A novel energy optimization approach for electrical vehicles in a smart city
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Abstract
Electric Vehicles (EVs) have emerged rapidly across the globe as a powerful eco-friendly initiative that if integrated well with an urban environment could be iconic for the host city’s commitment to sustainable mobility and be a key ingredient of the smart city concept. This paper examines ways that will help us to develop a better understanding of how EVs can achieve energy use optimization and be connected with a smart city. As a whole, the present study is based on an original idea that would be useful in informing policy-makers, automotive manufacturers and transport operators of how to improve and embrace better EV technologies in the context of smart cities. The proposed approach is based on vehicles’ and buildings’ communication to share some special information related to the vehicles’ status and to the road conditions. EVs can share their own information related to their energy consumption experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, this paper exposes a new approach to power management inside an electric vehicle based on two-way communication between vehicles and buildings. The principle of this method is established in two sections; the first one is related to vehicles’ classification and the second one is attached to the buildings’ recommendations, according to the car position. The classification problem is resolved using the support vector classification method. The recommendation phase is resolved using the artificial intelligence principle and a neural network was employed to give the best decision. The optimal decision will be calculated inside the building, according to its position and using the old vehicle’s data, and transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding building zone. Different possibilities and situations in this approach were discussed. The proposed power management methodology was tested and validated using Simulink/Matlab tool. Results related to the battery state of charge and to the consumed energy were compared at the end of this work, to show the efficiency of this approach.
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Data-driven smart urbanism is increasingly providing a plethora of applied innovative solutions to deal with the environmental pathologies pertaining to energy consumption, pollution, traffic congestion, mobility ineffectiveness, hazardous waste, unsustainable material, resource depletion, biodiversity loss, and climate change crisis.
“data-driven smart urbanism is increasingly providing a plethora of applied innovative solutions to deal with the environmental pathologies pertaining to energy consumption, pollution, traffic congestion, mobility ineffectiveness, hazardous waste, unsustainable material, resource depletion, biodiversity loss, and climate change crisis.”
From Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review (2023) · cites this paper as Aymen and Mahmoudi 2019
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Petersen, D., & Behr, A. (2024) · Research Square · doi:10.21203/rs.3.rs-4351903/v1 · Cited by 2 · see citing works
Velho, S. R. K., Barbalho, S. C. M., Vanderlinde, A. S. G., & Almeida, A. H. A. (2024) · Production · doi:10.1590/0103-6513.20240012 · Cited by 1 · see citing works
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Pudi, A., Raju, C. V. V. N., Varalatchoumy, M., Ramya, R., Premalatha, R., & Prasad, A. R. (2025) · Advances in civil and industrial engineering book series · doi:10.4018/979-8-3693-7663-8.ch010 · Cited by 1 · see citing works
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A Novel Energy Optimization Technique for Smart City
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- Authors
- Aymen, F., & Mahmoudi, C.
- Affiliations
- Laboratory of proceeding, energetic, environment and electric systems LR18E534, National school of engineering of Gabès, University of Gabès, Gabès 6072, Tunisia
- University of Gabès (TN)
- ORCID
- Aymen, Flah
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2019-03-10
- Volume
- 12
- Issue
- 5
- Pages
- 929
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2912013683
- Cited by
- 59
- References
- 54
- Citations by year
- 2025: 3 · 2024: 9 · 2023: 10 · 2022: 12 · 2021: 9 · 2020: 8 · 2019: 8
- FWCI
- 3.33
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- electric vehicles, energy optimization, smart cities, vehicle-to-vehicle communication, power management, battery state of charge, support vector machine classification, vehicle classification, sustainable transport
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