Search Results (19)

This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article

The escalating demand for efficient thermal management in lithium-ion batteries necessitates precise characterization of their thermal behavior under diverse operating conditions. This study develops a three-dimensional (3D) electrochemical–thermal coupling model grounded in porous electrode theory and energy conservation principles. The model solves multi-physics equations such as Fick’s law, Ohm’s law, and the Butler–Volmer equation, to resolve coupled electrochemical and thermal dynamics, with temperature-dependent parameters calibrated via the Arrhenius equation. Simulations under varying discharge rates reveal that high-rate discharges exacerbate internal heat accumulation. Low ambient temperatures amplify polarization effects. Forced convection cooling reduces surface temperatures but exacerbates core-to-surface thermal gradients. Structural optimization strategies demonstrate that enhancing through-thickness thermal conductivity reduces temperature differences. These findings underscore the necessity of balancing energy density and thermal management in lithium-ion battery design, proposing actionable insights such as preheating protocols for low-temperature operation, optimized cooling systems for high-rate scenarios, and material-level enhancements for improved thermal uniformity. Full article

Due to climate change, the electric vehicle (EV) industry is rapidly growing and drawing researchers interest. Driving conditions like mountainous roads, slick surfaces, and rough terrains illuminate the vehicles inherent nonlinearities. Under such scenarios, the behavior of power sources (fuel cell, battery, and super-capacitor), power processing units (converters), and power consuming units (traction motors) deviates from nominal operation. The increasing demand for FCHEVs necessitates control systems capable of handling nonlinear dynamics, while ensuring robust, precise energy distribution among fuel cells, batteries, and super-capacitors. This paper presents a DSMC strategy enhanced with Robust Uniform Exact Differentiators for FCHEV energy management. To optimally tune DSMC parameters, reduce chattering, and address the limitations of conventional methods, a hybrid metaheuristic framework is proposed. This framework integrates moth flame optimization (MFO) with the gravitational search algorithm (GSA) and Fractal Heritage Evolution, implemented through three spiral-based variants: MFOGSAPSO-A (Archimedean), MFOGSAPSO-H (Hyperbolic), and MFOGSAPSO-L (Logarithmic). Control laws are optimized using the Integral of Time-weighted Absolute Error (ITAE) criterion. Among the variants, MFOGSAPSO-L shows the best overall performance with the lowest ITAE for the fuel cell (56.38), battery (57.48), super-capacitor (62.83), and DC bus voltage (4741.60). MFOGSAPSO-A offers the most accurate transient response with minimum RMSE and MAE FC (0.005712, 0.000602), battery (0.004879, 0.000488), SC (0.002145, 0.000623), DC voltage (0.232815, 0.058991), and speed (0.030990, 0.010998)—outperforming MFOGSAPSO, GSA, and PSO. MFOGSAPSO-L further reduces the ITAE for fuel cell tracking by up to 29% over GSA and improves control smoothness. PSO performs moderately but lags under transient conditions. Simulation results conducted under EUDC validate the effectiveness of the MFOGSAPSO-based DSMC framework, confirming its superior tracking, faster convergence, and stable voltage control under transients making it a robust and high-performance solution for FCHEV. Full article

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen is essential for any water quality management structure. This research was conducted to develop a novel Nitrate Portable Measurement System (NPMS) to monitor nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on the Beer–Lambert law for measuring nitrate concentrations in water samples through light absorption in the spectral range of 295–315 nm. The system is equipped with a ready-to-use ultraviolet sensor, light emission diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box, which allows a very compact self-contained equipment with high portability, enabling field and near-real-time measurements. The proposed methodology and the developed instrument were used to analyze multiple nitrate standard solutions. The performance was evaluated in comparison to the Nicolet Evolution 300, a classical UV–Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO₃⁻/L. Full article

