World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
45
Citations
8077
World Ranking
3558
National Ranking
566

Fengxiang Wang publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Fengxiang Wang sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 326 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Fengxiang Wang D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Fengxiang Wang sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 45 D-Index — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Fengxiang Wang is affiliated with the Chinese Academy of Sciences in China. Their research primarily focuses on the field of Engineering, with significant contributions to Electrical and Electronic Engineering, Control and Systems Engineering, Mechanical Engineering, Automotive Engineering, and Aerospace Engineering.

The scientist's work covers various specialized topics, including:

  • Multilevel Inverters and Converters
  • Sensorless Control of Electric Motors
  • Advanced DC-DC Converters
  • Microgrid Control and Optimization
  • Electric Motor Design and Analysis
  • Iterative Learning Control Systems
  • Magnetic Bearings and Levitation Dynamics

Fengxiang Wang has published extensively, with frequent appearances in high-impact venues. The main publication venues include:

  • IEEE Transactions on Industrial Electronics
  • IEEE Transactions on Power Electronics
  • IEEE Transactions on Energy Conversion
  • 2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE)
  • IEEE Journal of Emerging and Selected Topics in Power Electronics

Some of their recent published papers are:

  • "Latest Advances of Model Predictive Control in Electrical Drives-Part I: Basic Concepts and Advanced Strategies," 2021, IEEE Transactions on Power Electronics
  • "Latest Advances of Model Predictive Control in Electrical Drives-Part II: Applications and Benchmarking With Classical Control Methods," 2021, IEEE Transactions on Power Electronics
  • "Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)," 2020, IEEE Transactions on Industrial Electronics
  • "FPGA-Based Predictive Speed Control for PMSM System Using Integral Sliding-Mode Disturbance Observer," 2020, IEEE Transactions on Industrial Electronics
  • "Robust Continuous Model Predictive Speed and Current Control for PMSM With Adaptive Integral Sliding-Mode Approach," 2021, IEEE Transactions on Power Electronics

Fengxiang Wang collaborates regularly with a group of frequent coauthors. These include:

  • José Rodríguez
  • Dongliang Ke
  • Ralph Kennel
  • Haotian Xie
  • Yao Wei

Best Publications

  • Finite-Control-Set Model Predictive Torque Control With a Deadbeat Solution for PMSM Drives

    Wei Xie;Xiaocan Wang;Fengxiang Wang;Wei Xu

  • Latest Advances of Model Predictive Control in Electrical Drives. Part I: Basic Concepts and Advanced Strategies

    Jose Rodriguez;Cristian Garcia;Andres Mora;Freddy Flores-Bahamonde

  • Model-Based Predictive Direct Control Strategies for Electrical Drives: An Experimental Evaluation of PTC and PCC Methods

    Fengxiang Wang;Shihua Li;Xuezhu Mei;Wei Xie

  • Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives

    Yuanlin Wang;Xiaocan Wang;Wei Xie;Fengxiang Wang

  • Latest Advances of Model Predictive Control in Electrical Drives. Part II: Applications and Benchmarking with Classical Control Methods

    Jose Rodriguez;Cristian Garcia;Andres Mora;Alireza Davari

  • Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control

    Fengxiang Wang;Zhenbin Zhang;Zhenbin Zhang;Xuezhu Mei;Xuezhu Mei;José Rodríguez

  • Synthesis and characterization of PAn/clay nanocomposite with extended chain conformation of polyaniline

    Q. Wu;Z. Xue;Z. Qi;F. Wang

  • A Very Simple Strategy for High-Quality Performance of AC Machines Using Model Predictive Control

    Margarita Norambuena;Jose Rodriguez;Zhenbin Zhang;Fengxiang Wang

  • Using Full Order and Reduced Order Observers for Robust Sensorless Predictive Torque Control of Induction Motors

    S. A. Davari;D. A. Khaburi;Fengxiang Wang;R. M. Kennel

  • Parameter and performance comparison of doubly fed brushless machine with cage and reluctance rotors

    Fengxiang Wang;Fengge Zhang;Longya Xu

  • Parallel Predictive Torque Control for Induction Machines Without Weighting Factors

