Integrated energy, environmental, and economic optimization for energy management systems in PHEVs considering traffic conditions
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| Gepubliceerd in: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 25927 |
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| Online toegang: | Citation/Abstract Full Text Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3231092013 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-10197-6 |2 doi | |
| 035 | |a 3231092013 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Montazeri-Gh, Morteza |u Iran University of Science and Technology, Department of Mechanical Engineering, System Simulation and Control Laboratory, Tehran, Iran (GRID:grid.411748.f) (ISNI:0000 0001 0387 0587) | |
| 245 | 1 | |a Integrated energy, environmental, and economic optimization for energy management systems in PHEVs considering traffic conditions | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The growing dependence on fossil fuels has depleted their reserves and significantly contributed to environmental pollution. In recent years, plug-in hybrid electric vehicles (PHEVs) have garnered attention for their ability to reduce fuel consumption and emissions while offering an increased driving range, mainly due to their large battery packs. In these vehicles, critical concerns include the reduction of fuel consumption, control of pollution, and the costs associated with battery degradation. This study introduces a multi-objective optimization approach for the energy management strategy (EMS), focusing on minimizing energy consumption, environmental impact, and the economic implications of battery aging (E3). To achieve this, a plug-in hybrid electric vehicle is modeled based on the Samand vehicle using experimental data. A genetic algorithm is then employed to perform sizing optimization of the components. Additionally, a fuzzy logic controller is developed for the EMS. Ultimately, the multi-objective optimization of the energy management system is conducted across three scenarios: one objective function, two objective functions, and three objective functions evaluated over five driving cycles. The results demonstrate that the optimization approach utilizing three objective functions outperforms other scenarios. Focusing on a single objective function leads to a 13.5% reduction in average battery degradation, though fuel consumption increases by 3%. With two objective functions, battery degradation decreases by 10%, while fuel consumption and emissions rise by 1.9% and 5.7%, respectively. Considering three objective functions leads to average reductions of 3.3% in emissions and 4.4% in battery degradation, with approximately a 0.02% rise in fuel consumption. | |
| 653 | |a Fuel cells | ||
| 653 | |a Hydrocarbons | ||
| 653 | |a Deep learning | ||
| 653 | |a Emissions control | ||
| 653 | |a Traffic | ||
| 653 | |a Degradation | ||
| 653 | |a Optimization | ||
| 653 | |a Hydrogen | ||
| 653 | |a Hybrid vehicles | ||
| 653 | |a Energy storage | ||
| 653 | |a Energy consumption | ||
| 653 | |a Pollution control | ||
| 653 | |a Environmental impact | ||
| 653 | |a Economics | ||
| 653 | |a Machine learning | ||
| 653 | |a Emissions | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Fossil fuels | ||
| 653 | |a Electric vehicles | ||
| 653 | |a Neural networks | ||
| 653 | |a Objective function | ||
| 653 | |a Energy management | ||
| 653 | |a Design | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Algorithms | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Cost control | ||
| 653 | |a Operating costs | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Alimohammadi, Ehsan |u Iran University of Science and Technology, Department of Mechanical Engineering, System Simulation and Control Laboratory, Tehran, Iran (GRID:grid.411748.f) (ISNI:0000 0001 0387 0587) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 25927 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231092013/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3231092013/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231092013/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |