Olivine LiFePO4 Cathodes for High-Performance Lithium-Ion Batteries.

Lithium iron phosphate (LiFePO4, LFP), an olivine-type cathode material, represents a highly suitable cathode option for lithium-ion batteries that is widely applied in electric vehicles and renewable energy storage systems. This work employed the ball milling technique to synthesize LiFePO4/carbon (LFP/C) composites and investigated the effects of various doping elements, including F, Mn, Nb, and Mg, on the electrochemical behavior of LFP/C composite cathodes. Our comprehensive work indicates that optimized F doping could improve the discharge capacity of the LFP/C composites at high rates, achieving 113.7 mAh g-1 at 10 C. Rational Nb doping boosted the cycling stability and improved the capacity retention rate (above 96.1% after 100 cycles at 0.2 C). The designed Mn doping escalated the discharge capacity of the LFP/C composite under a low temperature of -15 °C (101.2 mAh g-1 at 0.2 C). By optimizing the doping elements and levels, the role of doping as a modification method on the diverse properties of LFP/C cathode materials was effectively explored.

Bus Voltage Stabilization of a Sustainable Photovoltaic-Fed DC Microgrid with Hybrid Energy Storage Systems

Renewable energy sources play a great role in the sustainability of natural resources and a healthy environment. Among these, solar photovoltaic (PV) systems are becoming more economically viable. However, as the utility of solar energy conversion systems is limited by the availability of sunlight, they need to be integrated with electrical energy storage systems to be more sustainable. This paper aims to improve the control performance of a hybrid energy storage system (HESS) with PV power generation as the primary power source. HESSs stabilize DC microgrid systems by compensating for demand generation mismatches. Batteries and supercapacitors are chosen as energy storage elements; batteries have a high energy density and are capable of supplying and absorbing energy over a long duration, while supercapacitors can store and deliver energy very quickly. To enhance the stability of the system, each storage element is connected to the DC bus using a bidirectional Ćuk converter, which offers high efficiency, a continuous current, and minimal switching losses. This study proposes a proportional–integral (PI) controller combined with the fast nonsingular integral terminal sliding mode control (FNITSMC) for HESSs to adjust the power balance in a DC microgrid. FNITSMC has the advantage of enhancing the system states to reach the equilibrium point of a long sliding surface with a fast convergence rate. The reference current for FNITSMC is obtained using a PI controller combined with a low-pass filter (LPF), which eliminates the peaking current spikes on the battery and diverts them towards the supercapacitor. The effectiveness of the proposed control scheme is validated through the real-time hardware-in-the-loop (HIL) simulations on Typhoon™ HIL-402 with added uncertainties, including load variations at various temperatures and irradiances.

Cryptocurrency mining as a novel virtual energy storage system in islanded and grid-connected microgrids

Renewable electrical energy (such as: solar and wind energies) generation in microgrids (MGs), is gaining attention to reduce greenhouse gas emissions. Microgrid operators (MOs) aim to create self-sufficient, environmentally sustainable grids, increasing the capacity of renewable energy sources (RESs) by up to 100%. Despite of the benefits of this trend, challenges arise from non-controlled characteristics of these power generations and their seasonal variations, causing fluctuations and renewable energy curtailment. Although the technical solutions; such as: the demand response (DR) programs, and the conventional electrical energy storage systems (EESSs) can help, however those may face limitations in countries with high seasonal energy generation and consumption variations. This paper introduces cryptocurrency mining loads (CMLs) as innovative virtual energy storage systems (VESSs), named cryptocurrency energy storage systems (CESSs). It proposes a structure to store excess renewable energy in cryptocurrency units (CCUs) like Bitcoin (BTC). CESSs can be charged during off-peak intervals and, conversely, they discharge during high-demand periods to reduce the overall operational cost of MGs. Furthermore, it presents a new energy management system (EMS) formulation for the optimal operation of MGs in the presence of CESSs, providing an opportunity to generate additional electricity from RESs and to mitigate renewable energy curtailment. This paper explores the optimal operation conditions of both islanded and grid-connected MG with the proposed CESS. Utilizing a dataset from an island in Finland as a practical MG, its effectiveness is demonstrated through several case studies. The results of one case study in this paper demonstrate that the proposed CESS can decrease the operating cost of the MG by about 46.5%. Additionally, it is showed that by application of CESS the renewable energy curtailment is significantly reduced, and approached zero.

