Power Management Strategy Research Articles

Smart Animal Repelling Device: Utilizing IoT and AI for Effective Anti-Adaptive Harmful Animal Deterrence

The coexistence of human populations with wildlife often leads to conflicts in which harmful animals cause damage to crops and property and threaten human welfare. Certain limitations influence the effectiveness and environmental impacts of traditional methods used to repel animals. The present research outlines a growth of solutions that utilize the Internet of Things and machine learning techniques to address this issue. This study centers on a Smart Animal Repelling Device (SARD) that seeks to safeguard crops from ungulate assaults, substantially reducing production expenditures. This is achieved by developing virtual fences that use Artificial Intelligence (AI) and ultrasonic emission. This study introduces a comprehensive distributed system for resource management in Edge or Fog settings. The SARD framework leverages the principle of containerization and utilizes Docker containers to execute Internet of Things (IoT) applications in microservices. The software system inside the suggested structure can include various IoT applications and resources and power management strategies for Edge and fog computing systems. The experimental findings demonstrate that the intelligent animal-repellent system effectively uses animal detection on power-efficient computational methods. This implementation ensures the system maintains high mean average accuracy (93.25%) while simultaneously meeting real-time demands for anti-adaptive harmful animal deterrence.

Revolutionizing Air Quality Monitoring: IoT-Enabled E-Noses and Low-Power Devices

The integration of Internet of Things (IoT) technology and low-power devices presents a transformative approach to revolutionizing air quality monitoring, addressing the critical global challenge of air pollution. In this paper, we propose an innovative solution leveraging IoT-enabled e-noses, a gateway, and an IoT platform to facilitate comprehensive air quality assessment. Our system incorporates a diverse array of sensors capable of measuring 11 essential air quality parameters, including PM10, PM2.5, CO2, CO, VOCs, ozone, and nitrogen dioxide. Crucially, our solution achieves remarkable power efficiency, with the electronic noses consuming as low as 10 during deep sleep mode. Through meticulous power management strategies and optimized battery capacity calculations, we ensure sustained functionality and operational efficiency of the monitoring system. Indoor and outdoor deployment scenarios are considered, with the system exhibiting robustness and versatility across diverse environmental contexts. Real-time data collection, transmission, and analysis enable timely detection of air quality fluctuations, empowering stakeholders with actionable insights to mitigate the adverse effects of air pollution on public health and environmental sustainability.

SPICE modelling and analysis of hybrid energy harvester combiner topologies

Harvesting energy from multiple hybrid sources and efficiently combining the harvested energies is critical for enabling self-powered devices. Designing an efficient energy combiner is a technical challenge and is non-trivial. Various factors viz. kinds of sources, harvestable energy level and range from the sources, electrical characteristics of sources (low current and high voltage, high current and low voltage, capacitive, inductive etc.), impedance matching (resistive, resistive-reactive, modulus, complex conjugate etc.) of sources, sources scheduling algorithms for combiner, sources switching and control/trigger circuit losses, power conversion and management etc. influence the overall energy combiner’s efficiency. Considering that, this article presents a SPICE modelling and simulation framework for analyzing hybrid energy harvester combiner topologies such as Inductor sharing, voltage level detection and powerORing for its power and energy flow characteristics, regulation, and energy combining efficiency. Such analysis through simulation enables arriving efficient combiner architecture for the chosen harvestable resources, source models, power management circuits and schemes etc. Based on a case study with three different kinds of sources, it has been observed that the voltage level detection technique with DC-DC converters results in the highest efficiency as compared with the other two topologies for such a scenario.

