Search Results (4)

This paper proposes a hierarchical framework-based solution to address the challenges of vehicle state estimation and lateral stability control in four-wheel independent drive electric vehicles. First, based on a three-degrees-of-freedom four-wheel vehicle model combined with the Magic Formula Tire model (MF-T), a hierarchical estimation method is designed. The upper layer employs the Kalman Filter (KF) and Extended Kalman Filter (EKF) to estimate the vertical load of the wheels, while the lower layer utilizes EKF in conjunction with the upper-layer results to further estimate the lateral forces, longitudinal velocity, and lateral velocity, achieving accurate vehicle state estimation. On this basis, a hierarchical lateral stability control system is developed. The upper controller determines stability requirements based on driver inputs and vehicle states, switches between handling assistance mode and stability control mode, and generates yaw moment and speed control torques transmitted to the lower controller. The lower controller optimally distributes these torques to the four wheels. Through closed-loop Double Lane Change (DLC) tests under low-, medium-, and high-road-adhesion conditions, the results demonstrate that the proposed hierarchical estimation method offers high computational efficiency and superior estimation accuracy. The hierarchical control system significantly enhances vehicle handling and stability under low and medium road adhesion conditions. Full article

The objective of this study was to experimentally determine the mathematical correlations between the loading of the tire, being longitudinal and lateral forces, and the emission of particulate matter (PM) from the tire–road contact. Existing emission factors (EF, emission per vehicle and distance traveled) are the result of long-term measurements, which means that no conclusion can be drawn about the exact driving condition. To determine meaningful emission factors, extensive driving tests were conducted on an internal drum test bench while measuring PM emissions from the tire–road contact in real-time. This showed that the increases in emission over longitudinal and lateral forces can be approximated with fourth-order functions, with lateral forces leading to significantly higher emissions than longitudinal forces for the summer tire investigated. Using the emission functions obtained, a three-dimensional map was created that assigns an EF to each load condition consisting of different longitudinal and lateral forces for one vertical load. With known driving data, the map can be used for future simulation models to predict the total emission of real driving cycles. Furthermore, the results show that the average particle size increases with increasing horizontal force. The particles collected during the tests were analyzed to determine the proportions of tire and road material. According to the results, the tire contributes only about 20% of the particle mass, while 80% is attributable to the road surface. In terms of volume, these shares are 32% and 68%, respectively. Full article

The road friction coefficient and the forces between the tire and the road have a significant impact on the stability and precise control of the vehicle. For four-wheel independent drive electric vehicles, an adaptive tire force calculation method based on the improved Levenberg–Marquarelt multi-module and self-organizing feedforward neural networks (LM-MMSOFNN) was proposed to estimate the three-directional tire forces of four wheels. The input data was provided by common sensors amounted on the autonomous vehicle, including the inertial measurement unit (IMU) and the wheel speed/rotation angle sensors (WSS, WAS). The road type was recognized through the road friction coefficient based on the vehicle dynamics model and Dugoff tire model, and then the tire force was calculated by the neural network. The computational complexity and storage space of the system were also reduced by the improved LM learning algorithm and self-organizing neurons. The estimation accuracy was further improved by using the Extended Kalman Filter (EKF) and Moving Average (MA). The performance of the proposed LM-MMSOFNN was verified through simulations and experiments. The results confirmed that the proposed method was capable of extracting important information from the sensors to estimate three-directional tire forces and accurately adapt to different road surfaces. Full article

In the field of transportation, the accurate estimation of tire–road peak friction coefficient (TRPFC) is very important to improve vehicle safety performance and the efficiency of road maintenance. The existing estimation algorithms rarely consider the influence of road roughness and road texture on the estimation results. This paper proposes an estimation method of TRPFC considering the effective contact characteristics between the tire and the three-dimensional road. In the longitudinal and lateral directions of tire–road contact, the model is optimized by incorporating the effective contact area ratio coefficient into the LuGre tire model. The optimized model characterizes the road texture by road power spectrum and establishes the relationship between road texture and tire force. In the vertical direction of tire–road contact, the force transfer between tire and road is represented by a new multi-point contact method. By combining the above models with the normalization method and the unscented Kalman filter (UKF), the timely and accurate estimation of the peak friction coefficient of the tire and the 3D road is realized. Simulation and real vehicle experiments verify the effectiveness of the estimation algorithm. Full article