Residential Prosumer Energy Management System with Renewable Integration Considering Multi-Energy Storage and Demand Response

Rising energy demands, economic challenges, and the urgent need to address climate change have led to the emergence of a market wherein consumers can both purchase and sell electricity to the grid. This market leverages diverse energy sources and energy storage systems to achieve significant cost savings for consumers while providing critical grid support for utilities. In this study, an energy management system has been employed to tackle the optimization problem associated with various energy sources. This approach relies on mixed-integer linear programming (MILP) to optimize energy utilization while adhering to diverse constraints, yielding a feasible energy solution. This model is applied to real-world energy system consumption data and forecasts the most cost-effective day-ahead energy plans for different types of loads engaged in demand response. Furthermore, time-based charging and discharging strategies for electric vehicles and energy storage systems are considered, conducting a comprehensive analysis of energy costs across various storage devices. Our findings demonstrate that implementing this model can lead to an 18.26% reduction in operational costs when using lithium batteries and a remarkable 14.88% reduction with lead–acid batteries, particularly when integrating solar power and an EV into the system, while GHG is reduced by 36,018 grams/day for a load of 25 kW in one particular scenario. However, the analysis reveals that integrating wind power is not economically viable due to its comparatively higher operational costs.

Performance improvement of a standalone PV system using supercapacitors: modeling and energy management

Standalone photovoltaic (PV) systems are the most common and practical application in remote areas and communities far from the power grid. However, in the case of supplying a pulsating load with only a battery as a storage unit, their performance degraded. Therefore, hybrid electrical energy storage (HEES) systems represent a viable solution. This paper investigates the impact of utilizing a supercapacitor (SC) to work cooperatively with a battery storage unit to enhance the overall system behavior. Two scenarios of battery storage systems with/without SC are considered. A comprehensive modeling and sizing approach is established and presented in detail. Then, an energy management system (EMS) is proposed to enhance the HEES system’s performance. A proportional-integral (PI)-based controller is designed and examined to control the power electronic converters and hence improve energy management. The HEES system operation is simulated and evaluated using MATLAB/Simulink to feed a pulsating load, where the drawn pulsated load current is composed of two components: one component is supplied by battery, and the other component is fed from SC. Finally, the performance of the two hybrid configurations is evaluated in terms of battery voltage and current fluctuations, transient response, and load voltage and current ripples. The obtained results demonstrate the effectiveness of introducing SCs into HEES system.

Enhanced S‐ARIMAX model performance and state‐of‐health prediction accuracy with battery pack degradation factor combinations

AbstractThis study addresses the critical need for accurate state‐of‐health (SOH) predictions in lithium‐ion batteries, crucial for maintaining efficient battery operation and extending lifespan. We propose a novel approach utilizing the Seasonal autoregressive integrated moving average with exogenous variables (S‐ARIMAX) model, integrating multiple battery degradation factors as exogenous variables for precise SOH estimation. Our methodology involves correlation analysis to identify optimal factors, which are then incorporated into distinct cases of the S‐ARIMAX model. We evaluate the model's performance using mean absolute percentage error (MAPE) and identify voltage deviation as a highly correlated exogenous variable significantly impacting predictive accuracy. Notably, our findings underscore the S‐ARIMAX model's superiority in longer cycles, demonstrating a MAPE as low as 0.1113 at 160 cycles, emphasizing the model's adeptness at capturing cyclic patterns for precise long‐term SOH forecasts. Comparing S‐ARIMAX and ARIMAX models highlights the pivotal role of seasonality, particularly in long‐term predictions. The study's quantitative findings emphasize the necessity of integrating highly correlated variables and accounting for seasonal patterns in optimizing lithium‐ion battery SOH predictions. Our approach presents practical implications for maintaining battery performance and lifespan in electric vehicles and energy storage systems, underlining the importance of selecting strongly correlated variables and integrating seasonal components for accurate and reliable SOH forecasts.

Aging Mechanisms of Lithium-ion Batteries

&lt;p&gt;Modern society is making numerous efforts to reduce reliance on carbon-based energy systems. A notable solution in this transition is the adoption of lithium-ion batteries (LIBs) as potent energy sources, owing to their high energy and power densities. Driven by growing environmental challenges, the application scope of LIBs has expanded from their initial prevalence in portable electronic devices to include electric vehicles (EVs) and energy storage systems (ESSs). Accordingly, LIBs must exhibit long-lasting cyclability and high energy storage capacities to facilitate prolonged device usage, thereby offering a potential alternative to conventional sources like fossil fuels. Enhancing the durability of LIBs hinges on a comprehensive understanding of the reasons behind their performance decline. Therefore, comprehending the degradation mechanism, which includes detrimental chemical and mechanical phenomena in the components of LIBs, is an essential step in resolving cycle life issues. The LIB systems presently being commercialized and developed predominantly employ graphite anode and layered oxide cathode materials. A significant portion of the degradation process in LIB systems takes place during the electrochemical reactions involving these electrodes. In this review, we explore and organize the aging mechanisms of LIBs, especially those with graphite anodes and layered oxide cathodes.&lt;/p&gt;