Automatic regenerative braking of EVs based on the Driver-Characteristic-Oriented Deceleration model in car-following conditions

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• Chapter 1 Introduction 1
• 1.1. Research background 1
• 1.2. Related works 2
• 1.3. Research objectives 2
• 1.4. Outline 3
• Chapter 2 System overview 4
• Chapter 3 State recognition algorithm 6
• 3.1. Driving state recognition 6
• 3.2. Deceleration condition recognition 7
• Chapter 4 Parametric deceleration model 8
• 4.1. Split of braking section 8
• 4.2. Driver parameters 11
• 4.3. Deceleration model based on braking sections 11
• 4.3.1. Coasting section 11
• 4.3.2. Initial section 12
• 4.3.3. Adjustment section 13
• 4.3.4. Termination section 16
• Chapter 5 Online learning algorithm 17
• 5.1. Learning vectors 17
• 5.1.1. Driver parameters and correlated indices 17
• 5.1.2. Learning and effective probability vectors 18
• 5.1.3. Calculation of the driver parameters 21
• 5.2. Online update of learning vectors 21
• Chapter 6 Validation 27
• 6.1. Acceleration controller 27
• 6.2. Test vehicle configuration 27
• 6.3. Vehicle test environment 28
• 6.4. Vehicle test results 29
• 6.5. Learning results 32
• Chapter 7 Conclusion 36