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An empirical study on real-time data analytics for connected cars: Sensor-based applications for smart cars

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  • Jonghyuk Kim
  • Hyunwoo Hwangbo
  • Soyean Kim

Abstract

Connected cars, which are vehicles connected to wireless networks through the convergence of automotive and information technologies, have become an important topic of academic and industrial research on automobiles. In this research, we conducted a field experiment to understand vehicle maintenance mechanisms of a connected car platform. Specifically, we investigated the feasibility of prognostics and health management under different driving circumstances, with varying vehicle models, vehicle conditions, drivers’ propensity for speeding, and road conditions. We collected sensor data through a two-stage model of vehicle communication using an on-board diagnostics scanner and data transmission using wireless communication. We found that device defects can be predicted based on driving situations such as the driving mode, mechanical characteristics, and a driver’s speeding propensity.

Suggested Citation

  • Jonghyuk Kim & Hyunwoo Hwangbo & Soyean Kim, 2018. "An empirical study on real-time data analytics for connected cars: Sensor-based applications for smart cars," International Journal of Distributed Sensor Networks, , vol. 14(1), pages 15501477187, January.
  • Handle: RePEc:sae:intdis:v:14:y:2018:i:1:p:1550147718755290
    DOI: 10.1177/1550147718755290
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    References listed on IDEAS

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    1. Hu, Chao & Youn, Byeng D. & Chung, Jaesik, 2012. "A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation," Applied Energy, Elsevier, vol. 92(C), pages 694-704.
    2. Kwok L. Tsui & Nan Chen & Qiang Zhou & Yizhen Hai & Wenbin Wang, 2015. "Prognostics and Health Management: A Review on Data Driven Approaches," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-17, May.
    3. Millo, Federico & Giacominetto, Paolo Ferrero & Bernardi, Marco Gianoglio, 2012. "Analysis of different exhaust gas recirculation architectures for passenger car Diesel engines," Applied Energy, Elsevier, vol. 98(C), pages 79-91.
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