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Significant efforts are devoted to optimizing the efficiency of triboelectric energy harvesting systems, particularly through the design of an advanced power management system (PMS) for Triboelectric Nanoenerators (TENGs). A critical aspect of PMS is the design and control of switches. However, existing switches face significant limitations. For spark switches, precise control cannot be achieved, and electronic switches can only operate at voltages below several hundred volts which is limited by the risk of electrical breakdown. To address these limitations, a hybrid electronic‐spark switch power management system (HESS) is proposed. HESS changes the connection of capacitors from parallel to series by deploying a maximum voltage tracking switch components at the peak voltage point, resulting in a much‐elevated voltage level to activate the spark switch. This approach achieves precise control of the spark switch for the first time and significantly reduces the operating voltage of electronic switches. Through simulation and experimental verification, HESS achieves the control at a voltage level of 1.8 kV for spark switch, with an electrical component breakdown voltage of only 450 V. The power density of the HESS is 29.8 mW Hz−1 m−2, which is a new record for electronic switches. [ABSTRACT FROM AUTHOR]

Advancements in the reduction of carbon dioxide emissions from ships are driving the development of more efficient onboard power systems. The proposed non-equivalent parallel running operation system is explored in this study, which improves the efficiency of the main power generation source compared with traditional equal load-sharing methods used in power management systems. However, the asymmetric method reduces the efficiency of the auxiliary power sources. To address this issue, we propose a control method that integrates a battery system with an efficiency-based algorithm to optimize the overall system performance. The proposed approach involves establishing operation command values based on the characteristics of the power generation source and adjusting these commands according to the battery's state of charge (SOC). MATLAB/Simulink simulations confirmed the effectiveness of this method across various operating modes and revealed no operational issues. When applied to a ship's operating profile over 222 h, the method reduced fuel consumption by approximately 2.98 tons (5.57%) compared with conventional systems. Over 38 annual voyages, this reduction equates to savings of 115.96 tons of fuel or approximately 96.47 million Korean won. This study demonstrates that integrating an optimal efficiency algorithm into the energy management system significantly enhances both the propulsion and overall energy efficiency of ships. [ABSTRACT FROM AUTHOR]

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Abstract This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

It is an increasingly mature application solution that triboelectric nanogenerator (TENG) supplies power to electronic devices through its power management system (PMS). However, the previous PMS is able to manage a limited voltage magnitude and the energy storage elements are limited to capacitors. This work proposes an ultrahigh voltage PMS (UV‐PMS) to realize the charging of commercial lithium cells (LCs) by TENG. The design of UV‐PMS enables energy management of TENGs with ultrahigh open‐circuit voltages up to 3500 V and boosts the peak charging current from 30.9 µA to 2.77 mA, an increase of 89.64 times. With the introduction of UV‐PMS, the effective charging capacity of LC charged by a TENG at a working frequency of 1.5 Hz for 1 h comes to 429.7 µAh, making a 75.3 times enhancement compared to charging by TENG directly. The maximum charging power comes to 1.56 mW. The energy storage efficiency is above 97% and the overall charge efficiency can be maintained at 81.2%. This work provides a reliable strategy for TENG to store energy in LC, and has promising applications in energy storage, LC's life, and self‐powered systems. [ABSTRACT FROM AUTHOR]

In the present paper is analysed the possibility of transforming the current ship electrical energy system of an oil/chemical tanker from the current generation of ships in operation into a hybrid ship electrical energy system by installing electrical energy storage systems. Storage systems made up of Lithium-Ion type batteries offer the possibility of using diesel generators in optimal mode with minimum specific fuel consumption values, achieving reductions in fuel consumption. By transforming ship electrical energy network into hybrid electrical energy system, the operating time of diesel generators is reduced to less than half that leads to significant reductions in maintenance costs of diesel generators. [ABSTRACT FROM AUTHOR]

Recent years have seen an increase in electrical crises due to the proliferation of automated inductors and electrical applications such as electric vehicles and mobile devices. The greater dispersion in the smart grid exposes it to risks like cyber-attacks, attenuation, and faulty detections that were not prevalent in conventional methods. The proposed machine learning-based renewable energy smart grid protector and controller (ReSGPC) using Kalman filters effectively controls and detects noise faults, cyberattacks, and attenuation, addressing the mentioned problems. Additionally, the proposed method has increased the efficiency of the smart grid due to its superior performance compared to conventional methods. This method provides an additional layer of protection for the system, safeguarding grid information. An optimal control law is developed to ensure the stability of the power network. The controller demonstrates significant improvements in effectiveness regardless of the initial values. Numerical simulations verify the developed approach, showing that the recommended method offers a more powerful line of attack. This strategy provides a crucial energy management framework for the smart grid, representing a reliable and system-based communication infrastructure with applications integrating renewable resources. Performance analysis reveals substantial improvements, with the proposed method achieving an efficiency increase of 0.25%, 0.42%, 0.32%, and 0.34% in Mean Squared Error (MSE) for ∆1, ∆2, ∆3, and ∆4 scenarios respectively, compared to existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a single-lever control method based on Power Management System (PMS) and improved Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for turboprop engines. In this approach, power level angle command, which is the single-lever command, is decoupled into controlled variable commands by PMS, and the controller based on improved TD3 algorithm can ensure that controlled variables track their commands rapidly and accurately. To achieve the optimal conversion relationship between different commands, an offline optimization process is used to design PMS. By optimization, specific fuel consumption and propeller efficiency are both improved after conversion. To deal with strong interactions between different control loops of a turboprop engine, TD3 algorithm which is a deep reinforcement learning algorithm is adopted. Two improvements which are the design method of observation state and prioritized experience replay are made to enhance the tracking accuracy. Simulation results show that improved TD3 algorithm can learn an optimal control policy to guarantee good control effect with fast response and small overshoot. The maximum settling time is less than 0.25s and the maximum overshoot is less than 0.1%. It also has a good robustness performance when the plant exists model uncertainties. The maximum fluctuations are less than 0.05%. [ABSTRACT FROM AUTHOR]

