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Multi-Level Deceleration Planning Based on Reinforcement Learning Algorithm for Autonomous Regenerative Braking of EV
Open AccessArticle

Automatic Longitudinal Regenerative Control of EVs Based on a Driver Characteristics-Oriented Deceleration Model

1
Department of Automotive Electronics and Controls, Hanyang University, Seoul 04763, Korea
2
Department of Automotive Engineering, Hanyang University, Seoul 04763, Korea
3
Research & Development Division, Hyundai Motor Company, Hwaseong 18280, Korea
*
Author to whom correspondence should be addressed.
World Electr. Veh. J. 2019, 10(4), 58; https://doi.org/10.3390/wevj10040058
Received: 31 July 2019 / Revised: 5 September 2019 / Accepted: 11 September 2019 / Published: 20 September 2019
(This article belongs to the Special Issue Autonomous Driving of EVs)
To preserve the fun of driving and enhance driving convenience, a smart regenerative braking system (SRS) is developed. The SRS provides automatic regeneration that is appropriate for the driving conditions, but the existing technology has a low level of acceptability and comfort. To solve this problem, this paper presents an automatic regenerative control system based on a deceleration model that reflects the driver’s characteristics. The deceleration model is designed as a parametric model that mimics the driver’s behavior. In addition, it consists of parameters that represent the driver’s characteristics. These parameters are updated online by a learning algorithm. The validation results of the vehicle testing show that the vehicle maintained a safe distance from the leading car while simulating a driver’s behavior. Of all the deceleration that occurred during the testing, 92% was conducted by the automatic regeneration system. In addition, the results of the online learning algorithm are different based on the driver’s deceleration pattern. The presented automatic regenerative control system can be safely used in diverse car-following situations. Moreover, the system’s acceptability is improved by updating the driver characteristics. In the future, the algorithm will be extended for use in more diverse deceleration situations by using intelligent transportation system information. View Full-Text
Keywords: smart regenerative braking system (SRS); advanced driver assistance system (ADAS); driver characteristics; driver behavior; automatic regeneration smart regenerative braking system (SRS); advanced driver assistance system (ADAS); driver characteristics; driver behavior; automatic regeneration
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Sim, G.; Ahn, S.; Park, I.; Youn, J.; Yoo, S.; Min, K. Automatic Longitudinal Regenerative Control of EVs Based on a Driver Characteristics-Oriented Deceleration Model. World Electr. Veh. J. 2019, 10, 58.

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