Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles.

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ID: 55455
2019
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Abstract
The connected powertrain control, which uses intelligent transportation system information, has been widely researched to improve driver convenience and energy efficiency. The vehicle state prediction on decelerating driving conditions can be applied to automatic regenerative braking in electric vehicles. However, drivers can feel a sense of heterogeneity when regenerative control is performed based on prediction results from a general prediction model. As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with an automatic regenerative braking control. Thus, in this paper, we proposed a deceleration prediction model based on the parametric mathematical equation and explicit model parameters. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics. Furthermore, the explicit model parameters are updated according to individual driver characteristics using the driver's braking data during real driving situations. The proposed algorithm was integrated and validated on a real-time embedded system, and then, it was applied to the model-based regenerative control algorithm as a case study.
Reference Key
min2019vehiclesensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Min, Kyunghan;Sim, Gyubin;Ahn, Seongju;Sunwoo, Myoungho;Jo, Kichun;
Journal sensors
Year 2019
DOI
E4171
URL
Keywords
driver behavior modeling driver characteristics online learning electric vehicle control vehicle speed prediction

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