Battery Energy Storage System Research Articles (Page 1)

Abstract - The rapid global rise of Electric Vehicles (EVs) poses a major challenge to traditional power grids. It requires a smart and sustainable charging system. This paper introduces an Energy Management System (EMS) for an EV charging station that works with renewable energy sources (RES), mainly solar panels (PV), and a Battery Energy Storage System (BESS). The main goal of the EMS is to effectively manage the power flow between the PV system, BESS, utility grid, and EVs. The system focuses on charging vehicles directly from solar energy to maximize the use of renewables. Any extra solar power is stored in the BESS for future use when energy production is low or electricity prices are high. The EMS uses smart control algorithms that take into account real-time data, such as solar irradiance, grid electricity prices, the state-of-charge (SoC) of the BESS, and the charging needs of EVs. By carefully scheduling when to charge and discharge, the EMS aims to lower operational costs, ease peak load stress on the utility grid, and reduce the charging station's carbon footprint. This setup offers a reliable, affordable, and eco-friendly solution, which is essential for incorporating EVs into a sustainable transportation system. Key Words: Energy Management System, Electric Vehicles, Renewable Energy Sources, Photovoltaic, Battery Energy Storage System, EV Charging Station,Smart Grid, Optimization, Power Flow Control, Vehicle-to-Grid.

The increasing demand for efficient and sustainable energy solutions has accelerated the development of smart grid systems by integrating different types of advanced energy storage technologies. Among various available solutions hydrogen storage is emerging as a key solution for addressing the variability of renewable energy sources while enhancing grid stability and energy efficiency. This research study investigates the design and optimization of smart grid systems with integrated hydrogen storage. The goal is to improve energy efficiency, enhance system resilience, and support the transition to near-zero energy buildings (NZEBs). The proposed system combines hydrogen storage with battery energy storage systems (BESS) to mitigate fluctuations in solar and wind power generation. A cloud-based monitoring and control system enables real-time data access, predictive maintenance, and optimal energy dispatch. The study also evaluates the environmental impact, energy efficiency, and economic feasibility. Results indicate that properly designed smart grid systems with hydrogen storage significantly enhance energy sustainability and reliability. The integration of hydrogen as a long-term storage solution reduces dependence on fossil fuels, accelerates NZEB development, and supports carbon neutrality goals. Hydrogen storage plays a crucial role in advancing renewable energy systems. The research provides insights into its potential as a key enabler in smart grid infrastructures, contributing to a more sustainable and resilient energy future. Nevertheless, this method has several drawbacks, such as a poor conversion rate and expensive infrastructure. Hydrogen synthesis through electrolysis usually has an efficiency of less than 70%, with further energy losses taking place during storage and fuel cell reconversion to electricity. Furthermore, a major obstacle to broad adoption is still the high upfront expenditures of fuel cells, storage tanks, and electrolysers. Notwithstanding these drawbacks, continued developments in electrolysis technology, better techniques for compressing hydrogen, and fuel cell cost reductions should increase viability. To support a more robust and sustainable energy future, this research addresses the present issues with hydrogen while also shedding light on its potential as a major facilitator in smart grid systems.

This paper presents an integrated approach to optimizing microgrid management and electric truck logistics for transportation research. The experiment involves a 100 kW solar photovoltaic system, a 500 kWh battery energy storage system, the electric demand of a commercial building, and a heavy-duty vehicle charging system. The study aims to demonstrate how synchronized optimization of a microgrid control algorithm and a truck route algorithm can reduce overall system costs. The microgrid management system is designed to meet the constraints and requirements of a commercial electric truck charging scheduler. This integrated approach is an improvement over previous systems as it uses the scheduler’s outputs—such as time frames and energy requirements—as constraints for microgrid management. The truck scheduling algorithm iteratively learned to optimize charging times, ensuring that charging occurs during low-cost periods or when renewable energy is available. The electric vehicle scheduler adjusted truck arrival times based on the availability of clean energy sources, creating a feedback loop that continuously improves cost efficiency. Results indicated significant cost savings, with electric utility costs for electric vehicle (EV) charging being only 0% to 20% of the original value while the transportation system is only 23% to 64%, compared with the baseline scenario without the co-optimization framework. These findings suggest that the proposed integrated approach can effectively reduce costs and improve the efficiency of microgrid and electric truck operations. Uncoordinated charging schedules leads to a higher power demand than a well-organized battery electric truck (BET) dispatching strategy. Optimizing truck charging times and energy needs based on microgrid conditions can significantly reduce electricity and transportation costs.

