Optimal energy consumption algorithm based on speed reference generation for urban electric vehiclesReportar como inadecuado

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1 RITS - Robotics & Intelligent Transportation Systems Inria Paris-Rocquencourt

Abstract : Power consumption and battery life are two of the key aspect when it comes to improve electric transportation systems autonomy. This paper describes the design, development and implementation of a speed profile generation based on the calculation of the optimal energy consumption for electric Cybercar vehicles for each of the stretches that are covering. The proposed system considers a commuter daily route that is already known. It divides the pre-defined route into segments according to the road slope and stretch length, generating the proper speed reference. The developed system was tested on an experimental electric platform at Inria-s facilities, showing a significant improvement in terms of energy consumption for a pre-defined route. I. INTRODUCTION Electric vehicles EVs are getting more and more attention because their contribution toward eco-friendly cities. Non-emission vehicles will definitely help to improve cit-izen-s daily life, reducing dramatically both noise and pollution 1. For this reason, some governments–i.e. Canada 2–have carried out studies to figure out the emissions according to the kind of vehicle. Results showed that light duty gas vehicles are the biggest producer of CO 2 and the second greatest producer of N 2 O and methane, which makes them the main contributors towards gas emissions–because of their higher market penetration. In United States 3, the transportation sector consumes three-quarters of the total burned petroleum, which makes it the second largest carbon emitter in the country. Because of this, electric vehicles are an adequate solution to reduce greenhouse gas GHG emissions and pollution produced by road transport systems. On the other hand, EVs present serious limitations for their market deployment. Specifically, there are two unsolved challenges: 1 battery charge 4: how long it takes to fully load when running off at driving; and 2 battery life 5: how much energy will last when driving. Recent years have shown a lot of development on battery technologies– i.e. the mixed structures using supercapacitors 6 or fuel cells 7. However, EVs autonomy remains considerably lower than gas-powered vehicles. This paper deals with this second challenge, proposing an intelligent modular algorithm that provides better performance in order to improve EVs autonomy. Specifically related with the solution to the EVs autonomy problem, there are two main ways for improving battery

Autor: Carlos Flores - Vicente Milanés - Joshué Pérez - David González - Fawzi Nashashibi -

Fuente: https://hal.archives-ouvertes.fr/


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