    Fengxiang Wang;Haotian Xie;Qing Chen;S. Alireza Davari

  • Model predictive control for electrical drive systems-an overview

    Fengxiang Wang;Xuezhu Mei;Jose Rodriguez;Ralph Kennel

  • Generalized Proportional Integral Observer Based Robust Finite Control Set Predictive Current Control for Induction Motor Systems With Time-Varying Disturbances

    Junxiao Wang;Fengxiang Wang;Gaolin Wang;Shihua Li

  • Zynq Implemented Luenberger Disturbance Observer Based Predictive Control Scheme for PMSM Drives

    Long He;Fengxiang Wang;Junxiao Wang;Jose Rodriguez

  • Dynamic Loss Minimization of Finite Control Set-Model Predictive Torque Control for Electric Drive System

    Wei Xie;Xiaocan Wang;Fengxiang Wang;Wei Xu

  • Design and Implementation of Disturbance Compensation-Based Enhanced Robust Finite Control Set Predictive Torque Control for Induction Motor Systems

    Junxiao Wang;Fengxiang Wang;Zhenbin Zhang;Shihua Li

  • Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)

    Mateja Novak;Haotian Xie;Tomislav Dragicevic;Fengxiang Wang

  • Finite Control Set Model Predictive Torque Control of Induction Machine With a Robust Adaptive Observer

    Fengxiang Wang;S. Alireza Davari;Zhe Chen;Zhenbin Zhang

  • FPGA-Based Experimental Investigation of a Quasi-Centralized Model Predictive Control for Back-to-Back Converters

    Zhenbin Zhang;Fengxiang Wang;Tongjing Sun;Jose Rodriguez

  • An Encoderless Predictive Torque Control for an Induction Machine With a Revised Prediction Model and EFOSMO

    Fengxiang Wang;Zhenbin Zhang;S. Alireza Davari;Reza Fotouhi

  • Active Disturbance-Rejection-Based Speed Control in Model Predictive Control for Induction Machines

    Liming Yan;Fengxiang Wang;Manfeng Dou;Zhenbin Zhang

  • Nonlinear Direct Control for Three-Level NPC Back-to-Back Converter PMSG Wind Turbine Systems: Experimental Assessment With FPGA

    Zhenbin Zhang;Fengxiang Wang;Junxiao Wang;Jose Rodriguez

  • Encoderless Finite-State Predictive Torque Control for Induction Machine With a Compensated MRAS

    Fengxiang Wang;Zhe Chen;Peter Stolze;Jean-Francois Stumper

Frequent Co-Authors

Ralph Kennel
Ralph Kennel Technical University of Munich
Jose Rodriguez
Jose Rodriguez San Sebastián University
Cristian Garcia
Cristian Garcia Andrés Bello University
Shihua Li
Shihua Li Southeast University
Yongchang Zhang
Yongchang Zhang North China Electric Power University
Tomislav Dragicevic
Tomislav Dragicevic Technical University of Denmark
Zaicheng Sun
Zaicheng Sun Beijing University of Technology
Tobias Geyer
Tobias Geyer ABB (Switzerland)
Robert D. Lorenz
Robert D. Lorenz University of Wisconsin–Madison
Ziruo Hong
Ziruo Hong University of California, Los Angeles

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring related online degrees can greatly enhance your opportunities in Electronics and Electrical Engineering. Many universities offer flexible programs, catering to diverse needs such as military families. If you are looking for programs that support this community, consider exploring options highlighted in online universities for military spouses.

Flexibility is another key factor for many students. Online schools with multiple start dates allow you to begin your studies when it suits you best, preventing delays in advancing your education or career. Check out online schools with multiple start dates to find programs that fit your schedule.

For those seeking quicker entry into the workforce, 6-month certificate programs that pay well offer a fast-track option to develop specific technical skills and improve earning potential without a full degree commitment.

Additionally, career pathways in this field often suit a range of personality types. If you prefer a more focused and independent work environment, consider reviewing the high paying careers for introverts to find roles that match your style while leveraging your engineering expertise.

Best Scientists Citing Fengxiang Wang

Trending Scientists

Recently Published Articles