Zhumu Fu

To address the coordinated control problem of safety and comfort for connected logistic fuel cell hydrogen electric vehicles platoons in a vehicle-following environment, a Lyapunov drift-plus-penalty cooperative tracking control strategy based on speed planning is proposed. The designed algorithm is structured into two layers, the upper-layer controller, which aims to optimize energy consumption and comfort, utilizes the sequential quadratic programming algorithm to plan the overall future speed trajectory of the platoon of fuel cell hydrogen electric vehicles, while adhering to constraints on traction force and speed. The lower-layer controller focuses on platoon stability, employing Lyapunov optimization principles to transform the long-term stability control problem into a constrained optimization problem for each time slice. Using the drift-plus-penalty algorithm, the minimum Lyapunov drift and the objective function are resolved to establish the control command for tracking the platoon’s position and speed trajectory. The proposed speed-planning-based distributed nonlinear fuel cell hydrogen electric vehicles platoon control strategy achieves low computational cost, enhanced comfort, and improved energy efficiency. Simulation results demonstrate that, compared to existing classical algorithms, the proposed speed-planning-based platoon control algorithm achieves higher computational efficiency, better comfort, and optimal fuel economy.

The quality of the yarn directly determines the performance of the fabric. Existing methods lack three-dimensional rapid evaluation of yarn quality. To overcome the above problem, this paper designs a low-cost multi-camera collaborative system to simultaneously and accurately capture the same segment of yarn image from different perspectives. Specifically, this study introduces a novel yarn image segmentation method, including Local vertical interruption, Horizontal clearing algorithm, and Morphological operation, to accurately obtain the appearance diameter value of the same yarn segment from four views. Subsequently, a 3D model of yarn core for solving elliptical parameters based on multiple camera positions is established and validated. Then, the elliptical cross-sectional area of the yarn and its equivalent circular diameter are obtained. Four types with the same kind of yarns are tested using the proposed method and Uster Evenness Tester 5. By comparing the short-term, periodic, and long-term variation of yarn core, including the diameter value, three-dimensional unevenness coefficient, spectrogram, and variation length curve under different cutting lengths, it is demonstrated that the proposed method is consistent with the Uster measurement results. The experimental results highlight that the proposed method can accurately and effectively evaluate the yarn unevenness.

Aiming to solve the problems with easy false detection of small targets in river floating object detection and deploying an overly large model, a new method is proposed based on improved YOLOv5s. A new data augmentation method for small objects is designed to enrich the dataset and improve the model’s robustness. Distinct feature extraction network levels incorporate different coordinate attention mechanism pooling methods to enhance the effective feature information extraction of small targets and improve small target detection accuracy. Then, a shallow feature map with 4-fold down-sampling is added, and feature fusion is performed using the Feature Pyramid Network. At the same time, bilinear interpolation replaces the up-sampling method to retain feature information and enhance the network’s ability to sense small targets. Network complex algorithms are optimized to better adapt to embedded platforms. Finally, the model is channel pruned to solve the problem of difficult deployment. The experimental results show that this method has a better feature extraction capability as well as a higher detection accuracy. Compared with the original YOLOv5 algorithm, the accuracy is improved by 15.7%, the error detection rate is reduced by 83% in small target task detection, the detection accuracy can reach 92.01% in edge testing, and the inference speed can reach 33 frames per second, which can meet the real-time requirements.

In this paper, in order to extend battery lifespan and lift power performance in hybrid batteries/supercapacitors electrical vehicles, a new energy management strategy is proposed based on model prediction control and adaptive method. Firstly, models of batteries and supercapacitors are built, which are then adapted and simplified to develop state space expression. Secondly, a series of reference values of battery power and supercapacitor state of charge for model prediction methods are properly calculated by the adaptive method. Thereafter, an energy management strategy based on the model prediction control method is designed to allocate the output power of batteries and supercapacitors within constraints, which guarantees batteries lifespan and the power performance of vehicle. Finally, simulation and experiment results are provided to evaluate battery lifespan and power performance of vehicles under HWFET and UDDS road conditions. The results obtained show that the proposed strategy, compared with the former methods, reduce average power and power variation of batteries, and effectively utilize supercapacitors depending on the power demand, which can extend battery lifespan and lift the power performance of vehicle.

To improve hybrid electric vehicle (HEV) fuel efficiency further, the decision as to whether the internal combustion engine (ICE) should start or stop is important. This paper presents a novel optimization method of the ICE start-stop by using the model predictive control (MPC) based on equivalent consumption minimization strategy (ECMS). The optimization method and flow of the ICE start-stop are described in detail. Three torque-split control strategies are proposed for the comparison purpose. From the ICE operating points, the fuel consumption and the battery SOC, simulation results reveal that the transient MPC strategy with ICE start-stop has a huge potential for improving the overall fuel economy.