Chaochun Yuan

Potholes are typical negative road obstacles that can significantly compromise vehicle safety and ride comfort when traversed at inappropriate speeds. To address this issue, this paper proposes a pothole-detection-based, comfort-oriented pothole traversal algorithm that integrates multi-sensor fusion perception, comfort-constrained speed planning, and fuzzy control. A camera and a single-point ranging LiDAR are first fused to extract key geometric features of potholes, including contour, area, and depth. Based on these features, a vehicle–pothole dynamic model is developed in ADAMS to quantify the influence of pothole area and depth on vehicle vertical vibration. The vertical frequency-weighted root-mean-square (RMS) acceleration is adopted as the ride comfort indicator, based on which the maximum allowable traversal speed under different pothole geometries is determined. Furthermore, a longitudinal pothole traversal control strategy based on fuzzy theory is designed to regulate vehicle acceleration, enabling the vehicle to reach the comfort-constrained limiting speed within a finite preview distance while ensuring braking safety. The proposed method is validated through multi-scenario co-simulations using MATLAB/Simulink and CarSim, as well as real-vehicle experiments. Results demonstrate that the proposed strategy can effectively adjust vehicle speed before pothole traversal, satisfying comfort constraints and improving ride comfort without sacrificing driving safety.

Motion planning of platoon vehicles (PVs) in complex environments plays a crucial role in balancing traffic efficiency and safety. When front vehicles exhibit risky behaviors such as emergency braking or abrupt lateral deviation, relying solely on deceleration to ensure the safety of PVs inevitably compromises formation efficiency. Strategic spacing among PVs, guided by the Automatic Emergency Steering (AES) mechanism, is essential for collision avoidance and operational efficiency in diverse emergency scenarios. Accordingly, a dynamic risk assessment model is integrated into the decision-making module to evaluate PV risk levels based on their current states and surrounding interactions. A formation control mode suitable for complex scenarios is designed, supporting both simultaneous and sequential lane-changing maneuvers for PVs. In the motion planning module, a quintic* algorithm integrating scatter point planning and curve fitting is used to generate multiple feasible routes. Finally, Model Predictive Control (MPC) is applied to track the planned trajectories, verifying the practicality and effectiveness of the proposed techniques. Experimental results demonstrate the effectiveness of the proposed method in preventing collisions under complex conditions while maintaining traffic efficiency within the formation.

Remaining useful life (RUL) serves as a pivotal metric for quantifying lithium-ion batteries’ state of health (SOH) in electric vehicles and plays a crucial role in ensuring their safety and reliability. In order to achieve accurate and reliable RUL prediction, a novel RUL prediction method which employs a back propagation (BP) neural network based on the Harris Hawks optimization (HHO) algorithm is proposed. This method optimizes the BP parameters using the improved HHO algorithm. At first, the circle chaotic mapping method is utilized to solve the problem of the initial value. Considering the problem of local convergence, Gaussian mutation is introduced to improve the search ability of the algorithm. Subsequently, two key health factors are selected as input features for the model, including the constant-current charging isovoltage rise time and constant-current discharging isovoltage drop time. The model is validated using aging data from commercial lithium iron phosphate (LiFePO₄) batteries. Finally, the model is thoroughly verified under an aging test. Experimental validation using training sets comprising 50%, 60%, and 70% of the cycle data demonstrates superior predictive performance, with mean absolute error (MAE) values below 0.012, root mean square error (RMSE) values below 0.017 and mean absolute percentage error (MAPE) within 0.95%. The results indicate that the model significantly improves prediction accuracy, robustness and searchability.

This study develops an energy management strategy (EMS) for hybrid combine harvesters to address fluctuating power demands in agricultural operations. By segmenting harvesting processes into quasi-periodic cycles linked to machine dynamics, the method integrates component-specific power models (header, conveyor, drum) for accurate energy estimation. Real-time feed rate adjustments are achieved through dynamic responses of critical components, optimizing cycle duration and power allocation. A genetic algorithm synchronizes energy distribution and cycle timing to minimize fuel consumption. Validated via AMESim/Simulink co-simulation with dual engine models, the strategy reduces fuel use by 21.1% compared to conventional systems. Key innovations include quasi-periodic load segmentation, component-response-based feed rate prediction, and GA-driven multi-objective optimization. The approach enhances adaptability to variable harvesting conditions, offering a scalable framework for energy-efficient electrification in agriculture. Results demonstrate significant potential for hybrid systems in reducing operational costs and emissions while maintaining productivity under dynamic workloads.