Power Management System Research Articles (Page 1)

Smart sensor networks play important roles in structural monitoring, health diagnosis, and data transmission. Given their extensive distributed energy requirements, piezoelectric energy harvesting, which aims to convert mechanical vibrational energy into electrical power, can serve as a viable alternative or supplement to power supplies owing to its compact size, high power density, and excellent stability. Piezoelectric energy harvesting involves three key components: piezoelectric materials responsible for mechanical-to-electrical energy conversion, mechanical structures enabling mechanical-to-mechanical energy transmission, and power-management systems used to efficiently extract electrical energy. For electromechanical conversion, state-of-the-art piezoelectric materials, including crystals, ceramics, polymers, and composites, are analyzed. Regarding mechanical energy transmission, the focus is on methodologies to achieve high power output, wide bandwidth, and multi-directional vibration capability. Several widely adopted electrical circuits are comprehensively reviewed in terms of power management. From an application perspective, practical energy harvesters are categorized into magneto-mechano-electric, fluid-based, biomechanical, and ultrasound-induced types. Additionally, future theoretical and practical challenges in piezoelectric energy harvesting are discussed.

Real-time optimal hierarchical energy and power management system for fuel cell–battery hybrid electric vehicles

The integration of renewable energy sources (RES) into power systems requires sophisticated control strategies to ensure stable operation. This study presents a comprehensive framework that combines Machine Learning (ML) techniques—specifically Artificial Neural Networks (ANNs) and Reinforcement Learning (RL)—with traditional Proportional-Integral (PI) controllers to enhance microgrid control performance. Traditional PI controllers, while essential for microgrid operation with RES technologies such as solar and wind systems, face challenges in parameter tuning. Suboptimal selection of proportional gain left({K}_{p}right) and integral gain left({K}_{i}right) values can result in system instability or degraded performance. Our proposed ML-enhanced framework dynamically adjusts {K}_{p} based on real-time operational data and historical performance metrics, addressing these limitations. We evaluate three control strategies—traditional PI, ANN-based PI, and RL-based PI controllers—through extensive simulations of a microgrid with distributed energy resources (DERs). The RL-based controller demonstrates superior performance by reducing voltage Total Harmonic Distortion (THD) to 0.43%, compared to 16.99% for traditional PI control. The ANN-based controller achieves a THD of 0.58%, representing a 96.6% improvement over conventional methods. Both ML-enhanced approaches exceed IEEE 1547 requirements while improving settling time by 75% and frequency stability by 93%. These results validate the effectiveness of ML and deep learning techniques in enhancing microgrid stability and reliability, providing practical solutions for advanced RES management in modern power systems.

Artificial Intelligence-Based Power Management System for a DC Micro-Grid

Power management and optimization of a hybrid power system for remote communities: a case study

The development of load-side flexible regulation resources has emerged as a pivotal strategy to mitigate the declining source-side regulation capabilities in modern power systems. Among potential contributors, industrial parks-particularly those integrating large-scale electrolytic aluminum production and mineral heat furnace operations-exhibit substantial flexibility potential due to their controllable and high-capacity loads. However, effectively leveraging this potential is hindered by complex high-dimensional nonlinear coupling constraints, which arise from the intricate interactions of network current balance, load dynamics, and source-load characteristics within these parks. Accurately determining the power feasible region under these conditions is therefore a nontrivial challenge. To address this problem, this paper develops a comprehensive power feasible domain model tailored for industrial parks, explicitly incorporating the nonlinear coupling constraints between active and reactive power. A novel ray-emission-based sampling approach is proposed, which systematically explores the P-Q coupling plane by iteratively solving the optimal power flow (OPF) problem along multiple directions. This method efficiently identifies boundary points, enabling a precise and high-fidelity characterization of the feasible power region. The effectiveness and accuracy of the proposed modeling framework and sampling strategy are validated through a detailed case study on a five-node industrial park network. Results demonstrate that the approach not only captures the nonlinear interactions among loads and network constraints but also provides actionable insights for the real-time utilization of load-side flexibility. These findings underscore the potential of industrial park loads as a valuable resource for enhancing system regulation and operational resilience, paving the way for more adaptive and efficient power system management.

