I am a dual-degree Bachelor of Engineering student in Automotive Engineering at Chang'an University and University College Dublin. I am also a research assistant at the University of Alberta NODE Lab and Tsinghua University's State Key Laboratory of Intelligent Green Vehicle and Mobility. My research connects multimodal perception, state estimation, motion planning, model predictive control, and low-carbon vehicle energy management.
Multimodal sensor fusion, state estimation, and robust environmental understanding.
Vehicle trajectory prediction, autonomous-driving decision-making, and motion planning.
Learning-enhanced model predictive control and low-carbon energy management for hybrid vehicles.
Bachelor of Engineering in Automotive Engineering, Dual Degree, Xi'an, China
Bachelor of Engineering in Automotive Engineering, Dual Degree, Dublin, Ireland
Intelligent Vehicles, Energy Management, Machine Learning, Optimization, MPC
Python, PyTorch, TensorFlow, Computer Vision
MATLAB/Simulink, ROS, SolidWorks, LaTeX, Robotics
Research Assistant, Edmonton, Canada
Research Assistant, Beijing, China
Research Assistant, Xi'an, China
Zhengxian Chen, Qihang He, Chenggang Zhang, Han Hai, Yize Sun, Jieyu Wang, Chaosheng Huang, and Jun Li. Energy, 360, 141763 (2026). DOI: 10.1016/j.energy.2026.141763.
Chenggang Zhang, Yize Sun, Jiaming Zhang, Chaosheng Huang, and Jun Li. Accepted at the IEEE Intelligent Transportation Systems Conference (ITSC 2026, Oral).
Zhengxian Chen, Chenggang Zhang, Yuyang Pan, Jingyue Zheng, Fojin Zhou, Peng Ni, Yichong Li, Junyi Wang, Tianyang Gong, Xun Hu, Minhao Gao, Yuxing Wu, Mingyang Li, Pengyi Xiao, Mingqiang Wang, and Jun Li. Presented at the Applied Energy Symposium and Forum: Renewable Energy Matrix (REM 2025, Oral), and published in Energy Proceedings, 60 (2026). DOI: 10.46855/energy-proceedings-12047. Selected as an Outstanding Paper and invited to submit an extended version to a special issue of Applied Energy.
Zhengxian Chen, Yuyang Pan, Tianyang Gong, Chenggang Zhang, Yunwei Li, Jieyu Wang, Huanan Wang, Wenfeng Guo, Mingqiang Wang, Chaosheng Huang, and Jun Li. Scientific Data (2026). DOI: 10.1038/s41597-026-07640-2.
Jiaju Kang, Puyu Han, Xu Wang, Weichao Pan, Ruida Liu, Chenggang Zhang, and Keyan Jin. In Advanced Intelligent Computing Technology and Applications, pp. 343-353 (2026). Oral paper at ICIC 2026. DOI: 10.1007/978-981-92-3560-5_28.
Tianyang Gong, Zhengxian Chen, Yuyang Pan, Yuxing Wu, Chenggang Zhang, Peng Ni, Mingqiang Wang, and Jun Li. In the 3rd IEEE International Conference on Artificial Intelligence and Automation Control (AIAC), pp. 427-431 (2025). DOI: 10.1109/AIAC68175.2025.11332328.
Developing a learning-based multimodal sensor-fusion framework for robust state estimation, perception, and motion planning in autonomous vehicles. The work fuses onboard sensor and visual data and is validated with real vehicle data and high-fidelity ROS simulations.
OngoingDeveloped a prediction-embedded MPC framework integrating Attention-LSTM short-horizon power-demand prediction with multi-source energy management. The strategy reduced equivalent ammonia consumption by 1.45%, increased system efficiency from 42.18% to 42.80%, maintained execution below 20 ms per control step, and was validated through hardware-in-the-loop testing.
Developed a rule-based strategy coordinating the diesel engine, hydrogen fuel cell, and battery, with key parameters optimized using the Sparrow Search Algorithm. MATLAB simulation identified efficient operating ranges of 40-45 kW for the diesel engine and 5-10 kW for the fuel cell.
Led the design of a mechanical-metamaterial-inspired isolator integrating positive- and negative-stiffness elements in a monolithic 3D-printed TPU structure. Parametric design, finite-element simulation, prototyping, and dynamic verification produced a 9.75-10.30 N force plateau and an effective vibration-isolation region.
I am glad to connect with researchers and students working on autonomous driving, multimodal perception, motion planning, model predictive control, and sustainable vehicle energy management.