World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
11164
World Ranking
4592
National Ranking
276

Kang Li publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Kang Li sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 487 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Kang Li D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Kang Li sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 54 D-Index — 69th percentile

69% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Kang Li is affiliated with the University of Leeds in the United Kingdom and has a research focus primarily within the field of Engineering, with significant contributions to Electrical and Electronic Engineering, Control and Systems Engineering, Automotive Engineering, Industrial and Manufacturing Engineering, and Mechanical Engineering.

Their published work spans several key topics, including:

  • Advanced Battery Technologies Research
  • Microgrid Control and Optimization
  • Smart Grid Energy Management
  • Electric Vehicles and Infrastructure
  • Energy Load and Power Forecasting
  • Railway Systems and Energy Efficiency
  • Advancements in Battery Materials

Kang Li's frequent coauthors include Zhile Yang, Dajun Du, Yihuan Li, Li Zhang, and Xuan Liu.

Their publications appear repeatedly in leading venues such as:

  • IEEE Transactions on Smart Grid
  • SSRN Electronic Journal
  • Applied Energy
  • Energy
  • IEEE Sensors Journal

Selected recent papers illustrate the scope and application of their research:

  • "Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model" (2020, IEEE Transactions on Smart Grid)
  • "Lithium-ion battery capacity estimation - A pruned convolutional neural network approach assisted with transfer learning" (2021, Applied Energy)
  • "A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements" (2022, Applied Energy)
  • "A comprehensive review on deep learning approaches in wind forecasting applications" (2022, CAAI Transactions on Intelligence Technology)
  • "Radar active antagonism through deep reinforcement learning: A Way to address the challenge of mainlobe jamming" (2021, Signal Processing)

Kang Li's research contributions include advances in machine learning applications for battery management, energy storage optimization, and signal processing techniques. Their investigations frequently incorporate deep learning methodologies to solve complex engineering challenges related to energy systems and electronic signal interference.

Best Publications

  • A brief review on key technologies in the battery management system of electric vehicles

    Kailong Liu;Kang Li;Qiao Peng;Cheng Zhang

  • A survey of methods for monitoring and detecting thermal runaway of lithium-ion batteries

    Zhenghai Liao;Shen Zhang;Kang Li;Guoqiang Zhang

  • A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

    Qun Niu;Letian Zhang;Kang Li

  • Model selection approaches for non-linear system identification: a review

    X. Hong;R. J. Mitchell;S. Chen;C. J. Harris

  • A fast nonlinear model identification method

    Kang Li;Jian-Xun Peng;G.W. Irwin

  • Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

    Jun Cao;Dan Harrold;Zhong Fan;Thomas Morstyn

  • An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

    Qun Niu;Hongyun Zhang;Kang Li

  • Incremental Learning From Stream Data

    Haibo He;Sheng Chen;Kang Li;Xin Xu

  • Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

    Zhile Yang;Kang Li;Aoife Foley

  • Charging Pattern Optimization for Lithium-Ion Batteries With an Electrothermal-Aging Model

    Kailong Liu;Changfu Zou;Kang Li;Torsten Wik

  • Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning

    Yihuan Li;Kang Li;Xuan Liu;Yanxia Wang

  • A two-stage algorithm for identification of nonlinear dynamic systems

    Kang Li;Jian-Xun Peng;Er-Wei Bai

  • Support vector machine classification for large data sets via minimum enclosing ball clustering

    Jair Cervantes;Xiaoou Li;Wen Yu;Kang Li

  • Real-time estimation of battery internal temperature based on a simplified thermoelectric model

    Cheng Zhang;Kang Li;Jing Deng

  • Hybrid Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process

    Juan Yan;Kang Li;Er-Wei Bai;Jing Deng

  • Intelligent Control and Automation

    De-Shuang Huang;Kang Li;George William Irwin

  • Hierarchical management for integrated community energy systems

    Xiandong Xu;Xiaolong Jin;Hongjie Jia;Xiaodan Yu

  • Improved Realtime State-of-Charge Estimation of LiFePO $_{oldsymbol 4}$ Battery Based on a Novel Thermoelectric Model

    Cheng Zhang;Kang Li;Jing Deng;Shiji Song

  • Brief paper: Convergence of the iterative algorithm for a general Hammerstein system identification

    Er-Wei Bai;Kang Li

  • A Hybrid Forward Algorithm for RBF Neural Network Construction

    Jian-Xun Peng;Kang Li;De-Shuang Huang

  • Recovering large-scale battery aging dataset with machine learning.

    Xiaopeng Tang;Kailong Liu;Kang Li;Widanalage Dhammika Widanage

Frequent Co-Authors

George W. Irwin
George W. Irwin Queen's University Belfast
Minrui Fei
Minrui Fei Shanghai University
Zhile Yang
Zhile Yang University of Chinese Academy of Sciences
Aoife Foley
Aoife Foley University of Manchester
Er-Wei Bai
Er-Wei Bai University of Iowa
Shaoyuan Li
Shaoyuan Li Shanghai Jiao Tong University
De-Shuang Huang
De-Shuang Huang Tongji University
Min Tan
Min Tan Chinese Academy of Sciences
Hongjie Jia
Hongjie Jia Tianjin University
Haibo He
Haibo He University of Rhode Island

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