Title: A method for the prediction of future driving conditions and for the energy management optimisation of a hybrid electric vehicle

Authors: Teresa Donateo; Damiano Pacella; Domenico Laforgia

Addresses: Dipartimento d'Ingegneria dell'Innovazione, University of Salento, Complesso Ecotekne - edificio 'Corpo O', Via per Monteroni, 73100 Lecce, Italy ' Dipartimento d'Ingegneria dell'Innovazione, University of Salento, Complesso Ecotekne - edificio 'Corpo O',Via per Monteroni, 73100 Lecce, Italy ' Dipartimento d'Ingegneria dell'Innovazione, University of Salento, Complesso Ecotekne - edificio 'Corpo O', Via per Monteroni, 73100 Lecce, Italy

Abstract: Vehicular communications are expected to enable the development of Intelligent Cooperative Systems for solving crucial problems related to mobility: road safety, traffic management etc. Information and Communication Technologies could also play an important role in order to optimise the energy management of conventional, hybrid and electrical vehicles and, thus, to reduce their environment impact. In particular, vehicular communications could be used to predict driving conditions with the objective to determine future load power demand. An adaptive energy management strategy for series Hybrid Electric Vehicles (HEVs) based on genetic algorithm optimised maps and the Simulation of Urban Mobility (SUMO) predictor is presented here.

Keywords: hybrid electric vehicles; ecological vehicles; plug-in HEVs; energy management strategy; vehicular communications; traffic simulation; simulation based prediction; genetic algorithms; clustering algorithms; K-Means algorithm; vehicle design.

DOI: 10.1504/IJVD.2012.047385

International Journal of Vehicle Design, 2012 Vol.58 No.2/3/4, pp.111 - 133

Received: 01 Dec 2010
Accepted: 22 Feb 2011
Published online: 31 Dec 2014 *

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