In order to solve the problem of clutch overheating during the launching of a Dual Clutch Transmission (DCT) vehicle in the Drive mode of D1 gear under extreme working conditions, this paper proposes a dual-clutch launching control method to improve the climbing and escape performance of the vehicle. Based on the analysis of the hardware structural conditions, the clutch plate temperature model and the specific process of DCT launching, this paper proposes to use the D2 gear clutch to undertake the useless slipping power at the beginning of the steady speed stage of launching, and then switch back to the D1 gear clutch when the engine torque gradually increases to exceed the threshold, the paper then elaborates in detail the activation conditions and specific implementation process of the control method. Vehicle test verification shows that the dual-clutch launching has obvious advantages in clutch temperature control and driving smoothness compared with the single-clutch launching, and the vehicle climbing and escape performance has been significantly improved.

In order to explore the underlying mechanisms of driver distraction in turning and straight driving scenarios, this study uses a driving simulator to create straight-driving and turning virtual scenarios. It also collects driving performance and eye-movement data of drivers in different driving states. The KNNImputer algorithm is employed to handle missing data during data collection. Then, a paired samples T test is used to analyze significant differences and extract significant difference feature indexes from sample data with a time window of 1 s length and 75% overlap. Based on these features, an XGBoost classifier is used to build cognitive distraction recognition models for different scenarios. The results show that compared with straight driving, drivers in turning scenarios have higher mental workload, indicated by lower pupil diameter change frequency, higher saccade speed and higher fixation duration percentage. The built cognitive distraction recognition model achieves an accuracy of 91.30% for straight-driving and 83.28% for turning scenarios. This suggests that cognitive distraction behavior in turning scenarios is more dangerous and harder to recognize.

In order to solve the problem of inaccurate subjective evaluation of vehicle driving and the inability of objective evaluation to reflect subjective feelings, an evaluation model based on Stacking ensemble learning method is proposed. First, the acceleration condition characteristics of vehicles are studied and objective evaluation indicators of driverability are defined. Then the evaluation indicators are used as input features to train the Stacking ensemble model. Moreover, the Improved Harris Hawk Optimization algorithm was used to optimize the hyperparameters in the Stacking ensemble model to improve the model prediction performance. Finally, the road test proves that the performance of the HHO-Stacking ensemble model is superior to that of a single machine learning model. The qualification rate of the HHO-Stacking ensemble model is 95%. The HHO-Stacking ensemble model can complete the drivability evaluation more effectively.

Aiming at the issues of low high-speed tracking accuracy and weak robustness in the trajectory tracking control of distributed electric drive bearing platforms, a hierarchical lateral motion control strategy based on desired front wheel angle tracking is designed. Taking the distributed electric drive bearing platform as the research object, a comprehensive dynamic model integrating rubber wheels, vehicle body, and electric drive modules is constructed based on the dynamic analysis of each subsystem and the interaction relationship between the wheels and the ground. By constructing a hierarchical motion control strategy with upper-level Model Predictive Control (MPC) trajectory tracking and lower-level steering motor angle control, high-precision control of the platform’s lateral position can be achieved. A holistic dynamic simulation model of the bearing platform is built using Simulink. The simulation results show that the lateral motion control strategy designed in this research can achieve multi-scenario trajectory tracking with high precision at various speeds. Compared with the sliding mode controller, the control accuracy of this strategy is improved by 33%, and the control stability is significantly enhanced.

To avoid the incidence of vehicle - pedestrian collision accidents under emergency scenarios involving visual impairments, this study focuses on typical vehicle-pedestrian Automatic Emergency Braking (AEB) test scenarios with visual impediments. Three AEB triggering strategies, namely the conservative, regulatory, and aggressive strategies, are proposed, each characterized by distinct Time to Collision (TTC) values and braking deceleration profiles. Under the circumstances of two prototypical accident scenarios with visual impairments, accident scenarios and vehicle dynamics models are constructed using simulation platforms such as PreScan, MATLAB/Simulink, and CarSim. Subsequently, an AEB strategy model is developed. A comparative analysis is then conducted to evaluate the braking effectiveness of the three AEB triggering strategies. The results show that all the AEB models in this study can achieve collision avoidance in typical vehicle-pedestrian accident scenarios, with the regulatory braking strategy having the best avoidance effect.

