Intelligent Energy Management for Full-Active Hybrid Energy Storage Systems in Electric Vehicles Using Teaching–Learning-Based Optimization in Fuzzy Logic Algorithms

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 67665 - 67680

Published: Jan. 1, 2024

Electric vehicles (EVs) are a compelling alternative for mitigating CO ₂ -equivalent emissions. In the context of EVs, architecture and operational efficiency hybrid energy storage system (HESS) pivotal. The present study focuses on HESS model based parallel full-active configuration that integrates lithium-ion (Li-ion) battery with an ultracapacitor facilitated by two direct current-to-direct current converters. management strategy governing emerges as critical element in overall performance EVs. Conventionally, fuzzy control strategies have been extensively utilised at supervisory high-control levels; these largely dependent expert experience. This paper introduces innovative employs teaching–learning-based optimisation (TLBO) domain intelligent mechanism aims to optimise various metrics – such consumption, lithium output current, peak power. fine-tuning parameters constituting rule base membership functions. Simulation results substantiate adaptability TLBO-based efficiently allocating power across driving conditions. Comparative analyses conducted respect power-sharing capabilities proposed TLBO-fuzzy (TLBO-F), particle swarm optimisation-fuzzy (PSO-F), non-optimisation-fuzzy (NO-F) under distinct conditions: urban dynamometer schedule European extra-urban cycle. main objective this research is extend lifetime minimising both consumption. way, not only prolong lifespan Li-ion batteries but also mitigate associated costs charging. Ultimately, method enhances sharing during span 1400 seconds. It confirms ratio power, R.P_b , decreased (TLBO-F) 6.4% compared PSO-F 35% relative NO-F algorithm. battery's state charge increased 14% implementing TLBO-F 30% diminishes charging discharging rate cycle while extending adheres ultracapacitor's state-of-charge constraints.

Language: Английский

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Enhancement of a Hybrid Electric Shipboard Microgrid’s Frequency Stability With Triangulation Topology Aggregation Optimizer-Based 3DOF-PID-TI Controller

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 66625 - 66645

Published: Jan. 1, 2024

This work addresses the important issue of frequency stability in shipboard microgrids (SMGs), which are confronted with difficulties because alternating power insertion from green sources and low system inertia. In order to sustain a consistent (despite uncertainties), study precisely calibrated regulator is necessary. Therefore, this article proposed new combined 3 degree freedom proportional integral derivative (3DOF-PID) tilted-integral (TI) regulators for achieving enhanced performance, robustness, enhance performance grid. The superior suggested 3DOF-PID-TI has been assured by comparing it three distinct controller architectures (FOPID, PDPID2 2DOF-PID) multi-energy SMG system. investigation also took into account temporal delays brought on communication links between sensor regulator. triangulation topology aggregation optimizer (TTAO), relatively meta-heuristic technique that hasn't employed load control (LFC) issues until recently, was adjust controllers' parameters. TTAO's outcomes were thoroughly contrasted those other optimization algorithms (i.e., Chimp, Whale, Gradient-Based Optimization Algorithms) so as evaluate efficacy optimizer. findings showed controller, whose parameters tuned TTAO, outperformed its competitors terms overshoot (15.87mHz), undershoot (-29.04mHz), settling time (2.49sec) index ITAE(0.0171)). Additionally, controller's robustness assessed range operating situations ex. (Random Multi-Step Variation Load, Real Data Stochastic Power Fluctuations, Energy Storage System Impact sensitivity analysis). acquired data amply proved when crucial experience substantial variation, gains set under normal circumstances do not need be re-tuned.

Language: Английский

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A review of control strategies for optimized microgrid operations

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: 18(14), P. 2785 - 2818

Published: July 19, 2024

Abstract Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities remote areas. Integrating diverse renewable energy sources into the grid has further emphasized need effective management sophisticated control strategies. This review explores crucial role of strategies optimizing MG operations ensuring efficient utilization distributed resources, storage systems, networks, loads. To maximize source overall system performance, various are implemented, including demand response, management, data generation‐load management. Employing artificial intelligence (AI) optimization techniques enhances these strategies, leading to improved performance MGs. The delves their architectures, highlights significant contributions AI emerging technologies advancing

Language: Английский

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Enhancing Grid-Connected DFIG’s LVRT Capability Using Dandelion Optimizer Based the Hybrid Fractional-Order PI and PI Controlled STATCOM

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 120181 - 120197

Published: Jan. 1, 2024

This study aims to improve the low-voltage ride-through (LVRT) capabilities of wind power plants (WPPs) during various types grid faults, such as line-to-ground (L-G), double (LL-G), line-to-line (L-L), and three-phase-to-ground (LLL-G) faults. The paper proposes an enhanced cascaded Fractional-Order Proportional-Integral (FOPI-PI)-controlled static synchronous compensator (STATCOM). Dandelion Optimizer (DO), a novel optimization approach, is employed fine-tune STATCOM being studied. DO optimizer was selected for its exceptional performance robustness. efficacy algorithm assessed in comparison three well-established methods Water Cycle Approach (WCA), Particle Swarm (PSO), hybrid technique that combines WCA PSO. analysis focuses on two test systems: 9MW plant connected infinite bus system over 30 km transmission line, IEEE 39 system. results are displayed using MATLAB (R2018b) time-domain simulation. suggested monitors multiple LVRT metrics, including WPP active reactive power, voltage, speed well DC link capacitor voltage. Upon conducting comparative analysis, it found both proposed (FOPI-PI) conventional PI controller-based outperform other methods, algorithm, PSO optimizer, even combination WCA/PSO algorithm. findings indicate FOPI-PI controller demonstrates superior compared controller. When has fault, recommended restricts voltage drop specific percentages. Specifically, limited 3% L-G 5% L-L 6% LL-G 10% LLL-G Finally, much improved by adopting optimized all controllers scenarios.

Language: Английский

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A Novel State Machine-Fractional Order PID Control Strategy for Energy Management Framework to Optimize the Consumption from Fuel Cell in DC Microgrid

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 136160 - 136182

Published: Jan. 1, 2024

Language: Английский

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Accurate Key Parameters Estimation of PEM fuel cells using Self-Adaptive Bonobo Optimizer

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 192, P. 108894 - 108894

Published: Oct. 11, 2024

Language: Английский

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Unprecedented Optimum Combination of 2dof+Tid and Fopd Controllers for Frequency Stability of Electric Vehicle-Integrated Hybrid Power Grids

Published: Jan. 1, 2024

Language: Английский

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