The stability of brake control is an important guarantee for the safety of heavy-duty vehicles (HDVs) at high speeds. However, the electro-hydraulic actuation braking systems often exhibit a significant delay in seconds, which makes braking performance forecasting and control difficult. To address the torque tracking control problem with time delay, a deep inference and control method is proposed. First, a theoretical delay time under different rotating speeds is identified with a data-driven model. Then, a fast end-to-end prediction model is established to estimate the torque performance of the next step with delay information. The deep Q-network (DQN) learning approach is proposed to learn the experimental data by exploring and seeking the optimal control strategy in the time delay environment. A comparative simulation of the proposed DQN-based controller with or without considering time delay, and the rule-based method with or without considering time delay is implemented, and an online processor-in-the-loop(PIL) test with the edge computing device NVIDIA Jetson Xavier NX is performed on the robustness condition. The simulation results and PIL test results demonstrate that the proposed control framework achieves a great improvement in the torque tracking effect with time efficiency.

This paper discusses the heat transfer process for a typical stator–rotor machinery-hydrodynamic retarder from the perspective of computational fluid dynamics and experimental means. Fluid cooling is an essential step in the working process of hydrodynamic retarders, and changes in viscosity along with temperature rise will affect the performance of braking. To investigate the heat transfer process of stator–rotor machinery, a novel computational fluid dynamics (CFD) method, combined with a dynamic thermophysical property transfer algorithm, is proposed. A heat-flow coupling numerical method with experimental verification is proposed, in which the density and the viscosity are variable with the temperature in an effectiveness–number of transfer units (P-NTU) method. The results show that the numerical results are in good agreement with the experimental data, with a 0.1–2.5% error. The influence of an asymmetric structure on heat transfer characteristics is discussed. The results show that the optimal braking performance, along with the liquid cooling performance, is achieved under outlets with an inlet passage set as 90 degrees.

Temperature rise is a salient characteristic of the braking device of heavy-duty vehicles (HDVs) during long downhill braking. The temperature-independent effect on braking performance is an inevitable challenge for the longitudinal controller, which also causes the overheating of mechanical or hydrodynamic braking devices and may bring about brake failures for heavy-duty vehicles. To this end, a universal Bi-level control framework combining a temperature hierarchy system performance prediction method and a deep reinforcement learning (DRL)-based controller is proposed for long downhill braking of heavy-duty vehicles. Firstly, the temperature-independent characteristic is clustered to predict the braking performance under various rotating speeds. Secondly, a data-driven model for temperature rising is built for the long-time braking thermal prediction and estimates the safety remaining braking time using the auxiliary braking. Thirdly, a temperature-hierarchy environmental perceptive control framework with Double Deep Q Network (DDQN) algorithm is exploited to achieve the target speed tracking accuracy. Thermal safety is ensured with the application of fast calculating for the thermal rising, along with the effective estimation of remaining braking performance on endurance braking. The proposed Bi-level longitudinal controller is compared with the average-temperature strategy and the classic PID strategy to validate its superiority in terms of speed-tracking accuracy on robust conditions. The simulation results show that the proposed strategy improves the speed tracking accuracy by 37.31% on constant slope conditions and 68.11% on varying slope conditions compared with the classic PID strategy. Furthermore, a processor-in-the-loop test experiment verifies its real-time application.