Hao Zhang

Hao Zhang

Ph.D.
Research Fellow

I am the Associate Director of the ETAIC (Embodied Technology for Autonomy, Intelligence, and Control) Research Lab at the University of Texas at Arlington, working with Prof. Eric Tseng, a member of the National Academy of Engineering. I am currently conducting postdoctoral research in the Safe AI Lab at Carnegie Mellon University, working with Prof. Ding Zhao. Prior to this, I worked as a Research Fellow at Tsinghua University and a Visiting Researcher at University College of London. I received my Ph.D. from School of Vehicle and Mobility at Tsinghua University, co-advised by Prof. Zhi Wang and Prof. Shengbo Eben Li.

I was the recipient of the Outstanding Ph.D. Graduate, the Outstanding Doctoral Dissertation Award, and the “Shuimu Scholar” talent program at Tsinghua University in 2024. My doctoral research directly contributed to the successful industry deployment of reinforcement learning methods in developing advanced driver-assistance systems and energy management systems, significantly enhancing the operational efficiency of connected and automated vehicles. Notably, the control systems I developed have been implemented in leading automotive companies such as BYD Auto, Dongfeng Motor, SAIC Motor, and start-up automotive companies such as Hybot.

I have authored over 40 peer-reviewed SCI journal and conference papers and am a co-inventor on more than 20 patents. My current research focuses on multi-agent reinforcement learning theory, the integration of large language models with closed-loop control, and human–robot collaboration using game theory. I aim to advance AI agents for real-world deployment in autonomous systems (including robotics and vehicles) and distributed energy systems.

💻 Research Interests

  • Embidied AI theory: Multi-agent reinforcement learning, LLM with closed-loop capability, and game theory
  • Micro-mobility Device: Trustworthy AI methods for motion control of assistive and mobile robots
  • Automotive/Energy: Scalable AI foundation for ADAS, EMS, and related systems

🔥 News

📝 Featured Publications

IEEE TITS
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Multi-Scale Reinforcement Learning of Dynamic Energy Controller for Connected Electrified Vehicles

Hao Zhang, Nuo Lei, Shengbo Eben Li, Junzhi Zhang, Zhi Wang
Preprint

  • We proposed a multi-horizon reinforcement learning (MHRL) featuring a novel state representation and coordinated training of sub-networks across multiple time scales, which greatly improves fuel economy in real-world driving.
IEEE TITS
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Bi-Level Transfer Learning for Lifelong-Intelligent Energy Management of Electric Vehicles

Hao Zhang, Nuo Lei, Wang Peng, Bingbing Li, Shujun Lv, Boli Chen, Zhi Wang
In IEEE Transactions on Intelligent Transportation Systems

Industrial Collaborator: BYD Auto

  • We proposed a bi-level transfer approach with MAML to realize cross-platform transferable and online-adaptive EMS for REEVs. It contributed to the successful industry deployment of RL methods, implemented in leading automotive company - BYD Auto, significantly enhancing the REEV efficiency.
Energy
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Modeling and control system optimization for electrified vehicles: A data-driven approach

Hao Zhang, Nuo Lei, Boli Chen, Bingbing Li, Rulong Li, Zhi Wang
In Energy

Industrial Collaborator: Dongfeng Motor

  • This paper develops a high-fidelity PHEV model integrating physical and data-driven approaches, and proposes a real-vehicle control framework that combines horizon-extended reinforcement learning with ECMS to improve practical energy management.
IEEE TVT
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Theory-Constrained Neural Network with Modular Interpretability for Fuel Cell Vehicle Modelling

Nuo Lei, Hao Zhang* (Corresponding Author), Hong Wang, Zunyan Hu, Hu Chen, Jingjing Hu, Zhi Wang
In IEEE Transactions on Vehicular Technology

Industrial Collaborator: Hybot

  • We proposed a theory-constrained neural network (TCNN) that integrates physical principles without sacrificing accuracy. A theory-guided filter ensures sub-module interpretability, and sub-networks are individually trained under theoretical constraints with a CNN-BiLSTM-MHSA architecture enhances overall accuracy. Results demonstrate significant improvements in fitting accuragy for fuel cell modeling.
Aplied Energy
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Coupled velocity and energy management optimization of connected hybrid electric vehicles for maximum collective efficiency

Hao Zhang, Boli Chen, Nuo Lei, Bingbing Li, Chaoyi Chen, Zhi Wang
In Applied Energy

  • This paper proposes an efficient nested parallel optimization (NPO) strategy based on the ‘1+n’ mixed platoon concept, integrating Pontryagin’s minimum principle into a constrained control framework to jointly optimize speed planning and energy control of heterogeneous CAVs. The method reduces the control state-action dimensions while balancing traffic efficiency and fuel economy across intersections.
IEEE TTE
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Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

Hao Zhang, Boli Chen, Nuo Lei, Bingbing Li, Rulong Li, Zhi Wang
In IEEE Transactions on Transportation Electrification

Industrial Collaborator: Dongfeng Motor

  • This research proposes a model-free multistate deep reinforcement learning (DRL) algorithm for integrated thermal and energy management (ITEM) of multimode connected PHEVs, leveraging AI control and traffic preview to enhance EMS performance under cold climate conditions.

Preprints

  • Zhang H, Lei N, Li E S, et al. Multi-scale reinforcement learning of dynamic energy controller for connected electrified vehicles. IEEE Transactions on Intelligent Transportation Systems, 2025, to be published.
  • Zhang H, Dong J, Lei N, et al. Optimal Vehicle Dynamics and Powertrain Control of Carbon-Free Autonomous Vehicles: Large Language Model Assisted Heterogeneous-Agent Learning. Energy, 2025, under review.
  • Li B, Wang K, Zhang H, et al. A Globally Tuned Load-Leveling Strategy for Energy Management of Hybrid Electric Vehicles. Energy, 2025, under review.

Selected Papers

  • Zhang H, Lei N, Chen B, et al. Bi-level transfer learning for lifelong intelligent energy management of electric vehicles. IEEE Transactions on Intelligent Transportation Systems, 2025, Early Access.
  • Zhang H, Yang G, Lei N, et al. Scenario-Aware Electric Vehicle Energy Control with Enhanced Vehicle-to-Grid Capability: A Multi-Task Reinforcement Learning Approach. Energy, 2025,138189.
  • Zhang H, Xu J, Lei N, et al. Surrogate-enhanced multi-objective optimization of on-board hydrogen production device for carbon-free heavy-duty vehicles. Energy, 2025,333:137369.
  • Lei N, Zhang H, Hu J, et al. Sim-to-real design and development of reinforcement learning-based energy management strategies for fuel cell electric vehicles. Applied Energy, 2025,393:126030.
  • Lei N, Zhang H* (Corresponding Author), Wang H, et al. Theory-Constrained Neural Network with Modular Interpretability for Fuel Cell Vehicle Modelling. IEEE Trans. on Vehicular Technology, 2025, Early Access.
  • Zhang H, Lei N, Chen B, et al. Modeling and control system optimization for electrified vehicles: A data-driven approach. Energy, 2024,311:133196.
  • Zhang H, Chen B, Lei N, et al. Coupled velocity and energy management optimization of connected hybrid electric vehicles for maximum collective efficiency. Applied Energy, 2024,360:122792.
  • Li B, Zhuang W, Zhang H, et al. Traffic-aware ecological cruising control for connected electric vehicle. IEEE Trans. on Transportation Electrification. 2024,10:5225-5240.
  • Zhang H, Chen B, Lei N, et al. Integrated thermal and energy management of connected hybrid electric vehicles using deep reinforcement learning. IEEE Trans. on Transportation Electrification, 2024,10:4594-4603.
  • Lei N, Zhang H, and Wang Z. A comprehensive study of various carbon-free vehicle propulsion systems utilizing ammonia-hydrogen synergy fuel. eTransportation, 2024,20:100332.
  • Zhang H, Lei N, Wang Z. Ammonia-hydrogen propulsion system for carbon-free heavy-duty vehicles. Applied Energy, 2024,369:123505.
  • Sun H, Li B, Zhang H, et al. Ecological electric vehicle platooning: an adaptive tube-based distributed model predictive control approach. IEEE Trans. on Transportation Electrification, 2024,11:1048-1060.
  • Lei N, Zhang H, Li R, et al. Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization. Energy Conversion and Management, 2024;299:117814.
  • Zhang H, Lei N, Chen B, et al. Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles. Energy, 2023,283:128514.
  • Li B, Zhuang W, Zhang H, et al. A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes. Energy, 2023,289:129916.
  • Lei N, Zhang H, Wang H, et al. An improved co-optimization of component sizing and energy management for hybrid powertrains with high-fidelity model. IEEE Trans. on Vehicular Technology, 2023,72:15585-15596.
  • Zhang H, Liu S, Lei N, et al. Learning-based supervisory control of dual mode engine-based hybrid electric vehicle with reliance on multivariate trip information. Energy Conversion and Management, 2022,257:115450.
  • More publications can be found on my Google Scholar homepage.

