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

Area of Science:

• Automotive Engineering
• Control Systems
• Human-Computer Interaction

Background:

• Increasingly strict CO2 emission regulations are driving the adoption of electric vehicles (EVs).
• EVs offer versatile longitudinal dynamics due to advanced motors and regenerative braking.
• Individual driving styles significantly impact driver satisfaction with vehicle performance.

Purpose of the Study:

• To quantitatively characterize individual driving styles in electric vehicles.
• To develop a personalization algorithm for EV accelerating behavior.
• To enhance driver satisfaction by tailoring vehicle performance to personal preferences.

Main Methods:

• Investigated quantitative characterization of personal driving styles.
• Defined accelerating features reflecting distinct driving tendencies.
• Developed a personalization algorithm using statistical expected values and correction factors for motor torque control.

Main Results:

• Successfully quantified individual driving style characteristics.
• The proposed algorithm calculates correction factors for traction motor controller target torque.
• Simulations confirmed the algorithm's ability to replicate varied acceleration behaviors for identical pedal inputs.

Conclusions:

• Personalized motor torque adjustment can significantly improve EV driving satisfaction.
• The developed algorithm effectively adapts EV acceleration to individual driver styles.
• This approach addresses driving heterogeneity and enhances the overall EV user experience.