Battery Energy Research Articles

Smart Integration of Renewable Energy Sources Employing Setpoint Frequency Control—An Analysis on the Grid Cost of Balancing

The increasing installation of Renewable Energy Sources (RES) presents significant challenges to the stability and reliability of power systems. This paper introduces an advanced control method to mitigate the adverse effects of intermittent generation from RES on the power system frequency stability. The proposed approach emphasizes the critical role of Battery Energy Storage Systems (BESS) and RES in enhancing the resilience of modern power networks. The Generation Export Management Schemes (GEMS) are employed to curtail the excessive export of RES, thereby contributing to improved frequency stability. This research involves a comprehensive analysis of the dynamic behavior of the network under various operational scenarios, particularly focusing on power exchanges between RES, BESS, and synchronous generation units. Furthermore, this paper focuses on the economic implications of integrating RES into the grid, with a detailed cost of balancing (COB) modelling and analysis conducted to assess the financial viability of the proposed frequency management solutions. The analysis encompasses both short-term and long-term perspectives, providing insights into the development of economically sustainable smart power networks that effectively integrate renewable energy and storage technologies while maintaining system stability.

Case Studies of Battery Energy Storage System Applications in the Brazilian Transmission System

This paper presents the preliminary results of studies aiming to use a battery energy storage system (BESS) in the Brazilian transmission system. The main objective of the BESS is to solve congestion problems caused mainly by the large increase in variable renewable generation in certain system areas. The studies were conducted based on actual forecasted system scenarios using a full representation of the electric grid available from the Brazilian system operator data base. In this work, only the steady-state modeling was considered as this may be the first stage in the assessment of a new technology. A qualitative economic comparison of the BESS application with other possible solutions to the congestion problems is also included.

A Comprehensive Multi-Objective Energy Management Approach for Wearable Devices with Dynamic Energy Demands

Recent advancements in low-power electronics and machine-learning techniques have paved the way for innovative wearable Internet of Things (IoT) devices. However, these devices suffer from limited battery capacity and computational power. Hence, energy harvesting from ambient sources has emerged as a promising solution for powering low-energy wearables. Optimal management of the harvested energy is crucial for achieving energy-neutral operation and eliminating the need for frequent recharging. This task is challenging due to the dynamic nature of harvested energy and battery energy constraints. To tackle this challenge, we propose tinyMAN, a reinforcement learning-based energy management framework for resource-constrained wearable IoT devices. tinyMAN maximizes the target device utilization under battery energy constraints without relying on the harvested energy forecast, making it a prediction-free approach. It achieves up to 17% higher utility while reducing battery constraint violations by 80% compared to prior work. We also introduce tinyMAN-MO, a multi-objective extension of tinyMan for applications with time-varying energy demands. It learns the tradeoff between meeting the application’s energy demand and maintaining the battery energy level. We deployed our framework on a wearable device prototype using TensorFlow Lite for Micro, leveraging its small (less than 120 KB) memory footprint. Evaluations show that tinyMAN-MO operates within 10% of the Pareto-optimal solutions with only 1.98 ms execution time and 23.17 μJ energy consumption overhead.

Modeling and Control of Dual Active Bridge‐Modular Multilevel Converter‐Based Solid State Transformer for Grid Integration of SPV and Battery Energy Storage

ABSTRACTThis article deals with the modeling and control of a solid‐state transformer (SST) based on a dual active bridge (DAB) and modular multilevel converter (MMC) for integrating solar photovoltaic (SPV) and battery energy storage (BES) systems into the grid. SST uses DABs for bidirectional DC‐DC conversion and an MMC for DC‐AC conversion. A hybrid control method is developed in this study, which combines proportional‐integral (PI) controllers and model predictive control (MPC) to achieve the multiple objectives of the SST. The DABs' control system uses PI controllers for power extraction from the SPV system and to regulate the charging and discharging of the BES according to power generation and demand fluctuations. MPC is applied to control the active power injection, regulate the DC‐link and sub‐module capacitor voltages of the MMC. Moreover, the developed hybrid control method ensures reliable SST performance even under adverse conditions like grid voltage distortion, unbalance, and frequency variations. An energy management strategy is developed based on total power generation, reference active power injection, and the state of charge of the BES. This strategy ensures accurate reference power injection to the grid despite dynamic changes in operating conditions. The article also presents a detailed mathematical model of the DAB and MMC components, and the stability of the control algorithm is analyzed theoretically. The effectiveness of the SST and the proposed hybrid control scheme is demonstrated through MATLAB/Simulink simulations and hardware‐in‐the‐loop experimental results under various operating conditions.