🎖 Honors and Awards

  • 2024 Plenary Talk at 2024 China SAE Annual Conference on Advanced Powertrains
  • 2024 “Shuimu Tsinghua Scholar” Talents Program, Tsinghua University
  • 2024 Outstanding Doctoral Dissertation Award, Tsinghua University
  • 2024 Outstanding Ph.D. Graduate (top 4%), Tsinghua University
  • 2023 Comprehensive Excellence Scholarship, Tsinghua University
  • 2022 Best Paper Award in the 2022 CSICE Conference on TEIP in Shanghai, China
  • 2022 Comprehensive Excellence Scholarship, Tsinghua University
  • 2021 Excellent Student Leader, Tsinghua University
  • 2021 Comprehensive Excellence Scholarship, Tsinghua University
  • 2020 Comprehensive Excellence Scholarship, Tsinghua University
  • 2018 National Scholarship, Ministry of Education of China
  • 2018 Best Paper Award in the 2018 IEEE ACES Conference in Denver, U.S.
  • 2017 National Scholarship, Ministry of Education of China
  • 2016 National Scholarship, Ministry of Education of China

💬 Invited Talks

  • 2024.08, Plenary Talk, APC 2024: Joint Annual Conference on Advanced Powertrains - China SAE, “Data-Driven Modeling of Electric Powertrains and Reinforcement Learning-Based Optimal Control,” China SAE, Zhenjiang, China

📚 Service

Reviewer

  • Reviewer, Journal, IEEE Transactions on Intelligent Transportation Systems
  • Reviewer, Journal, IEEE Transactions on Intelligent Vehicles
  • Reviewer, Journal, IEEE Transactions on Transportation Electrification
  • Reviewer, Journal, IEEE Transactions on Vehicular Technology
  • Reviewer, Journal, Renewable and Sustainable Energy Reviews
  • Reviewer, Journal, Applied Energy
  • Reviewer, Journal, Energy
  • Reviewer, Journal, Energy Conversion and Management
  • Reviewer, Journal, Engineering Applications of Artificial Intelligence
  • Reviewer, Conference, IEEE Intelligent Transportation Systems Conference (ITSC)

Teaching

  • Guest Lecturer, Fundamentals of Automotive Powertrains，Tsinghua University, Fall 2023
  • Teaching Assistant, Frontiers in Dynamic Systems and Control，Tsinghua University, Fall 2021

Mentorship

  • Since 2024, independently mentored postgraduate and undergraduate students from THU, ZJU, UCL, etc. Several undergraduate mentees have published papers in top-tier journals such as Applied Energy, Energy, etc.
  • Since 2020, assisted in the supervision of 3 Ph.D. students, 5 master’s students; mentored over a dozen undergraduate students, their work received multiple honors including Tsinghua University and Beijing Outstanding Undergraduate Thesis Awards.