World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
37
Citations
5646
World Ranking
8383
National Ranking
2322

Mahdi Shahbakhti publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Mahdi Shahbakhti sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 192 publications — 45th percentile

45% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Mahdi Shahbakhti D-index placement in Engineering and Technology in 2026

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

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 37 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Mahdi Shahbakhti is affiliated with Michigan Technological University in the United States. Their research spans multiple areas within Engineering and Chemical Engineering, with extensive work in subfields including Automotive Engineering, Fluid Flow and Transfer Processes, Control and Systems Engineering, Mechanical Engineering, and Materials Chemistry.

Their scholarly output reflects focus on several main topics of study, such as:

  • Advanced Combustion Engine Technologies
  • Vehicle emissions and performance
  • Combustion and flame dynamics
  • Electric and Hybrid Vehicle Technologies
  • Catalytic Processes in Materials Science
  • Advanced Control Systems Optimization
  • Advancements in Solid Oxide Fuel Cells

Some of Shahbakhti's recent publications, indicating areas of applied research and collaboration, include:

  • Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions (2021), published in Progress in Energy and Combustion Science
  • A green hydrogen energy storage concept based on parabolic trough collector and proton exchange membrane electrolyzer/fuel cell: Thermodynamic and exergoeconomic analyses with multi-objective optimization (2022), published in International Journal of Hydrogen Energy
  • Design, thermodynamic, and economic analyses of a green hydrogen storage concept based on solid oxide electrolyzer/fuel cells and heliostat solar field (2023), published in Renewable Energy
  • Integrating Machine Learning and Model Predictive Control for automotive applications: A review and future directions (2023), published in Engineering Applications of Artificial Intelligence
  • Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle (2020), published in Applied Energy

The frequent co-authors in Shahbakhti's work include:

  • Charles Robert Koch
  • Jeffrey Naber
  • Saeid Shahpouri
  • Armin Norouzi
  • Gordon McTaggart-Cowan

The primary publication venues for Shahbakhti's research are notable for their relevance to control systems, energy, and engineering applications:

  • IFAC-PapersOnLine
  • Energies
  • Control Engineering Practice
  • SSRN Electronic Journal
  • SAE technical papers on CD-ROM/SAE technical paper series

Shahbakhti's multidisciplinary research ranges from theoretical machine learning applications in engine control to practical thermodynamic and economic analyses of green hydrogen energy systems. This body of work reflects integration of advanced combustion engine technologies with emerging sustainable energy solutions and control optimization techniques.

Best Publications

  • A green hydrogen energy storage concept based on parabolic trough collector and proton exchange membrane electrolyzer/fuel cell: Thermodynamic and exergoeconomic analyses with multi-objective optimization

    Unknown

  • Handling model uncertainty in model predictive control for energy efficient buildings

    M. Maasoumy;M. Razmara;M. Shahbakhti;A. Sangiovanni Vincentelli

  • Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions

    Masoud Aliramezani;Charles Robert Koch;Mahdi Shahbakhti

  • Design, thermodynamic, and economic analyses of a green hydrogen storage concept based on solid oxide electrolyzer/fuel cells and heliostat solar field

    Unknown

  • Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks

    Javad Rezaei;Mahdi Shahbakhti;Bahram Bahri;Azhar Abdul Aziz

  • Modeling and analysis of fuel injection parameters for combustion and performance of an RCCI engine

    M. Nazemi;M. Shahbakhti

  • Building-to-grid predictive power flow control for demand response and demand flexibility programs

    Meysam Razmara;Guna Bharati;Drew Hanover;Mahdi Shahbakhti

  • Optimal exergy control of building HVAC system

    M. Razmara;M. Maasoumy;M. Shahbakhti;R.D. Robinett

  • Bilevel Optimization Framework for Smart Building-to-Grid Systems

    Meysam Razmara;Guna R. Bharati;Mahdi Shahbakhti;Sumit Paudyal

  • Integrating Machine Learning and Model Predictive Control for automotive applications: A review and future directions

    Unknown

  • Understanding and detecting misfire in an HCCI engine fuelled with ethanol

    Bahram Bahri;Azhar Abdul Aziz;Mahdi Shahbakhti;Mohd Farid Muhamad Said

  • Characterizing the cyclic variability of ignition timing in a homogeneous charge compression ignition engine fuelled with n-heptane/iso-octane blend fuels:

    M Shahbakhti;C R Koch

  • Modeling and controller design architecture for cycle-by-cycle combustion control of homogeneous charge compression ignition (HCCI) engines – A comprehensive review

    Morteza Fathi;Omid Jahanian;Mahdi Shahbakhti

  • Optimal exergy-based control of internal combustion engines

    M. Razmara;M. Bidarvatan;M. Shahbakhti;R.D. Robinett

  • Predicting Start of Combustion Using a Modified Knock Integral Method for an HCCI Engine

    Kevin Swan;Mahdi Shahbakhti;Charles Robert Koch

  • Integrated cabin heating and powertrain thermal energy management for a connected hybrid electric vehicle

    S. Hemmati;N. Doshi;D. Hanover;C. Morgan

  • Reactivity controlled compression ignition engine: Pathways towards commercial viability

    Amin Paykani;Antonio Garcia;Mahdi Shahbakhti;Pourya Rahnama

  • Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

    Armin Norouzi;Hamed Heidarifar;Mahdi Shahbakhti;Charles Robert Koch

  • Deep learning based model predictive control for compression ignition engines

    Unknown

  • Optimization of performance and operational cost for a dual mode diesel-natural gas RCCI and diesel combustion engine

    Ehsan Ansari;Mahdi Shahbakhti;Jeffrey D. Naber

  • Cycle-to-cycle modeling and sliding mode control of blended-fuel HCCI engine

    M. Bidarvatan;M. Shahbakhti;S.A. Jazayeri;C.R. Koch

  • Physics Based Control Oriented Model for HCCI Combustion Timing

    Mahdi Shahbakhti;Charles Robert Koch

  • Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control

    Mehdi Maasoumy;Barzin Moridian;Meysam Razmara;Mahdi Shahbakhti

  • Modeling and experimental study of an HCCI engine for combustion timing control

    Mahdi Shahbakhti

  • Modeling of combustion phasing of a reactivity-controlled compression ignition engine for control applications:

    Kaveh Khodadadi Sadabadi;Mahdi Shahbakhti;Anand N Bharath;Rolf D Reitz

  • A SKELETAL KINETIC MECHANISM FOR PRF COMBUSTION IN HCCI ENGINES

    Patrick Kirchen;Mahdi Shahbakhti;Charles Robert Koch

Frequent Co-Authors

Jeffrey Naber
Jeffrey Naber Michigan Technological University
J. Karl Hedrick
J. Karl Hedrick University of California, Berkeley
John H. Johnson
John H. Johnson Michigan Technological University
Aria Alasty
Aria Alasty Sharif University of Technology
Jing Sun
Jing Sun University of Michigan–Ann Arbor
Rolf D. Reitz
Rolf D. Reitz University of Wisconsin–Madison
Gordon G. Parker
Gordon G. Parker Michigan Technological University
Alberto Sangiovanni-Vincentelli
Alberto Sangiovanni-Vincentelli University of California, Berkeley
Antonio García
Antonio García Universitat Politècnica de València

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