Techno-economic configuration of a hybrid backup system within a microgrid considering vehicle-to-grid technology: A case study of a remote area

This paper assesses the feasibility of the hybrid backup system within the microgrid that incorporates vehicle-to-grid (V2G) technology. The proposed strategy is verified through a real case study in a remote area of Egypt. Several operating configurations for the hybrid backup system are studied. In this study, the proposed backup sources are the battery energy storage system (BESS), the hydrogen energy storage system (HESS), and the electric vehicle battery (EVB). To improve the robustness and reliability of the system, an enhanced power management strategy for the hybrid backup system is implemented. The main objectives are to minimize the operational costs, the wasted energy in the dummy load, and the loss of power supply probability (LPSP). The optimization problem is performed using the african vultures optimization algorithm (AVOA). The results clearly indicate that the proposed AVOA outperforms other recent algorithms, specifically the reptile search algorithm (RSA) and the snake optimizer (SO). The AVOA shows a 14.377 % reduction in computational time compared to the RSA, while it is nearly equal to the computational time of SO. Based on the technical and economic analysis, the configuration that combines the three backup sources has superiority over the other configurations. When using AVOA, the cumulative operating cost throughout the day for this configuration is approximately 13066.21$. The BESS/EVB backup system has an operating cost of 13120.66 $, while the BESS/HESS and BESS backup systems have operating costs of 13084.27 $ and 13,085 $, respectively. Regarding accuracy, the three algorithms achieve close results.

INNOVATIVE POWER MANAGEMENT IN ELECTRO-MECHANICAL SYSTEMS: EXPLORING THE NEW PARADIGMS OF ENERGY EFFICIENCY AND SYSTEM LONGEVITY

This study presents a comprehensive review of innovative power management strategies in electro-mechanical systems, with a focus on enhancing energy efficiency and extending system longevity. The research encompasses a systematic literature review, analyzing studies from 2007 to 2023, to explore the evolution, current trends, and future prospects in the field. Key areas of investigation include the historical development of power management technologies, current advancements in energy-efficient systems, and the integration of novel approaches for system longevity. The study also examines the environmental and economic impacts of these innovations, the role of policy and industry standards, and the implications for various stakeholders. Significant findings reveal that advancements in nano-mechanical energy harvesting, bio-electro-Fenton systems, and smart power management technologies are driving the field towards greater sustainability and efficiency. The research highlights the critical role of stakeholder engagement and policy in facilitating the adoption of these technologies. Future trends indicate a shift towards more integrated and intelligent power management solutions, particularly in sectors like aeronautics and renewable energy. Strategic recommendations are provided for industry practitioners and policymakers, emphasizing the need for embracing technological innovations, investing in research and development, and creating supportive regulatory frameworks. The study concludes with suggestions for future research, particularly in assessing the long-term socio-economic impacts of power management technologies and exploring their scalability and societal acceptance. This study offers valuable insights and a roadmap for stakeholders in the field of electro-mechanical systems, highlighting the importance of innovative power management in achieving sustainable and efficient energy solutions.
 Keywords: Power Management, Electro-Mechanical Systems, Energy Efficiency, System Longevity.

Integrating self-powered disaster recovery networks with environmental monitoring for enhanced disaster preparedness and response

This paper investigates a comprehensive approach to enhancing environmental monitoring services within a self-powered Disaster Recovery Network (DRN) infrastructure. The study introduces a variety of solutions aimed at overcoming logistical challenges associated with establishing an environmentally conscious DRN infrastructure. Moreover, the research explores the intrinsic factors governing the system’s behavior, defines essential evaluation metrics, and delineates performance measurements. The Wireless Solar Router (WSR) is specifically introduced using the Ubicom IP 2022 platform to realize the Ad hoc wirelessly networked nodes of the DRN infrastructure. To advance the field further, the paper proposes an experimental platform for comprehensive evaluation, assessing network performance, practicality, power efficiency, and resilience to various scenarios. A comprehensive design process is illustrated, and the required values of the system elements, i.e., the number of solar cell panels, the capacity of the battery cells, etc., are adjusted to fulfill the design purposes. In order to reduce the power utilization of the recommended WSR and to lengthen the duration of their batteries, a new distributed power management scheme called Duty Cycle Estimation-Event Driven Duty Cycling (DCE-EDDC) was suggested and installed locally in the WSRs in order to decrease their power consumption and extend the lifetime of their batteries. The suggested method is compared with other duty cycling methods, and the proposed DRN system is also compared with other real-world implementations to show its usefulness in building a green DRN infrastructure.

