Design and Verification of Intelligent Driving Braking Energy System Based on Model Predictive Control

Given that the actual endurance Mileage of the vehicle is much lower than the theoretical endurance Mileage under the traditional intelligent driving mode, this paper develops an Adaptive Cruise Control (ACC) model based on dynamic predictive time-domain management. The developed Adaptive Cruise Control model is not only considering actuator efficiency and time delay, but also improving system safety by predicting the vehicle's motion state. This paper also verifies the feasibility of the Adaptive Cruise Control Model through simulation conditions by building a hardware in the loop simulation test platform based on Carsim/Veristand/MATLAB. It will use test platform conducting virtual simulations of cruise control acceleration, deceleration, and stopping to verify the effectiveness and accuracy of the intelligent driving system based on model predictive control. It also compares and verify the system designed in this paper on a real vehicle with the operating conditions of adaptive cruise control, start && stop, and China Light-duty Vehicle Test Cycle (CLTC). It turns out that the overall comprehensive endurance Mileage of intelligent driving was improved by 17.5% after adding energy recovery mode. And energy consumption reduces by 1.75 kwh per 100 kilo meters. The braking deceleration process is more linear, with a comfort level improvement of 6.46%, an average deceleration reduction by 0.2m/s2, and an average reduction of 4bar in maximum braking pressure, further avoiding braking energy loss.