Covalently Anchoring an Ultrathin Conformal SiOx Coating on Polyolefin Separator for Enhanced Lithium Metal Battery Performance

AbstractSurface modification of separators with inorganic oxide ceramics such as SiOx, Al2O3, and TiO2 has emerged as a promising strategy to suppress lithium dendrite growth in lithium metal batteries, thereby enhancing safety and extending battery life. However, the binder‐dependent nature of these modifications often leads to increased separator thickness and a heightened risk of detachment during lithium plating, ultimately compromising both battery performance and energy density. In this study, a conformal, ultrathin (≈20 nm) SiOx coating with strong covalent bonding is grown on a porous separator through a liquid polymer‐derived method. Compared to the pristine polyethylene (PE) separators, this SiOx‐co‐PE separator exhibits significantly improved electrolyte wettability, mechanical strength, and thermal stability without any increase in thickness, leading to vastly enhanced cycling stability and dendrite resistance in cells with Li metal anodes. In a practical demonstration, a 402.9 Wh·kg−1 Li‐S pouch cell, with a high sulfur loading of 9 mg cm−2 and a low E/S ratio of 3.3 µL mg−1, is assembled with the SiOx‐co‐PE separator, achieving stable cycling over 70 cycles at 25 °C.

Energy Sources and Battery Thermal Energy Management Technologies for Electrical Vehicles: A Technical Comprehensive Review

Electric vehicles are increasingly seen as a viable alternative to conventional combustion-engine vehicles, offering advantages such as lower emissions and enhanced energy efficiency. The critical role of batteries in EVs drives the need for high-performance, cost-effective, and safe solutions, where thermal management is key to ensuring optimal performance and longevity. This study is motivated by the need to address the limitations of current battery thermal management systems (BTMS), particularly the effectiveness of cooling methods in maintaining safe operating temperatures. The hypothesis is that immersion cooling offers superior thermal regulation compared to the widely used indirect liquid cooling approach. Using MATLAB Simulink, this research investigates the dynamic thermal behaviour of three cooling systems, including air cooling, indirect liquid cooling, and immersion cooling, by comparing their performance with an uncooled battery. The results show that immersion cooling outperforms indirect liquid cooling in terms of temperature control and safety, providing a more efficient solution. These findings challenge the existing literature, positioning immersion cooling as the optimal BTMS. The main contribution of this paper lies in its comprehensive evaluation of cooling technologies and its validation of immersion cooling as a superior method for enhancing EV battery performance.

A bi-objective optimization framework for configuration of battery energy storage system considering energy loss and economy

To address a bi-objective optimization configuration problem of battery energy storage system (BESS) in distributed energy system (DES) considering energy loss and economy, a perturbation and observation approach (P&O) is proposed in this article. First, in a DES, the configuration model of BESS is established. Then, a novel way is designed that transforming a bi-objective optimization problem into a single objective optimization problem with variable conditions. And the P&O process of the proposed method is presented. Finally, in a simulation case, compared with a single optimization objective of energy loss or economy, the P&O method improves 5.71-fold or 2.94-fold in each direction, respectively and effectively balances the contradiction between them. In addition, with the efficiency and electricity cost of BESS increasing, the rated capacity of BESS changes by approximately 10%. And the location is a key factor affecting the configuration scheme of BESS in DES.

Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

Design and Implementation of a Modular Multilevel Series-Parallel Converter for Second-Life Battery Energy Storage Systems

Battery Energy Storage Systems (BESS) offer scalable energy storage solutions, especially valuable for remote, off-grid applications. However, traditional battery packs with fixed series-parallel configurations lack reconfigurability and are limited by the weakest cell, hindering their application for second-life batteries. The Modular Multilevel Series-Parallel Converter (MMSPC) addresses these limitations by enabling dynamic reconfiguration, optimizing cell balancing, and enhancing energy control. This paper experimentally evaluates a single-phase BESS based on the MMSPC with an output power equivalent to 2 kW and two battery units (155V), demonstrating stable output and reduced internal losses across varied battery parameters.

Efficient Hybrid Electric Vehicle Power Management: Dual Battery Energy Storage Empowered by Bidirectional DC–DC Converter

