Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based $H$ -infinite feedback algorithm for vehicle dynamics stability control, and this algorithm controls vehicle steering system and brake system via direct yaw moment control (DYC) and active front steering control (AFS). The presented controller is robust against vehicle parametric uncertainties, including the vehicle mass and vehicle longitudinal velocity. A linear parameter varying lateral model is constructed utilizing polytopic uncertainty method considering time-varying vehicle longitudinal velocity and mass, where a polytope that contains finite vertices is established to contain all of the possible selections for uncertainty parameters. Then, the $H$ -infinite feedback controller integrating DYC and AFS is derived via LMI technique. Finally, experimental results based on hardware-in-the-loop (HIL) platform illustrate that the presented controller has better performance of ensuring autonomous vehicle dynamics stability than other controllers.

It is a difficult and important project to coordinate active front steering (AFS) and direct yaw moment control (DYC), which has great potential to improve vehicle dynamic stability. Moreover, the balance between driver’s operation and advanced technologies’ intervention is a critical problem. This paper proposes a human-machine-cooperative-driving controller (HMCDC) with a hierarchical structure for vehicle dynamic stability and it consists of a supervisor, an upper coordination layer, and two lower layers (AFS and DYC). The range of AFS additional angle is constrained, with consideration of the influence of AFS on drivers’ feeling. First, in the supervisor, a nonlinear vehicle model was utilized to predict vehicle states, and the reference yaw rate, and side slip angle values were calculated. Then, the upper coordination layer decides the control object and control mode. At last, DYC and AFS calculate brake pressures and the range of active steering angle, respectively. The proposed HMCDC is evaluated by co-simulation of CarSim and MATLAB. Results show that the proposed controller could improve vehicle dynamic stability effectively for the premise of ensuring the driver’s intention.

An accurate state of charge (SOC) estimation is the basis of the Battery Management System (BMS). In this paper, a new estimation method which considers fractional calculus is proposed to estimate the lithium battery state of charge. Firstly, a modified second-order RC model based on fractional calculus theory is developed to model the lithium battery characteristics. After that, a pulse characterization test is implemented to obtain the battery terminal voltage and current, in which the parameter identification is completed based on least square method. Furthermore, the proposed method based on Fractional Unscented Kalman Filter (FUKF) algorithm is applied to estimate the battery state of charge value in both static and dynamic battery discharging experiment. The experimental results have demonstrated that the proposed method shows high accuracy and efficiency for state of charge estimation and the fractional calculus contributes to the battery state of charge estimation.

Gear-shift control is essential in automated manual transmission (AMT) systems, because it has a significant influence on comfort of vehicle and lifespan of AMT. Gear engagement process is the most important part of gear-shift process, and it has multistage and nonlinear characteristics, which make it difficult to realize smooth and fast control. This paper proposes a position and force switching control scheme for gear engagement. At the beginning and the end of gear engagement process, the combination sleeve is supposed to reach the desired position quickly with a small resistance for which a position controller is designed by using sliding mode control (SMC) method. A force controller is designed for the midsynchronizing stage, because the combination sleeve is blocked at this stage until the synchronization is finished. Simulations and experiments are carried out to show that gear-shift quality is improved and gear-shift shock is reduced greatly by the proposed method.

Delayed measurements can create difficulties for real-time feedback control. To address the advanced driver assistant system (ADAS) in practical application with delayed measurements, false alarms, false dismissals and disturbances, we propose a novel mixed $H_{2}/H_\infty $ observer-based controller in this study, which enables object tracking and car-following. Especially, the additive and multiplicative noises of the adaptive cruise control (ACC) system can be attenuated by the proposed mixed $H_{2}/H_\infty $ method. We first analyze the adaptive cruise and Radar tracking characteristics. Then, a definition of $H_{2}/H_\infty $ guarantee performance is introduced to ensure satisfying target tracking and safety car-following performances. Based on $H_\infty $ theory, the design criterion of the proposed mixed $H_{2}/H_\infty $ observer-based controller for ACC is established by linear matrix inequality (LMI) technique. Lastly, some experiment scenarios are given to demonstrate the effectiveness of the proposed method.

A novel distributed cooperative formation control method inspired by the aggregate behaviors of fish groups is proposed for plug-in hybrid electric vehicle (PHEV) formation. Firstly, a hierarchical control architecture is established for formation keeping with fuel consumption (FC) optimization simultaneously. The top layer is to generate a leader with the optimal performance based on the nonlinear model predictive control (MPC) technique. A fish swarm optimization (FSO) algorithm is proposed to solve the nonlinear MPC problem by imitating the predation behaviors of the fish swarm. The middle layer is a decentralized intelligent cruise control (ICC) for follower vehicles to track their leader imitated the behaviors of fish swarm, and some design criteria are presented based on the Lyapunov stability theory. The under layer is to achieve a satisfying performance for the hybrid powertrain systems of followers. Finally, the bio-inspired method applied for PHEV formation is verified with a satisfying robustness, fuel economy, car-following and also real-time processing performances. According to the results, the PHEV formation using the proposed method represents a better car-following performance compared with the normal adaptive cruise control (ACC) method and a 21.26% improvement of FC compared with the rule-based energy management strategy (EMS). The computational burden is also reduced by the bio-inspired method.