Automated and electrified ground vehicles depend on optimal chassis control to synergistically orchestrate different actuators. This integration aims to improve performance, comfort, handling, and stability. However, this optimal control problem necessitates several key parameters that are expensive or impractical to measure. Virtual sensing emerges as an efficient and cost-effective alternative, utilizing existing vehicle sensors to overcome these challenges. The present study proposes a Virtual Sensing Module (VSM) designed to cope with a chassis controller. It is capable of estimating tire forces, the vehicle’s SideSlip Angle (SSA), and the Tire-Road Friction Coefficient (TRFC). Furthermore, the VSM has the benefit of inferring the vehicle mass and adjusting the tire parameters. Importantly, the VSM operates effectively across coupled dynamics and under both standard and aggressive driving conditions. The proposed framework adopts a Data to Features to Decision structure: The Feature segment tracks the tire forces and SSA via Model-Based (MB) dynamic estimators. Fed by the outputs of the previous sub-module, the Decision block tackles the TRFC estimation challenge using a novel MB strategy. This strategy was benchmarked against various Data-Driven techniques for validation. The effectiveness of the VSM was verified through both simulation and real-world experimental data under varying road conditions. The findings demonstrate the module’s high accuracy, with minimal estimation errors, and its efficiency in both low and high excitation scenarios.

Real-time knowledge of the vehicle mass is valuable for several applications, mainly: active safety systems design and energy consumption optimization. This work describes a novel strategy for mass estimation in static and dynamic conditions. First, when the vehicle is powered-up, an initial estimation is given by observing the variations of one suspension deflection sensor mounted on the rear. Then, the estimation is refined based on conditioned and filtered longitudinal and lateral motions. In this study, we suggest using these extracted events on two different algorithms, namely: the recursive least squares and the prior-recursive Bayesian inference. That is to express the results in a deterministic and statistical sense. Both simulations and experimental tests show that our approach encompasses the benefits of various works in the literature, preeminently, robustness to resistive loads, fast convergence, and minimal instrumentation.

Many Advanced Driver Assistance Systems (ADAS) and chassis systems can be found within the same passenger car. Most of these systems are controlled separately using standalone controllers. Several studies show the benefits of coordinating several systems especially in simultaneous operations. However, most of the studies tend to ignore the time-delays that exist in the control loop. This time-delays can destabilize the vehicle in severe situations. This paper provides a solution to face time-delays in the context of integrated ADAS and chassis systems. Results show the benefits of the control logic proposed in terms of stability and performance. This brings one step closer towards standardization of global vehicle motion control.

Most of automotive researches focus on autonomous vehicles. Studies regarding trajectory planning and trajectory tracking became preponderant. As in case of commercial ground vehicles there is a driver in the loop, one should raise the important question of how the trajectory should be tracked. In this paper, we investigate the influence of controlling integrated chassis systems on the vehicle's behavior. A fixed Model Predictive Control is used to track the trajectory. Tunable vehicle motion control is however used to provide different motion feelings. Results show that a specific trajectory could be followed in different manners. Therefore, vehicle dynamics can be and should be controlled in such a way to generate adaptive trust feelings to passengers in case of autonomous driving.