Accurate detection of road surface information is crucial for enhancing vehicle driving safety and ride comfort. To overcome the limitation that traditional suspension systems struggle to respond to road excitations in real time due to time delays in signal acquisition and control, suspension preview control technology has attracted significant attention for its proactive adjustment capability, with efficient road surface information perception being a critical prerequisite for its implementation. This paper systematically reviews road surface information detection technologies for suspension preview, focusing on the identification of potholes and speed bumps. Firstly, it summarizes relevant publicly available datasets. Secondly, it sorts out mainstream detection methods, including traditional dynamic methods, 2D image processing, 3D point cloud analysis, machine/deep learning methods, and multi-sensor fusion methods, while comparing their applicable scenarios and evaluation metrics. Furthermore, it emphasizes the core role of elevation information (e.g., pothole depth, speed bump height) in suspension preview control and summarizes elevation reconstruction technologies based on LiDAR, stereo vision, and multi-modal fusion. Finally, it prospects future research directions such as optimizing robustness, improving real-time performance, and reducing labeling costs. This review provides technical references for enhancing the accuracy of road surface information detection and the control efficiency of suspension preview systems, and it is of great significance for promoting the development of intelligent chassis.

To solve the low frequency vibration problem faced by heavy truck drivers, a positive real network inertial suspension structure combined with a skyhook inertial control strategy is adopted. This integrated approach effectively reduces low-frequency vibrations at the seat and human body levels. Specifically, this research aims to mitigate the acceleration experienced on the seat surface within the low-frequency range. Firstly, a human–seat dynamics model is established. Subsequently, based on the principles of network synthesis, the derivation of transfer functions for both first- and second-order systems is discussed, and the network parameters are also optimized. This paper further compares the optimization outcomes of first- and second-order skyhook seat inertial suspensions. An adaptive fuzzy sliding-mode controller (AFSMC) has been developed for an electromechanical inerter, ensuring it closely tracks optimal control performance. The findings demonstrate that the new suspension system achieves a 29.9% reduction in the root-mean-square value of seat surface acceleration and a 43.1% decrease in the road-bump peak acceleration compared to a conventional suspension system. The results show that the inertial suspension with skyhook inertial control is highly effective in completely suppressing seat surface acceleration within the low-frequency domain.

Hub motor-driven vehicles (HMDVs) suffer from poor handling and stability due to an increased unsprung mass and unbalanced radial electromagnetic forces. Although traditional ground-hook control reduces the dynamic tire load, it severely worsens the body acceleration. This paper presents a generalized ground-hook control strategy based on impedance transfer functions to address the parameter redundancy in structural methods. A quarter-vehicle model with a switched reluctance motor wheel hub drive was used to study different orders of generalized ground-hook impedance transfer function control strategies for dynamic inertial suspension. An enhanced fish swarm parameter optimization method identified the optimal solutions for different structural orders. Analyses showed that the third-order control strategy optimized the body acceleration by 2%, reduced the dynamic tire load by 8%, and decreased the suspension working space by 22%. This strategy also substantially lowered the power spectral density for the body acceleration and dynamic tire load in the low-frequency band of 1.2 Hz. Additionally, it balanced computational complexity and performance, having slightly higher complexity than lower-order methods but much less than higher-order structures, meeting real-time constraints. To address time-domain deviations from generalized ground-hook control in semi-active systems, a dynamic compensation strategy was proposed: eight topological structures were created by modifying the spring–damper structure. A deviation correction mechanism was devised based on the frequency-domain coupling characteristics between the wheel speed and suspension relative velocity. For ride comfort and road-friendliness, a dual-frequency control criterion was introduced: in the low-frequency range, energy transfer suppression and phase synchronization locking were realized by constraining the ground-hook damping coefficient or inertance coefficient, while in the high-frequency range, the inertia-dominant characteristic was enhanced, and dynamic phase adaptation was permitted to mitigate road excitations. The results show that only the T0 and T5 structures met dynamic constraints across the frequency spectrum. Time-domain simulations showed that the deviation between the T5 structure and the third-order generalized ground-hook impedance model was relatively small, outperforming traditional and T0 structures, validating the model’s superior adaptability in high-order semi-active suspension.

To address the limitations of existing quarter-vehicle models in capturing pitch motion and front-rear coupling effects, this paper proposes a half-vehicle mechatronic suspension system based on the electromechanical analogy. Traditional methods often overlook non-ideal effects and the dynamic interaction between the front and rear wheels. This paper constructs an equivalent electrical network model for the half-vehicle suspension system. To ensure the physical realizability of the system, parameter optimization is performed under positive-real constraints using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This approach achieves an optimal trade-off between vertical vibration suppression and pitch control. Simulation results under random road input at a vehicle speed of 20 m/s indicate that while the unconstrained mechatronic suspension improves ride comfort, it increases the dynamic tire load by 19.18%. In contrast, the constrained mechatronic suspension reduces RMS vertical body acceleration by 19.54% and pitch angular acceleration by 2.22% compared to the standard passive suspension. Additionally, a reduction of 8.29% was observed in the suspension working space RMS, alongside a 1.26% decrease in the dynamic tire load. These results demonstrate that introducing appropriate positive-real constraints effectively balances ride comfort and road-holding performance, providing a systematic modeling and optimization framework for half-vehicle mechatronic suspensions.