Rapid population growth propels technological advancement, heightening electricity demand. Obsolete fossil fuel-based power facilities necessitate alternative energy sources. Photovoltaic (PV) energy relies on weather conditions, posing challenges for constant energy consumption. This hybrid energy source system (HESS) prototype employs extreme learning machine (ELM) power management to oversee PV, fossil fuel, and battery sources. ELM optimally selects power sources, adapting to varying conditions. A bidirectional converter (BDC) efficiently manages battery charging, discharging, and secondary power distribution. HESS ensures continuous load supply and swift response for system reliability. The optimal HESS design incorporates a single renewable source (PV), conventional energy (PNL and genset), and energy storage (battery). Supported by a BDC with over 80% efficiency in buck and boost modes, it stabilizes voltage and supplies power through flawless ELM-free logic verification. Google Colab online testing and hardware implementation with Arduino demonstrate ELM's reliability, maintaining a direct current (DC) 24 V interface voltage and ensuring its applicability for optimal HESS. [ABSTRACT FROM AUTHOR]

Abstract It is an increasingly mature application solution that triboelectric nanogenerator (TENG) supplies power to electronic devices through its power management system (PMS). However, the previous PMS is able to manage a limited voltage magnitude and the energy storage elements are limited to capacitors. This work proposes an ultrahigh voltage PMS (UV‐PMS) to realize the charging of commercial lithium cells (LCs) by TENG. The design of UV‐PMS enables energy management of TENGs with ultrahigh open‐circuit voltages up to 3500 V and boosts the peak charging current from 30.9 µA to 2.77 mA, an increase of 89.64 times. With the introduction of UV‐PMS, the effective charging capacity of LC charged by a TENG at a working frequency of 1.5 Hz for 1 h comes to 429.7 µAh, making a 75.3 times enhancement compared to charging by TENG directly. The maximum charging power comes to 1.56 mW. The energy storage efficiency is above 97% and the overall charge efficiency can be maintained at 81.2%. This work provides a reliable strategy for TENG to store energy in LC, and has promising applications in energy storage, LC's life, and self‐powered systems.

Advancements in the reduction of carbon dioxide emissions from ships are driving the development of more efficient onboard power systems. The proposed non-equivalent parallel running operation system is explored in this study, which improves the efficiency of the main power generation source compared with traditional equal load-sharing methods used in power management systems. However, the asymmetric method reduces the efficiency of the auxiliary power sources. To address this issue, we propose a control method that integrates a battery system with an efficiency-based algorithm to optimize the overall system performance. The proposed approach involves establishing operation command values based on the characteristics of the power generation source and adjusting these commands according to the battery’s state of charge (SOC). MATLAB/Simulink simulations confirmed the effectiveness of this method across various operating modes and revealed no operational issues. When applied to a ship’s operating profile over 222 h, the method reduced fuel consumption by approximately 2.98 tons (5.57%) compared with conventional systems. Over 38 annual voyages, this reduction equates to savings of 115.96 tons of fuel or approximately 96.47 million Korean won. This study demonstrates that integrating an optimal efficiency algorithm into the energy management system significantly enhances both the propulsion and overall energy efficiency of ships.

In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply. [ABSTRACT FROM AUTHOR]

