It is a striking fact that the characteristics of parametric uncertainties, external disturbance, time-varying and nonlinearities are available in the constructed model of autonomous independent-drive vehicles; therefore, in this paper, the robust model predictive control (MPC) with the finite time horizon is proposed to realize the coordinated path tracking and direct yaw moment control (DYC) for autonomous four in-wheel motor independent-drive electric vehicles (AMIDEV). Firstly, considering the time-varying and uncertain feature of the tire cornering stiffness and the vehicle velocity in the state space equation constructed by 2 degrees of freedom (DoF) vehicle model and the path tracking preview model, the linear parameter varying (LPV) discrete model with four polytypic vertexes is constructed. Then, based on the linear matrix inequality (LMI) method, the novel robust MPC theory with the finite time horizon is put forward to solve the min-max optimization problem after updating four polytypic vertexes in real time, which could deal with the inevitable model mismatch problem caused by the time-varying, uncertain vehicle dynamic characteristics and external disturbance. Finally, the simulation and experimental results have verified that the proposed novel robust MPC theory could emerge from the stranglehold exercised by the conservativeness of the traditional robust MPC theory with the infinite time horizon, which strengthens the robustness of this control system as well as achieves better path tracking accuracy and handling ability of AMIDEV.

Efficient operation technique has always been one of the common goals for researches both in automobile industrial and academic areas. With the great progress of automobile technology, hybrid electric vehicle/plug‐in hybrid electric vehicle (HEV/PHEV) has already become the main achievement of transportation electrification, due to its excellent fuel‐saving performance. Energy management strategy (EMS) is an important link during the HEV/PHEV design procedure, which can govern the energy flow between the fuel tank and the electric energy storage by solving the energy distribution problem. As the continuous development of intelligent connected vehicle technology, designing an efficient EMS with vehicle to infrastructure/vehicle to vehicle (V2I/V2V) information for HEV/PHEV is still a challenge and hot issue. This study presents a deep review of the various EMSs for both conventional HEV/PHEV and that using V2I/V2V information, providing a thorough survey of EMSs using different methodologies. In terms of single‐vehicle and multi‐vehicle scenarios, the EMSs for HEV/PHEV under intelligent transport system is in‐depth reviewed. Finally, the challenges for future research are also identified. This study could provide a comprehensive reference for researchers in field of HEV/PHEV.

With the rapid development of autonomous driving technology, collision avoidance has become a research hotspot since it has the potential to increase safety. To obtain a collision-free path, the artificial potential field (APF) is widely used as a path planning method. APF is capable of establishing functional relationships between the vehicle and surrounding objects. However, the function features of the traditional APF method can cause autonomous vehicles to fall into the local minimum, and the generated zigzag path may be difficult to follow. Motivated by these challenges, this paper proposes a real-time path planning method for autonomous vehicles using the dynamic enhanced firework algorithm-APF. Firstly, to improve the safety and smoothness of the planned path by the traditional APF method, the constraints of the vehicle dynamics and different types of obstacles are taken into consideration. Secondly, an optimization problem is formulated to find an optimal path with the least cost in the driving area. Finally, the proposed method is verified with both a simulation and a hardware-in-loop test environment. The results show that the studied autonomous vehicle successfully avoids obstacles and arrives at the goal position by using the proposed path-planning method, and the path smoothness is improved.

How to reasonably coordinate and control the torque of four wheels to enhance vehicle performance is an important research problem of the four in-wheel-motor independent-drive electric vehicle (4MIDEV). In this paper, a coordinated hierarchical control strategy for the dynamic wheel torque of 4MIDEV steering is proposed, which considers both safety and energy-saving performance. The innovation of this paper lies in two new valuable algorithms in the top and bottom level controller, respectively. Besides the optimal control method on the basis of the 2-degree of freedom (DoF) ideal model in the top level controller, a novel model predictive control method based on the 7-DoF dynamic model is put forward to obtain two virtual generalized forces required during vehicle driving. In the bottom level controller, a novel optimal allocation (OA) algorithm is brought forward to improve the safety and energy-saving performance. The tire stability margin and the electric power of driving system balancing the driving energy consumption and regenerative braking energy recovery are considered in the quadratic objective function of OA algorithm at the same time. Carsim-Simulink joint simulation results under open-loop and closed-loop steering conditions illustrate that, compared with the 2-DoF LQR control method, the 7-DoF MPC method could effectively improve vehicle handling stability and safety. The bottom optimal allocation algorithm can not only improve the vehicle handling stability, but also make the driving system more energy-saving.

This paper proposes a stochastic predictive energy management strategy (PEMS) based on fast rolling optimization for plug-in hybrid electric vehicles (PHEVs). Firstly, combined with a large number of real-world driving cycle data, the stochastic driving behaviors are modeled as probability transition matrices of vehicle demand torque based on Markov chains. Secondly, to solve the torque split problem in parallel hybrid powertrain, the SMPC framework is built. Thirdly, the continuation/generalized minimum residual (C/GMRES) algorithm is employed to execute the fast rolling optimization. The effectiveness of the proposed strategy is validated in both simulations and test bench, and its performance is compared with the SMPC by dynamic programming (DP) optimization and the equivalent minimum fuel consumption strategy (ECMS). Simulation results show that under real-world driving cycle, PHEV using the proposed strategy could obtain 4.8% energy consumption reduction comparing with that uses ECMS. In terms of computational time, the proposed strategy dramatically reduces the running time comparing with that of SMPC by DP optimization. Furthermore, the similar results can be obtained in the experiment. Under real-world driving cycle, 4.6% fuel economy improvement is obtained using the proposed strategy compared with that using ECMS, which clearly shows that the proposed strategy is effective.

Four-wheel independent drive autonomous vehicle (4WIDAV) is constantly influenced by uncertainties, including parameter fluctuation, modeling error, and external disturbance during the path following process. If those uncertainties are not handled properly, 4WIDAV may deviate from the desired path or even become unstable. Motivated by this issue, a path following lateral control scheme based on modified sliding mode prediction control (SMPC) is proposed for 4WIDAV. First, an SMPC framework is built, which introduces the prediction process into the approaching process of sliding mode dynamic trajectory to alleviate the chattering phenomenon. Second, a lumped uncertainty term containing the modeling error, parameter fluctuation, and external disturbance is introduced in the prediction process of the SMPC framework. The accurate estimation of lumped uncertainty aforementioned is realized through an extended state observer (ESO). Third, the torque distribution method based on tire force observation is designed to ensure the driving stability of the 4WIDAV. Finally, typical driving maneuvers are performed in simulations and experiments to verify the effectiveness of the proposed scheme. Compared with the existing methods, the proposed control scheme can improve the tracking performance up to 69.4% and 52.8% in simulation and experiment, respectively. Therefore, the proposed scheme can be used as a theoretical reference for path following lateral control of autonomous vehicle.