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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Wang, Ning Gong, Xun Hu, Yunfeng Sun, Yao State Key Laboratory of Automotive Simulation and Control College of Artificial Intelligence Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Telecommunications Engineering Jilin University Changchun China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Changchun China

ISBN: (纸本)9798350340488

Selective Catalytic Reduction (SCR) is a module used in the aftertreatment system to mitigate the nitrogen oxides (NOx) emissions, and it can be affected by cross-sensitivity, resulting in a decrease in control performance and even controller failure. A commonly adopted approach is estimating the cross-sensitivity factor to correct the downstream NOx concentration, but the current estimation strategies often incur relatively high costs. In this paper, we propose a novel and more cost-effective method based on data-driven for estimating the cross-sensitivity factor. Specifically, we design a network architecture based on Long Short-Term Memory (LSTM) and attention mechanism. LSTM is utilized to capture the non-linear relationship between input features, historical information, and the cross-sensitivity factor. The introduced attention mechanism allows the model to focus more on important information, thereby further improving the estimation accuracy. This method eliminates the need for complex mechanism modeling and parameter calibration, and reduces the computational burden during online operation, resulting in significant time and computational resource *** simulation experiments conducted transient bench data validate the excellent performance of the designed cross-sensitivity factor estimator, as evidenced by comparative analysis with three alternative methods, and demonstrate the rationality of feature selection through the feature ablation analysis. © 2023 IEEE.

关键词： aftertreatment attention cross-sensitivity factor Long Short-Term Memory

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Frame Optimization Design Based on Improved Grey Relational Analysis

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 1071-1080页

作者： WANG Shuang WANG Dengfeng NING Zhanjin HU Zhongjian Department of Emergency Rescue China Fire and Rescue InstituteBeijing 102202China State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China

For effectively improving the overall performance of fire truck frame structure,and solving the complexity of previous methods in the frame optimization design process,the traditional grey relational grade ranking needs to be ***,the first-order modal test was conducted to verify the validity of the initial frame ***,based on this model,a high-strength steel frame was designed to reduce deformation,maximum stress,and frame mass,and increase the fatigue life and the frequencies of the first bending modal and first torsional *** groups of sample points were generated through Hammersley ***,improved grey relational analysis with principal component analysis was proposed to realize the optimal design of the frame ***,the optimal combination of design parameters for the frame was obtained using the proposed ***,the optimized frame structure is found by comparing the models before and after optimization,and the mass is reduced by 14.8%.Moreover,the computational cost can be reduced by 135%when the proposed method is compared with the previous ***,the proposed method can effectively improve the performance of the frame and improve the computational efficiency.

关键词： fire truck frame structure optimization design improved grey relational analysis principal component analysis

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Rational adjustment to interfacial interaction with carbonized polymer dots enabling efficient large-area perovskite light-emitting diodes

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Light(Science & Applications) 2023年第6期12卷 1029-1039页

作者： Fan Yang Qingsen Zeng Wei Dong Chunyuan Kang Zexing Qu Yue Zhao Haotong Wei Weitao Zheng Xiaoyu Zhang Bai Yang State Key Laboratory of Supramolecular Structure and Materials College of ChemistryJilin UniversityChangchun 130012China Department of Materials Science Key Laboratory of Mobile Materials MOEState Key Laboratory of Automotive Simulation and ControlJilin UniversityChangchun 130012China Institute of Theoretical Chemistry and Laboratory of Theoretical&Computational Chemistry Jilin UniversityChangchun 130023China

Film uniformity of solution-processed layers is the cornerstone of large-area perovskite light-emitting diodes,which is often determined by the‘coffee-ring effect’.Here we demonstrate a second factor that cannot be ignored is the solid-liquid interface interaction between substrate and precursor and can be optimized to eliminate rings.A perovskite film with rings can be formed when cations dominate the solid-liquid interface interaction;whereas smooth and homogeneous perovskite emitting layers are generated when anions and anion groups dominate the *** is due to the fact that the type of ions anchored to the substrate can determine how the subsequent film *** interfacial interaction is adjusted using carbonized polymer dots,who also orient the perovskite crystals and passivate their buried traps,enabling a 225 mm^(2) large-area perovskite light-emitting diode with a high efficiency of 20.2%.