An Innovative Power Management Strategy for Hybrid Battery–Supercapacitor Systems in Electric Vehicle

Currently, batteries and supercapacitors play a vital role as energy storage systems in industrial applications, particularly in electric vehicles. Electric vehicles benefit from the high energy density of lithium batteries as well as the high power density of supercapacitors. Hence, a robust and efficient energy management system is required to coordinate energy flows between these two storage systems, ensuring road safety. In this study, we develop a novel rule-based strategy called “Continuous Regulation with Dynamic Battery Power Limiting” to establish robust control between the lithium-ion battery and the supercapacitor. A comparative analysis is conducted to evaluate the performance of this proposed approach in comparison to conventional methods. The results show that this approach significantly enhances driving comfort and prevents depletion of the main energy source, resulting in a gain of nearly 30% compared to a lithium-ion battery electric vehicle. Additionally, this new rules-based strategy ensures that the supercapacitor is charged at the end of each drive cycle.

Deterministic power management strategy for fast charging station with integrated energy storage system

With the increasing expansion of fast-charging stations (FCS) and the emergence of high-power electric vehicles (EVs), the development of management strategies to address potential grid disturbances becomes necessary. In this context, this paper proposes an optimized power management strategy for an FCS with integrated battery energy storage systems (BESS). The proposed strategy aims to monitor the variation in AC voltage at the point of common coupling (PCC) and the state of charge (SOC) of the BESS, with the objective of establishing a deterministic formulation to find the optimal instantaneous BESS power level. Therefore, by using the proposed approach, it is possible to compensate for potential fluctuations in AC voltage while maintaining the BESS SOC at appropriate levels. The results are obtained through a real-time hardware-in-the-loop (HIL) platform to verify the system behavior under distinct parameters or operating conditions. These results demonstrate the control flexibility by prioritizing the AC voltage regulation or the BESS charging. Furthermore, the impact of the proposed power management strategy is investigated by performing a comparison with the system without BESS integration. It is observed that the proposed control strategy effectively regulates the PCC voltage and maintains it within acceptable limits.

Power Management Strategy of Hybrid Fuel Cell Drones for Flight Performance Improvement Based on Various Algorithms

In recent years, there has been a growing demand for multipurpose drones that can handle surveillance, environmental monitoring, and urgent deliveries. This trend has spurred the need for increased power and longer flight times for drones. Hence, many researchers introduced various hybrid systems to enhance endurance. In particular, a hybrid system that integrates solar cells, fuel cells, and batteries can substantially enhance a drone’s endurance. However, linking multiple power sources necessitates a control strategy that prioritizes safety and durability. It is also essential to analyze the control characteristics of each component as the dynamic behavior of individual components, coupled with environmental factors, significantly impacts the overall dynamic characteristics of drone systems. This study introduces a PEMFC–battery drone model. The model’s dynamic characteristics can be evaluated based on changes in environmental conditions and the control strategies of primary components. The validity of this model is confirmed by analyzing the dynamic characteristics of drone systems. As a result, the MRAC logic applied to the flight-level control and thrust motor of the drone was found to amplify the characteristics of the underlying PI and IP controllers. These control characteristics can lead to the development of control strategies for improving the flight performance or power durability of the aircraft by being properly applied to the flight environment of the drone.

A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy

The Internet of Things (IoT) plays a critical role in the digitalization of numerous industries, enabling increased automation, connectivity, and data collection in areas such as manufacturing, healthcare, transportation, and smart cities. This paper introduces a power-aware method for IoT networks using mobile stations and a dynamic power management strategy. The proposed method aims to improve power consumption and total packets received compared to the static-station balanced data traffic method. The proposed method uses a mobile station to dynamically adapt its transmission power based on the network conditions and the strength of the received signal. Furthermore, a dynamic power management strategy is employed to further decrease the power usage of the network by adjusting the power state of each station and IoT node according to its level of activity, data traffic, and communication requirements. Simulation results showed that the proposed method reduced power consumption by up to 64%, increased total packets received by 72%, and, as a result, increased network coverage and lifetime compared to the balanced data traffic method with static stations. This method can be employed in various IoT applications to improve power efficiency and increase network reliability.

