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.

To address the high computational and storage demands of object detection algorithms that limit real-time performance of edge devices, this study proposes an improved lightweight road object detection algorithm based on YOLOv7-tiny. First, prior anchor boxes optimized for road object detection are generated using the K-means++ clustering algorithm. Secondly, the backbone network is streamlined by modifying the ELAN structure, while a Lightweight Multi-scale Feature (LMS) module is introduced to optimize the neck network. Finally, the Sigmoid Linear Unit (SiLU) activation function is adopted to accelerate model convergence, and the MPDIoU loss function is employed to further improve detection accuracy. Experimental results demonstrate that the improved model achieves an 18.3% reduction in parameters, a 15.0% decrease in computational complexity, and a 1.1% increase in mean average precision across all categories. When deployed on Jetson TX2 with TensorRT acceleration, the detection speed reaches 48 frames per second, essentially meeting real-time requirements for road object detection applications.

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%.

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.

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.

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.