This paper presents the third-generation design of Bimu, a compact wearable inertial measurement unit (IMU) tailored for advanced human motion tracking. Building on prior iterations, Bimu R2 focuses on enhancing thermal stability, data integrity, and energy efficiency by integrating onboard memory, redesigning the power management system, and optimizing the communication interfaces. A detailed performance evaluation-including noise, bias, scale factor, power consumption, and drift-demonstrates the device's reliability and readiness for deployment in real-world applications ranging from clinical gait analysis to high-speed motion capture. The improvements introduced offer valuable insights for researchers and engineers developing robust wearable sensing solutions.

Purpose This paper aims to present the fast battery charging system with low power dissipation. Design/methodology/approach In this study, a battery power management system is presented, which involves field-programmable gate array (FPGA) controller, voltage monitoring, buck converter and a reconfigurable charge switching circuit. The voltage of each battery is continuously sensed and recorded with a differential method. Based on the sensed voltage, the FPGA controller determines the appropriate charging mode. A reconfigurable charge circuit is proposed to optimize the charging process and save charging energy. This circuit enables the early skipping of fully charged batteries, lowering the supply charge voltage from the buck converter and thereby reducing the charging power. This helps prevent overcharging, extending the battery’s lifespan. Findings In experiments with four battery modules, the proposed approach achieved a reduction in charging power ranging from 25% to 75%. This battery management system, integrated with FPGA control and a reconfigurable charge circuit, provides efficient and optimized charging for batteries, contributing to energy savings and extending the battery lifecycle. Research limitations/implications The proposed approach achieved a maximum saving of about 75% in charging power. Originality/value A new concept of battery charging architecture is presented, which will be benefit for the application of energy storage system and electric vehicles. Also, this concept can save the energy during charge period.

The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challenges, we propose Generative Reconstruction and Adaptive Identification via Latents (GRAIL), a unified, end-to-end framework that integrates generative modeling with adaptive clustering to discover latent structures and representative scenarios in PV datasets. GRAIL operates through a closed-loop mechanism where clustering feedback guides a cluster-aware data generation process, and the resulting generative augmentation strengthens partitioning in the latent space. Evaluated on a real-world, multi-site PV dataset with a high missing data rate of 45.4%, GRAIL consistently outperforms both classical clustering algorithms and deep embedding-based methods. Specifically, GRAIL achieves a Silhouette Score of 0.969, a Calinski–Harabasz index exceeding 4.132×106, and a Davies–Bouldin index of 0.042, demonstrating superior intra-cluster compactness and inter-cluster separation. The framework also yields a normalized entropy of 0.994, which indicates highly balanced partitioning. These results underscore that coupling data generation with clustering is a powerful strategy for expressive and robust structure learning in data-sparse environments. Notably, GRAIL achieves significant performance gains over the strongest deep learning baseline that lacks a generative component, securing the highest composite score among all evaluated methods. The framework is also computationally efficient. Its alternating optimization converges rapidly, and clustering and reconstruction metrics stabilize within approximately six iterations. Beyond quantitative performance, GRAIL produces physically interpretable clusters that correspond to distinct weather-driven regimes and capture cross-site dependencies. These clusters serve as compact and robust state descriptors, valuable for downstream applications such as PV forecasting, dispatch optimization, and intelligent energy management in modern power systems.

Abstract A critical challenge in the safe operation of autonomous vessels is ensuring that control commands are executed accurately and promptly by both shore-side and onboard systems. In this paper, we build on a use case of an autonomous ship, where the control hierarchy includes Human Operators on the shoreside, along with the Ship Motion Controller, Power Management System, and Battery Management System, among other controllers on the shipside. Incorrect execution of control actions by these controllers can lead to hazardous situations of varying severity. This study aims to identify and analyze hazards related to these four controllers and provide insights into how inadequate control may occur and create hazardous situations with the controllers. Recently, STPA has emerged as the mainstream approach for identifying hazards resulting from control action failures. Therefore, this study applies the System Theoretic Process Analysis (STPA) method to explore how control actions of different controllers might fail, ensuring safe operation. A control structure hierarchy has been developed that identifies (1) control actions and (2) feedback signals between controllers based on their responsibilities. Using STPA, a total of 127 unsafe control actions are identified that could result in hazards. We classify the significance of Unsafe Control Actions based on hazard severity, operational mode, and suggest the level of attention each controller requires. The results offer a structured foundation for prioritizing safety–critical control actions in battery-powered autonomous ships, facilitating more effective risk mitigation strategies for designers, operators, and regulators.