Pontryagin’s Minimum Principle-Based Power Management of Plug-In Hybrid Electric Vehicles to Enhance the Battery Durability and Thermal Safety

Temperature affects the battery aging and stability, bringing about significant effects on the electric vehicle’s economy, reliability, and safety. Therefore, it is necessary to consider the battery thermal condition when designing the power management scheme. In this paper, an optimal power management strategy for plug-in hybrid electric vehicles is proposed based on Pontryagin’s Minimum Principle (PMP). Two situations, normal operation and cooling system failure, are considered. The impacts of battery aging and temperature rise are characterized based on durability analysis model and thermal model. The temperature variation rate of the battery is incorporated into the Hamiltonian function, and the correlation between the cost optimization and temperature rise is disclosed by shooting method. Then the optimal co-state variables in different cases are determined. Based on above efforts, the PMP-based multi-objective optimal control strategy is proposed and evaluated based on the temperature statistical data in Shenyang, China. The results show that the proposed method can improve the durability of the battery and inhibit the excessive temperature rise. In case of the cooling system failure, it can realize the trade-off between safety and economy to reduce the risk of battery thermal runaway.

Data-efficient modeling for power consumption estimation of quadrotor operations using ensemble learning

Electric Take-Off and Landing (eVTOL) aircraft is considered the major aircraft type in emerging urban air mobility. Accurate power consumption estimation is crucial to eVTOL, supporting advanced power management strategies and improving the efficiency and safety performance of flight operations. In this study, a framework for power consumption modeling of quadrotor drones, as a representative of eVTOL aircraft, was established. We employed an ensemble learning method, namely stacking, to develop a data-driven model using flight records of three different types of quadrotors. Random forest and extreme gradient boosting, showing advantages in prediction, were chosen as base-models, and a linear regressor was used as the meta-model. The established stacking model can accurately estimate the power of a quadrotor. Error analysis shows that about 80% prediction errors fall within one standard deviation interval and less than 0.5% error in the prediction for an entire flight can be expected with a confidence of more than 80%. Our model outperforms the existing models in two aspects: firstly, our model has a better prediction performance, and secondly, our model is more data-efficient, requiring a much smaller dataset. Our model provides a powerful tool for operators of eVTOL aircraft in mission management and contributes to promoting safe and energy-efficient urban air traffic.

Optimization-based power management for battery/supercapacitor hybrid energy storage system with load estimation capability in a DC microgrid

This paper proposes a novel optimization-based power management strategy (PMS) for a battery/supercapacitor hybrid energy storage system (HESS) with a semi-active structure in a DC microgrid application. As the DC bus voltage regulation is the main purpose of the HESS, the supercapacitor control due to direct connection to the DC bus becomes more important. While, in the semi-active structure, there is no control over the supercapacitor. The proposed PMS solved this challenge by considering the supercapacitor current as a control target in determining the reference current of the battery. Extending the battery life span by drawing smooth current from the battery and responding the supercapacitor to load current changes, and charging the battery with a constant current as a new objective function, are the other optimization targets. This multi-objective problem is solved by the Karush–Kuhn–Tucker (KKT) condition which makes the proposed PMS suitable for real-time applications. In addition, in contrast to previous related studies, (i) the load current (grid excess/deficiency power) as one of the important inputs of PMS is estimated by the adaptive Kalman filter (AKF), and (ii) the converter control signal is determined by system average model instead of conventional PI controller. Finally, using simulation, the performance of the proposed PMS has been verified by comparing the results with fuzzy logic, droop control, and adaptive filter-based methods. As well, the presented control strategy has been implemented on an experimental setup, and its performance is investigated.

