Regenerative Braking Control Strategy Based on AI Algorithm to Improve Driving Comfort of Autonomous Vehicles
Myeong Hwan Hwang, Gye Seong Lee, Eugene Kim, Hyeon-Woo Kim, Seungha Yoon, Teressa Talluri, Hyun Rok
Abstract
Recent studies on autonomous vehicles focus on improving driving efficiency and ignore driving comfort. Because acceleration and jerk affect driving comfort, we propose a comfort regenerative braking system (CRBS) that uses artificial neural networks as a vehicle-control strategy for braking conditions. An autonomous vehicle driving comfort is mainly determined by the control algorithm of the vehicle. If the passenger’s comfort is initially predicted based on acceleration and deceleration limits, the control strategy algorithm can be adjusted, which would be helpful to improve ride comfort in autonomous vehicles. We implement numerical analysis of the control strategy, ensuring reduced jerk conditions. In addition, backward propagation was applied to estimate the braking force limits of the regenerative braking systems more accurately. The developed algorithm was verified through the Car Sim and MATLAB/Simulink simulations by comparing them with the conventional braking system. The proposed CRBS offers effective regenerative braking within limits and ensures increased driving comfort to passengers.