Test cycle simulation is an essential part of the vehicle-in-the-loop test, and
the deep reinforcement learning algorithm model is able to accurately control
the drastic change of speed during the simulated vehicle driving process. In
order to conduct a simulated cycle test of the vehicle, a vehicle model
including driver, battery, motor, transmission system, and vehicle dynamics is
established in MATLAB/Simulink. Additionally, a bench load simulation system
based on the speed-tracking algorithm of the forward model is established.
Taking the driver model action as input and the vehicle gas/brake pedal opening
as the action space, the deep deterministic policy gradient (DDPG) algorithm is
used to update the entire model. This process yields the dynamic response of the
output end of the bench model, ultimately producing the optimal intelligent
driver model to simulate the vehicle’s completion of the World Light Vehicle
Test Cycle (WLTC) on the bench. The results indicate that the algorithm exhibits
good convergence in the simulation, throughout the WLTC simulation, the driver
always kept the vehicle speed error within 1 km/h, and the response time is less
than 0.5 s under the vehicle’s starting condition. In comparison to the PID
control algorithm and the model predictive control (MPC) algorithm, it
demonstrates smaller speed error and response time, ensuring accuracy, high
efficiency, and safety during the indoor vehicle-in-the-loop test.
