An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Driving (original) (raw)

Applied Sciences

Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people’s health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption ...

Development of a Novel Driver Model Offering Human like Longitudinal Vehicle Control in Order to Simulate Emission in Real Driving Conditions

2017

Toyota would like to simulate emissions in real-world conditions and support future engine development newly regulated by Real Driving Emission from 2017. A realistic driver model is necessary to simulate representative vehicle emissions. This paper presents a new driver model trained using real-world data including GPS localization and recorded engine ECU parameters. From a geolocalisation webservice, the proposed approach extracts the road attributes that influence human driving behaviour such as traffic signs, road cross, etc. The novel BiMap innovative algorithm, is then used to learn and map the driver behaviour with respect to the road properties while a regression tree algorithm is used to learn a realistic gear selection model. Experimental tests, executed within Carmaker™ vehicle simulation platform, show that the resulting model can drive along arbitrary real-world routes, generated using a map service. Moreover, it exhibits a human-like driving behaviour while being robus...

Evaluation of Eco-driving using Smart Mobile Devices

The methods of measuring driving behaviour and the quality of drive in road transport are important factors in data acquisition and subsequent analysis of driving. The prevalence of smart terminal devices and cost effectiveness of On-Board Diagnostics (OBD) sensor devices provide great potential and the availability of the aforementioned technologies. This study shows the possibility of using information and communication technologies (ICT) and sensor devices for measuring the effectiveness of eco-driving. The ease of implementation of ICT elements, the accuracy of collected data and their storage for later data analysis offer a number of possibilities to use. This study shows the technical solution of the system and analysis of collected data on actual driving samples. By comparing normal and eco-driving modes, the advantage of using eco-driving modes is demonstrated through reduced fuel consumption and CO2 emissions amounting to almost 23% and 31%, respectively.

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