ABSTRACTThis work offers a fuel cell power system with the ability to distribute power to the load from the electrical source and charge an auxiliary battery utilizing regenerative power flows created by the load. The approach is established on a bidirectional closed‐loop DC converter. A bidirectional DC–DC converter is presented as a means of achieving extremely high voltage energy storage systems (ESSs) for a DC bus or supply of electricity in power applications. This paper presents a novel dual‐active‐bridge (DAB) bidirectional DC–DC converter power management system for hybrid electric vehicles (HEVs). The proposed system makes it possible to charge an additional battery with regenerative power flows and distributes power from the electrical source to the load efficiently. The two main stages of the DAB converter, which are the focus of this work, are an interleaved buck/boost converter on the battery and a three‐phase wye‐wye series resonating converter on the DC bus. Each switch's current stress is greatly reduced by this design, which lowers transmission losses and enhances thermal performance. The interleaved buck conversion on the battery allows for lesser current stress in each switch, resulting in lower transmission loss. The increasing complexity and power of automotive embedded electronic systems have made the use of more potent power electronic converters in automobiles necessary. In recent years, many dual volt (42 V/14 V) bidirectional inverter topologies for automotive systems have been presented. However, the majority of them are either inefficient or use a huge number of transistors and magnetic devices in both parallel and series arrangements. As a result, in this study, a bidirectional high‐efficiency inverter with fewer components is provided. The design, modes of operation, and performance metrics of the DAB converter are examined, emphasizing its ability to achieve zero‐voltage switching (ZVS) and zero current switching (ZCS) throughout its operating range. The suggested system seeks to maximize EV power management, guaranteeing high dependability and efficiency. To test all of the aforementioned qualities, an evaluation version was created, with an average efficiency of 97.5%. This research could have a substantial impact on the advancement of power electronic converters for automotive applications, leading to better EV power management, increased system reliability, and increased overall efficiency.

Enhancing distribution system performance by optimizing electric vehicle charging station integration in smart grids using the honey badger algorithm

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

Employing battery energy storage systems for flexible ramping products in a fully renewable energy power grid: A market mechanism and strategy analysis through multi-Agent Markov games

In high-proportion renewable energy power systems, flexible ramping products (FRPs) are critical for mitigating the volatility of renewable energy outputs and enhancing the adaptability of power systems. This paper aims to explore a fully renewable energy power system, with a battery energy storage system (BESS) as the sole provider of FRPs. An innovative market mechanism and new approaches to market equilibrium analysis are introduced. Initially, a common day-ahead (DA) market is established, covering both electricity and ancillary services with FRPs. The setup allows FRP providers to make bidding decisions and get rewarded based on actual ramping mileages. A bi-level multi-agent Markov game (MG) model is formulated. The upper level integrates the proposed strategies of wind power plants (WPP), photovoltaic power plants (PVP), and BESS, while the lower level encompasses the joint market clearing mechanism. An iterative solution algorithm, using reinforcement learning, is also introduced to address the model, effectively circumventing the privacy concerns associated with traditional mathematical programming methods. The proposed solution models and strategies are validated and demonstrated to be effective through two illustrative examples, highlighting their application and relevance in fully renewable energy power systems.

A new analytical technique for obtaining the optimal sizing, location, and scheduling of BESS in a grid-connected microgrid with multiple cases of heavy DG penetration

This paper aims to provide an optimal location, power, and energy rating for a battery energy storage system (BESS) in a grid-connected microgrid. The microgrid is pre-installed with heavy renewable distributed generations like solar and wind power plants. The paper suggests a straightforward optimal operating schedule for BESS, considering real-time data on solar irradiance, wind speed, load profile, and electricity tariff obtained from the US Energy Information Administration. The proposed method analytically identifies the optimal size and location of the storage system using the modified Q-PQV load flow technique. The method also proposes incorporating seasonal variations of the real-time data to obtain the optimal BESS size. A detailed cost-benefit analysis is exhibited to validate the economic feasibility. The methodological superiority is established by comparing the proposed algorithm with other soft computing techniques like PSO, GA, and JAYA algorithms. The novel technique is executed on 33-bus and 136-bus test systems with multiple cases of solar and wind-distributed generations. The proposed method provided a significant reduction in annual energy loss with a definite improvement in the voltage profile and overall profit of the system.

Machine learning-enhanced vision systems for cutting tool notch detection in new energy battery manufacturing

Abstract This paper presents a study on the problem of burrs on the electrodes of new energy batteries, which are a major factor contributing to battery short-circuits and explosions. During the process of electrode cutting, the use of cutting tools with a notch is likely to cause burrs on the electrode. Therefore, it is essential to accurately detect the notch of the cutting tool. This paper explores the issue of cutting tool notch detection using machine learning-enhanced vision systems. Firstly, a set of cutting tool image acquisition devices is used to capture high-quality images of the cutting tool edge. Next, an algorithm for removing attachments based on concave point matching is proposed, effectively eliminating edge attachments by analyzing the concave point information of the edge. Additionally, we propose an enhanced Zernike moment sub-pixel edge extraction method, which achieves sub-pixel edge extraction while preserving the edge characteristics of the cutting tool. Furthermore, a notch detection algorithm based on quartic Hermite interpolation is introduced to detect the notch of the cutting tool by initially identifying the tool’s edge. The proposed algorithms are compared with other state-of-the-art methods, demonstrating faster and more accurate extraction of sub-pixel cutting tool edges and detection of cutting tool notches.

