Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

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dc.contributor.authorLv, Chen
dc.contributor.authorXing, Yang
dc.contributor.authorLu, Chao
dc.contributor.authorLiu, Yahui
dc.contributor.authorGuo, Hongyan
dc.contributor.authorGao, Hongbo
dc.contributor.authorCao, Dongpu
dc.date.accessioned2018-03-19T09:19:00Z
dc.date.available2018-03-19T09:19:00Z
dc.date.issued2018-02-21
dc.description.abstractThe recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian Mixture Model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on Artificial Neural Networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.en_UK
dc.identifier.citationChen Lv, Yang Xing, Chao Lu, et al., (2018) Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle. IEEE Transactions on Vehicular Technology, Volume 67, Issue 7, July 2018, pp. 5718-5729en_UK
dc.identifier.issn0018-9545
dc.identifier.urihttp://dx.doi.org/10.1109/TVT.2018.2808359
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13099
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectBraking Intensityen_UK
dc.subjectHybrid Learningen_UK
dc.subjectGaussian Mixture Modelen_UK
dc.subjectRandom Foresten_UK
dc.subjectArtificial Neural Networksen_UK
dc.subjectElectric Vehicleen_UK
dc.titleHybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicleen_UK
dc.typeArticleen_UK

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