How to further improve the fuel economy and emission performance of hybrid vehicles through scientific and reasonable energy management strategies has become an urgent issue to be addressed at present. This paper proposes an energy management model based on speed prediction using Long Short-Term Memory (LSTM) neural networks. The initial learning rate and dropout probability of the LSTM speed prediction model are optimized using a Double Deep Q-Network (DDQN) algorithm. Furthermore, the LSTM speed prediction function is implemented within a Model Predictive Control (MPC) framework. A fuzzy logic-based driving mode recognition system classifies driving cycles and identifies real-time conditions. The fuzzy logic-based driving mode is used to divide the typical driving cycle into different driving modes, and the real-time driving modes are identified. The LSTM-MPC method achieves low RMSE across different prediction horizons. Using predicted power demand, battery SOC, and real-time power demand as inputs, the model implements MPC for real-time control. In our experiments, four prediction horizons (5 s, 10 s, 15 s, and 20 s) were set. The energy management strategy demonstrated optimal performance and the lowest fuel consumption at a 5 s horizon, with fuel usage at only 6.3220 L, saving 2.034 L compared to the rule-based strategy. Validation under the UDDS driving cycle revealed that the LSTM-MPC-DDQN strategy reduced fuel consumption by 0.2729 L compared to the rule-based approach and showed only a 0.0749 L difference from the DP strategy.

In this paper, the influence of temperature on the bonding strength of aluminum alloy joints under the full temperature field is studied. Based on the service temperature range of vehicle bonding structures, the failure strength of aluminum alloy joints at different temperature points, namely −40 °C, −20 °C, 0 °C, 25 °C (RT), 40 °C, 60 °C and 80 °C, is tested. The results showed that compared with the failure strength of the adhesive at −40 °C, it decreased by 47.69% and 68.15% at RT and 80°C, respectively; the Young’s modulus of the adhesive decreased by 57.63% and 75.42% at RT and 80°C, respectively; with the increase of temperature, the young’s modulus, tensile strength and failure strain of the adhesive decreased. In addition, the failure strength of aluminum alloy joints varied with temperature. To be specific, the stiffness of joints decreased gradually from 25 °C to 80 °C and increased gradually from −40 °C. Based on the failure strength data of bonded joints at different temperature points, the secondary stress failure criteria of bonded joints at different temperatures were obtained. Then, the surface function of failure criteria under the full temperature field was established to provide reference for failure prediction of bonded structures under different temperatures and stresses.

The bonding properties of BFRP composites have been demonstrated in previous studies, satisfying the strength and durability criteria. In this paper, a further in-depth study is carried out to bond Basalt Fibre Reinforced Polymer (BFRP) to Aluminum Alloy 5052 using two bonding agents, Aralite^® 2012 and Aralite^® 2015, respectively. The salt sprays under 80 °C, 3.5% NaCl environment; 80 °C, 5% NaCl environment; and pure water environment are also considered for comparison. Experimental results show that joints created with Araldite^® 2012 adhesives show higher average breaking strength (10.66 MPa at 720 h) and better ductility in a 5% NaCl environment. While the Araldite^® 2015 adhesive joint exhibits a combination of tear failure and interface failure, along with thin-layer cohesion failure. In the SEM images of the two adhesive joints’ failure, fiber pullout due to tension and damage at the interface between fiber and resin is apparent. To validate the experimental outcomes, water absorption testing, DSC, TGA-DTG, and FTIR experiments were conducted on dog-bone-shaped adhesive specimens to elucidate the results.

Object detection is essential for the perception systems of intelligent driving vehicles. RT-DETR has emerged as a prominent model. However, its direct application in intelligent driving vehicles still faces issues with the misdetection of occluded or small targets. To address these challenges, we propose a High-Precision Real-Time object detection algorithm (HPRT-DETR). We designed a Basic-iRMB-CGA (BIC) Block for a backbone network that efficiently extracts features and reduces the model’s parameters. We thus propose a Deformable Attention-based Intra-scale Feature Interaction (DAIFI) module by combining the Deformable Attention mechanism with the Intra-Scale Feature Interaction module. This enables the model to capture rich semantic features and enhance object detection accuracy in occlusion. The Local Feature Extraction Fusion (LFEF) block was created by integrating the local feature extraction module with the CNN-based Cross-scale Feature Fusion (CCFF) module. This integration expands the model’s receptive field and enhances feature extraction without adding learnable parameters or complex computations, effectively minimizing missed detections of small targets. Experiments on the KITTI dataset show that, compared to RT-DETR, HPRT-DETR improves mAP50 and FPS by 1.98% and 15.25%, respectively. Additionally, its generalization ability is assessed on the SODA 10M dataset, where HPRT-DETR outperforms RT-DETR in most evaluation metrics, confirming the model’s effectiveness.

Defect detection is crucial for quality control in industrial products. The defects in industrial products are typically subtle, leading to reduced accuracy in detection. Furthermore, industrial defect detection often necessitates high efficiency in order to meet operational demands. Deep learning-based algorithms for surface defect detection have been increasingly applied to industrial production processes. Among them, Swin-Transformer achieves remarkable success in many visual tasks. However, the computational burden imposed by numerous image tokens limits the application of Swin-Transformer. To enhance both the detection accuracy and efficiency, this paper proposes a linear attention mechanism based on pyramid pooling. It utilizes a more concise linear attention mechanism to reduce the computational load, thereby improving detection efficiency. Furthermore, it enhances global feature extraction capabilities through pyramid pooling, which improves the detection accuracy. Additionally, the incorporation of partial convolution into the model improves local feature extraction, further enhancing detection precision. Our model demonstrates satisfactory performance with minimal computational cost. It outperforms Swin-Transformer by 1.2% mAP and 52 FPS on the self-constructed SIM card slot defect dataset. When compared to the Swin-Transformer model on the public PKU-Market-PCB dataset, our model achieves an improvement of 1.7% mAP and 51 FPS. These results validate the universality of the proposed approach.