The paper focuses on the sideslip problem in the small overlap crash test in the vehicle structural safety field. A two-degree-of-freedom first-order dynamic model of the vehicle is established, based on which a segmented sideslip motion planning method is proposed, and a vehicle sideslip motion planning software for small-overlap crash conditions is developed. The experimental results show that the proposed planning method enhances the vehicle's lateral displacement by 30%, thereby facilitating successful sideslip. This elevates the vehicle structural rating from Acceptable (A) to Good (G) and significantly improves the safety performance in the small-overlap ceashes.

To improve the body crashworthiness of a battery electric SUV under extreme conditions, the research employs a collision energy management method to conduct simulation under two side impact conditions of the China Insurance Automotive Safety Index (C-IASI) and truck. The body transmission path, load-bearing target definitions for critical structures, structural deformation and failure modes are compared and analyzed. Furthermore, the body structure is optimized targeting the extreme truck side impact condition. Simulation results and physical vehicle test results demonstrate that the body crashworthiness under extreme condition is significantly improved after optimization.

In the analysis of large-scale traffic accident data, traditional statistical methods suffer from low computational efficiency and limited ability to identify causes, making it difficult to effectively uncover deep-seated associations behind accidents. To address this issue, this paper proposes a traffic accident cause analysis method based on the Human Factors Analysis and Classification System (HFACS) framework combined with parallel Apriori algorithms. The method is developed from five aspects, including feature engineering, HFACS framework mapping and transactional structure transformation. Experimental results show that this approach significantly enhances the efficiency and accuracy of cause analysis in large-scale traffic accident data compared to traditional statistical analysis methods, providing more scientific data support for traffic accident prevention and safety management.

To address the issue that static path planning methods struggle to respond to dynamic traffic conditiond and easily cause local congestion in underground parking lot guifance, this paper proposes a parking space guidance method tailored for Intelligent Connected Vehicles (ICV). The proposed method leverages Vehicle-to-Everything (V2X) communication technology to acquire real-time information on parking space availability and traffic flow. It employs a time window model to predict potential spatiotemporal conflicts during vehicle movement and coordinates vehicle passage according to predefined priority rules. To mitigate congestion arising from these conflicts, the estimated waiting time due to conflicts is quantified as a dynamic increment to the edge weights of the affected road sections. On this basis, the D*Lite algorithm is integrated to perform path re-planning, achieving spatiotemporal cooperative optimization. Based on a model replicating a real parking structure, Simulation tests demonstrate that compared to traditional methods, the proposed approach significantly reduces the average parking search time across various scenarios with different occupancy levels within the facility, thereby effectively enhancing the overall parking efficiency.

To address the issue of high fuel consumption in heavy-duty truck druing hill-climing conditions, a fuel comsumption predicton model balancing accuracy and real-time performance is developed by inregrating parameters such as engine speed and torque. This paper proposes a fuel-saving strategy for heavy trucks using Predictive Adaptive Cruise Control (PACC) on sloped roads. A hierarchical architecture is developed: the upper layer employs Dynamic Programming (DP) for global economic speed planning, while the lower layer uses Model Predictive Control (MPC) for secondary planning to ensure safe following and energy efficiency. A fuel consumption model considering engine torque and speed is integrated. Vehicle test demonstrates an average fuel reduction of 1.5% per kilometer.

Aiming at the stability issues of disributed drive electric vehicles attributed to the response discrepancies of in-wheel motors, such as loss of control, understeer and oversteer, the paper proposes a hierarchical vehicle stability control framework. The upper layer incorporates an additional yaw moment controller designed using Model Predictive Control (MPC), which computes the required corrective yaw moment online to track both the desired yaw rate and the sideslip angle at the vehicle's center of mass. The lower layer performs optimal four-wheel torque allocation while coordinating vehicle stability and energy efficiency. A co-simulation platform combining Simulink and CarSim is developed, and straight-line driving and double-lane-change maneuvers are selected for validation. To assess the effectiveness of the MPC-based controller, comparative simulations are conducted against a reference upper-layer controller based on Sliding Mode Control (SMC). The results indicate that the proposed hierarchical strategy effectively compensates for motor response discrepancies, enhances vehicle stability, and improves overall driving economy. The simulation results demonstrate that under the straight-line driving scenario, the proposed MPC strategy reduces the lateral deviation from 13.01 m to 1.71 m, achieving a control efficiency of 86.9%, while simultaneously decreasing motor energy consumption by approximately 6.8%. In the double lane change maneuver, the strategy effectively suppresses the oscillations of both yaw rate and sideslip angle. These results validate that the proposed hierarchical control strategy can effectively compensate for the impact of hub motor response discrepancies, thereby significantly enhancing driving stability and improving overall vehicle energy efficiency.