Nowadays, the industrial world is undergoing a disruptive transformation towards more environmentally sustainable solutions. In the blue economy, this new approach is not only expressed in the domain of actual vessels, but also in the development of charging infrastructure, displaying a notable transition towards more eco-friendly solutions. The key focus lies in adopting flexible power systems capable of integrating renewable energy sources and storage technologies. Such systems play a crucial role in enabling a shift towards low-emission maritime transport. The emissions reduction goal extends beyond onboard shipboard distribution systems, encompassing also the design of supplying platforms and marinas. This study explores the implementation of a controlled DC microgrid tailored to efficient management of power flows within a yacht marina. Once having established the interfaces for the vessels at berth, the integration between the vessels, the onshore photovoltaic plant and the battery storage unit is made possible thanks to the coordinated management of multiple power converters. The overarching goal is to curtail reliance on external energy sources. Within this DC microgrid framework, a centralized controller assumes a pivotal role in orchestrating the power sources and loads. This coordinated management is essential to achieve sustainable operations, ultimately leading to the reduction of emissions from both ships and onshore power plants. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy has become a considerably more attractive source of power, with costs continually declining. Along with the implementation of this sustainable energy system, the power supply stability should be extensively investigated, particularly in off-grid systems where electricity storage becomes a limitation. The performance of the standalone photovoltaic battery system (SPVBS) is mainly reliant on the DC-bus voltage and its capacitor. The shortcomings of utilizing a small film DC-bus capacitor in SPVBS include the instability and low-frequency ripple of DC-bus voltage and the system's dynamic MPPT techniques performance. Therefore, this study proposes a new technique of regulating the various system components that stabilizes the DC-bus voltage, extracts stable output power despite harsh sudden load and radiation fluctuations, and maintains the system's functionality with or without the battery storage system (BSS). The proposed approach uses a hybrid salp swarm algorithm and particle swarm optimization (SSA-PSO) combined with an intelligent direct sliding mode controller (DSMC) to eliminate the instability and ripples of DC-bus voltages. Moreover, it employs DSMC for bidirectional DC-DC converter and boost converters to enhance the system's dynamic MPPT techniques capabilities. Four different modes are considered according to the energy availability, demand, and battery's state of charge (SOC). Additionally, this easy-to-use technique keeps the DC-bus voltage stable even when the battery storage system is not available. Simulation and experimental results revealed the resilience performance of the proposed intelligent SSA-PSO-DSMC technique in terms of DC-bus regulation, output power extracted, and current harmonics reduction compared to PSO, SSA, cuckoo search optimization (CSO), and grey wolf optimization (GWO). • A novel SSA-PSO-DSMC control is designed for PV converter to achieve MPPT and regulate DC-bus voltage. • The performance of SSA-PSO-DSMC controller was investigated on several stringent and realistic environmental profiles. • SSA-PSO-DSMC improves global MPP tracking speed and dynamic performance. • HIL experiment validates the implementation feasibility. [ABSTRACT FROM AUTHOR]

Polymer electrolyte membrane fuel cells (PEMFCs) are employed in trucks and large commercial vehicles utilizing hydrogen as fuel due to their rapid start-up characteristics and responsiveness. However, addressing the requirement for high power output in the low-current section presents a challenge. To solve this issue, a multi-stack can be applied using two stacks. Furthermore, thermal management, which significantly affects the performance of the stacks, is essential. Therefore, in this study, a hydrogen electric truck system model was developed based on a Hyundai Xcient hydrogen electric truck model using MATLAB/SimscapeTM 2022b. In addition, the system's performance and thermal characteristics were evaluated and analyzed under different road environments and wind conditions while driving in Korea. [ABSTRACT FROM AUTHOR]

This paper proposes a self-supplied power management system to efficiently rectify and regulate the AC voltage received from wireless power transmission techniques to power or recharge biomedical devices. The proposed power management system comprises three integrated functional units, namely, a fully cross-coupled rectifier, a self-biased reference voltage, and a capacitor-less low-dropout regulator (LDO). To reduce the current complexity of designing capacitor-less LDOs, a new architecture based on a pair of diode-connected transistors at the load of the LDO is devised which alleviates the need for a large load capacitor. The proposed power management system is implemented in a 65-nm CMOS process with an active chip area of 0.0810 mm2. Experimental results indicate that this system is capable of rectifying an AC signal up to 5 V at a frequency of 6.78 MHz. This rectified signal is then regulated to a fixed DC voltage of 1.75 V, while the load current can vary between 0 and 75 mA, with a maximum voltage dropout of 170 mV. Advantageously, the proposed power management system is significantly robust to temperature, as a 55 °C change in ambient temperature leads to only a 9% degradation in its overall performance. Furthermore, the ability of the power management system to drive low-power consumer electronics is demonstrated, and its superiority is evidenced by a performance comparison with the latest integrated power management systems presented in the literature. [ABSTRACT FROM AUTHOR]

In this work, we discuss and demonstrate how multi-engine marine power plants with weak power grids efficiently can be set up and simulated in a distributed co-simulation framework. To facilitate configuration switching such as starting and stopping, connecting and disconnecting arbitrary gensets online, the generator models are modelled as hybrid causality component models. This implementation enables seamless and energy conservative model switching. Also, the proposed simulator framework is scalable such that the number of gensets in the power plant can be set by a single parameter, which automatically scales the power management system and the tailored simulator master algorithm accordingly. To control the number of active gensets being connected to the power grid while running the simulation, a simple mixed integer linear programming formulation is proposed. A simulation case study including a marine power plant configuration with four equal-sized gensets is conducted in the end to demonstrate the features of the proposed simulator framework, which also can be applied to, e.g. a small wind farm, or an isolated number of islands with interconnected power generators. [ABSTRACT FROM AUTHOR]

