World's Best Scientists 2026 revealed!

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Computer Science 54 4592 4463 276 274 487 11164

Kang Li publications per year

The chart shows the history of publications by Kang Li between 1992 and 2026, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Kang Li published across 35 years, from 1992 to 2026, averaging 18.5 papers a year. Output peaked at 74 publications in 2014. 19 of the 647 publications appeared in the last two years.

No. of publications
20 40 60
Bar chart. Horizontal axis: year, 1992 to 2026. Vertical axis: number of publications, 0 to 74. Peak 74 publications in 2014. 1992: 1 publication 1993: 0 publications 1994: 0 publications 1995: 0 publications 1996: 2 publications 1997: 1 publication 1998: 0 publications 1999: 2 publications 2000: 4 publications 2001: 2 publications 2002: 6 publications 2003: 6 publications 2004: 7 publications 2005: 8 publications 2006: 30 publications 2007: 25 publications 2008: 20 publications 2009: 17 publications 2010: 48 publications 2011: 36 publications 2012: 35 publications 2013: 45 publications 2014: 74 publications 2015: 40 publications 2016: 41 publications 2017: 28 publications 2018: 26 publications 2019: 22 publications 2020: 27 publications 2021: 33 publications 2022: 11 publications 2023: 9 publications 2024: 22 publications 2025: 18 publications 2026: 1 publication
1992 2026

647 publications in total across all disciplines

View publications per year as a table
Kang Li: publications per year, 1992 to 2026
Year Publications
1992 1
1993 0
1994 0
1995 0
1996 2
1997 1
1998 0
1999 2
2000 4
2001 2
2002 6
2003 6
2004 7
2005 8
2006 30
2007 25
2008 20
2009 17
2010 48
2011 36
2012 35
2013 45
2014 74
2015 40
2016 41
2017 28
2018 26
2019 22
2020 27
2021 33
2022 11
2023 9
2024 22
2025 18
2026 1
Total 647
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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.

No. of scientists
200 400 600
Bar chart with 97 bars. Horizontal axis: publications, 32–41 to 991+. Vertical axis: number of scientists, 0 to 609. Most scientists, 609, have 142–151 publications. The last bar groups every scientist with 991 publications or more. The highlighted bar, 482–491 publications, is where this scientist sits. 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
32–41 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.

View publications distribution as a table
Number of Computer Science scientists by publication count, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
Publications Scientists This scientist
32–41 7
42–51 22
52–61 82
62–71 134
72–81 249
82–91 324
92–101 421
102–111 420
112–121 497
122–131 544
132–141 555
142–151 609
152–161 559
162–171 534
172–181 556
182–191 583
192–201 519
202–211 508
212–221 490
222–231 437
232–241 423
242–251 408
252–261 377
262–271 301
272–281 335
282–291 320
292–301 293
302–311 250
312–321 238
322–331 206
332–341 209
342–351 208
352–361 162
362–371 176
372–381 127
382–391 158
392–401 128
402–411 104
412–421 94
422–431 99
432–441 83
442–451 108
452–461 73
462–471 77
472–481 69
482–491 84 487
492–501 62
502–511 54
512–521 57
522–531 51
532–541 51
542–551 32
552–561 38
562–571 28
572–581 43
582–591 33
592–601 41
602–611 32
612–621 28
622–631 25
632–641 27
642–651 17
652–661 20
662–671 17
672–681 15
682–691 14
692–701 21
702–711 13
712–721 12
722–731 19
732–741 14
742–751 12
752–761 10
762–771 10
772–781 11
782–791 10
792–801 11
802–811 8
812–821 8
822–831 7
832–841 11
842–851 10
852–861 5
862–871 9
872–881 4
882–891 6
892–901 3
902–911 6
912–921 3
922–931 2
932–941 2
942–951 2
952–961 3
962–971 3
972–981 3
982–990 5
991+ 100
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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.

No. of scientists
200 400 600 800
Bar chart with 52 bars. Horizontal axis: D-Index, 30–31 to 131+. Vertical axis: number of scientists, 0 to 990. Most scientists, 990, have 36–37 D-Index. The last bar groups every scientist with 131 D-Index or more. The highlighted bar, 54–55 D-Index, is where this scientist sits. 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
30–31 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.

View D-Index distribution as a table
Number of Computer Science scientists by D-index, Research.com 2026 ranking edition. Based on 14,188 ranked scientists.
D-Index Scientists This scientist
30–31 879
32–33 983
34–35 918
36–37 990
38–39 968
40–41 907
42–43 821
44–45 763
46–47 689
48–49 543
50–51 543
52–53 518
54–55 500 54
56–57 458
58–59 400
60–61 337
62–63 308
64–65 292
66–67 249
68–69 213
70–71 192
72–73 189
74–75 165
76–77 139
78–79 119
80–81 121
82–83 113
84–85 88
86–87 87
88–89 75
90–91 69
92–93 57
94–95 46
96–97 38
98–99 34
100–101 36
102–103 27
104–105 37
106–107 18
108–109 31
110–111 19
112–113 16
114–115 12
116–117 20
118–119 15
120–121 5
122–123 20
124–125 8
126–127 5
128–129 7
130 3
131+ 98
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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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