World's Best Scientists 2026 revealed!

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
55
Citations
11433
World Ranking
899
National Ranking
37

Xianke Lin 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 Xianke Lin 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: 121 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

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

Xianke Lin 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 Xianke Lin 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: 55 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

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

Overview

Xianke Lin is affiliated with the University of Ontario Institute of Technology in Canada and specializes in research within the engineering discipline. Their work is primarily concentrated in several subfields, including automotive engineering, electrical and electronic engineering, computer vision and pattern recognition, control and systems engineering, as well as safety, risk, reliability, and quality.

The scientist's research covers a range of main topics related to advanced battery technologies and electric vehicles. These topics include advanced battery technologies research, electric vehicles and infrastructure, advancements in battery materials, electric and hybrid vehicle technologies, advanced battery materials and technologies, reliability and maintenance optimization, and vehicle emissions and performance.

Lin has contributed to numerous peer-reviewed publications. Some of their recent papers include:

  • "Battery Lifetime Prognostics," 2020, published in Joule
  • "Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures," 2020, published in IEEE Industrial Electronics Magazine
  • "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," 2020, published in Energy
  • "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," 2020, published in Renewable and Sustainable Energy Reviews
  • "Lithium Plating Mechanism, Detection, and Mitigation in Lithium-Ion Batteries," 2021, published in Progress in Energy and Combustion Science

Lin collaborates frequently with several researchers, including Xiao Hu, Xiaolin Tang, Zhongwei Deng, Yi Xie, and Yunhong Che.

The scientist's publications are often featured in established journals and conferences. Their frequent publication venues include:

  • IEEE Transactions on Transportation Electrification
  • IEEE Transactions on Vehicular Technology
  • Energy
  • IEEE Transactions on Power Electronics
  • arXiv (Cornell University)

Best Publications

  • Battery Lifetime Prognostics

    Xiaosong Hu;Le Xu;Xianke Lin;Michael Pecht

  • Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

    Xiaosong Hu;Kai Zhang;Kailong Liu;Xianke Lin

  • Lithium Plating Mechanism, Detection, and Mitigation in Lithium-Ion Batteries

    Xianke Lin;Kavian Khosravinia;Xiaosong Hu;Ju Li

  • Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression

    Zhongwei Deng;Xiaosong Hu;Xianke Lin;Yunhong Che

  • Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data

    Unknown

  • Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends

    Haifeng Dai;Bo Jiang;Xiaosong Hu;Xianke Lin

  • Remaining Useful Life Prediction Using a Novel Feature-Attention-Based End-to-End Approach

    Unknown

  • Battery health estimation with degradation pattern recognition and transfer learning

    Unknown

  • Health prognostics for lithium-ion batteries: mechanisms, methods, and prospects

    Unknown

  • Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning

    Xiaosong Hu;Yunhong Che;Xianke Lin;Simona Onori

  • A Comprehensive Capacity Fade Model and Analysis for Li-Ion Batteries

    Xianke Lin;Jonghyun Park;Lin Liu;Yoonkoo Lee

  • Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning

    Unknown

  • Simulation and Experiment on Solid Electrolyte Interphase (SEI) Morphology Evolution and Lithium-Ion Diffusion

    Unknown

  • Predictive Battery Health Management With Transfer Learning and Online Model Correction

    Yunhong Che;Zhongwei Deng;Xianke Lin;Lin Hu

  • Enabling high-fidelity electrochemical P2D modeling of lithium-ion batteries via fast and non-destructive parameter identification

    Unknown

  • A thermal-electrochemical model that gives spatial-dependent growth of solid electrolyte interphase in a Li-ion battery

    Lin Liu;Jonghyun Park;Xianke Lin;Ann Marie Sastry

  • General Discharge Voltage Information Enabled Health Evaluation for Lithium-Ion Batteries

    Zhongwei Deng;Xiaosong Hu;Xianke Lin;Le Xu

  • A Review of Second-Life Lithium-Ion Batteries for Stationary Energy Storage Applications

    Unknown

  • Health Prognosis for Electric Vehicle Battery Packs: A Data-Driven Approach

    Xiaosong Hu;Yunhong Che;Xianke Lin;Zhongwei Deng

  • A review of equalization strategies for series battery packs: variables, objectives, and algorithms

    Fei Feng;Xiaosong Hu;Jianfei Liu;Xianke Lin

  • A Comparative Study of Control-Oriented Thermal Models for Cylindrical Li-Ion Batteries

    Xiaosong Hu;Wenxue Liu;Xianke Lin;Yi Xie

  • Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering

    Lin Hu;Xiaosong Hu;Yunhong Che;Fei Feng

  • State of health prognostics for series battery packs: A universal deep learning method

    Yunhong Che;Zhongwei Deng;Penghua Li;Xiaolin Tang

  • An MPC-Based Control Strategy for Electric Vehicle Battery Cooling Considering Energy Saving and Battery Lifespan

    Yi Xie;Chenyang Wang;Xiaosong Hu;Xianke Lin

  • Oxygen Vacancies Lead to Loss of Domain Order, Particle Fracture, and Rapid Capacity Fade in Lithium Manganospinel (LiMn2O4) Batteries

    Xiaoguang Hao;Xianke Lin;Wei Lu;Bart M. Bartlett

  • Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios

    Xiaosong Hu;Xiaoqian Zhang;Xiaolin Tang;Xianke Lin

  • Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios

    Siyang Wang;Xianke Lin

  • Ensemble Reinforcement Learning-Based Supervisory Control of Hybrid Electric Vehicle for Fuel Economy Improvement

    Bin Xu;Xiaosong Hu;Xiaolin Tang;Xianke Lin

Frequent Co-Authors

Xiaosong Hu
Xiaosong Hu Chongqing University
Michael Pecht
Michael Pecht University of Maryland, College Park
Simona Onori
Simona Onori Stanford University
Aoife Foley
Aoife Foley University of Manchester
Huei Peng
Huei Peng University of Michigan–Ann Arbor
Satadru Dey
Satadru Dey Pennsylvania State University

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