Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108179 - 108179
Опубликована: Март 1, 2024
Язык: Английский
Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108179 - 108179
Опубликована: Март 1, 2024
Язык: Английский
Energy Strategy Reviews, Год журнала: 2024, Номер 54, С. 101478 - 101478
Опубликована: Июнь 29, 2024
This study investigates the techno-economic impacts analysis of renewable energy-based hybrid energy storage system integrated grid electric vehicles charging station (EVCS) in Malaysia. Focusing on three potential locations namely Pulau Pinang, Johor Bharu and Kuala Terengganu, research aims to address increasing electricity demand from expanding vehicle (EV) infrastructure while mitigating instability issues caused by sudden load surges, increased electrical losses, overload high voltage devices that leads power quality issues. Using HOMER Pro platform, models optimises an EVCS configuration-based incorporates sources (RES) such as photovoltaic (PV), wind turbines (WT), lithium-ion (Li-ion) batteries, hydrogen (H2) tank, fuel cell (FC) electrolysers considering various geographical meteorological conditions. The configuration offers a long-term solution, surpassing current batteries' capabilities providing stable supply for sustainable system. results demonstrated favourable outcomes, with total Net Present Cost (NPC) ranging $1.4 million $3.4 across all locations, Energy (COE) $0.03/kWh $0.16/kWh. These findings suggest optimization methodology is adaptable implementation diverse different innovation beneficial developing renewable-based infrastructures, which can support economic growth In addition, emphasizes necessity advanced control algorithms manage during peak suggests future field trials validate system's real-world performance. Additionally, optimized EVCS-based contributes reducing carbon dioxide (CO2) emissions, promoting cleaner environment eco-friendly ecosystem.
Язык: Английский
Процитировано
21Journal of Cleaner Production, Год журнала: 2023, Номер 421, С. 138471 - 138471
Опубликована: Авг. 17, 2023
Язык: Английский
Процитировано
25Applied Soft Computing, Год журнала: 2024, Номер 161, С. 111789 - 111789
Опубликована: Май 22, 2024
Язык: Английский
Процитировано
10Expert Systems with Applications, Год журнала: 2023, Номер 228, С. 120374 - 120374
Опубликована: Май 10, 2023
Язык: Английский
Процитировано
21Biomimetics, Год журнала: 2024, Номер 9(4), С. 242 - 242
Опубликована: Апрель 18, 2024
Due to the high pollution of transportation sector, nowadays role electric vehicles has been noticed more and by governments, organizations, environmentally friendly people. On other hand, problem vehicle routing (EVRP) widely studied in recent years. This paper deals with an extended version EVRP, which (EVs) deliver goods customers. The limited battery capacity EVs causes their operational domains be less than those gasoline vehicles. For this purpose, several charging stations are considered study for EVs. In addition, depending on domain, a full charge may not needed, reduces operation time. Therefore, partial recharging is also taken into account present research. formulated as multi-objective integer linear programming model, whose objective functions include economic, environmental, social aspects. Then, preemptive fuzzy goal method (PFGP) exploited exact solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired nature, including MOSA, MOGWO, MOPSO, NSGAII_TLBO, utilized large-sized results obtained from solving numerous test problems demonstrate that algorithm can provide efficient solutions terms quality non-dominated all performance was compared four indexes: time, MID, MOCV, HV. Moreover, statistical analysis performed investigate whether there significant difference between algorithms. indicate MOSA performs better time index. NSGA-II-TLBO outperforms HV indexes.
Язык: Английский
Процитировано
5Mathematics, Год журнала: 2025, Номер 13(1), С. 145 - 145
Опубликована: Янв. 2, 2025
Autonomous electric vehicle (AEV) services leverage advanced autonomous driving and technologies to provide innovative, driverless transportation solutions. The biggest challenge faced by AEVs is the limited number of charging stations long times. A critical maximizing passenger travel satisfaction while reducing AEV idle time. This involves coordinating transport tasks via leveraging information from stations, transport, data. There are four important contributions in this paper. Firstly, we introduce an integrated scheduling model that considers both tasks. Secondly, propose a multi-level differentiated threshold strategy, which dynamically adjusts based on battery levels availability competition among vehicles minimizing waiting Thirdly, develop rapid strategy optimize selection combining geographic deviation distance. Fourthly, design new evolutionary algorithm solve proposed model, buffer space introduced promote diversity within population. Finally, experimental results show compared existing state-of-the-art algorithms, shortens running time algorithms 6.72% reduces 6.53%, proves effectiveness efficiency algorithm.
Язык: Английский
Процитировано
0Energy Conversion and Management X, Год журнала: 2025, Номер unknown, С. 100867 - 100867
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0E-Commerce Letters, Год журнала: 2025, Номер 14(01), С. 2452 - 2462
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Advanced Engineering Informatics, Год журнала: 2025, Номер 65, С. 103133 - 103133
Опубликована: Фев. 3, 2025
Язык: Английский
Процитировано
0Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126875 - 126875
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
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