To reduce energy consumption or improve energy efficiency, the regenerative devices recently have drawn the public’s eyes. In this paper, a novel hydraulic energy-regenerative shock absorber (HERSA) is developed for vehicle suspension to regenerate the vibration energy which is dissipated by conventional viscous dampers into heat waste. At first, the schematic of HERSA is presented and a mathematic model is developed to describe the characteristic of HERSA. Then the parametric sensitivity analysis of the vibration energy is expounded, and the ranking of their influences is k1≫m2>m1>k2≈cs. Besides, a parametric study of HERSA is adopted to research the influences of the key parameters on the characteristic of HERSA. Moreover, an optimization of HERSA is carried out to regenerate more power as far as possible without devitalizing the damping characteristic. To make the optimization results more close to the actual condition, the displacement data of the shock absorber in the road test is selected as the excitation in the optimization. The results show that the RMS of regenerated energy is up to 107.94 W under the actual excitation. Moreover it indicates that the HERSA can improve its performance through the damping control.

This study addresses the limitations of traditional active and semi-active suspension control systems in terms of adaptability and nonlinear handling, by exploring the potential of Deep Reinforcement Learning (DRL) techniques. Initially, a framework based on the Twin Delayed Deep Deterministic policy gradient (TD3) specific to active suspension systems was developed. Building on this, an enhanced TD3 algorithm, TD3-PSC (Physically Safe Constraint TD3), incorporating physical safety constraints was proposed. The TD3-PSC algorithm extends the state space to enhance understanding of suspension dynamics and improve adaptability. To accommodate the physical constraints and actuator characteristics inherent in suspension systems, TD3-PSC introduces guided training with real physical constraints and employs immediate termination and high penalty mechanisms to ensure safety and practicality of the algorithm. The simulation results demonstrate that TD3-PSC significantly outperforms the linear quadratic regulator (LQR), deep deterministic policy gradient (DDPG), and standard TD3 baseline, achieving improvements in control performance of 73.81%, 43.72%, and 32.14% under standard Class C road conditions, respectively. Additionally, it exhibits excellent generalization capabilities.

The types of road surfaces on which intelligent connected cars operate are complicated and varied, and current research lacks the achievement of real-time and reasonably high accuracy for road surface categorization. In this research, we provide a deep learning-based technique for classifying and identifying road surfaces that makes use of an improved (VGGNet-16) model, in conjunction with a transfer learning strategy, to gather data from the road surface in front of the car using an on-board camera. To accurately classify data based on obtained road surface photos, the dataset is first preprocessed, then pretrained weights are frozen, and the network is initialized using transfer learning parameters. In order to explore the accuracy analysis of the various models regarding the identification of six types of road surfaces, comparisons were made via the VGG16, AlexNet, InceptionV3, and ResNet50 models, using the same parameter values. The experimental findings demonstrate that the improved VGGNet-16 model, combined with the transfer learning approach, achieves 96.87% accuracy for the classification and recognition of pavements, demonstrating the improved network model’s superior accuracy for these tasks. Additionally, the driving recorder of the vehicle may be used as the sensor to complete pavement detection, which has significant financial advantages.

A novel suspension system, the hydraulic integrated interconnected regenerative suspension (HIIRS), has been proposed recently. This paper demonstrates the vibration and energy harvesting characteristics of the HIIRS. The HIIRS model is established as a set of coupled, frequency-dependent equations with the hydraulic impedance method. The mechanical–fluid boundary condition in the double-acting cylinders is modelled as an external force on the mechanical system and a moving boundary on the fluid system. By integrating the HIIRS into a half car model, its free and forced vibration analyses are conducted and compared with an equivalent traditional off-road vehicle. Results show that the natural frequency and the damping ratio of the HIIRS-equipped vehicle are within a proper range of a normal off-road vehicle. The root mean square values of the bounce and roll acceleration of the HIIRS system are, respectively, 64.62 and 11.21% lower than that of a traditional suspension. The average energy harvesting power are 186.93, 417.40 and 655.90 W at the speeds of 36, 72 and 108 km/h for an off-road vehicle on a Class-C road. The results indicate that the HIIRS system can significantly enhance the vehicle dynamics and harvest the vibration energy simultaneously.

To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

The electromechanical braking (EMB) system is an important component of intelligent vehicles and is also the core actuator for longitudinal dynamic control in autonomous driving motion control. Therefore, we propose a new mechanism layout form for EMB and a feedforward second-order linear active disturbance rejection controller based on clamping force. This solves the problem of excessive axial distance in traditional EMB and reduces the axial distance by 30%, while concentrating the PCB control board for the wheels on the EMB housing. This enables the ABS and ESP functions to be integrated into the EMB system, further enhancing the integration of line control and active safety functions. A feedforward second-order linear active disturbance rejection controller (LADRC) based on the clamping force of the brake caliper is proposed. Compared with the traditional clamping force control methods three-loop PID and adaptive fuzzy PID, it improves the response speed, steady-state error, and anti-interference ability. Moreover, the LADRC has more advantages in parameter adjustment. Simulation results show that the response speed is increased by 130 ms, the overshoot is reduced by 9.85%, and the anti-interference ability is increased by 41.2%. Finally, the feasibility of this control algorithm was verified through the EMB hardware-in-the-loop test bench.