battery management system (BMS)

Control systems are central to modern engineering, enabling precise and reliable operation in fields such as robotics, aerospace, automotive, and energy. Over time, control approaches have evolved from simple feedback laws to advanced model-based and learning-driven techniques. This paper presents a comprehensive review of five major families of control strategies: classical proportional-integral-derivative (PID) control, optimal methods exemplified by the linear quadratic regulator (LQR), robust control including H∞-based designs, adaptive control schemes that adjust to system uncertainties, and intelligent approaches such as fuzzy logic and neural-network-based controllers. For each category, we outline fundamental principles, typical design procedures, tuning practices, and implementation challenges. Comparative analysis and application-oriented insights are provided to guide the selection of suitable strategies that balance performance, robustness, and complexity in practical engineering systems.

This paper proposes and evaluates the design of a battery-backed IoT-based incubator to ensure stable temperature and humidity during tempeh fermentation. The system integrates a microcontroller (ESP32), precision sensors (SHT31/DHT22), heating and ventilation actuators, and a battery management subsystem (BMS) for backup operation. The evaluation includes battery sizing, comparative stability tests under power outage conditions, and assessment of fermentation quality (time to full mycelium coverage, pH, texture, sensory tests). Results are expected to show that battery integration reduces environmental fluctuations and minimizes fermentation failure risks under unstable power supply.

The traditional Intelligence Cycle—requirements, planning, collection, processing, analysis, and
dissemination—is criticised in this paper for being out of step with the threat dynamics of the twenty-first
century. It makes the case that the linear model from the Cold War era overlooks operational delays and
cognitive blind spots to handle terrorism, disinformation, hybrid threats, and fast cyberwarfare. Cross-agency
integration and timely all-source analysis are impeded by structural pathologies, particularly bureaucratic
stovepipes and an uneven emphasis on TECHINT versus HUMINT. The study emphasises how demand-driven
analysis and the "wrong puzzle" syndrome can cause cognitive errors that result in missed or incorrect
conclusions. In order to realign culture, technology, and processes towards non-linear, recursive, and
hypothesis-driven intelligence appropriate for 21st-century security challenges, it recommends implementing the
Adaptive Intelligence Systems framework.

The rapid electrification of the automotive industry demands continual advancements in electric vehicle (EV) powertrain technology. This review paper presents a comprehensive overview of recent innovations in electrical engineering that are reshaping EV powertrains, focusing on electric motors, power electronics, energy storage, and thermal management systems. Key developments include AI-driven control strategies enhancing motor performance, the adoption of wide bandgap semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) for more efficient power conversion, breakthroughs in solid-state battery technology, and novel thermal management approaches using phase change materials and AI optimization. The paper also discusses modular powertrain architectures and highlights industry advances such as next-generation heat pump systems. Collectively, these innovations contribute to improving EV efficiency, range, and reliability, thereby accelerating the transition toward sustainable transportation.

This paper deals with the optimal scheduling of prosumers equipped with energy storage facilities within renewable energy communities, and proposes a novel strategy for optimizing storage usage within a price-volume demand response framework. The problem is formulated as a scalable, low-complexity mixed-integer linear program. Furthermore, a heuristic procedure is introduced to ensure redistribution of demand response rewards among participants according to their contribution to achieving demand-response goals. The proposed approach is designed to enhance the benefits for prosumers operating within a community compared to running independently.

Hard carbon (HC), an amorphous carbon-based material, is a promising anode for sodium-ion batteries (SIBs) due to its sustainability and electrochemical performance. Direct carbonization offers a simple and energy-efficient synthesis route with relatively high initial coulombic efficiency (ICE), though often at the expense of capacity. To overcome this limitation, both pretreatment and post-treatment strategies have been developed to enhance HC properties. pre-treatment methods modify structural characteristics during synthesis by increasing structural disorder, surface activity, and defect density. In contrast, post-treatment methods improve the electrochemical behavior of the final product, yet remain comparatively underexplored. These two approaches serve complementary functions and, when integrated, offer potential for optimizing performance. This review discusses the methodologies, benefits, limitations, and impact of various pre-and post-treatment strategies for HC anodes in SIBs. Advancing understanding in this area is essential for the development of high-performance and sustainable SIB technologies. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Increasing use of electrical energy at domestic consumer level due to increased usage of electrical appliances, compounded with increased electric vehicle usage will cause severe stress on existing distribution networks necessitating exp ensive infrastructural changes. An alternative would be to increase consumer level energy production through renewable sources so that dependence on the grid is eliminated altogether, or minimized. This would also help reduce overall carbon footprint. This pap er discusses a viable implementation of a DC micro-grid with battery storage that is compatible with the majority of existing loads and also suggests modifications in certain loads to make them compatible. The micro-grid also allows for DC charging of electric vehicles at different voltage levels. The compatibility with the proposed Microgrid, of loads that can function without modifications and those that require minor changes is analyzed and verified experimentally.