ABSTRACTThe development of power quality control systems and methods is a credible step for the modernization of the power grid. In this context, this paper presents novel approaches for the control of power quality and voltage of the power grid using power converters connected to photovoltaic battery systems. The photovoltaic system permits to generate the required power to adjust the grid voltage. Indeed, a three‐level neutral point clamped (NPC) converter, connecting the PV‐batteries to the power grid, plays the function of a static compensator (STATCOM) in the case of a fault causing power grid voltage variation. The NPC converter is controlled by a direct current vector control method based on a hysteresis controller. The amplitude and phase of the NPC converter reference currents are generated based on the irradiation value, the battery state of charge, and the grid voltage. In addition to the NPC converter, the proposed approach uses three DC–DC converters with the aim to extract the maximum power from the PV system and control the charge–discharge of the batteries. The bidirectional converter associated with the batteries and the four‐quadrant chopper connected to the NPC converter are controlled by proportional integral (PI) regulators in aim to maintain the voltage and state of charge of the batteries within the acceptable range. To coordinate the different scenarios, a power management system is proposed in this paper to generate adequate control signals for the control of the different power converters. PI closed‐loop controllers have been proposed to ensure the highest performance and stability of voltage regulation in the power grid. The different control methods have been implemented and verified in MATLAB‐Simulink environment. The results prove the effectiveness of the proposed approach to regulate the voltage and the grid power quality. The results demonstrate that the proposed system is capable of maintaining the grid voltage within ±10% of the nominal value, even under fault conditions, with a voltage regulation efficiency of 98%. Additionally, the power quality improvements are quantified, showing a reduction in total harmonic distortion (THD) of the grid current to below 3%, ensuring compliance with international power quality standards.

Development of integrated system combining parallel-connected microbial fuel cells and microbial electrolysis cell for green hydrogen production.

ABSTRACTThis research compares the performance of plug‐in fuel cell electric vehicles (PFCEVs), battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs) using MATLAB Simulink. The simulations were run for 1800 s using the Worldwide Harmonized Light Vehicles Test Cycle (WLTC 3a), spanning a distance of 23 km, to assess important performance characteristics such as energy efficiency, consumption, emissions, and life cycle costs. The PFCEV architecture, which combines a medium‐sized fuel cell and a sizable battery pack, has a strategic advantage because it requires fewer charging stations than BEVs and fewer hydrogen filling stations than FCEVs. The findings reveal that PFCEVs provide a unique combination of high efficiency, low emissions, rapid recharging, and greater driving range while requiring minimal hydrogen infrastructure. Compared to BEVs, PFCEVs minimize range anxiety while improving grid stability, and unlike FCEVs, they maximize hydrogen utilization via a complicated power management system. This study highlighted PFCEVs as a viable choice for sustainable mobility, serving as a valuable link between BEVs and FCEVs in the evolution of electric transportation. The findings indicate that PFCEVs have a good possibility of becoming a preferred vehicle technology, bridging the gap between battery and hydrogen‐powered electric vehicles while addressing infrastructure and efficiency challenges.

The rapid adoption of electric vehicles (EVs) demands intelligent power management systems to support auxiliary and control subsystems like infotainment, lighting, cooling, and communication modules, which require stable power despite source fluctuations. Traditional single-input supplies risk instability, so this Paper develops a smart power switching and regulation system using multiple DC inputs from Switched-Mode Power Supplies (SMPS). An ATmega328 microcontroller with adaptive algorithms monitors input voltage, load demand, efficiency, and availability in real time, ensuring seamless source switching without disrupting output. MATLAB simulation of the adaptive power management system validates its performance, while hardware implementation confirms practical feasibility. A graphical interface provides real-time monitoring of voltages, currents, load status, and switching events. The system also integrates speed control of a DC motor using PI and PID control, ensuring efficiency, reliability, and safety across EVs, renewable microgrids, robotics, and industrial automation.

