Analyzing the performance characteristics and applicable environments of intelligent vehicle lateral control algorithms, this paper establishes the Model Predictive Control (MPC), Pure Pursuit (PP), and Linear Quadratic Regulator (LQR) algorithms and uses 2022b MATLAB software to simulate the lateral error, heading error, and algorithm execution time at speeds of 3 m/s, 7 m/s, and 10 m/s. Urban low-speed scenarios (3 m/s) require high-precision control (such as obstacle avoidance), while high-speed scenarios (10 m/s) require strong stability. Existing research mostly focuses on a single speed and lacks a quantitative comparison across multiple operating conditions. Although MPC has high accuracy, its time consumption fluctuates greatly. LQR has strong real-time performance but a wide range of heading errors. PP has poor low-speed performance but controllable high-speed time consumption growth. It is necessary to define the applicable scenarios of each algorithm through quantitative data. In response to the lack of multi-speed domain quantitative comparison in existing research, this paper conducts multi-condition simulations using MPC, PP, and LQR algorithms and finds that at a low speed of 3 m/s, the peak lateral error of PP (0.45 m) is 55% and 156% higher than MPC (0.29 m) and LQR (0.176 m), respectively. At a speed of 10 m/s, the lateral error standard deviation of MPC (0.08 m) is reduced by 68% compared to PP (0.25 m). In terms of algorithm time consumption, LQR maintains full-speed domain stability (0.11–0.44 ms), while PP time increases by 95% with speed from 3 m/s to 10 m/s. The results show that in terms of lateral error, the MPC and LQR algorithms perform more stably, while the PP algorithm has a larger error at low speeds. Regarding heading error, all algorithms have a relatively large error range, but the MPC and LQR algorithms perform slightly better than the PP algorithm at high speeds. In terms of algorithm execution time, the LQR algorithm has the shortest and most stable execution time, the MPC algorithm has a relatively longer execution time, and the PP algorithm’s execution time varies at different speeds. Through this simulation, if high control accuracy and stability are pursued, the MPC or LQR algorithm can be considered; if real-time performance and computational efficiency are more important, the PP algorithm can be considered.

Persistent discrepancies remain in the perceived far-field noise of automotive permanent-magnet synchronous motors (PMSMs) and the predictions of conventional NVH simulations. To bridge this gap, a Tri-source Electromagnetic Coupling NVH Integrated Framework (Tri-ECNVH) is developed, in which air-gap electromagnetic force harmonics, torque ripple, and cogging torque are treated as a coupled excitation system rather than as independent sources. Traditional workflows usually superpose their responses in the power domain, which tends to underestimate the radiating contribution of torque-related excitations and neglect their phase and order coupling with radial electromagnetic forces. In the proposed Tri-ECNVH framework, the three sources are mapped into the order domain, aligned by spatial order, and applied to the stator with phase consistency, so that inter-source coupling and cross terms are explicitly retained along a unified electromagnetic–structural–acoustic chain. Acoustic radiation is evaluated by prescribing the normal velocity on the stator outer surface as a Neumann boundary condition and computing the far-field A-weighted sound pressure level (SPL) using a boundary element method (BEM) model. Numerical results reveal pronounced cooperative amplification of the three sources at critical orders and within perceptually sensitive frequency bands; relative to independent-source modeling with power-domain summation, Tri-ECNVH predicts peak levels that are typically 5–10 dB higher and reproduces the spectral envelope and peak–valley evolution more faithfully. The framework therefore offers a practical, radiation-oriented basis for multi-source noise mitigation in traction PMSMs and helps narrow the gap between simulation and perceived sound quality in automotive applications.

The Rapidly Exploring Random Tree (RRT) is widely employed in the field of intelligent vehicles, but traditional RRT has issues like inefficient blind expansion, tortuous/discontinuous paths, and slow convergence. Thus, a multi-dimensional optimized RRT is proposed. First, a heuristic search method is adopted to reduce blind sampling, guiding sampling toward the target and cutting irrelevant searches. Second, to fix RRT’s inability to adjust step size dynamically (limiting complex road adaptability), step size is optimized based on environmental information. Third, since treating vehicles as mass points leads to unreasonable paths, sampling points are expanded for practicality. Finally, redundant points are removed via a greedy strategy, and paths are smoothed with quasi-uniform cubic B-splines to meet ride comfort needs. MATLAB R2022b simulations validate the algorithm: in simple scenarios, optimized RRT reduces sampling points to 232 (24.4% of traditional RRT), runtime to 3.25 s (79.4% cut), path length to 673.84 m (15.6% reduction); in complex scenarios, 636 points (37.0%), 11.07 s runtime (58.8% cut), 699.61 m path (21.6% reduction), outperforming traditional RRT and Q-RRT*.

This study takes the power battery pack of a pure electric vehicle as the research object, focusing on safety—a core concern widely emphasized in the automotive industry. In practical application scenarios, evaluating the safety of the power battery pack through a single operating condition fails to fully reflect its comprehensive safety performance throughout the vehicle’s entire life cycle. To overcome this limitation, a systematic analysis process was established. First, Catia geometric modeling software was used to simplify the battery pack structure, and HyperMesh was then employed for mesh generation. Second, three core analyses were conducted: static analysis, modal analysis, and extrusion condition analysis. A multi-condition safety evaluation system for electric vehicle battery packs during computer simulation analysis was proposed, which evaluates the battery pack from three dimensions: “dynamic stiffness-static strength-extrusion safety”. Results show that: modal analysis reveals the battery pack’s low-order natural frequencies exceed the vehicle’s excitation frequency (excitation point on the case cover); static analysis confirms it meets operational requirements; extrusion verification proves its safety complies with new national standards. The coupling effect of this multi-dimensional analysis breaks through the limitations of safety performance evaluation under a single operating condition, more realistically reflecting the battery pack’s comprehensive safety over its life cycle and providing a more systematic basis for power battery pack optimization.

Path planning is a pivotal technology for autonomous vehicles, directly influencing driving safety and comfort. Developing algorithms adaptable to diverse scenarios is critical for ensuring the safe operation of autonomous driving systems and advancing their engineering applications. The existing Rapidly exploring Random Tree (RRT) algorithm has limitations such as low efficiency and tortuous, lengthy paths. To address these issues, this study proposes the PAB-RRT algorithm, which integrates probabilistic goal bias, adaptive step size, and bidirectional exploration into RRT. Comparative simulations were conducted to evaluate PAB-RRT against traditional RRT, RRT*, and single-strategy improved variants (A-RRT, P-RRT, B-RRT). Results show that in static multi-obstacle scenarios, PAB-RRT completes planning with 30 iterations (6.99% of traditional RRT), 0.1255 s computation time (21.9% of traditional RRT), and a 130.83 m path length (7.2% shorter than traditional RRT). In dynamic obstacle scenarios, it requires 19 iterations (0.0434 s) at the initial stage and 37 iterations (0.0861 s) after obstacle movement, with path length stably around 130 m. Overall, PAB-RRT outperforms traditional algorithms in exploration efficiency, path performance, and robustness in complex settings, better meeting the efficiency and reliability requirements of autonomous vehicle path planning under complex scenarios and providing a feasible reference for related technology.