Battery is an important component in the implementation of renewable energy. One type of battery that is common used in the implementation of renewable energy is the Lead-Acid battery. Accurate estimation of SOC (state of charge) values becomes one of the important parameters in the design of BMS (Battery Management System). Establishing accurate SOC value estimates is necessary to avoid batteries operating in over charge and over discharges. One method of SOC estimation is easy and common used is "coulomb counting". Determination of SOC estimation with this method by summing the coulomb that in or come out of the battery with a certain period of time. The sum of the coulomb can be calculate by adding the current to the battery, either during charging and discharging. In this final project will designed a SOC monitoring tool using coulomb counting method to monitor the value of SOC battery both at charging and discharging with a certain period of time. In the implementation of this method is very dependent on the accurate initial SOC value and accuracy of current sensors used. The type of current sensor used in this SOC monitoring tool is ACS712 which has good accuracy. Based on the results of the tests performed, the amount of coulomb on the battery is proportional to the SOC battery value.

This study examines the role of aggregators in the context of digital energy and the integration of renewable energy sources (RES). The primary economic functions of aggregators are examined, including their role in optimizing energy markets and enhancing the flexibility and resilience of electricity systems. Different business models are presented, including the Energy as a Service (EaaS) model, and the effects of aggregators' participation in electricity markets and balancing markets are examined. Special attention is paid to models for optimizing trading strategies and energy storage management. A comparative assessment of two scenarios for the distribution of the energy mix between solar and wind energy in the period 2022-2024 is conducted, evaluating the necessary storage capacities to achieve energy sustainability. The study highlights the importance of aggregators for grid stability, the integration of RES, and achieving higher efficiency through digitalisation and decentralisation in the context of European energy policy and the transition to a low-carbon economy.

Transportation is a vital component of urban development and accessibility. In Madurai district, transportation services play a critical role in daily commuter activities, economic progress, and regional connectivity. However, passengers frequently face a range of challenges, including inadequate frequency, poor maintenance of vehicles, overcrowding, lack of punctuality, safety issues, and insufficient last-mile connectivity. This study investigates the multifaceted problems faced by passengers using both qualitative and quantitative data from surveys and interviews conducted across different transport hubs in Madurai. The findings highlight significant dissatisfaction with service quality, infrastructure, and policy implementation. The study applies descriptive statistics and thematic analysis to identify key issues and provide insights for policymakers and transportation authorities. Recommendations include enhancing fleet maintenance, introducing passenger feedback mechanisms, and adopting technology for real-time monitoring. The study contributes to existing literature by addressing region-specific challenges and provides a foundation for improving transport systems in Tier-II cities like Madurai.

This research focuses on enhancing the design and construction of a microcontroller-based rechargeable electric motor vehicle by integrating a fuzzy logic controller with an ultracapacitor system. The objective is to improve the vehicle's operational mechanism, particularly in terms of energy efficiency, responsiveness, and reliability. Traditional battery-only electric vehicles often face challenges such as slow response to load variations, limited battery lifespan, and inefficient energy utilization. To address these issues, this study incorporates a fuzzy-based control strategy to manage power distribution between a rechargeable battery and an ultracapacitor bank. A microcontroller (e.g., Arduino Uno) is used to execute the fuzzy logic algorithm, which dynamically controls the charge and discharge cycles of the ultracapacitor to support the electric motor during peak load demands and regenerative braking. The system is designed to intelligently allocate energy resources based on real-time data inputs, such as motor load, speed, and battery voltage. The integration of the ultracapacitor significantly reduces stress on the battery, extends its life cycle, and improves overall energy management. Experimental results from the prototype vehicle demonstrate improved acceleration, smoother motor control, and enhanced energy recovery. This project establishes a robust framework for developing smarter electric vehicle systems using fuzzy logic and energy storage technologies. The findings offer practical implications for sustainable transportation, particularly in lowcost or resource-constrained environments. The conventional Inadequate Motor Sizing that causes poor design and construction of microcontroller based rechargeable electric motor vehicle operating mechanism was 23%. On the other hand, when Fuzzy based ultra capacitor was integrated in the system, it simultaneously reduced it to17.3% and the conventional Substandard Motor Driver or Inverter Circuitry that causes poor design and construction of microcontroller based rechargeable electric motor vehicle operating mechanism was 9%. Meanwhile, when Fuzzy based ultra capacitor was imbibed into the system, it instantly reduced it to6.8%. Finally, with these results obtained, it signified that percentage enhancement in the design and construction of microcontroller based rechargeable electric motor vehicle operating mechanism when Fuzzy based ultra capacitor was incorporated into the system was 2.2%.