This study aims to enhance the lateral stability of distributed drive electric vehicles when traveling on highways and low-adhesion roads by adopting a hierarchical control strategy. The upper level is the torque decision-making layer, which designs a hierarchical Sliding Mode Control (SMC) based on a two-degree-of-freedom dynamic model to optimize the additional yaw moment and introduces Active Rear-wheel Steering (ARS), while simultaneously using PID control to achieve vehicle speed tracking. The lower level is the torque distribution layer, which optimizes the distribution of driving torque among the four wheels based on wheel load and road adhesion coefficient. Through co-simulation using Carsim and Matlab/Simulink, the control effectiveness was verified under double lane change, different adhesion road surface, and slalom conditions. The results indicate that the SMC+ARS control maintains high stability under high-speed double lane change conditions and outperforms SMC under various adhesion conditions, while also reducing energy consumption during turning.

To address the challenges associated with the complex steps and inability to identify the optimal hardware parameters of the CLLLC converter when designing parameters by using the Fundamental Harmonic Analysis (FHA) method, this paper proposes a CLLLC converter parameter design and optimization approach based on the fundamental wave analysis method and the Dung Beetle Optimizer (DBO) algorithm. Firstly, the FHA is employed to derive the design boundary of the converter parameters as the design constraints. Secondly, the efficiency function of the converter is established according to the relationship between converter efficiency and hardware parameters. Then, the DBO algorithm is used to optimize the objective function within the design constraints to obtain the hardware parameter values at the best efficiency point. The experimental results indicate that the efficiency of the designed prototype can reach 97%, further proving feasibility of this scheme.

In order to improve the accuracy of predicting the Remaining Useful Life (RUL) of batteries, an intelligent digital-analogue fusion method of Particle Swarm Optimization (PSO) optimized Extreme Learning Machine (ELM) combined with Random Perturbation Untraceable Particle Filtering (RP-UPF) is used to predict the RUL of batteries B0005, B0006 and B0018 based on fusion of the health indexes and the constructed battery capacity decline model. The research results show that the proposed intelligent digital-analogue fusion method not only significantly improves the accuracy of battery RUL prediction, but also maintains high prediction accuracy throughout the life cycle of the battery.

Based on heat pump technology, an integrated thermal management system that fully utilizes the waste heat of the motor has been designed. The system uses heat exchangers to connect the independent circuits and achieve efficient energy utilization. In order to address the difficulty of controlling thermal management systems, this paper proposes two optimization fuzzy control approaches, anti-saturation integral fuzzy control and multi-level fuzzy control. An integrated thermal management system model is built based on AMESim, and Simulink control strategy models for working mode switching and key components are established to jointly simulate and analyze the thermal management control effect of the entire vehicle. The simulation results show that at 0 ℃, the integrated thermal management system reduces the cabin heating time by about 27.8% compared with the independent thermal management systems of each circuit, and the energy efficiency ratio has been increased by an average of about 31.3%, in addition the winter driving range has been increased by about 9.57%. The control effect of optimized fuzzy control is significantly improved with the heating time of the cab in winter shortened by about 18.4%, and the fluctuation and overshoot of the cabin temperature in summer reduced, and the battery cooling time shortened by about 3.6%.

To address the issue of low accuracy in estimating the State of Charge (SOC) of lithium batteries using the Unscented Kalman Filter (UKF) algorithm, a combined ELM-UKF algorithm with a state detection mechanism is proposed, leveraging the complementary advantages of Extreme Learning Machine (ELM) and UKF for estimating the SOC of lithium batteries. Firstly, the algorithm uses the relevant filtering data estimated by UKF for battery SOC as a sample set to train the ELM model. The successfully trained ELM model is then used to online compensate for the SOC estimation error of UKF, thereby achieving real-time correction of estimation deviations. Secondly, the algorithm designs a state detection mechanism for the predictive output of the ELM model to reduce the impact of overfitting in the ELM model’s predictive output on the smoothness of the SOC estimation waveform. Experimental results show that, compared to single-type algorithms, the proposed combined algorithm exhibits good robustness and generalization, effectively enhancing the estimation performance of lithium battery SOC.