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein's PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a power management strategy to control a multiport converter as an interface among a three‐phase power grid, a renewable energy source (RES), and two energy storage resources (ESRs). The ESRs include electric energy storage (EES) and electric vehicle (EV). With the expansion of the use of EVs, batteries from EVs can be used as energy storage and support the integrated RES‐based electricity systems. EES and EV differ in availability, capacity, and charge/discharge rates. The multiport converter has a simple structure and reduced number of elements that allows simultaneous and independent use of resources. Two bidirectional DC ports enable two‐way power flow for ESRs. A two‐level power management system (TLPMS) is presented to deal with controlling battery charging, Vehicle‐to‐Grid (V2G) operation, and RES and power grid utilization. This way, TLPMS changes the case studies (CSs) based on the availability of EV and photovoltaic (PV) as well as batteries' state‐of‐charge (SOC). The performance of the proposed TLPMS has been evaluated by simulation results and experimental tests. The results show the fast and robust performance of the proposed TLPMS. So that the current on the power grid side, under consecutive changes in the CSs, achieves a steady state during a cycle (20 ms). Also, the speed of DC sources in achieving their reference values is high, which indicates the outstanding features of the proposed system. [ABSTRACT FROM AUTHOR]

In this work, a model predictive control (MPC) based energy management system (EMS) is designed and implemented to control the powertrain power flow of a series hybrid electric agricultural tractor. The proposed MPC-based EMS considers the battery SoC regulation and the fuel consumption minimization subject to power sources constraints. In addition to the control objectives established in the MPC, the analysis of the results shows the influence of the proposed scheme on the battery state of health (SoH), the battery temperature, the fuel economy and the engine performance based in the break specific fuel consumption (BSFC) map. For comparison purposes, a conventional rule-based (RB) EMS is also implemented. The results show that the MPC-EMS can achieve the main objectives with 7.2% fuel reduction while satisfactorily managing the systems constraints, and positively impacting the battery SoH and temperature in comparison with the RB-EMS.

This study developed four different power management system (PMS) prototypes based on the most popular mission computers used for offboard mission operation by the PX4 (open-source UAV flight controller software platform) user community for a photovoltaic (PV)-powered unmanned aerial vehicle (UAV). The four PMS prototypes were compared in terms of operational performance through an analysis of data obtained from a model-in-the-loop simulation and hardware-in-the-loop simulation using charging and discharging current patterns, saved from an outdoor PV panel power generation and multirotor UAV flight tests, respectively, under varying environmental temperature. Two types of simulations were performed, depending upon the additional placement of a supercapacitor in parallel with a Li-ion battery pack, to verify the effectiveness of the Li-ion battery pack life extension through high current shock mitigation. Simulation results demonstrated that the application of a supercapacitor in parallel with a Li-ion battery pack resulted in poor performance indicated by in-flow current fluctuation deviation measurements during the end of the constant current and constant voltage charging process. Moreover, this behavior was confirmed to be mainly because of the inherent high C-rate performance capability of the Li-ion battery pack compared to the supercapacitor. Among the four PMS candidates, under 25 °C without supercapacitor, Jetson TX2 was found to exhibit the best functionality in terms of power management (0.41 W of power fluctuation deviation), whereas AMega yielded the best functionality in terms of energy management (1165.4 s of flight time).

Abstract This article proposes a novel comprehensive multi‐layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi‐level optimization to address the participation of multi‐microgrids (MMGs) in day‐ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMG‐connected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not‐supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC‐power flow, reliability, security, and flexibility of the MGs. To obtain a single‐level model, the Karush–Kuhn–Tucker method is applied, and a hybrid stochastic‐robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi‐layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability).

A microbial fuel cell (MFC) is a renewable source of energy that produces electricity without harming the environment. This review paper describes the importance of MFCs for future power sources. The construction methods, history of MFC, different configuration of MFC and voltage generation of single-chamber as well as the two-chamber MFC have been discussed. Due to the bio-electrochemical features of the MFCs, when an electric load is applied to stack MFCs, Stack MFC’s voltage does not remain constant over time (known as reversal of voltage). Therefore, researchers have proposed various power management systems (PMS) to raise the voltage of the MFC to avoid voltage reversal. The future direction is focus on the design of a PMS adapted for MFC optimal operation using ultralow power consumption components.

The primary goal of power management in a hybrid microgrid is to maintain the active power balance among renewable energy sources, energy storage system, and loads. In a HMG system, an ESS is required to reduce power fluctuations caused by the intermittent behavior of RESs. A bi-directional dc–dc converter has been used to connect a battery to the dc bus. This paper proposes an improved power management scheme where a novel controller named as hybrid fuzzy integrated fractional order cascaded proportional derivative filter (1+proportional integral) is designed and implemented in a voltage-controlled loop of the BDC. This controller is introduced in this work to achieve optimum power flow management improves voltage stabilization, and enhances the system dynamic response. A hybrid modified sine cosine algorithm-pattern search algorithm is implemented here to tune the parameters of the proposed controller. The extensive time-domain analysis and robustness studies of the proposed controller have been performed and compared with the other conventional control methods Variable solar irradiance, solar temperature, wind speed, and load are used to test the effectiveness of proposed power management strategy. The proposed power management scheme with several case studies has been performed in MATLAB/Simulink and also validated through a Real-time digital platform (Opal-RT). • The proposed power management scheme is designed to maintain the active power balance in hybrid microgrid. • The proposed HFFOC PDF (1＋PI) controller is designed and implemented in the battery power management scheme to effectively regulate the dc bus voltage. • The extensive time-domain analysis and robustness studies have been performed here with different controllers. • Hybrid m-SCA-PS algorithm is used to tune the parameters of the proposed controller to confer the supremacy of the controller. • The proposed power management scheme with several case studies has been performed and also validated through Opal-RT. [ABSTRACT FROM AUTHOR]