This paper presents a novel approach to monitoring Electric Vehicle (EV) charging ecosystems, crucial for assessing their condition parameters. Leveraging sensor on the development of predictive models for battery and charging station conditions, particularly concerning voltage levels and associated variables. The core contribution lies in the design and implementation of tailored explicitly for Battery Management Systems (BMS). Unlike existing proposals, this system is founded upon a set of fundamental requirements that distinguish its design and functionality. Notably, it introduces a vari scheme for assessing the State of Health (SOH), enabling the prediction of battery degradation and facilitating fault detection within EV systems.

This project seeks to create a new system that applies IoT technology to mitigate the issues associated with batteries today. It employs various crucial components: the ESP8266 microcontroller, ZMPT101B voltage sensor, ACS712 current sensor, DHT11 temperature sensor, and the RTC DS1307 module. These parts work together to determine the condition of the battery by obtaining accurate information with timestamps. This system is not limited to simply showing real-time data. It also makes it possible for users to see the health status of their battery via a mobile application or website, no matter where they are. It also sends instant alerts if there are any problems, including overvoltage, over current, overheating, or if the charge in the battery drops very low. All the data gathered is stored for future usage to support maintenance and make decisions effectively in the future. This battery management system is energy-saving, costreducing, and scalable, which makes it suitable for applications from small electric cars to big energy storage installations. The project has a detailed study that demonstrates the system is technically feasible, simple to use, and economically viable. Its architecture is future-proof, ready to accommodate artificial intelligence or machine learning for deeper analysis, allowing it to evolve with evolving smart energy technologies. Essentially, this project presents an end-to-end battery management solution that outpaces conventional boundaries, addressing the increasing demand for smart, networked battery management. With the inclusion of IoT features, data monitoring, remote diagnostics, and safety features, not only does it improve battery performance and dependability but also helps ensure sustainability targets for future electric transport and smart grids.

This paper explores the uncertainty of expected returns by adopting the real options analysis method for the financial evaluation of renewable energy projects in Brazil. Energy transition is key to meeting climate targets, and real options analysis can play a pivotal role in evaluating renewable energy projects to meet those targets. The impact of the volatility of the chosen variables on the viability of the project is studied using Monte Carlo simulation in the R software. The results indicate that the lower the option value the higher the volatility of the project, leading to lower likelihood of the project being financed. The resulting model represents a simple instrument that can be incorporated in larger modelling frameworks (e.g., agent-based simulation) to assess the impact of real option analysis on different markets and environmental and socio-political conditions. These findings represent a strong case for the adoption of systems modelling to inform policy to support glob...

The proposed system provides a comprehensive and efficient machine learning framework for forecasting short-term solar power output. The approach combines essential steps, including data cleaning, feature preparation, model training, validation, and result visualization, into a single, cohesive system. It utilizes historical environmental parameters, including sunlight intensity, temperature, humidity, wind conditions, and cloud coverage, to extract relevant features for prediction tasks. The framework supports a variety of learning algorithms, including Random Forest, XG Boost, Long Short-Term Memory (LSTM) networks, Linear Regression, and Support Vector Regression (SVR). Models are trained on sequential time-based data using a configurable window size, and their performance is evaluated using key metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). The system features graphical outputs that enable comparisons of model predictions, highlight influential features, and assess relative performance. Developed using Python, the solution leverages popular libraries such as Scikit-learn, XG Boost, TensorFlow, and Matplotlib. With its focus on flexibility, clarity, and scalability, this framework offers a valuable tool for researchers and professionals seeking to enhance solar energy forecasting in intelligent energy systems and future grid planning.

With the increase in the lifestyle and technological advancements in automotive sector, the demand for the transport vehicular has increased rapidly. The dependency of personal automobile had encroached the traffic problems and also environmental problems. The hybrid system could not only increase the efficiency of the running engine but also reduce the exhaust waste to the environment. We only need to do a small configuration to our existing models and replace with our system for maximum efficiency.

The objective of this paper is to describe the optimization of a hybrid renewable energy system. The system is intended to supply a residential neighborhood with thermal and electrical power. A software based model has been developed to serve as an experimental tool for calculations and comparison. The aim of the research is to find an optimal size per each element of the system due to the demand to keep the system as independent from the public grid as it is possible. The proposed system model consists of a combined heat and power plant, thermal boiler and a photovoltaic array with an additional lithium-ion battery for electrical energy storage. A methodology based on optimal power production for maximal covering of consumer`s demands is applied in a combination with approximate prizes per natural gas, prizes per purchased and sold back electrical energy from and to the utility grid. Results formed as graphs and comparable cases are expected as outcomes, based on the most optimal calculations for lowest net present cost per purchases from the utility grid.