Rising fossil fuel costs and environmental concerns are driving the search for new energy sources, particularly renewable energy. Among these sources, solar photovoltaic (PV) is the most promising in southern European countries, mainly through the use of decentralised PV systems designed to produce electricity close to the point of demand and primarily to meet local energy needs. In an urban scenario, a decentralised energy system usually operates in parallel with the grid, allowing excess power generated to be injected into the grid. Solar carports and rooftop systems are excellent examples of distributed photovoltaic systems, which are far more sustainable than large centralised systems because they do not compete for land use. Despite their operational advantages, these decentralised photovoltaic production plants, which are in most cases financed by specific energy efficiency programs, present challenges in a regulated market where the injection of energy into the electricity grid is restricted by law and support programs. The aim of this work is to integrate two different photovoltaic systems within an academic campus where the only PV source currently available is a solar car park, a solution designed both to provide shaded space for vehicles and to produce energy to be consumed within the facilities. Due to legal restrictions, surplus electricity cannot be sold to the national grid, and solar batteries to store the generated energy are expensive and have a short lifespan. Therefore, since the campus has two different grid connections and a 102.37 kWp PV system, the newly designed system to be installed on the library roof must be calculated to support the installed electricity system during the most critical working hours, determining the specific angle and orientation of the solar panels. On this basis, the energy management of a school campus is fundamental to creating a collective self-consumption system, the basis of a local energy community that can meet energy, environmental, and social objectives. Full article

Currently, the urgent needs of sustainable mobility and green energy generation are driving governments and researchers to explore innovative energy storage systems. Concurrently, lithium-ion batteries are one of the most extensively employed technologies. The challenges of battery modeling and parameter estimation are crucial for building reliable battery management systems that ensure optimal battery performance. State of charge (SOC) estimation is particularly critical for predicting the available capacity in the battery. Many methods for SOC estimation rely on the knowledge of the open-circuit voltage (OCV) curve. Another significant consideration is understanding how these curves evolve with battery degradation. In the literature, the effect of cycle aging on the OCV is primarily addressed through the look-up tables and correction factors applied to the OCV curve for fresh cells. However, the variation law of the OCV curve as a function of the battery cycling is not well-characterized. Building upon a simple analytical function with five parameters proposed in the prior research to model the OCV as a function of the absolute state of discharge, this study investigates the dependency of these parameters on the moved charge, serving as an indicator of the cycling level. Specifically, the analysis focuses on the impact of cycle aging in the low-, medium-, and high-SOC regions. Three different cycle aging tests were conducted in these SOC intervals, followed by the extensive experimental verification of the proposed model. The results were promising, with mean relative errors lower than 0.2% for the low- and high-SOC cycling regions and 0.34% for the medium-SOC cycling region. Finally, capacity estimation was enabled by the model, achieving relative error values lower than 1% for all the tests. Full article

The transition to a net-zero energy system is being promoted in the energy sector, which has led to the creation of energy prosumers. These produce, consume, and trade energy using renewable energy systems installed in buildings or complexes. Here, a community was set as the target to apply the concept of an energy prosumer at the individual building and regional levels. Energy-sharing systems were divided into three categories: energy production, energy storage, and energy management. Energy-sharing systems centered on electrical energy—photovoltaic, battery energy storage, and energy management systems—were installed in two communities located in South Korea, and the energy-sharing effects of the system operation were reported. Monthly power consumption in spring and fall exhibited significant savings of approximately three times that of winter consumption, owing to the energy-sharing systems. Daily hourly power-consumption patterns differed on weekdays and weekends because of the weekday working and building-use hours of the communities. Energy could be shared between communities and buildings because of surplus energy. More surplus power was available for energy sharing on weekends because power consumption was lower. Because energy trading and sharing are restricted, the related laws are being revised. Therefore, a low-carbon community can be realized through surplus energy trading and sharing technology between communities and buildings as renewable energy systems spread owing to low carbonization. Full article

