Preprint Article Version 1 This version is not peer-reviewed

A novel energy optimization approach for electrical vehicles in a smart city

Version 1 : Received: 20 January 2019 / Approved: 22 January 2019 / Online: 22 January 2019 (11:18:42 CET)

How to cite: AYMEN, F.; mahmoudi, C.; sbita, L. A novel energy optimization approach for electrical vehicles in a smart city. Preprints 2019, 2019010214 (doi: 10.20944/preprints201901.0214.v1). AYMEN, F.; mahmoudi, C.; sbita, L. A novel energy optimization approach for electrical vehicles in a smart city. Preprints 2019, 2019010214 (doi: 10.20944/preprints201901.0214.v1).

Abstract

Smart cities and smart technologies have been incorporated into several axes to increase the comfort of life. The connected building's concept was introduced for this reason. However, it was utilized in power management for better organizing, greater buildings management, and monetary savings. Cars technologies and the number of vehicles are also involved; Nowadays, each house has at least one car. Technological evolution helped to make those cars intelligent and connected. In the latest versions, the majority of those cars were equipped with several sensors, several communication protocols and a principal electrical control unit (ECU), especially for the electric vehicle model. This type of architecture was an essential element in a smart city, thus, it helps to manage power and decide when a vehicle needs to be charged. Based on the smart city concept and using possible network communication between buildings and vehicles, EVs can share their own information related to the powerful 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, we propose in this paper a new approach for power management inside an electric vehicle based on bi-communication between vehicles and buildings. The proposed approach is founded on two essential parts; the first is related to vehicles’ classification and buildings’ recommendation according to different car positions. Two algorithms, related to the SVC and neural network was employed in this work for implementing the final process. Different possibilities and situations were discussed for this approach. The proposed method was tested and validated using Simulink/Matlab application. The state of charge of the used battery was compared at the end of this work, for two specified cases, for showing the contribution of this approach.

Subject Areas

Smart city; energy management; electric vehicle; classification; state of charge; intelligence.

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