Lithium-ion batteries play a pivotal role in enabling eco-friendly mobility, particularly in electric vehicles, but optimizing their charging process to improve battery lifespan, safety, and overall efficiency remains a significant challenge. Traditional predictive control methods are limited by their reliance on precise models, which are often hindered by uncertainties in battery parameters due to aging, production variability, and operational conditions. While stochastic predictive control policies can address these uncertainties by incorporating them directly into the optimization process, they typically introduce considerable computational complexity. In response to this challenge, this paper presents a novel approach that adapts imitation learning to efficiently approximate stochastic predictive control strategies, thus significantly reducing the computational burden through offline training. Specifically, the proposed method leverages the Dataset Aggregation algorithm to overcome the issue of distributional shift, a common limitation in imitation learning frameworks. Simulations based on a detailed electrochemical model demonstrate the effectiveness of the method, adhering to probabilistic constraints while offering a scalable and computationally efficient solution for advanced battery management systems.

An active battery management system for lithium ion battery stacks is described, that is robust and scalable. The architecture is based on an isolation unit consisting of a small equal turns ratio high isolation transformer with two diodes. One isolation unit is connected to each series connected cell in the battery stack and enables both accurate cell voltage monitoring and active cell balancing. The output of the isolation unit is completely galvanically isolated and thus suitable for high voltage packs with stringent safety requirements such as automotive and industrial electrical vehicles. Detailed measurements results are presented and a complete prototype for a LiFePO4 16s2p 2kWh battery module is constructed and its performance measured.

International collaboration between Algerian and French research teams.  Use the coulomb counting technic for battery SOC estimation under a variable discharge current.  Derivation of a theoretical expression for the battery model (generic model) for SOC estimation method.  Numerical calculation to compute the battery SOC estimation.  Experimental validation of theoretical expression on a battery system test bench

Battery state of charge assumes a critical part in advancing electric vehicle energy management, improving battery capacity and energy utilization, keeping batteries from overcharging and over-discharging, for the purpose to guarantee the security and long-term use of batteries lifespan and the expected vehicle autonomy with the indicated battery state of charge. This study presents a battery state of charge estimation using coulomb counting technique with a constant and variable discharging current for Lead-acid battery. In this way, the current, voltage, and temperature are measured during the battery cycle of charge and discharge and contrasted with the estimated value depending on the battery generic model. Additionally, exploratory outcomes are done to approve this investigation.

This paper comprehensively analyzes maximizing battery storage efficiency and return on investment (ROI) in grid-connected hybrid solar systems. The proposed framework optimizes battery charging/discharging cycles by incorporating real-time weather and load forecasting while ensuring demand fulfillment and extending battery life. Experimental results show that integrating machine learning-based forecasting techniques can improve overall system efficiency by 17.3% and increase ROI by 22.5% compared to conventional systems. The study evaluates multiple energy management strategies across diverse geographical locations and load profiles, providing valuable insights for system designers and energy managers seeking to enhance the economic viability of renewable energy storage solutions.

Baterai memegang peranan penting dalam sistem kelistrikan pesawat udara modern, tidak hanya sebagai sumber daya utama untuk menyalakan Auxiliary Power Unit (APU), tetapi juga sebagai penyokong sistem avionik darurat dan cadangan kelistrikan lainnya. Seiring berkembangnya teknologi penerbangan dan meningkatnya kebutuhan akan efisiensi energi serta keberlanjutan lingkungan, pemilihan jenis baterai yang optimal menjadi krusial. Penelitian ini melakukan analisis komparatif terhadap tiga jenis baterai utama yang umum digunakan dalam industri aviasi, yaitu Nickel-Cadmium (Ni-Cd), Lithium-Ion (Li-Ion), dan Lead-Acid. Kajian dilakukan berdasarkan parameter energi spesifik, siklus pakai, efisiensi pengisian, bobot, aspek keamanan, biaya, serta kepatuhan terhadap standar keselamatan penerbangan internasional. Hasil analisis menunjukkan bahwa baterai Li-Ion memiliki performa paling unggul dalam hampir seluruh aspek, dengan keunggulan utama pada kepadatan energi tinggi, umur pakai yang panjang, dan bobot ringan, meskipun memerlukan sistem proteksi termal tambahan seperti Battery Management System (BMS). Sementara itu, Ni-Cd masih digunakan karena keandalannya dalam kondisi ekstrem, dan Lead-Acid tetap dipertahankan untuk aplikasi dengan biaya rendah. Temuan ini memberikan dasar ilmiah dalam pemilihan baterai yang tepat untuk mendukung penggunaan tenaga listrik pada sistem pesawat dan pengembangan teknologi penerbangan masa depan yang lebih efisien dan ramah lingkungan.

Kata Kunci: Baterai pesawat, Lithium-ion, Nickel-cadmium, Lead-acid, Avionik, Sistem kelistrikan cadangan pesawat.