Following the European Climate Law of 2021 and the climate neutrality goal for zero-emission transportation by 2050, electric vehicles continue to gain market share, reaching 2.5 million vehicles in Q1 of 2023. Electric vehicles utilize an electric motor for propulsion powered by lithium batteries, which suffer from high temperatures caused by peak operation conditions and rapid charging, so hybridization with supercapacitors is implemented. In this paper, a fuzzy logic controller is employed based on a rule-based scheme and the Mamdani model to control the power distribution of the hybrid system, driven by the state of charge and duty cycle parameters. An active topology with one bi-directional DC-to-DC converter at each source is exploited in the MATLAB/Simulink environment, and five power states like acceleration and coasting are identified. Results show that the ideal duty cycle is within 0.40–0.50 as a universal value for all power states, which may vary depending on the available state of charge. Total efficiency is enhanced by 6%, sizing is increased by 22%, leading to a more compact layout, and battery life is extended by 20%. Future work includes testing with larger energy sources and the application of this management strategy in real-time operations. Full article

This paper combines two types of energy storage components, the battery and supercapacitor (SC), to form a fully active hybrid energy storage system (HESS) as a power source for electric vehicles (EVs). At the same time, a hierarchical coordinated energy management strategy based on model predictive control (HCEMS-MPC) is presented. Firstly, the mathematical model of the fully active HESS is obtained based on Kirchhoff’s law and state-space modeling technology. Secondly, considering the state of charge (SOC) of the battery, a fuzzy-control-based upper-level energy management strategy (EMS) is proposed to optimize power allocation and to generate a reference current for a lower-level current controller. Then, a lower-level current predictive controller is designed to achieve accurate current tracking. Finally, a lower-level voltage sliding mode controller is designed to stabilize the bus voltage. Compared with previous works, the HCEMS-MPC strategy only needs to adjust the weight matrix and the reaching term to avoid the problem of excessive controller parameters. The simulation results, under different driving conditions, show that the HCEMS-MPC strategy has a better performance with respect to its fast response, error reduction, and robust stability. In addition, the SOC of the battery decreases more slowly, and the final SOC value significantly increases, thereby extending the single-discharge cycle time of the battery and improving the service life of the battery. Full article

In this paper, optimal and multi-objective planning of a hybrid energy system (HES) with wind turbine and battery storage (WT/Battery) has been proposed to drop power loss, smooth voltage profile, enhance customers reliability, as well as minimize the net present cost of the hybrid system plus the battery degradation cost (BDC). Decision variables include the installation site of the hybrid system and size of the wind farm and battery storage. These variables are found with the help of a novel metaheuristic approach called improved Fick’s law algorithm (IFLA). To enhance the exploration performance and avoid the early incomplete convergence of the conventional Fick’s law (FLA) algorithm, a dynamic lens-imaging learning strategy (DLILS) based on opposition learning has been adopted. The planning problem has been implemented in two approaches without and considering BDC to analyze its impact on the reserve power level and the amount and quality of power loss, voltage profile, and reliability. A 33-bus distribution system has also been employed to validate the capability and efficiency of the suggested method. Simulation results have shown that the multi-objective planning of the hybrid WT/Battery energy system improves voltage and reliability and decreases power loss by managing the reserve power based on charging and discharging battery units and creating electrical planning with optimal power injection into the network. The results of simulations and evaluation of statistic analysis indicate the superiority of the IFLA in achieving the optimal solution with faster convergence than conventional FLA, particle swarm optimization (PSO), manta ray foraging optimizer (MRFO), and bat algorithm (BA). It has been observed that the proposed methodology based on IFLA in different approaches has obtained lower power loss and more desirable voltage profile and reliability than its counterparts. Simulation reports demonstrate that by considering BDC, the values of losses and voltage deviations are increased by 2.82% and 1.34%, respectively, and the reliability of network customers is weakened by 5.59% in comparison with a case in which this cost is neglected. Therefore, taking into account this parameter in the objective function can lead to the correct and real calculation of the improvement rate of each of the objectives, especially the improvement of the reliability level, as well as making the correct decisions of network planners based on these findings. Full article