关键词： interaction perovskite interaction

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An S-scheme artificial photosynthetic system with H-TiO2/g-C3N4 heterojunction coupled with MXene boosts solar H2 evolution

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材料科学技术（英文版） 2025年第8期211卷 22-29页

作者： Depeng Meng Xiaowen Ruan Minghua Xu Dongxu Jiao Guozhen Fang Yu Qiu Yueyang Zhang Haiyan Zhang Sai Kishore Ravi Xiaoqiang Cui State Key Laboratory of Automotive Simulation and Control School of Materials Science and EngineeringKey Laboratory of Automobile Materials of MOEJilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy MaterialsJilin UniversityChangchun 130012China School of Energy and Environment City University of Hong KongTat Chee AvenueKowloonSARHong KongChina

Solar hydrogenproduction via water splitting is pivotal for solar energy harnessing,addressing key challenges in energy and environmental ***,two critical issues persist with single-component photocatalysts:suboptimal carrier transport and inadequate light *** heterojunction-based artificial photosynthetic systems like Z-scheme photocatalysts have been explored,their charge recombination and light harvesting efficiency are still unsatisfactory.S-scheme heterojunc-tions have gained attention in photocatalysis,owing to their pronounced built-in electric field and su-perior redox *** this study,we introduce a MXene-based S-scheme H-TiO2/g-C3N4/Ti3C2 het-erojunction(TCMX),synthesized through electrostatic *** as-prepared TCMX exhibited an excellent photocatalytic hydrogen evolution rate of 53.67 mmol g-1 h-1 surpassing the performance of commercial Rutile TiO2,H-TiO2,g-C3N4,and *** effectiveness of TCMX is largely due to the built-in electric field in the S-scheme heterojunction and the cocatalytic activity of MXene promoting rapid separation of photogenerated charges and resulting in well-separated electron and hole enriched *** study offers a new approach to enhance photocatalytic hydrogen evolution efficiency and paves the way for the future design of S-scheme heterojunctions.

关键词： S-scheme MXene Heterojunction Hydrogen Charge transfer

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Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5377-5391页

作者： Chu, Liang Hou, Zhuoran Jiang, Jingjing Yang, Jun Zhang, Yuanjian Jilin University State Key Laboratory of Automotive Simulation and Control Changchun130022 China Loughborough University Department of Aeronautical and Automotive Engineering LeicestershireLE11 3TU United Kingdom

Traffic condition prediction is crucial for executing traffic control and scheduling tasks within intelligent transportation systems. With diversified data sources, effectively modeling the complex spatial-temporal dependencies in the whole traffic network and predicting nonlinear general traffic condition changes become primary challenges for intelligent transportation systems. In this article, a double-layer spatial-temporal feature extraction and evaluation (DL-STFEE) model is proposed, aimed at accurately predicting the traffic condition transferring of the whole traffic network. Firstly, a public traffic dataset is processed to extract the spatial and temporal features of vehicles, as well as the clusters of traffic conditions of the entire traffic network. Secondly, a double-layer deep learning traffic condition predictor is proposed. The model incorporates a spatial-temporal feature extraction layer, which leverages the graph convolution network (GCN) and attention mechanism to obtain spatiotemporal features across the whole traffic network. Additionally, a spatial-temporal combination layer employs a high-dimensional self-attention mechanism to integrate features across spatial-temporal combinations, bolstering prediction accuracy. Finally, through rigorous experiments, the contributions of neural network structures on the spatial-temporal feature extraction are comprehensively analyzed and the experiments also validate the effectiveness of traffic condition prediction by the proposed DL-STFEE model. © 2016 IEEE.

关键词： Feature extraction

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Correlation Analysis of Drivers' Natural Driving Behavior Based on Kernel Density Estimation

SAE International journal of Sustainable Transportation, Ene...

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SAE International journal of Sustainable Transportation, Energy, Environment, and Policy 2023年第2期4卷

作者： Sun, Tianjun Hu, Hongyu Cai, Ronggui Yu, Tong Yu, Feng Jilin University Changsha Automobile Innovation Research Institute China Jilin University State Key Laboratory of Automotive Simulation and Control China Jilin University College of Automotive Engineering China

To investigate the interplay between driver handling behaviors, this article collects data on vehicle kinematic parameters characterizing driver handling characteristics under natural driving, estimates the probability density curves of the parameters using the kernel density method, and fits the curve equations. On this basis, a percentile correlation analysis was performed between the parameters to obtain the influence relationship between the handling behaviors. The results show that longitudinal maneuvers are frequent and intense in the 0-10 km/h speed range, lateral maneuvers are more intense in the 10-30 km/h speed range, and the interaction between longitudinal and lateral maneuvers is more intense in the acceleration phase. This study enriches the natural driving dataset and illustrates the correlation of driving behavior under natural driving, providing a theoretical and data basis for the development of driver-oriented intelligent driving technologies. © 2023 SAE International. All rights reserved.

关键词： Curve fitting

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Attitude control of Active Suspension All-Terrain Vehicle Based on LQR

Attitude Control of Active Suspension All-Terrain Vehicle Ba...