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

Solar photovoltaic represent one of the most promising technologies for generating electrical energy from renewable sources. In fact, as a result of the technological advances in the field of microelectronics and power electronics, photovoltaic systems have reached historic lows in terms of costs, surpassing the other types of renewable energy sources. The potential of the solar photovoltaic systems is enormous, being capable of meeting the energy needs of today, without compromising future needs, and allowing a sustainable development. With this in mind, several incentives and, consequently, legislations have been implemented around the world. In the Portuguese case, depending on the type of contract, for photovoltaic solar installations with value of power up to 1500 Wp the production surplus is injected into the power grid without any financial compensation. Considering the high investment, particularly regarding the photovoltaic panels, the support structure and the power electronics converters, it could be interesting to implement a system capable of storing the surplus energy for later use by the owner of the photovoltaic installation. This paper presents a study on the viability of energy storage systems in photovoltaic installations up to 1500 Wp. For this study, different consumer profiles, types of installations and geographic locations were considered, in order to perceive the technological and economic viability of this solution.

A marine vessel used for dredging among other activities is subject to degradation. This is because of mechanical wear and tears associated with the rigorous dredging activities. The electrical sections of such vessels are not an exception. The electrical components of the panel can be made to carry more than the rated operating current. Also, certain faults can occur because of usage and operator deficiencies. This therefore calls for proper maintenance and diagnosis in the event of a fault. The twin and complimentary role of maintenance and diagnosis of a dredger electrical system is the subject of this book. This book covers in detail the diagnosis carried out on the electrical distribution system and the maintenance of the vessel drive system

The global transition towards cleaner energy systems and the relentless expansion of the digital economy are inextricably linked to the battery. Batteries are the silent workhorses powering electric vehicles (EVs), stabilizing national grids fed by intermittent renewable sources like solar and wind, and energizing the myriad portable electronic devices that define modern life. 1 Their centrality to achieving ambitious climate goals, such as those enshrined in the Paris Agreement, cannot be overstated. 1 However, this indispensable role comes at a profound cost. The global battery quest, while promising a cleaner future, has simultaneously unleashed a cascade of severe environmental, social, ethical, and geopolitical crises-a multifaceted "catastrophe" that demands urgent and comprehensive attention.

Electric vehicles (EVs) are becoming increasingly popular as a sustainable alternative to traditional gasoline-powered cars. However, the effects of continuous charging on EV batteries have raised concerns about their longevity and performance. This paper provides an overview of the effects of continuous charging on EV batteries, including the impact on battery life, charging speed, and battery capacity. Continuous charging can lead to battery degradation, reduced range, and decreased performance due to overheating and overcharging. It is crucial to manage the charging process properly to ensure the long-term health of EV batteries. This paper also discusses the strategies that can be employed to mitigate the negative effects of continuous charging, including smart charging systems, battery management systems, and thermal management technologies. The findings indicate that continuous charging can have a significant impact on EV battery performance, but with proper management and monitoring, it is possible to maintain battery health and extend battery life.

To solve the problems of energy crisis, sustainable energy has been developed and gained extensive attention. As an important player in sustainable energy, lithium-ion batteries have been widely used in many fields, such as electric cars and battery energy storage systems, due to their merits including high energy density, low self-discharge rate, low cost and rechargeable operation. Given the low-voltage and low-power characteristics of the battery cell, numerous cells must be connected in series and parallel to boost the pack's capacity for high-voltage, high-power applications. However, the same type of batteries made by the same manufacturer exhibit inconsistent performance when used due to variations in production processes, temperature, and other factors. To address this phenomenon, an effective evaluation of active battery cell equalization is required to improve the overall capacity and performance of the battery pack. battery equalization is an important standard for a battery management system to work properly. This study examines battery equalization topology and active equalization circuits, summarizing their operating principles and research advancements.

Recently, the number of prosumers is increasing significantly in the electrical system. Most prosumers use solar photovoltaics to generate electricity; some even use batteries to store excess energy. However, integrating solar photovoltaics with batteries increases the system costs. Therefore, optimal sizing of these storage systems and renewable generation is a critical requirement of the prosumers to reduce investment costs and increase system efficiency. Also, having the correct renewable generation and storage capacity sizes is crucial to profit from the system. This study presents an optimal sizing algorithm to size the solar photovoltaic system and battery storage system for domestic prosumers, giving the minimum cost using genetic algorithm. In the context of Sri Lanka, grid availability also uses as an input parameter to optimize the system capacities since the country had planned power outages continuously throughout a year. By contrast, the optimization results found that 14 solar panels with 8kWh battery capacity are sufficient for a load profile that has a peak demand of 4.5kW with a grid availability of 88.9%. Also, the optimized system has a 14-year payback period and a 6.8% of loss of load probability. With the proposed integrated system, power availability has been increased by 4.8%.