Enhanced control strategy and energy management for a photovoltaic system with hybrid energy storage based on self-adaptive bonobo optimization

Large-scale energy storage systems (ESSs) that can react quickly to energy fluctuations and store excess energy are required to increase the reliability of electricity grids that rely heavily on renewable energy sources (RESs). Hybrid systems, which combine different energy storage technologies such as batteries and supercapacitors, are becoming increasingly popular because no single technology can satisfy all requirements. In this study, a supercapacitor is used to stabilize quickly shifting bursts of power, while a battery is used to stabilize gradually fluctuating power flow. This paper proposes a robust controller for managing the direct current (DC) bus voltage to optimize the performance of ESS. The proposed controller combines a fractional-order proportional integral (FOPI) with a classical PI controller for the first time in the DC microgrid area. The hybrid (FOPI-PI) controller achieves an outstanding and superior performance in all transient and dynamic response specifications compared to other traditional controllers. The parameters of the suggested controller are incorporated with the self-adaptive bonobo optimizer (SaBO) to determine the optimal values. Furthermore, various optimization techniques are applied to the model and the SaBO’s output outperforms other techniques by minimizing the best objective function. In addition, the current study has utilized a novel power management strategy that includes two closed current loops for both batteries and supercapacitors. By using this method, batteries’ lifespans may be increased while still retaining optimal system performance. The suggested controller is implemented in MATLAB/Simulink 2022b, and the outcomes are reported for several case studies. The findings demonstrate that the control technique remarkably improves the transient response, such as transient duration, overshoot/undershoot, and the settling time. The proposed controller (FOPI-PI) with the SaBO optimizer is effective in maintaining the DC bus voltage under load and solar system variation.

Addressing Power and Thermal Challenges in Advanced Packaging for AI CPUs/GPUs: Insights into Multi-die Stacking Technology

The rapid advancement of artificial intelligence (AI) has propelled the demand for high-performance computing systems, leading to significant challenges in power and thermal management in advanced packaging for AI CPUs/GPUs. This research paper explores various strategies and technologies to address these challenges. It examines the principles and types of multi-die stacking technology, power optimization techniques, and advanced thermal management solutions. Furthermore, the paper discusses the integration of power and thermal management strategies and their synergistic effects. Case studies and numerical analyses highlight the effectiveness of these approaches in improving energy efficiency, performance, and reliability in AI semiconductor devices. The paper also identifies emerging technologies, such as heterogeneous integration and chiplet-based architectures, and outlines future directions for innovation in advanced packaging. Overall, by leveraging comprehensive research and collaboration, the semiconductor industry can drive the development of advanced packaging solutions that meet the growing demands of AI workloads and enable transformative advancements across industries and applications.

Design optimization and power management of a fuel cell-battery fixed-wing electric UAV

This study aims for endurance improvement and energy saving by addressing, on the one hand, the design optimization (DO) of a hybrid electric source (HES) consisting of a fuel cell (FC) and a battery, intended for a fixed-wing electric UAV (EUAV) and, on the other hand, the power management (PM) of the sources. The DO employed the particle swarm optimization (PSO) algorithm, which determines the FC power and the battery capacity that offer the sources the least weight; the findings resulted in a weight reduction of 8%; this gain saves a total energy of 90.28 kJ that can extend the “endurance stage” by more than 26 min. Three power management strategies (PMS) are examined: frequency separation (FS), which gives good performance in PM and protects the FC from dehydration by smoothing its response; power limitation (PL) which protects the FC from exceeding its maximum power, and fuzzy logic (FL), which allows for more flexible power distribution between the sources and has the main advantage of controlling the battery state of charge (SOC). A fourth strategy was developed by combining the benefits of these three strategies. This hybrid PMS (HPMS) saves 2% of FC energy compared to the FL strategy. FS and PL save, respectively, 41% and 44% of FC energy, but the SOC can drop significantly, especially for longer missions. The experimental findings, achieved using a laboratory test bench, validate the simulated results obtained from MATLAB/Simulink software, confirming the validity and performance of the proposed HPMS.