Forest fires have long been a significant global problem. How to reliably accomplish the early warning and real-time monitoring of forest fires has become a pressing issue in order to limit the damage caused by forest fires. A novel technological approach for forest fire monitoring has been made possible by the quick development of wireless sensor network (WSN) technology. Currently, batteries are the primary source of power for WSNs used in forests, but frequent battery replacement will compromise the network for monitoring. As a result, the power supply is the key limit to its application in forest areas. This paper puts forward the thermoelectric conversion based on the Seebeck effect. Its notable feature is to convert heat energy into electric energy through the temperature difference between shallow soil and air. In the process of testing the device, the maximum voltage was 803.36 mV. At the same time, a power management system (PMS) for a thermoelectric conversion device was designed. The main feature of this system is that there is no need for an external control module. In the laboratory test, the minimum input power of this system was 200 mV. When the load resistance was 8 KΩ, the output power was 0.55 mW, and the maximum efficiency could reach 65.38% when the input was 500 mV, which fulfills the requirements of low cost and high reliability, providing a feasible solution for solving the energy limitation problem of WSNs in a forest area. [ABSTRACT FROM AUTHOR]

In recent years, the growing demand for efficient and sustainable energy management has led to the development of innovative solutions for embedded systems. One such solution is the integration of hybrid nanogrid energy management systems into various applications. There are currently many energy management systems in different domains, such as buildings, electric vehicles, or even naval transport. However, an embedded nanogrid management system is subject to several constraints that are not sufficiently studied in the literature. Indeed, such a system often has a limited energy reserve and is isolated from any energy supply for a long time. This paper aims to provide a comprehensive overview of the current state of research, advancements, and challenges in the field of hybrid nanogrid energy management systems. Furthermore, it offers a comparative analysis between hybrid nanogrids and microgrids and the implications of their integration in embedded systems. This paper also discusses the key components, operation principles, optimization strategies, real-world implementations, challenges, and future prospects of hybrid nanogrid energy management systems. Moreover, it highlights the significance of such systems in enhancing energy efficiency, reducing carbon footprints, and ensuring reliable power supply.

Nowadays, the industrial world is undergoing a disruptive transformation towards more environmentally sustainable solutions. In the blue economy, this new approach is not only expressed in the domain of actual vessels, but also in the development of charging infrastructure, displaying a notable transition towards more eco-friendly solutions. The key focus lies in adopting flexible power systems capable of integrating renewable energy sources and storage technologies. Such systems play a crucial role in enabling a shift towards low-emission maritime transport. The emissions reduction goal extends beyond onboard shipboard distribution systems, encompassing also the design of supplying platforms and marinas. This study explores the implementation of a controlled DC microgrid tailored to efficient management of power flows within a yacht marina. Once having established the interfaces for the vessels at berth, the integration between the vessels, the onshore photovoltaic plant and the battery storage unit is made possible thanks to the coordinated management of multiple power converters. The overarching goal is to curtail reliance on external energy sources. Within this DC microgrid framework, a centralized controller assumes a pivotal role in orchestrating the power sources and loads. This coordinated management is essential to achieve sustainable operations, ultimately leading to the reduction of emissions from both ships and onshore power plants.

Polymer electrolyte membrane fuel cells (PEMFCs) are employed in trucks and large commercial vehicles utilizing hydrogen as fuel due to their rapid start-up characteristics and responsiveness. However, addressing the requirement for high power output in the low-current section presents a challenge. To solve this issue, a multi-stack can be applied using two stacks. Furthermore, thermal management, which significantly affects the performance of the stacks, is essential. Therefore, in this study, a hydrogen electric truck system model was developed based on a Hyundai Xcient hydrogen electric truck model using MATLAB/SimscapeTM 2022b. In addition, the system’s performance and thermal characteristics were evaluated and analyzed under different road environments and wind conditions while driving in Korea.

The management of energy in distribution networks has been gathering attention in recent years. The simultaneous control of generation and demand is crucial for achieving energy savings and can further lower energy pricing. The work aims to develop a control scheme for a hybrid microgrid that can provide stability to the bus voltage and effectively manage the power flow. Solar energy is the current trend in renewable energy sources (RES). There is a surge in the installation of solar PV systems both on a large scale and on a small scale, such as rooftop PV systems. Installation of RES at residential premises has to be conducted with a proper power management scheme. The hybrid microgrid for this work consists of a PV system with a boost converter to extract maximum power, a DC-DC bi-directional converter to charge or discharge the hybrid energy-storing devices, and a three-phase AC-DC interlinking converter for exchange of energy with the utility grid. The control and power management scheme checks the voltage of each unit and maintains the power flow according to operating conditions. Disturbances are introduced in the form of load switching and irradiance variation to check the system performance. The system is tested on the MATLAB (R2021a) Simulink platform for varying its different modes of operations. An experimental set-up has been developed with hardware-in-the-loop to validate the simulation results. [ABSTRACT FROM AUTHOR]

