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• W2976403210 endingPage "4171" @default.
• W2976403210 startingPage "4171" @default.
• W2976403210 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." @default.
• W2976403210 created "2019-10-03" @default.
• W2976403210 creator A5023628992 @default.
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• W2976403210 date "2019-09-26" @default.
• W2976403210 modified "2023-10-07" @default.
• W2976403210 title "Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles" @default.
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• W2976403210 doi "https://doi.org/10.3390/s19194171" @default.
• W2976403210 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6806341" @default.
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• W2976403210 hasPublicationYear "2019" @default.
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