Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction

As worldwide vehicle CO<sub>2</sub> emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wide...

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Main Author: Haksu Kim
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/12/3951
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spelling doaj-f3362bf086ce4be5b9fa7745411597332021-06-30T23:36:59ZengMDPI AGSensors1424-82202021-06-01213951395110.3390/s21123951Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving SatisfactionHaksu Kim0Future Technology Division, ControlWorks, Seoul 06222, KoreaAs worldwide vehicle CO<sub>2</sub> emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.https://www.mdpi.com/1424-8220/21/12/3951personalized EV performanceindividual driver behavior modelingdriving satisfactoryEV controltraction motor control
collection DOAJ
language English
format Article
sources DOAJ
author Haksu Kim
spellingShingle Haksu Kim
Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
Sensors
personalized EV performance
individual driver behavior modeling
driving satisfactory
EV control
traction motor control
author_facet Haksu Kim
author_sort Haksu Kim
title Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
title_short Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
title_full Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
title_fullStr Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
title_full_unstemmed Personalization of Electric Vehicle Accelerating Behavior Based on Motor Torque Adjustment to Improve Individual Driving Satisfaction
title_sort personalization of electric vehicle accelerating behavior based on motor torque adjustment to improve individual driving satisfaction
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2021-06-01
description As worldwide vehicle CO<sub>2</sub> emission regulations have been becoming more stringent, electric vehicles are regarded as one of the main development trends for the future automotive industry. Compared to conventional internal combustion engines, electric vehicles can generate a wider variety of longitudinal behaviors based on their high-performance motors and regenerative braking systems. The longitudinal behavior of a vehicle affects the driver’s driving satisfaction. Notably, each driver has their own driving style and as such demands a different performance for the vehicle. Therefore, personalization studies have been conducted in attempts to reduce the individual driving heterogeneity and thus improve driving satisfaction. In this respect, this paper first investigates a quantitative characterization of individual driving styles and then proposes a personalization algorithm of accelerating behavior of electric vehicles. The quantitative characterization determines the statistical expected value of the personal accelerating features. The accelerating features include physical values that can express acceleration behaviors and display different tendencies depending on the driving style. The quantified features are applied to calculate the correction factors for the target torque of the traction motor controller of electric vehicles. This driver-specific correction provides satisfactory propulsion performance for each driver. The proposed algorithm was validated through simulations. The results show that the proposed motor torque adjustment can reproduce different acceleration behaviors for an identical accelerator pedal input.
topic personalized EV performance
individual driver behavior modeling
driving satisfactory
EV control
traction motor control
url https://www.mdpi.com/1424-8220/21/12/3951
work_keys_str_mv AT haksukim personalizationofelectricvehicleacceleratingbehaviorbasedonmotortorqueadjustmenttoimproveindividualdrivingsatisfaction
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