The widespread adoption of Battery Energy Storage Systems (BESS) is crucial for integrating intermittent renewable sources like solar and wind into the power grid, thereby advancing the goals of green energy. Deploying BESS underground offers a sustainable solution to land constraints and safety concerns. However, the dynamic and complex thermal environment underground severely challenges the accurate State-of-Charge (SOC) estimation, which is vital for the safety, longevity, and operational efficiency of BESS. Data-driven SOC models often suffer from performance degradation due to data distribution shifts caused by temperature fluctuations, especially when operational data for specific underground temperatures is sparse. To tackle this issue, this paper proposes a transfer learning model based on adversarial domain adaptation. The model utilizes a Gated Recurrent Unit (GRU) network for feature extraction and incorporates a Gradient Reversal Layer (GRL) to learn temperature-invariant features through an adversarial training mechanism. This approach effectively transfers knowledge from a data-rich source domain (standard temperature) to data-sparse target domains (varied underground temperatures). Comprehensive experiments on a public battery dataset covering a wide temperature range (-20 ◦C to 40 ◦C) demonstrate that our method significantly reduces SOC estimation errors under unseen thermal conditions compared to conventional models. The proposed solution enhances the reliability and sustainability of underground BESS, contributing to more resilient and efficient green energy infrastructure.

The integration of renewable energy sources in micro-grids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response strategies are crucial for maintaining system stability and economic efficiency, particularly in micro-grids with high renewable penetration. The aim of the work is prediction of load demand for micro-grid using genetic algorithm, however, the objectives are to model and implement a demand response system of a 4kva 24v smart solar system using Genetic Algorithm, optimize energy consumption and maximize revenue. In addition, to evaluate the performance of the Demand Response System and quantify its impact on energy savings and minimize total cost. This paper presents a comprehensive (genetic algorithm) model for optimizing operations in a micro-grid with solar generation and (battery energy storage systems). The model incorporates load classification, dynamic price threshold, and multi-period coordination for optimal event scheduling. Analysis across four distinct operational scenarios demonstrates consistent peak load reduction of 68.6% while achieving energy cost savings ranging from 4.5% to 11.3%. The highest performance was observed in scenarios with high demand, where the model achieved 11.3% energy cost reduction through optimal coordination of renewable resources and actions. The results validate the model’s effectiveness in managing diverse operational challenges while maintaining system stability and economic efficiency. The research therefore recommends that not only should this proposed method (genetic algorithm) be included in the curriculum for higher programs, but also applied when solving modelling and optimization of demand response for micro-grids problems. Doing this will assist research students in accomplishing desired result(s), eliminate rigorous calculation processes and obtain optimally converging solutions. The financial evaluation should be enhanced by including certain factors such as potential loss of heat during generation, reduced maintenance on the operating device and the deferred replacement of machine components.

Nowadays, given ecological concerns, including greenhouse gas (GHG) emissions and climate change, it is critical to look into environmentally friendly and sustainable energy sources (SES). This study examines the viability of using photovoltaic (PV) and micro wind turbine (WT) energy systems for hybrid energy (HE) harvesting in rail transportation systems. To meet the energy requirements of Kazakhstan’s railway systems, this study investigates the importance of employing PV, WT, solar sleepers, and a battery energy storage system (BESS). The Talgo Tulpar passenger train traveling from Astana to Almaty has been selected for this study. The integration of solar sleepers over the rail track is also taken into consideration. This article proposes a strategic plan for the system’s operating principles. The methodology involved examining three distinct cases: PV/WT/Grid (Case 1), PV/WT/BESS/Grid (Case 2), and PV/WT/BESS/Grid/Solar sleeper (Case 3). This system was mathematically modeled and simulated using a MATLAB algorithm. The major findings indicate that Case 1 achieves higher energy savings compared to Case 2, while Case 3 results in significantly greater savings than both Case 1 and Case 2. The results suggest considerable cost savings because less grid electricity is needed, which in turn leads to a reduced CO 2 footprint.

