Опубликована: Сен. 18, 2024
Язык: Английский
IET Renewable Power Generation, Год журнала: 2025, Номер 19(1)
Опубликована: Янв. 1, 2025
Abstract Renewable energy sources (RES) are vital for addressing fossil fuel challenges and promoting environmental sustainability by reducing air pollution. Hybrid RES (HRES) in microgrids (MGs) enhance efficiency reliability but face issues like management, load demand, efficiency. Existing research on HRES MGs often lacks efficiency, reliability, accuracy. This model proposes a solution using ant lion colony optimization with particle swarm (ALCO‐PSO) Maximum Power Point Tracking (MPPT) to improve power The (ACO) algorithm offers higher better global search, suffers from limitations such as computational complexity premature convergence. (LOA) addresses these issues, enhancing the algorithm's robustness. However, ALCO faces limited scalability search ability, which overcome integrating (PSO). Additionally, direct current (DC) fault detection is enhanced an artificial neural network (ANN) solar data. model's performance evaluated power, voltage, quality metrics, achieving 99.56% accuracy, faster convergence (0.11 s), oscillation around 4.25 W, tracking time of 0.2 s, interruptible 0.009%, cost (COE) 0.0413%, penalty 0.94 $/kWh.
Язык: Английский
Процитировано
0
Solar Energy, Год журнала: 2025, Номер 295, С. 113513 - 113513
Опубликована: Апрель 22, 2025
Язык: Английский
Процитировано
0
Energy, Год журнала: 2025, Номер unknown, С. 136463 - 136463
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0
Elsevier eBooks, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Journal of Energy Storage, Год журнала: 2024, Номер 97, С. 112565 - 112565
Опубликована: Июль 4, 2024
Язык: Английский
Процитировано
2
Algorithms, Год журнала: 2024, Номер 17(7), С. 319 - 319
Опубликована: Июль 21, 2024
Hybrid energy storage systems significantly impact the renewable sector due to their role in enhancing grid stability and managing its variability. However, implementing these requires advanced control strategies ensure correct operation. This paper presents an algorithm for designing power stages of a hybrid system formed by battery, supercapacitor, bidirectional Zeta converter. The stage involves adaptive sliding-mode controller co-designed with circuit parameters. design ensures battery protection against high-frequency transients that reduce lifespan, provides compatibility low-cost microcontrollers. Moreover, continuous output current converter does not introduce harmonics microgrid, or load. proposed solution is validated through application example using PSIM electrical simulation software (version 2024.0), demonstrating superior performance comparison classical cascade PI structure.
Язык: Английский
Процитировано
2
MATEC Web of Conferences, Год журнала: 2024, Номер 392, С. 01168 - 01168
Опубликована: Янв. 1, 2024
This study examines the improvement of electric vehicle (EV) economy by using Model Predictive Control (MPC) in power electronics, with goal optimizing system performance. Experimental assessments done on different battery parameters have identified a spectrum capacities, ranging from 55 kWh to 75 kWh, and voltages, 380V 450V, that impact total energy storage production capabilities. The efficiency percentages recorded systems ranged 90% 95%, suggesting differences losses throughout operations charging discharging. Furthermore, examinations electronics control configurations highlighted significance PWM frequencies (varying 8 kHz 12 kHz) modulation indices (0.75 0.85) conversion. results indicated rates 94% 97%, emphasizing efficacy MPC-based techniques improving flow. assessment performance demonstrated driving ranges 140 km 180 km, consumption 50 60 kWh. metrics 2.5 km/kWh 3.0 km/kWh, were directly affected properties improvements electronics. Moreover, there was little change link between temperature variations (ambient 23°C 29°C 32°C 40°C) efficiency. highlights system's sensitivity external variables. In summary, this relationship characteristics, control, environmental conditions determining vehicles (EVs). emphasize importance customized setups based model predictive use increasing distance cars can travel. These findings provide valuable knowledge for development sustainable transportation solutions industry.
Язык: Английский
Процитировано
1
Energies, Год журнала: 2024, Номер 17(18), С. 4721 - 4721
Опубликована: Сен. 22, 2024
This article studies the transformation and assembly process of Volkswagen (VW) Crafter from conventional to hybrid vehicle department vehicles engineering, University Debrecen, uses a computer-aided simulation (CAS) design based on real measurement data (hardware-in-the-loop, HIL method) obtained an online CAN bus platform using MATLAB/Simulink/Simscape LabVIEW software. The powered by 6-speed manual transmission 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine transformed electrified powertrain are designed compare performance. A novel methodology is introduced Netcan plus 110 devices for analysis vehicle’s version. acquired raw analyzed decoded with help database (DBC) file into physical values. classical proportional integral derivative (PID) controller utilized in system manage consumption CO2 emissions. However, intricate nonlinearities other external environments could make its performance unsatisfactory. study develops energy management strategies (EMSs) basis enhanced derivative-based genetic algorithm (GA-PID), compares integral-based particle swarm optimization (PSO-PI) fractional order (FOPID) controllers, regulating speed, allocating optimal torque speed motor reducing fuel time absolute error (ITAE) proposed as fitness function optimization. GA-PID demonstrates superior performance, achieving efficiency 90%, extending battery pack range 128.75 km 185.3281 emissions 74.79 gCO2/km. It outperforms PSO-PI FOPID consuming less higher efficiency.
Язык: Английский
Процитировано
1
Опубликована: Май 3, 2024
Язык: Английский
Процитировано
0
Опубликована: Июнь 21, 2024
Язык: Английский
Процитировано
0