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Path tracking is the rudimentary capability and primary task for autonomous ground vehicles (AGVs). In this paper, a novel four-wheel-independent-steering (4WIS) and four-wheel-independent-drive (4WID) electric vehicle (EV) is proposed which is equipped with steer-by-wire (SBW) system. For path-tracking controller design, the nonlinear vehicle model with 2 degrees of freedom (DOF) is built utilizing the nonlinear Dugoff tire model. The nonlinear dynamic model of SBW system is conducted as well considering the external disturbances. As to the path-tracking controller design, an integrated four-wheel steering (4WS) and direct yaw-moment control (DYC) system is designed based on the model predictive control (MPC) algorithm to track the target path described by desired yaw angle and lateral displacement. Then, the fast terminal sliding mode controller (FTSMC) is proposed for the SBW system to suppress disturbances.

Traditional active suspension which is equipped with hydraulic actuator or pneumatic actuator features slow response and high power consumption. However, electromagnetic actuated active suspension benefits quick response and energy harvesting from vibration at the same time. To design a novel active and energy regenerative suspension (AERS) utilizing electromagnetic actuator, this paper investigates the benchmark cars available on the market and summaries the suspension features. Basing on the investigation, a design reference for AERS design is proposed. To determine the parameters of the actuator, a principle is proposed and the parameters of the actuator are designed accordingly. Compared the linear type and rotary type Permanent Magnet Synchronous Motor (PMSM), the rotary type is selected to construct the actuator of the AERS. Basing on the suspension structure of the design reference model and utilizing rotary type PMSM, a novel AERS structure is proposed.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

In this work, the influences of various real-timely available energy management strategies on vehicle fuel consumption (VFC) and energy flow of a range-extender electric vehicle were studied The strategies include single-point, multi-point, speed-following, and equivalent consumption minimization strategy. In addition, the dynamic programming method which cannot be used in real time, but can provide the optimal solution for a known drive situation was used for comparison. VFCs and energy flow characteristics with different strategies under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) were obtained through computer modeling, and the results were verified experimentally on a range-extender test bench. The experimental results are consistent with the modeled ones in general with a maximum deviation of 4.11%, which verifies the accuracy of the simulation models.

To improve the maneuverability and energy consumption of an electrical vehicle, a two-level speed control method based on model predictive control (MPC) is proposed for accurate control of the vehicle during downhill coasting. The targeted acceleration is planned using the anti-interference speed filter and MPC algorithm in the upper-level controller and executed using the integrated algorithm with the inverse vehicle dynamics and proportional-integral-derivative control model (PID) in the lower-level controller, improving the algorithm’s anti-interference performance and road adaptability. Simulations and vehicle road tests showed that the proposed method could realize accurate real-time speed control of the vehicle during downhill coasting. It can also achieve a smaller derivation between the actual and targeted speeds, as well as more stable speeds when the road resistance changes abruptly, compared with the conventional PID method.

To overcome the shortcoming that vehicles with multiple steering modes need to switch steering modes at parking or very low speeds, a dynamic switch method of steering modes based on MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) was proposed for 4WID-4WIS (Four Wheel Independent Drive-Four Wheel Independent Steering) electric vehicle, considering the smoothness of dynamic switch, the lateral stability of the vehicle and the energy economy of tires. First of all, the vehicle model of 4WID-4WIS was established, and steering modes were introduced and analyzed. Secondly, the conditions for the dynamic switch of steering modes were designed with the goal of stability and safety. According to different constraints, the control strategy was formulated to obtain the target angle of the active wheels. Then aiming at the smoothness of the dynamic switch, the active wheel angle trajectory was constructed based on the B-spline theory.

In order to avoid the influence of the change of the camera pitch angle and the variation of the height of the ground on the ranging accuracy, improve the real-time performance of the algorithm by substituting the current widely-used monocular vision ranging algorithm which builds the optical model based on the shadow of the vehicle floor and the lane line, as well as avoid the classification of vehicle detection, a vehicle distance measurement algorithm based on monocular vision and license plate width is established. Firstly, the target image acquisition and preprocessing are studied. Then the paper studies the license plate image location segmentation method based on accelerated template matching. On this basis, the algorithm for obtaining the ratio of license plate width to image width is studied, and the function of vehicle distance and license plate ratio width is established.

In this paper, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes.

With the increase of motor speed and the deterioration of operating environment, it is more difficult to predict the transient temperature field (TTF). Meanwhile, it is difficult to obtain the temperature test dataset of key nodes under various complete road conditions, so the cost of bench test or real vehicle test is high. Therefore, it is of great significance to establish a high fidelity, lightweight temperature prediction model which can be applied to real vehicle thermal management for ensuring the safe and stable operation of motor. In this paper, a physical model simulating electromagnetic-heat-flow multi-physical coupling of permanent magnet synchronous motor (PMSM) in electric drive gearbox (EDG) is established, and the correctness of the model is verified by the actual EDG bench test.

Accurate prediction temperature variation of electric drive transmission (EDT) can effectively monitor its abnormal temperature rise that may occur under high speed and heavy load working conditions, so as to ensure the vehicles’ safe operation. In this paper, combined with real temperature and input/output characteristic data collected from EDT test platform under different working conditions, a spatio-temporal relationship dynamic graph convolution neural network based on least square method (OLS-DRGCN) for temperature prediction is proposed. Firstly, OLS is used to estimate the EDT’s internal temperature based on partial sensor information as the input of OLS-DRGCN. Secondly, the spatial dependence relationship of each temperature node is dynamically learned through node embedding and the dynamic thermal network topology of EDT is constructed. Meanwhile, the timing rule of each temperature node is obtained through the gated recurrent unit.

The on-ramp merging driving scenario is challenging for achieving the highest-level autonomous driving. Current research using reinforcement learning methods to address the on-ramp merging problem of automated vehicles (AVs) is mainly designed for a single AV, treating other vehicles as part of the environment. This paper proposes a control framework for cooperative on-ramp merging of multiple AVs based on multi-agent deep reinforcement learning (MADRL). This framework facilitates AVs on the ramp and adjacent mainline to learn a coordinate control policy for their longitudinal and lateral motions based on the environment observations. Unlike the hierarchical architecture, this paper integrates decision and control into a unified optimal control problem to solve an on-ramp merging strategy through MADRL.