The increasing environmental concerns due to emissions from the shipping industry have accelerated the interest in developing sustainable energy sources and alternatives to traditional hydrocarbon fuel sources to reduce carbon emissions. Predominantly, a hybrid power system is used via a combination of alternative energy sources with hydrocarbon fuel due to the relatively small energy efficiency of the former as compared to the latter. For such a hybrid system to operate efficiently, the power management on the multiple power sources has to be optimised and the power requirements for different vessel types with varying loading operation profiles have to be understood. This can be achieved by using energy management systems (EMS) or power management systems (PMS) and control methods for hybrid marine power systems. This review paper focuses on the different EMSs and control strategies adopted to optimise power management as well as reduce fuel consumption and thus the carbon emission for hybrid vessel systems. This paper first presents the different commonly used hybrid propulsion systems, i.e., diesel–mechanical, diesel–electric, fully electric and other hybrid systems. Then, a comprehensive review of the different EMSs and control method strategies is carried out, followed by a comparison of the alternative energy sources to diesel power. Finally, the gaps, challenges and future works for hybrid systems are discussed. [ABSTRACT FROM AUTHOR]

This article proposes a novel comprehensive multi‐layer power management system (PMS) along with its smart distribution network (SDN) constraints as bi‐level optimization to address the participation of multi‐microgrids (MMGs) in day‐ahead energy and ancillary services markets. In the first layer of the proposed model, optimal programming of MMG‐connected SDN is considered, in which Microgrids (MGs) participation in the markets is performed to bidirectionally coordinate sources and active loads along with the operator of MGs. In the second layer, the bidirectional coordination of operators of MGs and SDN, that is PMS, is executed in which energy loss, voltage security, and expected energy not‐supplied (EENS) are minimized as weighted sum functions. The problem of the difference between costs and revenues of MGs in markets is minimized subject to constraints of linearized AC‐power flow, reliability, security, and flexibility of the MGs. To obtain a single‐level model, the Karush–Kuhn–Tucker method is applied, and a hybrid stochastic‐robust programming is implemented to model uncertainties associated with the load, renewable power, energy price, mobile storage energy demand, and network equipment accessibility. The contributions of this paper include the simultaneous modelling of several economic indicators, multi‐layer energy management modelling, and stochastic mixed modelling of uncertainties. The efficiency of this method is validated by simultaneously evaluating the optimum condition of technical and economic indices of several SDNs and MGs. Flexibility of 0.022 MW is obtained for the proposed scheme, which is close to zero (100% flexibility). The voltage security index is increased to 22 by the mentioned scheme, which is close to its normal value, that is, 24. The voltage deviation is below 0.07 p.u. Energy losses are reduced by about 30% compared with that in power flow studies, and the EENS reaches roughly 3 MWh, that is, close to zero (100% reliability). [ABSTRACT FROM AUTHOR]

This study employs a probabilistic methodology to forecast cruise ship emissions during the itinerary planning phase, considering environmental factors (current, waves, wind), fouling, shallow water, loading conditions, and Fresh Water (FW) production effects. Probability distributions of environmental parameters are established based on statistical data and utilized for generating deterministic scenarios through Monte Carlo sampling. The resulting scenarios are simulated to define probability distributions for carbon dioxide emissions, carbon intensity indicators, and other pertinent quantities. The simulation model is validated using data from an existing ship. To demonstrate the effectiveness of the proposed methodology in strategic itinerary planning, multiple alternatives for an existing itinerary are simulated. Specifically, a Mediterranean cruise is simulated in both the original sequence and in reverse, with and without FW production. The reverse sequence without FW production demonstrates a potential reduction of approximately 190 tCO2e/week emissions. Furthermore, in this scenario, a comparison is made between a standard Power Management System (PMS) with an equal load on all engines and an optimized PMS with optimized engine loads, resulting in an additional average reduction of 86 tCO2e. In the latter itinerary, the most probable rating according to Carbon Intensity Indicator is reduced from original E to C. • Itinerary planning is carried out years before deployment by cruise companies. • A probabilistic method forecasts cruise emissions considering environmental factors. • Monte Carlo sampling drives a simulation model specialized for cruise ships. • Simulations of fuel and freshwater consumption are validated with real ship data. • Itinerary planning and engine load optimizations show potential emission reductions. [ABSTRACT FROM AUTHOR]

