Management System of Smart Electric Vehicles Using Software Engineering Model

Croock, Muayad Sadik; Mahmood, Sahar Salman

doi:10.32985/ijeces.13.5.5

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In the end, this results in better energy distribution throughout the network and better resource utilization. Other studies were related to our research that can be used for more information [59][60][61][62][63].…”

Section: Energy Optimization-based Clustering Algorithmsmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

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In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.

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Section: Energy Optimization-based Clustering Algorithmsmentioning

confidence: 99%

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Ahmad¹,

Al-Nayar²,

Mahmood³

2022

MMEP

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“…The exponential growth of energy demand around the world has led to a massive increase in the emission of greenhouse gases, thereby sparking the interest of the global industrial and academic sectors in finding alternative energy sources [1]. The ecological and economical motivation for improving the transportation sector has led to the adoption of renewable energy-based electric vehicles, which are projected to lead the automobile market in the upcoming years [2,3].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks Based Power Management for a Battery/Supercapacitor and Integrated Photovoltaic Hybrid Storage System for Electric Vehicles

Bourenane¹,

Berkani²,

Negadi³

et al. 2023

JESA

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Integrating solar energy in electric vehicles (EV) is expected to play a dominant role in the decarbonization of the transportation sector as well as reducing the charging costs. These integrated photovoltaic automobiles are particularly adapted for urban driving that is to say the driving range is limited. On that account, the use of a feasible energy storage system is necessary to boost the driving mileage. This paper uses a hybrid energy system, containing a battery as the main storage device and a supercapacitor (SC) as a backup. This combination was suggested in order to negate the former's deficiencies. The use of a hybrid energy source leads to the necessity of introducing a robust power management strategy to guarantee the optimal power flow in electric vehicle components. An artificial neural network is then trained with model calculation for the power management approach for the traction chain. The results of the simulation indicate that the proposed system is efficient in improving the energy management of the vehicle. Moreover, the system's simplicity could potentially make it easier to implement in real-time using a DSP or a DSPACE platform.

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Management System of Smart Electric Vehicles Using Software Engineering Model

Cited by 2 publications

References 16 publications

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Investigation of Energy Efficient Clustering Algorithms in WSNs: A Review

Artificial Neural Networks Based Power Management for a Battery/Supercapacitor and Integrated Photovoltaic Hybrid Storage System for Electric Vehicles

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