In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

For lithium-ion batteries used in the electric vehicles, accurate prediction of capacity and remaining useful life online is extremely important. However, most of the researches focus on the prediction accuracy but neglect the complexity of the test environment, which makes many methods show poor robustness in application. To solve the problem, in this paper, we firstly introduce the simultaneous input and state estimation (SISE) algorithm into the online prediction of state-of-charge (SOC) and capacity, and combine the Gauss-Hermite extended particle filter (GH-EPF) to predict the remaining useful life. By setting different gradients of state noises in experiments, the proposed algorithm demonstrates the best accuracy and robustness in comparison with other algorithms. Through the two-factor authentication in simulations, the maximum error of capacity estimation is 35 mAh. For the prediction of remaining useful life, the minimum relative error of the proposed method is 0.4%. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified.

This paper presents the design of $μ$-synthesis control for four-wheel steering (4WS) vehicle and an experimental study using a hardware-in-the-loop (Hil) setup. First, the robust controller is designed and the selection of weighting functions is discussed in the framework of $μ$-synthesis control scheme, considering the varying parameters induced by running vehicle condition. Second, in order to investigate the feasibility of the four-wheel steering control system, the 4WS vehicle control system is built using dSPACE DS1005 platform. The experimental tests are performed using the Hil setup which has been constructed using the devised rear steering actuating system. The dynamics performance is evaluated by experiment using the Hil setup under the condition of parameter variations. Finally, experimental results show that the $μ$-synthesis controller can enhance good vehicle lateral maneuverability.

A 5-degrees-of-freedom shimmy model is established to analyse the dynamic responses of an electric vehicle with independent suspensions. Tyre elasticity is considered by means of Pacejka’s magic formula. Under the nonslip assumption for the leading contact point, tyre–road constraint equations are derived. Numerical simulation is conducted with different structural parameters and initial conditions to observe the shimmy phenomenon. Simulation results indicate that Hopf bifurcation occurs at a certain vehicle forward speed. Moreover, suspension structural parameters, such as caster angle, affect wheel shimmy. The linearized model of the system presents the stability boundaries, which agree with the simulation results. The results of this study not only provide a theoretical reference for shimmy attenuation, but also validate the effectiveness of the provided model, which can be used in further dynamic analysis of vehicle shimmy.

Autonomous vehicles have been widely equipped with radars, camera, and LiDARs due to their complementary capabilities of environment perception. However, it becomes a critical task to accurately track the trajectory of the preceding vehicle when the host vehicle suffers a LiDAR failure under challenging lighting conditions. This article proposes an integrated learning-based solution to address this critical issue. It is composed of a Q -learning-based Gaussian mixture model (QLGMM) for clustering dense radar data, a weight-scheduled method for radar data association, and a switchable recurrent neural network with dual-level long short-term memory (LSTM) cells for trajectory tracking under LiDAR failure and various illumination levels. Specifically, the QLGMM is first introduced to improve the conventional GMM-EM algorithm with the cluster number determined by a Q -learning approach. Then, the weight-scheduled method is presented to associate the data from multiple radars. Furthermore, a switchable dual-level LSTM network is developed to adaptively fuse the trajectories from the radar and camera streams based on three lighting modes. The training data and testing data were acquired on a fully instrumented autonomous vehicle. Experimental verification demonstrates that the proposed method can achieve a promising improvement for LiDAR-fault-tolerant trajectory tracking under different lighting conditions.