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Integrated energy, environmental, and economic optimization for energy management systems in PHEVs considering traffic conditions

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Publicado en:Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 25927
Autor principal: Montazeri-Gh, Morteza
Otros Autores: Alimohammadi, Ehsan
Publicado:
Nature Publishing Group
Materias:
Fuel cells
Hydrocarbons
Deep learning
Emissions control
Traffic
Degradation
Optimization
Hydrogen
Hybrid vehicles
Energy storage
Energy consumption
Pollution control
Environmental impact
Economics
Machine learning
Emissions
Dynamic programming
Fossil fuels
Electric vehicles
Neural networks
Objective function
Energy management
Design
Energy efficiency
Algorithms
Fuzzy logic
Cost control
Operating costs
Economic
Environmental
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2045-2322
DOI:10.1038/s41598-025-10197-6
Fuente:Science Database