With the intensification of global ocean exploration, there is an increasing need for extensive and dispersed wireless sensor networks (WSNs) to meet the requirements of real-time, in-situ, and high-spatial-temporal-density marine meteorological observations. Nevertheless, providing a reliable power source and ensuring the ongoing maintenance of these WSNs pose significant challenges. Here, an innovative self-powered wireless sensor node is developed to address these challenges, featuring an ultra-high-output triboelectric-electromagnetic hybrid generator (T-EHG) with a swinging mechanism. The T-EHG, which combines triboelectric (TENG) and electromagnetic (EMG) generation, assisted by a universal power management system (UPMS), efficiently converts wave energy into a consistent voltage output between 1.5 V to 3.3 V. Additionally, the self-powered WSNs integrated with T-EHG, UPMS circuits, meteorological sensors, and microcontroller units (MCUs) with LoRa wireless transmission functionality are capable of executing data collection and transmission operations every 10 s under simulated wave conditions. These systems can achieve a maximum wireless transmission range of 1.5 km. Notably, a marine meteorological monitoring system utilizes these autonomous sensor nodes, along with base stations, cloud servers, and terminal devices to effectively implement real-time, on-site measurements of temperature, humidity, and air pressure within the monitored zone. This research underscores the substantial practical utility and extensive application prospects of these advanced self-powered WSNs for marine meteorological surveillance. [Display omitted] • A swinging hybrid generator with ultra-high average power. • The management circuit for achieving the efficient coupling of the outputs of TENG and EMG. • A generic strategy for maritime sensor nodes equipped with long-range wireless transmission capabilities. • Realized real-time data transmission at a distance of 1.5 km. [ABSTRACT FROM AUTHOR]

The ship's power system is one of the most important systems on board. It is designed for uninterrupted power supply to all ship consumers under different conditions of exploitation. When designing a ship, various optimizations are conducted to build the ship as economically and environmentally friendly as possible. The paper aims to analyze the possibility of applying renewable energy sources (RES), particularly solar and wind energy, on an existing vessel by conducting technical and economic analysis. Data for the solar hour's number and wind distribution are gathered from the six locations in the Adriatic Sea over 32 years period. Firstly, it was investigated if data were position dependent or independent. Performing a Pearson correlation coefficient and an ANOVA analysis with F-test, it was concluded that the RES analysis is position-independent (p > 0.05, p = 0.826). Secondly, the energy system model created in Simulink was used for the analysis of the electrical network fundamental parameters. Finally, the object of the analysis is the total costs of procurement, installation, and maintenance of the system within a period of 25 years. Consequences are savings in the cost of exploitation and reduction of harmful gas emissions. The use of solar energy would result in savings of 111,556 L of diesel fuel, while the savings from wind energy would be 170,274 L of diesel fuel for 25 years. [ABSTRACT FROM AUTHOR]

There is an interest in harvesting energy from people's footsteps in crowded areas to power smart electronic devices with low consumption. The average power consumption of these devices is approximately 10 μW. The energy from our footsteps is green and free, because walking is a routine activity in everyday life. The energy floor is one of the most efficient pieces of equipment in vibration-based energy harvesting. The paper aims to improve the previous design of the energy floor—called Genpath—which uses a rotational electromagnetic (EM) technique to generate electricity from human footsteps. The design consists of two main parts of (1) the EM generator, including the lead-screw mechanism for translation-to-rotation conversion, and (2) the Power Management and Storage (PMS) circuit. The improvement was focused on the part of the EM generator. A thorough investigation of the design components reveals that the EM generator shaft in the previous Genpath design cannot continuously rotate when the floor-tile reaches the bottom end, resulting in no energy gain. Therefore, a one-way clutch is implemented to the system to disengage the generator shaft from the lead-screw motion when the floor-tile reaches the allowable displacement. During the disengagement, the EM generator shaft still proceeds with a free rotation and could generate more power. In our analysis, the dynamic model of the electro-mechanical systems with the one-way clutch was successfully developed and used to predict the energy performances of the VEH floors and fine-tune the design parameters. The analytical result is shown that the spring stiffness mainly affects the force transmitted to the EM generator, and then the induced voltage and power of the generator, thus, the value of the stiffness is one of the critical design parameters to optimize. Finally, the new prototype consisting of 12-V-DC generator, mechanisms of lead-screw and clutch, as well as coil springs with the optimal stiffness of 1700 N/m was built and tested. The average energy produced by the new prototype is 3637 mJ (or average power of 3219 mW), per footstep which is 2935 mJ greater than that of the previous design. Moreover, to raise the social awareness about energy usage, the sets of Genpath have been used to organize an exhibition, "Genpath Empower our Journey". The people who stroll forward on the paths can realize how much energy they gain from their footsteps. [ABSTRACT FROM AUTHOR]

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm.

Hybrid systems based on renewable energies for the electrification of remote sites controlled by power management systems (PMSs) aim to reduce fossil fuels and increase the efficiency of renewable energy sources to minimize greenhouse gas emissions. The influential role of the PMS contributes to improving the efficiency and effectiveness of these systems by ensuring a balance between the different sources and loads in all operating modes. However, the abrupt transitions between the various operational modes selected by the PMS generate power loss and imbalance. To handle this issue, a fuzzy logic controller (FLC)-based PMS controlling a photovoltaic (PV) and diesel hybrid system with a battery storage element connected to a DC bus is proposed in this paper. The proposed PMS is wholly based on FLC to ensure a smooth transition between the different modes of the system. The success of using the suggested PMS lies in how well the FLC parameters are chosen before the system is processed. For this purpose, the particle swarm optimization algorithm is adapted to tune the FLC parameters. The resulting optimal intelligent PMS is tested and compared with a classical one using comprehensive simulations performed in a Simscape ElectricalTM MATLAB® environment. The obtained results show an overshoot attenuation at the DC-bus voltage of 2% when changing the mode and an improvement in the PV generator efficiency by 99.5%. [ABSTRACT FROM AUTHOR]

In today's environment of increasing pressure to reduce fuel consumption and emissions of carbon dioxide (CO2) and nitrogen oxides (NOx), the LNG (Liquefied Natural Gas) transportation industry is growing in size and influence. In this context, further efforts are needed to improve the energy efficiency of LNG marine energy power plant. The LNG vessels and their equipment considered in this study have different power consumption requirements depending on the vessel's mode of operation (loading/unloading, maneuvering, anchoring, at sea, etc.). For each ship mode, where the power plant requirements are the same, the specific fuel consumption (SFOC) and exhaust emissions, NOx and CO2, are compared with a different number of engines in the network to find the optimal number of engines in the network, considering both the safety aspect and the port requirements. An analysis was performed showing the efficiency of the on-board power management system (PMS) in terms of manual load sharing between engines. A comprehensive analysis of the data and its comparison led to the conclusion that the manual distribution of power among the engines is the slightly better solution. The obtained results show that further analysis of the number of engines for a given load with minimum fuel consumption and CO2 and NOx emissions is required. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights: Use EIS to quickly and effectively predict the internal temperature changes of LIBs.No hardware temperature sensors and thermal model are required.The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed. [ABSTRACT FROM AUTHOR]

Shipboard power systems (SPS) are complex systems connecting multiple power sources, energy storage systems (ESSs) and various types of loads. However, the fluctuated propulsion loads may lead to a low power quality of the system. In this paper, a power management system (PMS) is designed for SPS in the framework of an optimization-based method. With equivalent consumption minimization strategy (ECMS), the proposed method is able to provide an optimal power-split between hybrid energy sources in real time, while minimizing the fuel consumption of the system. Furthermore, the equivalence factor is adjusted by model predictive control (MPC) of PMS. The performances of the proposed approach are evaluated by simulations.

Triboelectric nanogenerators (TENGs) are used to harvest high entropy energy. To optimize the energy harvesting, storage and effective utilization, a critical part is the power management system (PMS), which requires an ideal electrical switch as the key component. However, the current switch technologies cannot adapt and enhance the charging for the four operation modes of TENG simultaneously. Herein, a flexible film‐discharge‐switch (FDS), delivering an averaged charge‐enhance factor over 100 under four operation modes of TENG, compared to directly charging a capacitor with conventional rectifier is reported. More specifically, the charge‐enhancement factors of the FDS are 134.3, 64.7, 97.0, and 136.5 for the modes of contact‐separation, lateral sliding, single‐electrode, and freestanding triboelectric layer, respectively. Furthermore, the high flexibility, stability, simplicity, durability, and self‐driven design of FDS endows the switch the possibility of versatile applications in the four modes of TENGs and flexible PMSs in wearable TENG applications. The successful demonstration in TENGs shows the great potential of the FDS in various fields such as micro‐nano energy storage, distributed power supply, and self‐powered systems. [ABSTRACT FROM AUTHOR]

Supercapacitors, are commonly connected to the sediment microbial fuel cell (SMFC) and then serves as the input source for the power management system (PMS). To compare and analyze functions of supercapacitors in SMFC energy harvesting, PMSs (PMS I and PMS II) are powered by SMFC stack or charged supercapacitors as the input source. Tests indicate that the charged supercapacitor results in a higher input power and a larger output power. In addition, the overall efficiency of PMSs is rarely affected by the capacitance, but the initial voltage of the supercapacitor. By charging supercapacitors connected in parallel and then discharging them in series, the overall power efficiency of PMS II is increased from 44.33% to 69.52%. In conclusion, supercapacitors firstly storing SMFC energy is beneficial to provide sufficient energy, resulting in an improved PMS performance. Further, these results can be useful and informative to PMS design for efficiently harvesting and utilize MFC energy. • Two common power management systems (PMSs) are comparative studied. • The MPPT-based PMS extracts higher power from the SMFC stack and supercapacitor. • Charged supercapacitors provide higher input power for the PMSs. • PMS performance is affected by the initial voltage of supercapacitor. • Different arrangements of supercapacitors are studied. [ABSTRACT FROM AUTHOR]

Forest fires have long been a significant global problem. How to reliably accomplish the early warning and real-time monitoring of forest fires has become a pressing issue in order to limit the damage caused by forest fires. A novel technological approach for forest fire monitoring has been made possible by the quick development of wireless sensor network (WSN) technology. Currently, batteries are the primary source of power for WSNs used in forests, but frequent battery replacement will compromise the network for monitoring. As a result, the power supply is the key limit to its application in forest areas. This paper puts forward the thermoelectric conversion based on the Seebeck effect. Its notable feature is to convert heat energy into electric energy through the temperature difference between shallow soil and air. In the process of testing the device, the maximum voltage was 803.36 mV. At the same time, a power management system (PMS) for a thermoelectric conversion device was designed. The main feature of this system is that there is no need for an external control module. In the laboratory test, the minimum input power of this system was 200 mV. When the load resistance was 8 KΩ, the output power was 0.55 mW, and the maximum efficiency could reach 65.38% when the input was 500 mV, which fulfills the requirements of low cost and high reliability, providing a feasible solution for solving the energy limitation problem of WSNs in a forest area.