Eco‐driving assistance systems encourage economical driving behaviour and support the driver in optimising his/her driving style to achieve fuel economy and consequently, emission reductions. Energy efficiency is also one of the most pertinent issues related to the autonomy of fully electric vehicles. This study introduces a novel methodology for energy efficient routing, based on the realisation of dependable energy consumption predictions for the various road segments constituting an actual or potential vehicle route, performed mainly by means of machine‐learning functionality. This proposed innovative methodology, the functional architecture implementing it, as well as demonstrative experimental results are presented in this study.
Machine‐learning methodology for energy efficient routing
IET Intelligent Transport Systems ; 8 , 3 ; 255-265
2014-05-01
11 pages
Article (Journal)
Electronic Resource
English
energy conservation , machine learning methodology , fuel economy , road segments , ecodriving assistance systems , electrical engineering computing , energy consumption predictions , emission reductions , economical driving behaviour , road traffic , traffic engineering computing , vehicle route , machine learning functionality , electric vehicles , learning (artificial intelligence) , energy efficient routing , road vehicles