The performance of Electric Vehicles (EVs) undergoes rapid changes over time. EVs are equipped with an Energy Storage System (ESS) consisting of a battery, controller, charger, BMS, and a dynamically designed vehicle. The performance of an EV primarily depends on the mentioned parameters. Researchers are keen on designing and developing rigid, efficient, and long-lasting vehicles. Within the ESS field, lithium-ion batteries are known for their extended lifespan, making battery model parameter estimation a crucial aspect of EVs. Currently, many scholars present great relation studies on equivalent circuit models (ECM) for ESS i.e Li-ion batteries. They are focus on the primarily order model with resistor and capacitor (RC) and the secondarily order model with RC, which are commonly used in thevenin's model. This paper aims to simulate both I st and II nd order models with RC. MATLAB/Simulink software is utilized for simulation and analysis, providing insights into the correct evaluation of State of charge (SOC) and terminal voltage of the Energy Storage System in the cell (ESS). The paper concludes by presenting an accurate LIB model for thevenin's model parameter estimation, comparing the outputs of both models and providing comments. The simulation methodology helps in identifying the best method for accurate SOC estimation and analyzing the differences between conventional and computational methods under various operating conditions.

An experimental study on the application of air multiplier popularly known as bladeless fan in the automobile field and in the process comparing it with the conventional bladed fan that is used on a large scale in the industry are investigated. It consists of a brushless electric motor along with asymmetricallyaligned blades attached to a rotor. The frame is sloped at such an angle that by this pressure is increased. The surrounding air is rapidly sucked or 'entrained' inside resulting in a stronger air flow. By this process the air multiplier, multiplies the flow of air. This is how the air multiplier increases the flow of air by 15% compared to a conventional fan. The results of the comparison between the mainstream bladed fan and the bladeless fan are shown with the important factors such as efficiency, quietness, speed of the air flow and nature of the air flow are shown. These findings are then incorporated to apply the air multiplier in use in automobile industry to eliminate the use of bladed fans in the applications of HVAC fans, radiator fans, headlight cooling fans. The theoretical assumption is that the air multiplier will have a higher air flow output with a lower energy consumption. It will also have a continuous air flow and the issue of buffeting that is evident in the case of bladed fans will be nominated.

In this paper real time monitoring and state of charge(SOC) estimation of Li-ion battery parameters is done with the help of different techniques such as internal resistance method, open-circuit voltage method, coulombs counting method, modified coulombs counting method. With the advancement in technology and continuous surge in amount of Carbon-dioxide in the environment by the use of fossil fuel, many industrial applications like automobile are now relying on chemical Battery as their foremost energy sources. Our purpose is to interface voltage detecting sensor in circuit which is being operated by battery through voltage divider approach with the microprocessor Raspberry pi 3 and for sensing voltage part uno board is used which will transmit data serially to the Raspberry Pi which will control and monitor the ADC device.. As lithium-ion based batteries exhibit splendid electrical attribute like high energy density, steep voltage but they are highly prone to deeply discharging iss...

The escalating energy crisis, heightened environmental awareness and the impacts of climate change have driven global efforts to reduce carbon emissions. A key strategy in this transition is the adoption of green energy technologies particularly for charging electric vehicles (EVs). According to the U.S. Department of Energy, EVs utilize approximately 60% of their input energy during operation, twice the efficiency of conventional fossil fuel vehicles. However, the environmental benefits of EVs are heavily dependent on the source of electricity used for charging. This study examines the potential of renewable energy (RE) as a sustainable alternative for electric vehicle (EV) charging by analyzing several critical dimensions. It explores the current RE sources used in EV infrastructure, highlighting global adoption trends, their advantages, limitations, and the leading nations in this transition. It also evaluates supporting technologies such as energy storage systems, charging technologies, power electronics, and smart grid integration that facilitate RE adoption. The study reviews RE-enabled smart charging strategies implemented across the industry to meet growing global EV energy demands. Finally, it discusses key challenges and prospects associated with grid integration, infrastructure upgrades, standardization, maintenance, cybersecurity, and the optimization of energy resources. This review aims to serve as a foundational reference for stakeholders and researchers seeking to advance the sustainable development of RE based EV charging systems.

This article examines avalanche failures in battery banks, where one cell failure triggers a chain reaction. Main causes include internal defects, bad operating conditions, and poor maintenance. Early detection methods identify indicators like unusual temperature increases, voltage changes, and higher internal resistance before major failures. Real examples show how initial failures spread. Predictive maintenance using continuous monitoring and IEEE standards is recommended. Best practices and clear recommendations are provided to reduce risks, enhance reliability, and extend battery lifespan, with technical references included.

