Optimizing Energy of Electric Vehicles in Smart Cities

Advances in Data Mining and Database Management - Opportunities and Challenges for Blockchain Technology in Autonomous Vehicles ◽

10.4018/978-1-7998-3295-9.ch009 ◽

2021 ◽

pp. 147-164

Author(s):

Brahim Lejdel

Keyword(s):

Genetic Algorithm ◽

Electric Vehicle ◽

Energy Management ◽

Electric Vehicles ◽

Smart Cities ◽

Hybrid Approach ◽

Multi Agent System ◽

Optimal Method ◽

Multi Agent ◽

Near Future

In the near future, the electric vehicle (EV) will be the most used in the word. Thus, the energy management of its battery is the most attractive subject specialty in the last decade. Thus, if a driver uses an electric vehicle, he wants to find an optimal method that can optimize the energy battery of its electric vehicle. In this chapter, the authors propose a new concept of the smart electric vehicle (SEV) that can manage, control, and optimize the energy of its battery, in condition to satisfy the drivers' and passengers' comfort. Thus, they use a hybrid approach based on the multi-agent system and the genetic algorithm (MAS-GA).

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A Hybrid Approach for Shape Retrieval Using Genetic Algorithms and Approximate Distance

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2018010105 ◽

2018 ◽

Vol 8 (1) ◽

pp. 75-91

Author(s):

Saliha Mezzoudj ◽

Kamal Eddine Melkemi

Keyword(s):

Genetic Algorithm ◽

Hybrid Approach ◽

Multi Agent System ◽

Shape Context ◽

Similar Shape ◽

Complex Shapes ◽

Ga Algorithm ◽

Approximate Distance ◽

Multi Agent ◽

Part Structure

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.

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A Multi-Agent System for Smart Energy Management Devoted to Vehicle Applications: Realistic Dynamic Hybrid Electric System Using Hydrogen as a Fuel

Energies ◽

10.3390/en12030474 ◽

2019 ◽

Vol 12 (3) ◽

pp. 474 ◽

Cited By ~ 5

Author(s):

Benslama Sami ◽

Nasri Sihem ◽

Salsabil Gherairi ◽

Cherif Adnane

Keyword(s):

Fuel Cell ◽

Energy Management ◽

Electric Vehicles ◽

Energy Demand ◽

Hybrid Electric Vehicles ◽

Multi Agent System ◽

Electric System ◽

Multi Agent ◽

Zero Emission ◽

Hybrid Electric

Real-time simulation test beds for new zero-emission hybrid electric vehicles are considered as an attractive challenge for future transport applications that are fully recommended in the laboratory environment. In contrast, new zero-emission hybrid electric vehicles have a more complicated charging procedure. For this reason, an efficient simulation tools development for hydrogen consumption control becomes critical. In this vein, a New Zero Emission Hybrid Electric Vehicle Simulation (NZE-HEVSim) tool for the dynamic Fuel Cell Hybrid-Electric System is proposed to smartly control multisource activities. The designed system consists of a proton-exchange membrane fuel cell used to provide the required energy demand and a Supercapacitor system for energy recovery assistance in load peak or in fast transient. To regulate the supplied power, an efficient Real-Time Embedded Intelligent Energy Management (RT-EM-IEM) is implemented and tested through various constraints. The proposed intelligent energy management system aims to act quickly against sudden circumstances related to hydrogen depletion in the basis required fuel consumption prediction using multi-agent system (MAS). The proposed MAS strategy aims to define the proper operating agent according to energy demand and supply. The obtained results prove that the designed system meets the objectives set for RT-EM-IEM by referring to an experimental velocity database.

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Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽

10.3390/electricity2010006 ◽

2021 ◽

Vol 2 (1) ◽

pp. 91-109

Author(s):

Julian Wruk ◽

Kevin Cibis ◽

Matthias Resch ◽

Hanne Sæle ◽

Markus Zdrallek

Keyword(s):

Genetic Algorithm ◽

Electric Vehicle ◽

Electric Vehicles ◽

Low Voltage ◽

Network Planning ◽

Voltage Regulation ◽

Electric Vehicle Charging ◽

Vehicle Charging ◽

The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

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A GENETIC APPROACH FOR LONG TERM VIRTUAL ORGANIZATION DISTRIBUTION

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213011000152 ◽

2011 ◽

Vol 20 (02) ◽

pp. 271-295 ◽

Cited By ~ 2

Author(s):

VÍCTOR SÁNCHEZ-ANGUIX ◽

SOLEDAD VALERO ◽

ANA GARCÍA-FORNES

Keyword(s):

Genetic Algorithm ◽

Virtual Organization ◽

Genetic Material ◽

Multi Agent System ◽

Crossover Operator ◽

Agent Based ◽

Multi Agent ◽

Global Goal ◽

Computational Resources

An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios.

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