Ke Shi

The poor cruising range of electric vehicle (EV) is a problem preventing its popularity. To tackle this problem, methods such as battery technology, energy-based motion control technology are developed. This paper proposes a new solution from the perspective of path planning. Such a solution is called 3-D multi-object path planning method (3D-M method), in which both the energy consumption and distance are considered. The 3D-M method mainly realizes multi-object path planning by an energy consumption estimation model (ECEM) and a distance-integrated estimation model (DIEM). The ECEM can estimate the energy consumption between the neighbour position and the destination on the 3-D map, using a novel slope energy model considering energy consumption characteristic of the EV. The DIEM can estimate the integrated distance which includes the corresponding 2-D distance and 3-D distance, respectively. In the planning process, the outputs of ECEM and DIEM are combined to determine the cost of a path. In addition, a chaos-based multi-object optimizer (CBMOO) is used to search the optimal weights for the 3D-M method. The simulation experiments prove that the proposed method can generate an optimal path which saves much energy in comparison with the path provided by the distance-based method.

The path tracking is a basic function of the autonomous vehicle. Generally, it is difficult to realize the path tracking with high accuracy at high speeds. To solve this problem, an adaptivity-enhanced path tracking system (AEPTS) is investigated in this paper. The adaptive performance of this AEPTS is realized via three aspects. The first is to achieve preview distance adaptivity by a dynamic multi-point preview model (DMPPM), which provides the desired course deviation according to the vehicle speed and the reference path. The second is to improve the adaptivity of an adaptive fuzzy steering controller (AFSC) with an adaptive steering gain model which is built via the path curvature and vehicle speed. The third is that an optimal parameter surface is devised to enhance the adaptivity of the AEPTS at high speeds. Such a surface can provide the optimal coefficients for the AEPTS, and it is implemented using the particle swarm optimization (PSO) and a curve fitting method. At last, simulation experiments are given to verify the adaptivity of the proposed method.

The lateral and longitudinal stability is important and difficult for distributed drive electric vehicle (DDEV). Current control methods for the lateral and longitudinal stability are designed based on the static model and parameter without considering the uncertain characteristic in DDEV. To solve this problem, this paper presents a novel compensation-based robust decoupling control system (CRDCS), which consists of a robust decoupling controller (RDC) and a feedback compensation controller (FCC). The RDC can achieve the robust stability for DDEV and obtain the decoupling control signals, using a robust inverse model constructed by a radial basis function neural network (RBF-NN). The FCC can eliminate the control error caused by the uncertain characteristic in DDEV, utilizing the compensation control signals obtained by the designed reference model and the model predictive controller (MPC). The simulation is carried on the Carsim and Matlab, and the results show that the proposed CRDCS has good robust performance for the lateral and longitudinal stability of DDEV.

Path following is the basic technology of the autonomous vehicle (AV), many preview control methods have been widely applied to path following tasks. However, less of them take the variable vehicle velocities into account. In fact, the velocity is an important factor affecting the tracking accuracy. Especially, when an AV is in high velocity, it is not easy to achieve path following with high accuracy. To improve the adaptivity of path following in different velocities, an improved adaptive path following control system (PFCS) constructed by a course angle optimal referential model (CAORM) and a model predictive controller (MPC) is developed in this paper. The CAORM can provide the referential course angle, according to the vehicle longitudinal and lateral velocities, which significantly improves the adaptivity of the proposed PFCS in different velocities. And the CAORM is mainly implemented by a fuzzy inference system and a novel preview model, using human driving experience. The MPC is applied to realize the course control with high accuracy via manipulating the steering angle. Finally, the tracking performance of the PFCS is verified via simulation experiments on the Simulink-CarSim platform.

The yaw stability of distributed drive electric vehicle (DDEV) can be guaranteed by the active steering and torque distribution systems. Conventional yaw stability control strategy mainly focuses on the study of yaw moment, while the tyre lateral saturation and excessive longitudinal skid problems are difficult to be solved efficiently. This may lead to the unstable yaw phenomenon of the vehicle such as the sideslip or tail flick. To improve the yaw stability of DDEV under the tyre lateral saturation and excessive longitudinal skid situations, a double‐layer model predictive control system (DLMPCS) is presented in this work. The proposed DLMPCS consists of an anti‐saturation yaw stability controller (AYSC) and an anti‐slip torque distribution controller (ATDC). The AYSC is designed to guarantee the vehicle yaw stability under the tyre lateral saturation situation, through the active steering and yaw moment. The ATDC is designed to realise the torque distribution and keep the longitudinal slip ratio within the stable zone; then, the vehicle can be prevented from the excessive skid phenomenon. The simulation results confirm the improved yaw performance of the DLMPCS for DDEV under different situations.

Conventional yaw stability strategy of distributed drive electric vehicle (DDEV) is usually realised by torque distribution strategy. However, the instantaneous variations of four independent tyres slip ratio and the effect of disturbance have not been considered sufficiently. Therefore, it is difficult to realise the robustness of yaw stability for DDEV under various operating conditions. To solve this problem, a novel model predictive controller-based compensation control system (MPC-CCS) is proposed in this paper. The proposed MPC-CCS consists of two parts, an MPC based-feedback controller and a Kalman filter based-feedforward controller. In the feedback controller, a dual torque distribution scheme is adopted to obtain optimal torque values derived from the real-time signals of four independent tyres slip ratio, an MPC is designed to realise optimal torque values for vehicle yaw motion. In the feedforward controller, a Kalman filter is employed to attenuate the effect of the disturbance on yaw performance. In this way, the robustness of yaw stability for DDEV can be guaranteed by the proposed MPC-CCS. The proposed MPC-CCS is evaluated on eight degrees of freedom simulation platform and simulation results of different conditions show the effectiveness of the MPC-CCS.