This study examines the key factors contributing to the short operational lifespan of the light vehicle (LV) fleet managed by Engineers and Planners Company Limited at the Tarkwa Mine in Ghana. The research aims to identify the environmental, operational, and maintenance factors that negatively impact vehicle durability, and to offer recommendations for improving fleet management practices. A mixed-method approach was used, incorporating quantitative analysis of vehicle lifespan data, maintenance logs, and failure rates, alongside qualitative insights from interviews with fleet managers, maintenance staff, and vehicle operators. The findings revealed that harsh environmental conditions, including rough terrain and high temperatures, played a major role in accelerating vehicle wear and tear. Additionally, overloading of vehicles and inconsistent maintenance practices significantly contributed to increased failure rates. Vehicles that missed preventive maintenance sessions had a notably higher failure rate compared to those that followed maintenance schedules. The study also uncovered issues with part selection and procurement policies, where components not designed for the demands of the mining environment were frequently selected, leading to premature mechanical failures. The recommendations emphasize the importance of enforcing load limits, adopting preventive maintenance schedules, investing in advanced diagnostic tools, and revising procurement policies to prioritize durable parts suited for harsh mining conditions.

Bioluminescent materials have emerged as a significant innovation in the field of medical diagnostics and treatment, offering unique advantages in noninvasive imaging, realtime monitoring, and therapeutic applications. These materials, which emit light through biochemical reactions, are being harnessed for their ability to improve the sensitivity and accuracy of diagnostic tools. In particular, bioluminescence is used in various imaging techniques, such as bioluminescence imaging (BLI) and fluorescencebased assays, enabling the detection of specific biomarkers and cellular activities at the molecular level. This provides an invaluable tool for early disease detection, monitoring the progression of conditions like cancer, infection, and genetic disorders. Additionally, bioluminescent materials are being explored for their potential in targeted drug delivery, where light emission can help track the location and effectiveness of treatments in realtime. Furthermore, advances in bioluminescent nanoparticles and engineered biomolecules are paving the way for personalized medicine and precision therapeutics. This article highlights the growing potential of bioluminescent materials to transform medical diagnostics and therapeutic strategies, improving both patient outcomes and the efficiency of healthcare interventions.

Traction applications in automotive have traditionally used lead acid batteries. However, the situation is changing as new battery chemistries and supporting technologies have brought with them new technical and economic benefits, making battery power viable and more accessible for traction applications that were previously uneconomic or impractical. So, traction applications, and particularly electric vehicles, are being increasing their popularity with new type of batteries. In this case, electric cars use the energy stored in a battery (or series of batteries) for vehicle propulsion. Electric motors provide a clean and safe alternative to the internal combustion engine. Obviously, with the new technology for batteries some systems to improve them are arising. At the present, systems for monitoring and managing these batteries are common. In this article we summarize the Battery Management Systems and present some choices for some of the parts of these systems: communication protocols, microcontrollers, and the batteries.

Improving the Extended Kalman Filter's (EKF) State of Charge (SOC) prediction for EV battery packs is the
primary goal of this section. Optimised batteries management procedures rely on SOC estimate that is both
accurate and reliable. The EKF is a popular tool for estimating nonlinear states, but how well it works relies heavily
on which noise coefficient matrices are used (Q and R). Experimental testing and other conventional approaches
of calibrating these matrix systems are extremely costly and time-consuming. In order to tackle this, the section
delves into the integration of four state-of-the-art metaheuristic optimisation methods: GA, PSO, SFO, and HHO.
By minimising the mean square error (MSE) among the real and expected SOC, these techniques optimise the Q
and R matrices. When looking at preciseness, converging speed, and resilience, SFO-EKF comes out on top in
both static and dynamic comparisons. By greatly improving the reliability of SOC estimations, the numerical
results show that SFO-EKF obtains the lowest MSE & RMSE. This study advances electric car batteries by
providing a realistic scheme for combining optimisation methods with EKF to offer highly effective and exact
SOC estimates. When as opposed to TR-EKF, GA-EKF, PSO-EKF, and HHO-EKF, the SFO-EKF approach
shows the best accuracy, with an improvement of over 94%. This is a result of the suggested model's exceptional
efficiency in SOC estimates.

The rise of electric vehicles (EVs) has intensified the demand for efficient and reliable battery management systems (BMS) that ensure optimal performance, safety, and longevity. A crucial component of BMS is battery module balancing, which addresses the inherent disparities in cell voltage and capacity within battery packs. These imbalances, if left uncorrected, can lead to overcharging, undercharging, thermal instability, and ultimately premature degradation of the battery system-factors that directly impact commercial EV performance and lifecycle costs. This paper presents a comprehensive examination of battery balancing strategies employed in commercial electric vehicles, exploring both passive and active balancing techniques. Passive balancing, while cost-effective and simple, dissipates excess energy as heat and is less efficient for large-scale modules. In contrast, active balancing methods-such as capacitor-based, inductor-based, and charge shuttling-redistribute energy between cells and enhance overall energy efficiency, albeit with increased complexity and cost. The discussion further delves into real-time monitoring algorithms, thermal management integration, and the role of AI in predictive balancing. By analyzing case studies from commercial fleets and experimental EV platforms, the paper identifies key parameters influencing balancing effectiveness, including cell chemistry, depth of discharge cycles, operational temperature, and load distribution patterns. Ultimately, the study highlights that strategic selection and implementation of balancing techniques not only improve power delivery and range consistency but also extend battery lifespan, reduce maintenance frequency, and enhance vehicle reliability. These findings underscore the critical importance of intelligent, scalable balancing architectures in meeting the growing energy demands of modern commercial EV applications.