To address the issue of unstable motor output torque resulting from torque response discrepancies in Four-Wheel Independent Drive (FWID) electric vehicles, the paper proposes a drive antislip control strategy that accounts for motor response errors. On the basis of comprehensively analyzing the impact of the dynamic and steady-state responses of the motors on drive slip control, a target slip ratio tracking algorithm based on sliding mode control is designed, and the motor torque response errors are compensated at the control level. Through co-simulation of CarSim and MATLAB/Simulink, the proposed drive slip control strategy is verified under low-adhesion coefficient road and medium-adhesion coefficient road conditions. The results show that, compared with the drive slip control strategy that does not consider motor response errors, the proposed method increases the vehicle speed by 55.6% and 31.8% respectively to achieve a rapid start effect and effectively suppress the overshoot of the slip ratio in drive slip control, thus ensuring the safety and stability of vehicle driving.

To address the issue of severe drive wheel slippage in pure electric light-duty truck under operating conditions such as low-adhesion coefficient roads and full-throttle acceleration, a feedforward-feedback fusion anti-slip control strategy based on road adhesion coefficient is proposed. First, the road adhesion coefficient is calculated in real time based on the friction ellipse principle, and the basic driving torque that can be provided is pre-calculated in combination with wheel load. Simultaneously, the driving torque is computed based on the target angular velocity and actual angular velocity, and the feedforward and feedback components are superimposed for fusion control. A real-vehicle test is conducted on the pure electric light-duty truck under the full-throttle acceleration uphill condition. The results indicate that when the anti-slip function is disabled, the wheels experience severe slippage at the 4th, 11th, and 18th seconds, with the slip rate of the drive wheels exceeding 80%. When the proposed method is applied, the wheels slip at the 11th and 14th seconds, and the slip rate of the drive wheels is less than 60%, which significantly reduces the degree of drive wheel slippage.

Traditional Linear Quadratic Regulator (LQR) control in vehicle active suspension systems faces challenges in precisely directing optimization due to fixed weight coefficients, which fail to meet the demands of complex operating conditions. To address this issue, a permanent magnet linear motor actuator model, a seven-degree-of-freedom active suspension model and state-space equations are established. Multiple operating modes are defined according to different road profiles and vehicle speeds. The Analytic Hierarchy Process (AHP) is employed to determine weighted coefficients for the fitness function, and the Red-billed Blue Magpie Optimization (RBMO) algorithm is utilized to optimize the LQR weight coefficient matrices. Simulations under various operating conditions are conducted using MATLAB/Simulink. The results indicate that, compared with a passive suspension system, the proposed strategy improves vertical body acceleration, pitch angular acceleration and the dynamic deflection of the right front, left front, left rear, and right rear suspensions in comfort mode by 27%, 30%, 17%, 14%, 8%, and 15%, respectively. In safety mode, roll angular acceleration and the dynamic loads of the right front, left front, left rear, and right rear tires are improved by 41%, 15%, 18%, 15%, and 17%, respectively. In integrated mode, body acceleration pitch angular acceleration and roll angular acceleration are improved by 35%, 32%, and 48%, respectively. Further validation through CarSim/Simulink co-simulation demonstrates that the proposed control strategy achieves a notable level of performance improvement across different modes.

In order to improve smoothness and ride comfort of the vehicle, this paper proposes a Genetic Algorithm (GA) optimization method to address the subjectivity and limitations of performance optimization of Linear Quadratic Regulators (LQR) in the vertical dynamic system of vehicles. Firstly, a vertical dynamic model of the vehicle is established, an LQR controller is designed, and genetic algorithm is introduced for parameter optimization. The simulation results show that compared with passive suspension, the controller optimized by genetic algorithm demonstrates better control effect in performance index including body acceleration, suspension dynamic travel and tire dynamic displacement.