Optimal operating strategy of hybrid heat pump − boiler systems with photovoltaics and battery storage

The growing need to reduce energy consumption and greenhouse gas emissions is driving the search for more efficient heating solutions in buildings. Hybrid heating systems, which combine air-to-water heat pumps (AWHP) with traditional gas boilers, are a common solution after refurbishment investments. However, managing these systems effectively, particularly when integrated with photovoltaic (PV) panels and battery energy storage systems (BESS), remains a complex task. For instance, heat pumps perform poorly in very cold conditions, making boilers a more efficient option; however, it might be advantageous to use it to increase electricity self-consumption. Optimal management depends on multiple factors, including future forecast data. In this paper, a daily optimization program is developed by means of a brute-force approach using forecast data. The core innovation of this paper is the use of an artificial neural network (ANN) that, trained on predictive optimization results, can determine the optimal solution in real-time without the need for future forecasts. The ANN achieved a 99.16% accuracy in new scenarios, successfully optimizing costs, CO2 emissions, and primary energy use. Results indicate up to 19% cost savings in colder cities, a 12% reduction in CO2 emissions, and a 3% decrease in primary energy consumption. This approach holds significant potential for enhancing the integration of renewable energy sources, contributing to long-term sustainability goals.

Innovation and future development direction of hydrogen fuel cell vehicles

With the increasing problem of global climate change, reducing greenhouse gas emissions has become an urgent issue for all countries to solve. The pollution of the Earth is gradually becoming severe. The extensive use of fossil fuels is a major cause of this problem. In this critical situation, energy with the least carbon emissions should be used to minimize air pollution and the harm caused by it. Hydrogen energy is currently a clean energy alternative that can achieve low carbon emissions. As a new energy vehicle, hydrogen fuel cell vehicles have the advantages of being clean, efficient, and environmentally friendly. Compared with traditional fuel vehicles, hydrogen fuel cell vehicles only produce water and a small amount of oxygen during operation, and do not emit harmful substances such as carbon dioxide, which helps to significantly reduce carbon emissions in the transportation sector. So, hydrogen fuel cell vehicles are a viable and clean solution. In this paper, the performance evaluation, cost analysis, and future impact of hydrogen-powered vehicles are analyzed, and the future development trend of hydrogen energy battery vehicles is discussed. As a new energy vehicle, hydrogen fuel cell vehicles have important strategic significance and market prospects. The purpose of this paper is to provide a useful reference for the development of China's new energy vehicle industry through the study of hydrogen fuel cell vehicles.

Robust Distributed Load Frequency Control for Multi-Area Power Systems with Photovoltaic and Battery Energy Storage System

The intermittent power generation of renewable energy sources (RESs) interrupts the balance between power generation and demand load due to the increased frequency fluctuation, which challenges the frequency stability analysis and control synthesis of power generation systems. This paper proposes a robust distributed load frequency control (DLFC) scheme for multi-area power systems. Firstly, a multi-area power system is constructed by integrating photovoltaic (PV) and battery energy storage systems (BESSs). Then, by employing the linear matrix inequality (LMI) technique, the sufficient condition capable of ensuring that the proposed controller satisfies H∞ robust performance in the sense of asymptotic stability is derived. Finally, testing is conducted on a four-area renewable power system, and results verify the strong robustness of the proposed controller against load disturbance and intermittence of RESs.

Renewable Solar Energy Facilities in South America—The Road to a Low-Carbon Sustainable Energy Matrix: A Systematic Review

South America is a place on the planet that stands out with enormous potential linked to renewable energies. Countries in this region have developed private investment projects to carry out an energy transition from fossil energies to clean energies and contribute to climate change mitigation. The sun resource is one of the more abundant sources of renewable energies that stands out in South America, especially in the Atacama Desert. In this context, South American countries are developing sustainable actions/strategies linked to implementing solar photovoltaic (PV) and concentrated solar power (CSP) facilities and achieving carbon neutrality for the year 2050. As a result, this systematic review presents the progress, new trends, and the road to a sustainable paradigm with disruptive innovations like artificial intelligence, robots, and unmanned aerial vehicles (UAVs) for solar energy facilities in the region. According to the findings, solar energy infrastructure was applied in South America during the global climate change crisis era. Different levels of implementation in solar photovoltaic (PV) facilities have been reached in each country, with the region being a worldwide research and development (R&D) hotspot. Also, high potential exists for concentrated solar power (CSP) facilities considering the technology evolution, and for the implementation of the hybridization of solar photovoltaic (PV) facilities with onshore wind farm infrastructures, decreasing the capital/operation costs of the projects. Finally, synergy between solar energy infrastructures with emerging technologies linked with low-carbon economies like battery energy storage systems (BESSs) and the use of floating solar PV plants looks like a promising sustainable solution.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.