Battery Energy Research Articles

Design Battery Charger With CC-CV Method for Series Connected Lithium-Ion Batteries Using Fuzzy Logic Controller

Considering the many human needs, the existence of solar energy is considered very appropriate to help meet the energy needs consumed by humans. All the energy produced by solar panels can be stored using batteries which can then be converted into electrical energy. The battery will work when the energy stored in the battery is fulfilled, but if the use of the battery is carried out continuously it causes the energy in the battery to run out, so that the battery energy must be recharged immediately so that it can still be used again. charging which is used to recharge battery energy at this time unfortunately it still takes quite a long time to be recharged. Through the description of the problem, so that in study this is a tool fast charging use buck converter by using the method (Constant Current) CC-CV (Constant Voltage) on a Lithium-Ion battery. Buck converter This is necessary because the source used in this study is a solar panel which produces a very high voltage, so the voltage needs to be lowered by using buck converter. Using a 11.1V / 2.2 Ah LithiumIon battery. To keep the current and voltage constant, the charging process is carried out charging this current and voltage the output will be stabilized by Fuzzy Logic Controller (FLC). From the simulation results that have been carried out, it appears that the FLC has been able to stabilize the charging current of 2.2Ah and 11.1V.

Coordinated operation of pumped hydro energy storage with reversible pump turbine and co-located battery energy storage system

Abstract This publication examines the coordinated operation of pumped hydro energy storage and battery energy storage systems to improve profitability. While pumped hydro energy storages offer high storage capacity but have slower response times, battery energy storage systems have lower capacity but faster response times. A hybrid system combining both can thus harness synergies. A mixed-integer linear programming model was developed to depict the coordinated use of both systems in the German market. The proposed approach is also applicable to other regional markets where energy and balancing services are traded in a similar manner. In this model, the pumped hydro energy storage operates in the spot market and provides automatic Frequency Restoration Reserve, while the battery energy storage systems supplies Frequency Containment Reserve. The model takes into account the costs caused by degradation effects in both storage types. The results show a 10.05 % increase in revenue through coordination compared to the independent operation of both storage systems. This added value can be achieved through more efficient use of the power capacity, particularly that of the battery energy storage systems, in coordinated operation.

Optimizing bus charging infrastructure by incorporating private car charging demands and uncertain solar photovoltaic generation

Abstract Integrating solar photovoltaic (PV) and battery energy storage (BES) into bus charging infrastructure offers a feasible solution to the challenge of carbon emissions and grid burdens. The deployment costs and uncertain power outputs of solar PV and BES need to be considered by public transportation agencies. This study presents a data-driven approach to optimize bus charging infrastructure and incorporates sharing charging and uncertain solar PV generation using the Latin Hypercube Sampling method. A case study in Yinchuan, China, reveals that integrating solar PV and BES at a single bus depot reduces total costs by 37.35%, carbon emissions by 41.46%, and grid loads by 49.35% over the 10-year lifetime of BES. Sharing this infrastructure with private cars offers further economic benefits. Global analysis shows this approach’s varying economic and environmental advantages. The proposed model offers practical implications for developing cost-effective and environmentally friendly electric bus charging infrastructure to advance sustainable transport.

Decentralized dynamic safety control for battery energy storage system in autonomous DC microgrids

This paper is aiming to address a decentralized dynamic safety control issue for battery energy storage system in DC microgrids. A novel dynamic control barrier function (DCBF) based on nonlinear disturbance observers is devised in the decentralized converter control to restrain the current value while ensuring DC bus voltage regulation. First, a DCBF dependent on load variation is built in order to restrain the current value of each energy storage unit triggered by sudden load changes. Second, a decentralized composite control law, incorporating the DCBF via quadratic program (QP), achieves the objective of current constraint while ensuring the large-signal stability of the DC microgrids. The efficacy of the proposed method has been validated through both simulations and experimental comparison tests.

