As advanced driver-assistance systems become more common in commercial vehicles, there is a growing need for evaluating safety of vehicles. Low platform target robot systems play a crucial role in this evaluation process as they can assess safety performances of autonomous vehicles. Driving stability of a target robot during real vehicle tests depends significantly on its suspension system. Therefore, developing an appropriate suspension device for the target robot is of utmost importance. This study aimed to improve driving stability by comparing two different suspension configurations: a single rocker and a double rocker, both incorporating a crank rocker mechanism. Initially, a two-dimensional model that met constraints of the suspension device was developed, followed by an analysis of reaction forces. Subsequently, an optimal design was determined using design of experiments principles based on parameters of a 2D model. The manufactured suspension system model based on the optimal design underwent multi-body dynamics simulation to evaluate driving stability. Comparative analysis of driving stability for both configurations was performed using MBD simulation, offering insights into the superior suspension design for the target robot.

In recent years, research on machine learning techniques that can be integrated with existing suspension control algorithms for enhanced control effects has advanced considerably. Machine learning, especially involving neural networks, often requires many samples, which makes maintaining robust performance in diverse, changing environments challenging. The present study applied reinforcement learning, which can generalize complex situations not previously encountered, to overcome this obstacle and is crucial for suspension control under varying road conditions. The effectiveness of the proposed control method was evaluated on different road conditions using the quarter-vehicle model. The impact of training data was assessed by comparing models trained under two distinct road conditions. In addition, a validation exercise on the performance of the control method that utilizes reinforcement learning demonstrated its potential for enhancing the adaptability and efficiency of suspension systems under various road conditions.

In this study, the sensitivity of the power generation effect of the applied linear generator of the energy harvesting suspension system under various input conditions was analyzed. The energy-harvesting suspension generates electric energy through energy harvesting using the road surface vibration energy during driving. Before analyzing the power generation effect, we analyzed the structure of the eight-pole Outer PM (Permanent Magnet) linear generator model using the electromagnetic suspension system to design the efficient generator, PIANO (Process Integration and Design Optimization). The ANSYS MAXWELL program was used to perform electromagnetic simulations of a linear generator model installed inside an energy-harvesting suspension to determine the power generation of the linear generator under various input conditions. The sensitivity of each input variable was compared by comparing the power generation effect of the energy-harvesting suspension device according to road displacement, frequency, and vehicle speed. The sensitivity to the road surface frequency was 1.9451, the sensitivity to the road surface amplitude was 1.0502, and the sensitivity to the vehicle speed was 0.6258. It is confirmed that the maximum sensitivity to the road surface displacement was demonstrated.