This article examines the critical challenges and solutions in energy-efficient cloud networking within distributed data centers. The article explores multiple dimensions of energy optimization, including workload scheduling, dynamic voltage scaling, and renewable energy integration. The article encompasses green data center design principles, cloud-based video streaming optimizations, and IoT network efficiency improvements. The article analyzes current challenges in thermal management, resource utilization, and environmental impact while presenting strategic approaches for implementing energy-efficient technologies. Furthermore, it evaluates emerging trends and future directions in AI-driven power management, advanced cooling systems, and sophisticated monitoring tools, providing comprehensive insights into sustainable cloud infrastructure development.

This research evaluates impacts of net metering on residential photovoltaic (PV) adoption in Palestine, focusing on PV systems ranging from 2 to 5 kWp. The analysis considers key factors such as energy generation, self-consumption, self-sufficiency, and economic viability, with PV coverage meeting an annual demand of 6378 kWh. Under the current net-metering scheme, 10% discounted of exported energy, and unused energy is forfeited at year end. The 2 kWp system generated 3405 kWh annually, achieving a self-consumption rate of 63% and covering 35% of household energy needs. Conversely, the 5 kWp system produced 8514 kWh but exhibited a lower self-consumption rate of 31% and a self-sufficiency rate of 43%. In terms of economic performance, the 2 kWp system delivered the highest internal rate of return (IRR) at 23.60% and a discounted payback period (DPP) of 4.95 years. Meanwhile, the 3 kWp and 4 kWp systems demonstrated IRRs of 23.1% and 22.76%, with DPPs of 5.07 and 5.15 years, respectively. Although the 4 kWp system had a higher net present value of $5,626.8 compared to $2968.2 for the 2 kWp system, the latter remained more efficient with a higher IRR of 23.60%, vs. 22.76% for the 4 kWp system, suggesting that the 4 kWp system offers greater overall profit, but the 2 kWp system yields better returns on investment. Nevertheless, the 5 kWp system, despite generating the most energy, had the lowest IRR at 16.72% and the longest DPP of 7.20 years due to discounted and forfeited credits at year end. Therefore, the study recommends that households install PV systems closely aligned with their energy consumption to maximize self-consumption.

Thevehicles in use around the world have produced serious problems to the environment and the depletion of the earth’s petroleum is the motivation to the researchers to develop the alternative way to our daily life. This research paper focuses on development of overall electrical wiring system low current installation between the controller, high voltage installation and engine compartment installation for Electric Vehicle (EV) conversion. This conversion system used electrical energy stored in rechargeable lead acid batteriesto drive three phase AC Induction electric motor and equipped with controller build by Curtis. The vehicle successfully runs on full electric power but further improvement and modification need to take into action to improve the performance of the vehicle in tern of speed and motor torque by fine tuning of the controller.

A new scheduling method is proposed to manage efficiently the integration of renewable sources in microgrids (MGs) with energy storage systems (ESSs). The purpose of this work is to take into account the main stress factors influencing the ageing mechanisms of a battery energy storage system (BESS) in order to make an optimal dispatch of resources in the microgrid and enhance the storage system lifetime while minimizing the cost of electric consumption. The load demand and generation profiles are derived from the analysis of consumption and renewable production (solar photovoltaic sources and wind turbines) of the Western Denmark electric grid. Thus, the proposed microgrid is mainly fed by renewable sources and few electricity is coming from the main grid (which helps operating costs minimization). In this respect, a cost analysis is performed to find the optimal hourly power output of the BESS as well as the purchased electricity from the utility.

The article explores HESS-EV articles published over the prior ten years from 2014 to 2023 to understand the changes in scholarly engagement in this imperative field. With a growing demand for energy and increasing concern for the environment, improving HESS for EVs needed to support SDG 7 and SDG 13. This investigation aims to define key publications, leading works, and important topics in 83 papers, examine patterns of collaboration and co-authoring and experience of citations. Evidence shows that the rate at which articles are being published under HESS has been gradually rising, with the most articles being published in 2018, focusing on elements such as energy management, power density, and control strategies. Despite offering a rich source of information, this study is confined by the given set of databases that might leave out important articles that are not indexed in the used source. Theoretical sections show rich new directions for future research, and methodological discussions underscore the importance of more cross-disciplinary work. In practical terms, this paper provides an understanding of how to modify HESS for the purpose of EV application in the most efficient way possible for subsequent research to build upon to improve the viability of the technology for the broad use of ev's in particular. The novelty is based in a structured, empirical approach to documenting a decade's worth of advancements in HESS research as an essential reference for scholars and practitioners in the field of EV technology.