World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
69
Citations
19606
World Ranking
414
National Ranking
58

Electronics and Electrical Engineering

D-Index
69
Citations
19603
World Ranking
958
National Ranking
166

Fengchun Sun publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Fengchun Sun sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 187 publications — 39th percentile

39% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Fengchun Sun D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Fengchun Sun sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 69 D-Index — 88th percentile

88% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Overview

Fengchun Sun is affiliated with the Beijing Institute of Technology in China. The primary area of their scholarly focus is engineering, with a significant volume of work concentrated in automotive engineering and electrical and electronic engineering. They have also contributed to control and systems engineering, renewable energy, sustainability and the environment, and aerospace engineering.

Their research topics are concentrated on advanced battery technologies and electric vehicle systems. These include:

  • Advanced Battery Technologies Research
  • Electric Vehicles and Infrastructure
  • Electric and Hybrid Vehicle Technologies
  • Advancements in Battery Materials
  • Vehicle emissions and performance
  • Advanced Battery Materials and Technologies
  • Fuel Cells and Related Materials

Fengchun Sun has authored numerous papers, with frequent publications appearing in venues such as Applied Energy, Energy, IEEE Transactions on Vehicular Technology, SSRN Electronic Journal, and the Journal of Power Sources. Notable recent papers include:

  • Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives, 2020, Renewable and Sustainable Energy Reviews
  • Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles, 2020, Applied Energy
  • China's battery electric vehicles lead the world: achievements in technology system architecture and technological breakthroughs, 2022, Green Energy and Intelligent Transportation
  • Deep learning to estimate lithium-ion battery state of health without additional degradation experiments, 2023, Nature Communications
  • Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning, 2022, Energy Storage Materials

Frequent collaborating authors with Fengchun Sun include Rui Xiong, Chao Sun, Weixiang Shen, Zhenpo Wang, and Hongwen He.

Best Publications

  • Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles

    Rui Xiong;Jiayi Cao;Quanqing Yu;Hongwen He

  • State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model

    Hongwen He;Rui Xiong;Xiaowei Zhang;Fengchun Sun

  • A review of supercapacitor modeling, estimation, and applications: A control/management perspective

    Lei Zhang;Lei Zhang;Xiaosong Hu;Zhenpo Wang;Fengchun Sun

  • Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

    Fengchun Sun;Xiaosong Hu;Yuan Zou;Siguang Li

  • Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives

    Rui Xiong;Yue Pan;Weixiang Shen;Hailong Li

  • A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles

    Rui Xiong;Rui Xiong;Fengchun Sun;Zheng Chen;Hongwen He

  • Velocity Predictors for Predictive Energy Management in Hybrid Electric Vehicles

    Chao Sun;Xiaosong Hu;Scott J. Moura;Fengchun Sun

  • Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

    Rui Xiong;Hongwen He;Fengchun Sun;Kai Zhao

  • Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles

    Rui Xiong;Wanzhou Sun;Quanqing Yu;Quanqing Yu;Fengchun Sun

  • China’s Battery Electric Vehicles Lead the World: Achievements in Technology System Architecture and Technological Breakthroughs

    Unknown

  • Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles

    Chao Sun;Fengchun Sun;Hongwen He

  • Dynamic Traffic Feedback Data Enabled Energy Management in Plug-in Hybrid Electric Vehicles

    Chao Sun;Scott Jason Moura;Xiaosong Hu;J. Karl Hedrick

  • Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles

    Lei Zhang;Xiaosong Hu;Zhenpo Wang;Fengchun Sun

  • Deep learning to estimate lithium-ion battery state of health without additional degradation experiments

    Unknown

  • An Overview on Thermal Safety Issues of Lithium-ion Batteries for Electric Vehicle Application

    Jianan Zhang;Lei Zhang;Fengchun Sun;Zhenpo Wang

  • A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique

    Fengchun Sun;Rui Xiong;Hongwen He

  • Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system

    Shuo Zhang;Rui Xiong;Fengchun Sun

  • Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer

    Xiaosong Hu;Fengchun Sun;Yuan Zou

  • Battery Degradation Prediction Against Uncertain Future Conditions with Recurrent Neural Network Enabled Deep Learning

    Unknown

  • A Novel Fractional Order Model for State of Charge Estimation in Lithium Ion Batteries

    Rui Xiong;Jinpeng Tian;Weixiang Shen;Fengchun Sun

  • Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle

    Teng Liu;Yuan Zou;Dexing Liu;Fengchun Sun

  • Reinforcement learning-based real-time energy management for a hybrid tracked vehicle

    Yuan Zou;Teng Liu;Dexing Liu;Fengchun Sun

  • A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles

    Rui Xiong;Rui Xiong;Fengchun Sun;Xianzhi Gong;Chenchen Gao

Frequent Co-Authors

Rui Xiong
Rui Xiong Beijing Institute of Technology
Hongwen He
Hongwen He Beijing Institute of Technology
Zhenpo Wang
Zhenpo Wang Beijing Institute of Technology
Xiaosong Hu
Xiaosong Hu Chongqing University
David G. Dorrell
David G. Dorrell University of Turku
Weixiang Shen
Weixiang Shen Swinburne University of Technology
Scott J. Moura
Scott J. Moura University of California, Berkeley
Huei Peng
Huei Peng University of Michigan–Ann Arbor
Chao-Yang Wang
Chao-Yang Wang Pennsylvania State University
J. Karl Hedrick
J. Karl Hedrick University of California, Berkeley

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