Enhanced stability analysis of a delayed T–S fuzzy load frequency control system integrated with a battery energy storage system

A stochastic power-based distribution locational marginal pricing framework for frequency regulation in isolated micro-grids using battery energy storage systems

Techno-economic management of mobile battery energy storage systems in microgrids considering self-driving electric trucks and uncertainty of generation and consumption

Evaluating economic feasibility of lithium-ion battery energy storage systems in future US electricity markets

A comprehensive techno-economic performance assessment framework for large-scale battery energy storage systems: Analysis of Expected–Actual deviations and case study

Deep Q-network based battery energy storage system control strategy with charging/discharging times considered

This paper introduces a Hybrid Fuzzy–Neural Droop Control (FNDC) approach designed for renewable agricultural microgrids that combine photovoltaic (PV), biogas, and battery energy storage systems (BESS). The control framework merges the nonlinear adaptability of fuzzy logic with the learning ability of neural networks to improve voltage–frequency stability, speed up transient responses, and reduce steady-state errors amid fluctuating renewable generation and nonlinear agricultural loads. The fuzzy component dynamically adjusts droop coefficients based on voltage deviations, power imbalances, and load variation rates. Meanwhile, the neural component continuously refines fuzzy membership parameters through an online gradient-based learning law. The FNDC is implemented and tested on a Python-based microgrid simulation platform utilizing pandapower and NumPy/SciPy. Comparative results against standard and fuzzy-only droop controllers show that the FNDC reduces settling time by up to 67%, cuts mean absolute error (MAE) by 45% and decreases RMSE by over 50% for voltage and frequency regulation. Additionally, the adaptive and decentralized design of the FNDC provides robustness against communication delays and scalability for rural deployment. The proposed strategy offers an intelligent and efficient control framework for next-generation smart agricultural microgrids powered by hybrid renewable energy sources.

The increasing integration of distributed renewable energy sources into distribution networks results in significant voltage regulation challenges. To address these challenges, we introduce a novel data-driven approach for voltage regulation that utilizes predictive control mechanisms, specifically data-enabled predictive control (DeePC). This method exploits the capabilities of photovoltaic (PV) inverters and battery energy storage systems (BESS) to manage bus voltages within the distribution network. Unlike traditional model-based approaches that require a precise physical model of the network, the DeePC algorithm operates optimally by relying solely on historical data to predict and adjust bus voltages. By employing the DeePC algorithm, the proposed controller maintains voltage profiles and the state of charge (SoC) of BESSs within operational thresholds in an optimal and robust manner. To further reduce the computational complexity, a reformulation of DeePC is developed using scoring functions, where the DeePC algorithm is efficiently approximated via differentiable convex programming. We validate our approach through simulations on the IEEE 34-bus test system, demonstrating its efficiency in maintaining desired voltage levels without the need for a detailed physical system model.

This study presents a novel LSTM–CNN-based adaptive scheduling framework (LSTM-CNN–AS) designed to improve real-time energy management and extend the lifespan of lithium-ion Battery Energy Storage Systems (BESS) in rural and resource-constrained microgrids. In contrast to conventional methods that prioritize economic optimization, the proposed framework incorporates state of health (SOH) aware control and adaptive closed-loop scheduling to enhance operational reliability and battery longevity. The architecture combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for accurate SOH estimation, with lightweight Multi-Layer Perceptron (MLP) models supporting real-time scheduling and state of charge (SOC) regulation. Operational safety is maintained by keeping SOC within 20–80% and SOH above 70%. The proposed model training and validation are conducted using two real-world datasets: the Mendeley Lithium-Ion SOH Test Dataset and the DKA Solar System Dataset from Alice Springs, both sampled at 5-min intervals. Performance is evaluated across three operational scenarios, which are 2C charging with random discharge; random charging with 3C discharge; and fully random profiles, achieving up to 44% reduction in MAE and an R2; score of 0.9767. A one-month deployment demonstrates a 30% reduction in charging time and 40% lower operational costs, confirming the framework’s effectiveness and scalability for rural microgrid applications.

