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Calibration of ECO2 Emission control and driving style improvement in Automobile using Big Data and Machine Learning
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| Author:
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MR.SHAHUL HAMEED CHETTALI, DR. C.M. VELU
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| Abstract:
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A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a better way, with the purpose of optimizing the energy consumption. In the context of electric vehicles, equipped with regenerative brakes, the driver’s braking style can make a significant difference. In this paper, we propose an approach which is based on the combination of big data and machine learning techniques, with the aim of enhancing the driver's braking style through visual elements (i.e) displayed in the vehicle dashboard, as a Human-Machine Interface, actuating eco-driving behaviours. We have designed and developed a system prototype, by exploiting big data coming from an electric vehicle and a machine learning algorithm. Then, we have conducted a set of tests, with simulated and real data, and here we discuss the results we have obtained that can open interesting discussions about the use of big data, together with machine learning, so as to improve drivers' awareness of eco-behaviors.
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| Keyword:
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Machine learning, Human-Machine Interface, Eco-driving behaviors’
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| EOI:
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-
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| DOI:
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https://doi.org/10.31838/ijpr/2020.SP1.389
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| Download:
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