A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification

A multi-timescale estimator for state of energy and maximum available energy of lithium-ion batteries based on variable order online identification

Revealing the contribution of flame spread to vertical thermal runaway propagation for energy storage systems

The rapidly growing energy storage systems necessitate more high-capacity lithium iron phosphate batteries but pose significant safety concerns. In multi-layer battery clusters, if thermal runaway propagation occurs between modules, particularly in the vertical direction, the ensuing fire spread can further result in the accelerated propagation of battery and even an irrevocable catastrophe. Clarifying the contribution of flame spread to vertical thermal runaway propagation is the goal of this investigation. The unexpected propagation characteristics between the upper and lower modules are explored. Further, the critical heat and the percentage of heat that leads to thermal runaway in the upper battery are determined using the equivalent replacement battery. The flame heat transfer is finally decoupled using the thermal radiation model. And the mechanism of vertical thermal runaway propagation induced flame between modules is analyzed. The findings reveal that the higher module's thermal runaway and venting sequence differs from the lower module's, suggesting that flame spread dominated the thermal runaway propagation paths. The critical triggering energy of the upper battery is 1193.6 kJ, comprising 279 kJ of conductive heat, 750 kJ of flame heat, and 164.6 kJ of self-generation heat, with the flame heat accounting for approximately 1.18 % of the total heat released from the battery fire. In contrast to horizontal thermal runaway propagation, where thermal conduction is predominant, the convection heat from battery fire serves as the main heat source for vertical propagation. The findings serve as a foundation for both emergency response to fire incidents and the safe design of battery modules in existing energy storage systems.

Design and optimization for photovoltaic heat pump system integrating thermal energy storage and battery energy storage

Design and optimization for photovoltaic heat pump system integrating thermal energy storage and battery energy storage

Optimal planning of hybrid hydrogen and battery energy storage for resilience enhancement using bi-layer decomposition algorithm

Optimal planning of hybrid hydrogen and battery energy storage for resilience enhancement using bi-layer decomposition algorithm

Dataset of noise signals generated by smart attackers for disrupting state of health and state of charge estimations of battery energy storage systems.

This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF). The low-magnitude noise signals are specifically crafted to be stealthy, evading easy detection while still effectively causing malfunctions in the estimation processes. Additionally, we introduce a verification case using a different battery model and estimation algorithm to enhance generalization. This case involves high-noise signals with defined thresholds for current and voltage noise levels, which cause significant disruptions to Kalman Filters. These signals serve as a complementary example of adversarial attacks, demonstrating how such noise can destabilize estimation algorithms and lead to critical control errors. This dataset is valuable for researchers and engineers aiming to understand and mitigate the effects of smart cyber-physical attacks on BESS. These attacks can disrupt real-time BESS controllers by injecting false input data related to SoC and SoH, leading to physical control manipulations, increased energy costs, inefficiencies in demand-side management, and incorrect day-ahead scheduling, thereby destabilizing grid operations. The data is reusable in studies focused on enhancing the resilience of SoC and SoH estimation methods, as well as in developing robust defensive strategies against smart DRL-based adversarial attacks.

Carnot battery energy storage system integrated with liquid hydrogen cold energy: Thermodynamics, economic analysis and optimization

Carnot battery energy storage system integrated with liquid hydrogen cold energy: Thermodynamics, economic analysis and optimization

Field Verification Results on Long-term Operational Performance of a Large Battery Energy Storage System using Lithium-ion Batteries and their Uneven Degradation

Field Verification Results on Long-term Operational Performance of a Large Battery Energy Storage System using Lithium-ion Batteries and their Uneven Degradation

Study on performance effects for battery energy storage rack in thermal management system

Study on performance effects for battery energy storage rack in thermal management system

Active Water Optimization in Different Electrolyte Systems for Stable Zinc Anodes.

Zinc (Zn) metal, with abundant resources, intrinsic safety, and environmental benignity, presents an attractive prospect as a novel electrode material. However, many substantial challenges remain in realizing the widespread application of aqueous Zn-ion batteries (AZIBs) technologies. These encompass significant material corrosion challenges (This can lead to battery failure in an unloaded state.), hydrogen evolution reactions, pronounced dendrite growth at the anode interface, and a constrained electrochemical stability window. Consequently, these factors contribute to diminished battery lifespan and energy efficiency while restricting high-voltage performance. Although numerous reviews have addressed the potential of electrode and separator design to mitigate these issues to some extent, the inherent reactivity of water remains the fundamental source of these challenges, underscoring the necessity for precise regulation of active water molecules within the electrolyte. In this review, the failure mechanism of AZIBs (unloaded and in charge and discharge state) is analyzed, and the optimization strategy and working principle of water in the electrolyte are reviewed, aiming to provide insights for effectively controlling the corrosion process and hydrogen evolution reaction, further controlling dendrite formation, and expanding the range of electrochemical stability. Furthermore, it outlines the challenges to promote its practical application and future development pathways.