Integrating renewable energies into the grid creates many problems, including the injection of harmonics. This research aims to maximize the energy extracted from PV arrays and wind turbines while minimizing total harmonic distortion (THD) injected into the grid. For that, we propose to study a grid-connected hybrid power system with a hybrid storage system consisting of batteries and a supercapacitor. Several control loops are required for the system, such as: MPPT for wind systems, Machine-Side Converter for the Permanent Magnet Synchronous Generator (PMSG), Battery Energy Storage System (BESS), Supercapacitor, and Grid-Side Converter (GSC). Previous works have used traditional PI controllers in these loops, but in our work, we propose a cascade PI-PID controller optimized with the COOT bird algorithm and the results were compared with a GA-tuned PI controller. The proposed approach demonstrated superior performance in settling time and reducing current and voltage oscillations, achieving a 30% and 81% reduction in [Formula: see text] and [Formula: see text], respectively. To eliminate peaks produced by the PI-PID controller, a supercapacitor system was incorporated and it effectively reduced these peaks. Additionally, a more realistic simulation scenario was proposed to test the system, involving variable sunlight, fluctuating wind speed, battery limits, and a load similar to real-life scenarios. Our energy management strategy efficiently maximized renewable energy exploitation while ensuring system stability.

Existing Android systems exhibit energy inefficiency during mobile web browsing due to the lack of awareness of application-level context. Inferring such context from system-level data alone is challenging, but one promising opportunity is using the sequence of task wakeup events, where one task activates another. These sequences show correlation with the type of webpage being used. In this article, we present Ember, a lightweight and responsive power management system for mobile web browsing using only task wakeup sequences. Ember introduces a neural network–based approach to predict optimal CPU clamping values by addressing three key challenges: (1) embedding task names, given as natural-language strings, into meaningful vectors using a Word2Vec-based embedding scheme tailored for task wakeup sequences; (2) minimizing inference overhead with a touch-driven hierarchical inference method that combines lightweight logistic regression with high-accuracy neural networks to balance responsiveness and efficiency; and (3) adapting to within-page interaction dynamics through an interaction-adaptive clamping mechanism that adjusts constraints across different user interaction phases. Implemented on commercial Android smartphones, Ember reduced power consumption by 6.2%–31.2% across a wide range of webpages while maintaining user-perceived quality of experience (QoE).

LED TVs provide crystal-clear image quality and are energy-efficient, but are not free from voltage fluctuations, voltage spikes and power losses that can cause great damage to these TVs, especially in the areas where the power supply is not stabilized. These power problems can result in image quality degradation, flicker or permanent damage to the LED panel. In this paper, a novel power architecture is proposed where a rechargeable lithium-ion battery and an intelligent power management system are built within the LED TV frame. Unlike previous works, which either need infrastructure for solar power, or access to an external UPS unit, or partial battery EMI shielding, this is the first embedded system that offers sub-millisecond switching, adaptive brightness control and full battery management inside the television chassis. The system contains an AC-DC charger, bi-directional DC-DC converters, a microcontroller unit (MCU), a high-speed switchover circuit (HSSC) composed of MOSFETs and a reliable Battery Management System (BMS). Features include adaptive load management, undervoltage protection and soft start to reduce inrush current. Simulation results by using LTSpice and Proteus confirm that the system can achieve the switchover latency of less than 1 ms, keep the panel voltage within the range of ±0.5% during voltage sags and surges, and prolong the backup operation time of up to 2 hours for a 32″ LED panel. This integrated solution removes the requirement to use separate UPS systems, increases reliability and also enables future integration with renewable sources like rooftop solar. The architecture fits with the trends that have been emerging towards low voltage DC, smart grid standard and embedded energy resilience of consumer electronics appliances.