Electric vehicles (EVs) in severe cold regions face the real demand for fast charging under low temperatures, but low-temperature environments with high C-rate fast charging can lead to severe lithium plating of the anode material, resulting in rapid degradation of the lithium-ion battery (LIB). In this paper, by constructing an electrode–thermal model coupling solid electrolyte interphase (SEI) growth and lithium plating, the competition among different factors of capacity degradation under various ambient temperatures and C-rates are systematically analyzed. In addition, the most important cause of rapid degradation of LIBs under low temperatures are investigated, which reveal the change pattern of lithium plating with temperature and C-rate. The threshold value and kinetic law of lithium plating are determined, and a method of lithium-free control under high C-rate is proposed. Finally, by studying the average aging rate of LIBs, the reasons for the abnormal attenuation of cycle life at lower C-rates are ascertained. Through the chromaticity diagram of the expected life of LIBs under various conditions, the optimal fast strategy is explored, and its practical application in EVs is also discussed. This study can provide a useful reference for the development of high-performance and high-safety battery management systems to achieve fine management. Full article

The question of how energy resources can be efficiently used is likewise of fundamental and technological interest. In this opinion, we give a brief overview on developments of harvesting solar energy across different length scales and address some strategies to tackle economic and ecological challenges, in particular with a view to sustainability and toward a circular economy. On the mesoscopic scale, the emergence of thermodynamic laws in open quantum systems is of central importance and how they can be employed for efficient quantum thermal machines and batteries. The broad tunability of band gaps in quantum dot systems makes them attractive for hybrid photovoltaic devices. Complementary, machine learning-aided band gap engineering and the high-throughput screening of novel materials assist with improving absorption characteristics. On the device scale, hybrid concepts of optical control via metasurfaces enable a multitude of functionalities such as a directed re-emission of embedded photoluminescent materials or field enhancement effects from nanostructures. Advanced techniques in computational nanophotonics concern a topology optimization of nanostructured layers together with multiobjective optimization toward specific light management tasks. On the industrial level, modern manufacturers explore 3D printing and flexible solar cell platforms obtained from roll-to-roll technologies. The remote control of solar parks through applications via the Internet of Things opens up new strategies to expand to difficult terrain where human interaction is only required to a limited extent. Full article

Poland is dynamically changing its energy mix. As a result of this process, solar energy is increasing its share in energy production. The development of the solar energy market is determined by numerous factors. This paper aims to develop a roadmap for further development of the photovoltaic (PV) energy market in Poland. The scope of the research covers five areas of PV technology and market development in Poland: (i) technology; (ii) power grids; (iii) law; (iv) economic conditions; and (v) social conditions. With the use of a Technology Roadmapping Methodology (TRM), for each of the determined areas, several factors were analyzed, and their development paths were described. In addition, the article focuses on technological challenges (regarding PV cells, modules, components, power conversion and monitoring and management system, optimizers, batteries, and other energy storage systems), grid efficiency, recycling, production costs, subsidies, public awareness and education, and the energy exclusion problem. The main result of the research is the roadmap of the photovoltaic solar energy technology and market development in Poland. Further development of the PV market and technology requires parallel progress in all the identified areas. This study offers implications for policymakers, investors, managers, and technology and infrastructure developers regarding their involvement in photovoltaic market. Full article

The performance and efficiency of green energy sources in electric vehicles (EVs) are significantly affected by operation temperatures. To maintain the optimal temperatures of a hybrid energy system (HES), an innovative hybrid thermal management system (IHTMS) was designed. The IHTMS contains a coolant pump, a heat exchanger, a proportional valve for hybrid flow rates, five coolant pipes, and three electromagnetic valves to form two mode-switch coolant loops. A Matlab/Simulink-based simulator of the IHTMS was constructed by formulating a set of first-ordered dynamics of temperatures of coolant pipes and energy bodies using the theories of Newton’s law of cooling and the lumped-parameter technique. Parameters were majorly derived by measured performance maps and data from the experimental platform of the IHTMS. To properly manage the optimal temperatures, four control modes were designed for inner-loop form and outer-loop form. For the experimental platform to verify the simulator, two power supplies generated the waste heat of dual energy sources calculated by the driving cycle and vehicle dynamics. Simulation results show that the temperatures were controlled at their optimal ranges by proper mode/loop switch. With the inner-loop mechanism, the rise time of optimal temperature decreased 27.4%. The average simulation-experiment temperature error of the battery was $0.898$ °C; the average simulation-experiment temperature error of the PEMFC was $4.839$ °C. The IHTMS will be integrated to a real HES in the future. Full article