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2023中国汽车工程学会年会暨展览会

作者： Wu Liang Li Shucheng Xu Guangjian Zhang Weizhou Jia Weiwei The State Key Laboratory of Automotive Simulation and Control Jilin University Jilin University of Finance and Economics

ISBN: (纸本)9789819702510

In order to enhance the attitude control ability of all-terrain vehicles in complex terrain,a dual closed-loop control strategy based on LQR controller is designed with series active suspension all-terrain vehicles as the research ***,considering the kinematic and dynamic relationships between each mechanism of the all-terrain vehicle,a vehicle dynamic model based on the speed control of the actuator is constructed on the basis of the traditional active suspension force ***,a terrain estimation algorithm based on all-terrain vehicle model is studied,and a LQR controller suitable for attitude adjustment is ***,three pavement models are established to simulate real road conditions for verifying the accuracy of the dynamic model with terrain estimation model and the effectiveness of attitude *** results show that the collaborative control strategy proposed in this paper has good adaptability to working conditions,and the peak and Root mean square values of vertical displacement,pitch angle and roll angle of allterrain vehicle body are reduced by 30%-50%,which can greatly improve the ride comfort and attitude stability of all-terrain vehicle.

关键词： active suspension all-terrain vehicle attitude control LQR

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Modeling of contact temperatures and their influence on the tribological performance of PEEK and PTFE in a dual-pin-ondisk tribometer

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Friction 2023年第4期11卷 546-566页

作者： Zhibin LIN Ting QU Ke ZHANG Qingbin ZHANG Shengdao WANG Guibin WANG Bingzhao GAO Guowei FAN State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130025China Key Laboratory of High Performance Plastics Ministry of EducationJilin UniversityChangchun 130012China Clean Energy Automotive Engineering Center Tongji UniversityShanghai 201804China College of Instrumentation and Electrical Engineering Jilin UniversityChangchun 130061China

The direct blending of polyether ether ketone(PEEK)with a solid lubricant such as polytetrafluoroethylene(PTFE)improves its tribological performance,but compromises its outstanding mechanical properties and *** these negative effects might be circumvented via the hybrid wear method,the influence of the contact temperature between multiple sliding components acting together is not fully ***,an analytical temperature model considering the influence of both micro-and macro-thermal behavior is extended to predict the contact temperature of a dual-pin-on-disk hybrid wear *** interactions between several heat sources are investigated and experimentally *** analytical results show that the nominal temperature rise of the shared wear track is determined by the combined effect of the heat generated by both pin components,while the rise in flash temperature at the region in contact with each pin component is dependent upon its individual characteristics and working ***,while different temperature peaks can coexist in the shared wear track,the maximum value dominates the performance of the *** the experimentally investigated PEEK–PTFE–steel hybrid wear system,the formation of tribofilms is blocked,and the hybrid wear system fails,when the peak temperature exceeds the glass transition temperature of both pins due to an increase in applied load.

关键词： contact temperature frictional heating dual-pin-on-disk hybrid wear

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BPNN Based Energy Management Strategy for Pure Electric Vehicles 7

BPNN Based Energy Management Strategy for Pure Electric Vehi...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Zhang, Shilong Wang, Fei Lu, Henghui Yang, Shen State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China

ISBN: (纸本)9798350340488

In order to enhance the real-time performance of model predictive control (MPC) based energy management strategy (EMS), this paper proposes a real-time automotive energy management strategy based on backpropagation neural network (BPNN) approximation of MPC controller. Firstly, establish a pure electric vehicle (PEV) simulation model, then design a BP neural network, and the joint simulation model is established to predict the speed, the energy management is finally realized by combining the energy consumption evaluation model. Compared with the EMS based on MPC controller, the energy-saving control effect is marginally diminished, and the real-time performance of vehicle speed track prediction is greatly improved. The proposed EMS can optimize the driver's speed trajectory and control vehicle movement in real-time, resulting in better comprehensive energy-saving control effects compared to traditional MPC. © 2023 IEEE.

关键词： Backpropagation neural network Electric vehicles Energy management strategy Model predictive control Velocity prediction

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Lane change trajectory planning method for intelligent vehicles considering surrounding risk 7

Lane change trajectory planning method for intelligent vehic...

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7th CAA International Conference on Vehicular control and Intelligence, CVCI 2023

作者： Liu, Ke Wang, Honglin Fu, Yao Liu, Haoran The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China

ISBN: (纸本)9798350340488

Intelligent vehicle trajectory planning is a multi-objective coupling optimization problem with multiple constraints. Rapid generation of collision-free smooth trajectories that meet kinematic and dynamic constraints can improve the safety and comfort of intelligent vehicle lane-changing process. In order to improve the efficiency of trajectory planning, this paper uses the path and speed decoupling method to generate a feasible lane-changing trajectory set. First, the B-spline curve is used to generate a feasible lane-changing path, and then the speed planning is completed based on Dynamic programming (DP) and Quadratic programming (QP). Considering safety, comfort and efficiency, a comprehensive cost function is established, and the trajectory with the smallest cost function is selected as the output target. The safety cost function is based on the theory of driving risk field, and the safety potential energy index is used to evaluate the safety of the trajectory, which can quantify the risk level of the surrounding traffic factors on the whole process of lane-changing. The simulation results show that the proposed method can quickly plan a reasonable trajectory in the lane-changing scene, and can effectively avoid collision with dynamic obstacles while maintaining safety and efficiency. © 2023 IEEE.

关键词： comprehensive cost function intelligent vehicle surrounding risk trajectory planning

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