This article introduces a cost-effective, IoT-enabled flexible energy management system (EMS) for residential photovoltaic (PV) microgrids with battery storage, implemented on an ESP32 microcontroller. The proposed system achieves indirect control over commercial household inverters by altering wattmeter readings and utilizing Modbus communication, thereby avoiding expensive hardware modifications. A significant contribution of this work is enabling the injection of energy from the Battery Energy Storage System (BESS) into the grid, a capability often restricted by commercial inverters. Real-world experimentation validated robust performance of the proposed system, demonstrating its ability to dynamically manage energy flows, achieve minimal tracking errors, and optimize energy usage in response to both flexibility market signals and electricity prices. This approach provides a practical and accessible solution for prosumers to actively participate in energy trading and flexibility markets using widely available technology.

ABSTRACT This article proposes a composite fault‐tolerant control strategy combining zero‐mode control and half‐wave reconfiguration to address single IGBT open‐circuit faults in cascaded H‐bridge converters. The composite strategy enables continuous operation without bypassing faulty modules by dynamically adjusting the working modes of faulty modules and reconstructing modulation waves for cascaded converters. The zero‐mode control strategy, as the core of this study, adopts a digital logic‐based architecture and utilizes the zero‐mode equivalence of the two operating modes of the H‐bridge module to dynamically switch modes according to the fault type. Through collaboration with the half‐wave reconfiguration control strategy, precise compensation for missing positive or negative voltage levels caused by faulty modules is achieved within a specific interval, ensuring that the cascaded converter can maintain output characteristics even under single IGBT open‐circuit faults and meet the requirements of grid‐connected operation. The maximum power control strategy dynamically adjusts the output power of faulty and healthy modules to minimize power deviations between them, thereby optimizing energy distribution efficiency and extending the lifespan of the battery energy storage system. Experimental results validate the effectiveness of the strategy in addressing single IGBT faults in cascaded energy converters.

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust.

Energy management in Internet of Things-enabled hybrid microgrids plays a vital role in optimizing the coordination of distributed energy resources, including wind turbines, photovoltaic systems, battery energy storage systems, and the main grid. Despite advancements in the Internet of Things improving real-time control and monitoring, the variability of renewable sources presents significant challenges in ensuring consistent energy efficiency and cost minimization. To address these challenges, this study introduces an innovative method that integrates the builder optimization algorithm with a neural architecture search-guided physics-informed neural network. The optimization algorithm determines optimal energy distribution, while the neural framework uses Internet of Things data for accurate forecasting of generation and storage. This integration enables adaptive and intelligent energy management decisions. Implemented in MATLAB, the proposed method significantly outperforms existing models, achieving a total energy cost reduction of $321.06 and an energy efficiency of 99.1%.

Understanding the degradation behavior of lithium-ion batteries under realistic application conditions is critical for the design and operation of Battery Energy Storage Systems (BESS). This research presents a modular, cell-level simulation framework that integrates electrical, thermal, and aging models to evaluate system performance in representative utility and residential scenarios. The framework is implemented using Python and allows time-series simulations to be performed under different state of charge (SOC), depth of discharge (DOD), C-rate, and ambient temperature conditions. Simulation results reveal that high-SOC windows, deep cycling, and elevated temperatures significantly accelerate capacity fade, with distinct aging behavior observed between residential and utility profiles. In particular, frequency modulation and deep-cycle self-consumption use cases impose more severe aging stress compared to microgrid or medium-cycle conditions. The study provides interpretable degradation metrics and visualizations, enabling targeted aging analysis under different load conditions. The results highlight the importance of thermal effects and cell-level stress variability, offering insights for lifetime-aware BESS control strategies. This framework serves as a practical tool to support the aging-resilient design and operation of grid-connected storage systems.