Optimizing Energy of Electric Vehicles in Smart Cities

Optimizing Energy of Electric Vehicles in Smart Cities

Brahim Lejdel (Univeristy of El Oued, El Oued, Algeria)
DOI: 10.4018/978-1-7998-3295-9.ch009
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Abstract

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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1. Introduction

The Smart City (SC) is a concept which is appeared to have made a city more alive and more liveable. Thus, SC is an urban area that is a means to enhance the life quality of the citizens. This new concept has gained increasing importance in the agendas of policy makers (Paolo et al. 2014). The policy maker should introduce Electric Vehicles in smart city to replace traditional vehicles to reduce air pollution, improve energy efficiency and avoid the congestion in road traffic. We will treat in this paper, two main questions. The one is what is the optimal method to manage, reduce and control the energy consumption of the battery? The second is what is the optimal strategy which permits, finding the charging station?

In this paper, we propose to use a multi-agent system which allows to distribute the different tasks between the agents when each agent can perform Genetic Algorithms to optimize energy consumption of electric vehicle battery in real-time, thus adapting rapidly to battery consumption. Thus, all agents can cooperate and negotiate to find the best solution which can regulate the energy consumption battery in electric vehicle. Then, we develop a GIS system which allows knowing the position of electric vehicle and all data associated with it as electric vehicle-id, energy consumption, charging station, etc.

After a deep study of the subject, we have found that we have four factors that can affect the energy consumption of battery in electric vehicle, as the time of system peak, energy costs, peak energy demand and the quantity of energy per hours, etc. Also, we find two mainly energy equipment such as HVAC system and lighting systems, we can use HVAC-L system to designate these two systems.

This paper is organized as the following. Firstly, we will present a state of the art review for optimization of energy consumption of battery in electric vehicle and a state of the art review for the localization of the optimal charging station for electric vehicle. Then, we describe our proposed approach which is based on two approaches, the Multi Agent System and Genetic Agent (MAS-GA). Finally, we add a conclusion.

2. Energy Management of Battery Electric Vehicles

2.1. Overview

The electric vehicles are quickly merged in the smart city, but many problems are appearing as the high consumption cost, the limited capacities, and the long recharge time of their batteries. To increase of these batteries, multi-battery systems that combine a standard battery with super-capacitors are currently one of the most promising ways to increase battery life’s and reduce consumption costs. However, their performance essentially depends on how they are designed. In this paper, we focus on a complementary aspect of the problem that is optimizing the energy consumption of batteries in electric vehicles.

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