Adaptive Fuzzy Power Management Strategy for Extended-Range Electric Logistics Vehicles Based on Driving Pattern Recognition

The primary objective of an energy management strategy is to achieve optimal fuel economy through proper energy distribution. The adoption of a fuzzy energy management strategy is hindered due to different reasons, such as uncertainties surrounding its adaptability and sustainability compared to conventional energy control methods. To address this issue, a fuzzy energy management strategy based on long short-term memory neural network driving pattern recognition is proposed. The time-frequency characteristics of vehicle speed are obtained using the Hilbert–Huang transform method. The multi-dimensional features are composed of the time-frequency features of vehicle speed and the time-domain signals of the accelerator pedal and brake pedal. A novel driving pattern recognition approach is designed using a long short-term memory neural network. A dual-input and single-output fuzzy controller is proposed, which takes the required power of the vehicle and the state of charge of the battery as the input, and the comprehensive power of the range extender as the output. The parameters of the fuzzy controller are selected according to the category of driving pattern. The results show that the fuel consumption of the method proposed in this paper is 5.8% lower than that of the traditional fuzzy strategy, and 4.2% lower than the fuzzy strategy of the two-dimensional feature recognition model. In general, the proposed EMS can effectively improve the fuel consumption of extended-range electric vehicles.

Effective Power Management Strategy and Control of a Hybrid Microgrid With Hybrid Energy Storage Systems

Effective Power Management Strategy and Control of a Hybrid Microgrid With Hybrid Energy Storage Systems

A REVIEW ON INTEGRATION OF ELECTRIC VEHICLES AND OPTIMAL POWER MANAGEMENT STRATEGY FOR SUSTAINABLE DEVELOPMENT

The integration of electric vehicles (EVs) into our transportation system is a pivotal step towards sustainable development. EVs are gaining traction as a cleaner and more energy-efficient alternative to traditional internal combustion engine vehicles. Their adoption holds the promise of reducing greenhouse gas emissions and decreasing our reliance on fossil fuels, contributing significantly to environmental sustainability .One of the critical aspects of effective EV integration is the development and implementation of optimal power management strategies. These strategies play a pivotal role in ensuring that the charging and discharging of EV batteries are not only efficient but also seamlessly integrated with the existing power grid infrastructure. An optimal power management system takes into consideration factors such as load balancing, peak demand management, and the integration of renewable energy sources, making it a key component of a sustainable and resilient energy ecosystem. By efficiently scheduling when and how EVs charge or discharge, these strategies can reduce the strain on the electrical grid during peak periods, lower electricity costs, and minimize the carbon footprint associated with charging. Moreover, they can contribute to grid stability and reliability, making the entire energy system more sustainable. This research explores the synergistic potential of EVs and smart grid technologies, aiming to create a harmonious relationship between transportation and energy systems. Key Words: Electric Vehicles, Sustainable Development, Optimal Power Management, greenhouse gas emissions, fossil fuels, clean environment, smart grid technology.

Efficient power management and control of DC microgrid with supercapacitor-battery storage systems

This paper introduces a novel power management strategy (PMS) that aims to facilitate power-sharing between battery and supercapacitor (SC) energy storage systems. The proposed technique is employed to resolve the discrepancy between power demand and generation, as well as to regulate the voltage of the dc bus. The implementation of a hybrid adaptive fuzzy integrated fractional order PD-PI controller (HAFI FPD-PI controller), in the hybrid energy storage system (HESS) results in enhanced regulation of the dc bus, while simultaneously minimising the levels of stress imposed on the battery. Moreover, the proposed PMS improves the lifespan of batteries by redirecting unwaged battery currents to SCs for rapid compensations. In addition, the stability study of dc microgrid is performed. The efficacy of the proposed PMS is evaluated by the utilisation of varying solar irradiation, wind speed, and load. The effectiveness of the proposed PMS was examined by simulations, which involved a comparison of peak deviations in the dc bus voltage with conventional PMS techniques used in literature. The real-time verification of the results has been conducted using OPAL-RT real-time simulator.