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The power-split transmission is considered as one of the major technologies for hybrid electric vehicles. It utilizes two electric motors/generators (MGs) and a power-split device (planetary gear sets) to make the speed of internal combustion engine (ICE) independent from the vehicle speed, and in that way enables the ICE to operate in a high-efficiency region under all driving cycles. In this study, a compound power-split hybrid system integrated with a two-planetary gear train is proposed. To suppress the vehicle jerk intensity and improve the driving comfort during the transition from EV (Electric Vehicle) mode to HEV (Hybrid Electric Vehicle) mode, a torque coordinated control strategy is derived. Based on the analysis of mode transition in different sections, mathematical models of each section are deduced, respectively. Then a model-based torque coordinated control method is used to solve out the target output torques of ICE, MGs and brakes in each mode transition phase.

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.

Lane change obstacle avoidance is a common driving scenario for autonomous vehicles. However, existing methods for lane change obstacle avoidance in vehicles decouple path and velocity planning, neglecting the coupling relationship between the path and velocity. Additionally, these methods often do not sufficiently consider the lane change behaviors characteristic of human drivers. In response to these challenges, this paper innovatively applies the Dynamic Movement Primitives (DMPs) algorithm to vehicle trajectory planning and proposes a real-time trajectory planning method that integrates DMPs and Artificial Potential Fields (APFs) algorithm (DMP-Fs) for lane change obstacle avoidance, enabling rapid coordinated planning of both path and velocity. The DMPs algorithm is based on the lane change trajectories of human drivers. Therefore, this paper first collected lane change trajectory samples from on-road vehicle experiments.