As a result of the COVID-19 pandemic, institutions have changed their settings, policies, and methods for handing off knowledge from teachers to students. Online instruction has replaced the traditional methods of processing and exchanging student knowledge and abilities with professors and fellow students. Due to a lack of preparedness for the situation brought on by the virus’ spread, some schools and universities were forced to end the term. Quezon City University chose to hold classes online using Google Classroom, a free learning management system. Google Classroom has a set of online tools that enable teachers to assign tasks, receive student work, grade it, and return the results. It can deliver educational requirements and administer classes, document, track, and report data; it serves as an illustration of a learning management system. In this study, the researchers aim to assess the effectiveness of the Google Classroom in conducting online classes in Quezon City University, specifically Information Technology subjects. The respondents are the IT faculty and 4th Year level BSIT students, there are a total of 3 faculty and 707 students in the Information Technology department. The researchers based the design of their questionnaires on some validated instruments used in several research related to the study. They also utilized Slovin’s formula to compute the sample size; Likert Scale to measure the degree of response; Percentage to determine the percentage of the respondents; and Mean to determine the degree of the validity of the responses. Purposive Sampling was used in identifying the samples. The result of the survey conducted is that, most of the respondents identified that Device Compatibility is frequently the challenges encountered by most faculty in using the LMS. Among students, the teaching and learning environment is where they encounter challenges frequently. In terms of the factors affecting the outcomes using the LMS, frequently the LMS provides information that is easy to understand. Majority also stated that the LMS provides information that can be used in the classroom, and most of them also frequently said that the LMS contains enough information for instruction. On the other hand, the majority of respondents agree with information quality, system use, and the LMS’s perceived usefulness. The researchers draw the conclusion that Google Classroom is a powerful learning management system based on these findings.

The complexity of power systems has accelerated and this has emphasized the use of sophisticated maintenance strategies that would help in enhancing reliability in power systems, minimize down-time as well as reduce operational costs. Conventional corrective and preventive maintenance strategies have been found inadequate in managing the dynamism of large power grids that are increasingly incorporating smart grids, renewable energy sources and smart-incorporated devices. Predictive maintenance has become an even better replacement since it allows one to predict failing equipment to handle before they fail and conduct it proactively. The analysis of Big Data is fundamentally relevant in meeting this paradigm since it offers the ability to collect, analyze and extract meaning of large amounts of heterogenous data collected using a variety of sources including IoT, SCADA systems, and smart meters. This paper explores the ways by which predictive maintenance in the new power systems can be reinforced through Big Data Analytics. It presents the framework that combines superior data collection, scalable storage systems, and machine learning model to help detect faults and predict system health. Analyzing the impacts that Big Data-based predictive maintenance has to offer, the paper focuses on three positive dimensions of the change, including increased resilience of systems, economic feasibility, and sustainability of operations. By overcoming the problems of extensibility, instant response, and accuracy in the decision making, the suggested solution allows Big Data Analytics to become one of the main pillars of smart future power system management. DOI :https://doi.org/10.52783/pst.2349

This research proposes the design and application of a smart controller for a dual-fold Luo converter tailored specifically for E-vehicle applications. The dual-fold Luo converter, known for its ability to efficiently step up and step down voltage levels with reduced components, is augmented with a smart control strategy to enhance its performance in the context of electric vehicles. The smart controller utilizes advanced techniques, such as artificial neural networks or fuzzy logic, to adaptively regulate the converter's operation, thereby improving efficiency, transient response, and overall reliability. By leveraging real-time data from the E-vehicle system, the controller dynamically adjusts key parameters to optimize performance under varying load and operating conditions. Key design considerations include the selection and training of the smart controller to achieve desired voltage regulation, efficiency, and robustness in the face of uncertainties inherent in E-vehicle operation. The proposed design methodology is validated through simulation studies, demonstrating superior performance compared to conventional control techniques. The results illustrate the efficacy of the smart controller in enhancing the dynamic response of the dual-fold Luo converter, making it a promising solution for E-vehicle power management systems. This research contributes to the advancement of power electronics in electric transportation, facilitating the development of more efficient and reliable E-vehicle systems in the pursuit of sustainable mobility.