Temperature dependent dielectric performance of battery wrapping film for high voltage energy storage

Abstract The direct connection of Li-ion battery energy storage system to high voltage (HV) power grid through electronic equipment becomes promising. The overvoltage and high-frequency interference of the electronic circuit may result in the thermal runaway of battery cells and insulation failure of battery wrapping film. The key dielectric properties of the wrapping film for energy storage battery are investigated under DC to high frequency and room temperature to elevated temperature. The results indicate that high-frequency electric field and high temperature are both adverse to the insulation performance of the wrapping film. Under concurrent high-temperature and high-electric-field conditions, the breakdown strength can decrease by more than 70% compared to that under regular condition. The analysis of broadband dielectric response suggests that the interfacial defects inside the wrapping film may play an important role in the degradation of dielectric performance. This research provides theoretical basis for the insulation failure and improvement of the battery wrapping film used in high voltage energy storage system.

Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management

Driven by the global energy transition, the widespread use of electric vehicles has profoundly reshaped the transportation landscape and thrown many problems to the power system, and coordinating their charging needs with renewable energy generation has become a key part of ensuring the stable operation of regional clean energy power supply networks. This study focuses on the problem of vehicle charging dispatch to make a breakthrough, deeply analyzes the effect and efficiency of the clean energy grid, and then proposes a series of targeted measures to effectively improve the operational efficiency and reliability of the energy system. The comprehensive model integrates electric vehicle charging stations, distributed photovoltaic power generation systems, wind farms, and battery energy storage devices and enables the charging process to be accurately controlled with real-time monitoring and intelligent algorithms. In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. Experimental data strongly confirms that the optimization strategy has led to a 15% reduction in peak load on the grid, a 23% increase in the proportion of clean energy consumption, and a 10% reduction in total electricity consumption. For policymakers, these achievements can be used as a guide to help formulate energy policies and build a framework for adapting to the development of new energy. For practitioners, they serve as a guide to energy planning, grid dispatch, and technology research and development to improve effectiveness. The research promotes the growth of green energy, optimizes the energy structure, lays the foundation for a low-carbon and environmentally friendly society, affects the economy, environment, culture, and other fields, and becomes a key force driving sustainable development.

Battery pack technological considerations for hybrid-electric regional aircraft feasibility

Abstract This paper presents a study of the effects of the durability and level of energy storage technology on energy management strategies and the performance of hybrid electric turboprops. The results highlight the key role of battery energy density on the durability of the battery pack and the viability of the concept of hybrid electric aircraft. Additionally, the trade-off between zero-day environmental compatibility and battery lifetime is identified, caused by the size of the pack. The effective energy density would decrease with the aging of the cells, leaving a significant inert mass and increasing fuel consumption. Optimal energy management strategies are suggested in light of this new information. Higher specific energy of the pack would mitigate this aspect, along with a reduction in fuel consumption and ${\rm{N}}{{\rm{O}}_{\rm{x}}}$ emissions. Indeed, the improvement of environmental compatibility was found to be nonlinear with a positive rate, suggesting high returns in investing in great improvements in energy density over a gradual increase. This result relates to the results of the statistical technological forecast presented in this study, which, without an increase in funding, predicts the availability of the specific energy required to match the fuel-only baseline in the 2040–2050 decade.

Microgrid Management of Hybrid Energy Sources Using a Hybrid Optimization Algorithm

ABSTRACTThe microgrid of the renewable energy sources are used as photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), micro turbines (MT), diesel generators (DG), and battery energy storage systems (ESS), offers a promising solution. The issues posed by microgrid operators (MGOs) in managing energy from multiple sources, device as a storage, and response demand programs are addressed in this research study, which proposes a finest dispatch of energy approach for connected grid and microgrid freestanding. In order to accomplish successful energy management, the suggested strategy takes into account not only the minimization of operational expenses but also the reduction of power losses and greenhouse gas emissions. For microgrid energy management (MGEM), a new multi‐objective solution integrating a demand response program is incorporated into a mixed‐integer linear programming model. The optimization issue illustrates the techno‐commercial benefits and assesses the effect of demand response on optimal energy dispatch. Furthermore, a hybrid optimization technique combining the African Vultures Optimization technique (AVOA) and Artificial Rabbits Optimization (ARO) is projected to holder the problem of optimization, and an optimized fuzzy interface is built for scheduling the ESS. Finding the best trade‐offs between costs, emissions, and power losses is made possible by the algorithm, which offers insightful information for the microgrid energy management system. The outcome of the renewable energy sources of the all categories are examined.

A Multifunctional Surface Modifier Capable of Stabilizing 5.0 V Graphite Cathode via Reinforced Mechanical Strength and Preferential Anion Adsorption

AbstractWith the ever‐increasing demand for high power and high energy batteries, extensive researches devote to high voltage cathode materials. Graphite cathode‐based dual‐ion batteries (DIBs) possess the unique advantages of high working voltage (≈4.5−5.0) and high power, but still suffer from low coulombic efficiency and poor long‐term stability mainly resulted by the serious oxidative decomposition of electrolytes and significant structural deterioration of graphite cathode. From the perspectives of simultaneously reinforcing the mechanical strength of graphite cathode and suppressing the decomposition of electrolytes via a cathode/electrolyte interphase (CEI), polyacrylic acid (PAA) is adopted as the surface modifier of natural graphite (NG). The mechanical stability of graphite cathode is significantly improved by virtue of the bonding interaction between PAA and binder, which is validated through both theoretical calculation and experimental observation. In addition, PAA contributes to the formation of a LiF‐rich and homogeneous CEI through the preferential adsorption of anions, and effectively mitigates the cointercalation and decomposition of solvent. As the cathode material of DIBs, NG@PAA manifests fast charge/discharge capability and outstanding capacity retention of 73.9% after 8000 cycles. This work provides a surface modification strategy for optimizing the performance of electrode materials from multiple perspectives.

Enhancing Smart Microgrid Resilience Under Natural Disaster Conditions: Virtual Power Plant Allocation Using the Jellyfish Search Algorithm

Electric power networks face critical challenges from extreme weather events and natural disasters, disrupting socioeconomic activities and jeopardizing energy security. This study presents an innovative approach incorporating virtual power plants (VPPs) within networked microgrids (MGs) to address these challenges. VPPs integrate diverse distributed energy resources such as solar- and wind-based generation, diesel generators, shunt capacitors, battery energy storage systems, and electric vehicles (EVs). These resources enhance MG autonomy during grid disruptions, ensuring uninterrupted power supply to critical services. EVs function as mobile energy storage units during emergencies, while shunt capacitors stabilize the system. Excess energy from distributed generation is stored in battery systems for future use. The seamless integration of VPPs and networked technologies enables MGs to operate independently under extreme weather conditions. Prosumers, acting as both energy producers and consumers, actively strengthen system resilience and efficiency. Energy management and VPP allocation are optimized using the jellyfish search optimization algorithm, enhancing resource scheduling during outages. This study evaluates the proposed approach’s resilience, reliability, stability, and emission reduction capabilities using real-world scenarios, including the IEEE 34-bus and Indian 52-bus radial distribution systems. Various weather conditions are analyzed, and a multi-objective function is employed to optimize system performance during disasters. The results demonstrate that networked microgrids with VPPs significantly enhance distribution grid resilience, offering a promising solution to mitigate the impacts of extreme weather events on energy infrastructure.

The power of battery energy storage systems: unlocking home efficiency

Abstract This project explores the integration of battery energy storage systems (BESS) in residential settings to optimize energy management with a novel focus on standalone BESS configurations independent of solar photovoltaic (PV) systems. The objective is to analyze electricity price patterns, evaluate different BESS configurations, and develop strategies for efficient charging and discharging. This enables homeowners to store excess electricity during low-demand periods and discharge it during peak demand, reducing grid reliance and saving on electricity costs. The analysis reveals that a standalone BESS can reduce electricity costs by leveraging low-demand periods with electricity prices averaging 0.4 DKK/kWh, compared to peak prices exceeding 0.8 DKK/kWh. For June 2023, optimal charging (6:00–14:00, 18:00–22:00) and discharging (14:00–18:00, 23:00–6:00) periods were identified, leading to potential cost savings of up to 30 %. The findings highlight the potential of BESS to transform energy management and suggestions such as a net metering program are proposed to mitigate these challenges.