J.S. Wang

437 total citations
27 papers, 321 citations indexed

About

J.S. Wang is a scholar working on Condensed Matter Physics, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, J.S. Wang has authored 27 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Condensed Matter Physics, 13 papers in Electrical and Electronic Engineering and 11 papers in Control and Systems Engineering. Recurrent topics in J.S. Wang's work include Physics of Superconductivity and Magnetism (15 papers), Magnetic Bearings and Levitation Dynamics (9 papers) and Superconducting Materials and Applications (9 papers). J.S. Wang is often cited by papers focused on Physics of Superconductivity and Magnetism (15 papers), Magnetic Bearings and Levitation Dynamics (9 papers) and Superconducting Materials and Applications (9 papers). J.S. Wang collaborates with scholars based in China and Hong Kong. J.S. Wang's co-authors include Zunsong Ren, He Jiang, Minhao Zhu, Jun Zheng, Xiaorong Wang, Honghai Song, Weiqun Liu, Ming Jiang, Donghai Hu and Dagang Lu and has published in prestigious journals such as Journal of Cleaner Production, Applied Thermal Engineering and Energies.

In The Last Decade

J.S. Wang

24 papers receiving 305 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
J.S. Wang China	11	272	167	120	100	88	27	321
M. Igarashi Japan	13	348 1.3×	143 0.9×	208 1.7×	183 1.8×	76 0.9×	19	414
M. Yamaji Japan	11	271 1.0×	150 0.9×	197 1.6×	186 1.9×	42 0.5×	18	354
J. Kellers United States	11	296 1.1×	67 0.4×	253 2.1×	244 2.4×	39 0.4×	15	402
Felipe Costa Brazil	5	256 0.9×	229 1.4×	94 0.8×	129 1.3×	48 0.5×	14	311
A. Kawagoe Japan	10	175 0.6×	51 0.3×	194 1.6×	132 1.3×	38 0.4×	63	288
Shinichi Mukoyama Japan	11	297 1.1×	116 0.7×	260 2.2×	222 2.2×	46 0.5×	30	409
J. Wiezoreck Germany	10	243 0.9×	56 0.3×	207 1.7×	229 2.3×	26 0.3×	12	329
H. May Germany	10	167 0.6×	122 0.7×	97 0.8×	174 1.7×	102 1.2×	29	348
J. Kozak Poland	15	200 0.7×	117 0.7×	220 1.8×	466 4.7×	67 0.8×	49	526
S. Kozak Poland	15	197 0.7×	123 0.7×	226 1.9×	464 4.6×	59 0.7×	46	532

Countries citing papers authored by J.S. Wang

Since Specialization

Citations

This map shows the geographic impact of J.S. Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by J.S. Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J.S. Wang more than expected).

Fields of papers citing papers by J.S. Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.S. Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by J.S. Wang. The network helps show where J.S. Wang may publish in the future.

Co-authorship network of co-authors of J.S. Wang

This figure shows the co-authorship network connecting the top 25 collaborators of J.S. Wang. A scholar is included among the top collaborators of J.S. Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with J.S. Wang. J.S. Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lu, Dagang, Yu Chen, Song Ji, et al.. (2025). Research Progress in Multi-Domain and Cross-Domain AI Management and Control for Intelligent Electric Vehicles. Energies. 18(17). 4597–4597.

Wang, J.S., Donghai Hu, Dagang Lu, et al.. (2025). BL-DATransformer Lifespan Degradation Prediction Model of Fuel Cell Using Relative Voltage Loss Rate Health Indicator. World Electric Vehicle Journal. 16(6). 290–290.

Jiang, Hongwei�, et al.. (2025). Location-Route Planning for VTOL Airport and UAV Urban Logistics Network – A Case Study of Tianjin. PROMET - Traffic&Transportation. 37(2). 456–476. 1 indexed citations

Hu, Donghai, et al.. (2024). Design and material optimization of carbon fiber composite winding reinforcement layer for vehicle Type-IV hydrogen storage vessels. Journal of Energy Storage. 100. 113459–113459. 13 indexed citations

Wang, J.S., et al.. (2024). Lightweight Type-IV Hydrogen Storage Vessel Boss Based on Optimal Sealing Structure. World Electric Vehicle Journal. 15(6). 261–261. 2 indexed citations

Hu, Donghai, Jixiang Huang, Dagang Lu, & J.S. Wang. (2024). Hydrogen consumption estimation of fuel cell vehicle based on vehicle energy transfer. Sustainable Energy Technologies and Assessments. 68. 103854–103854. 2 indexed citations

Wang, Zilong, Guangbin Liu, Qichao Yang, et al.. (2024). Energy consumption analysis of building air-conditioning systems using centrifugal compressor with gas bearing under annual operating conditions. Case Studies in Thermal Engineering. 64. 105522–105522. 5 indexed citations

Hu, Donghai, et al.. (2024). An intelligent thermal comfort control strategy for air conditioning of fuel cell vehicles. Applied Thermal Engineering. 248. 123286–123286. 8 indexed citations

Hu, Donghai, et al.. (2024). A multi-algorithm fusion model for predicting automotive fuel cell system demand power. Journal of Cleaner Production. 466. 142848–142848. 4 indexed citations

10.

Hu, Donghai, et al.. (2024). Real-time prediction model of passenger thermal comfort for intelligent cabin. International Journal of Thermal Sciences. 207. 109370–109370.

11.

Yi, Fengyan, et al.. (2024). A novel sensor preference method for proton exchange membrane fuel cell flooding fault diagnosis based on multi-algorithm. International Journal of Green Energy. 21(16). 3823–3837. 2 indexed citations

12.

Zheng, Jun, et al.. (2013). Growth anisotropy effect of bulk high temperature superconductors on the levitation performance in the applied magnetic field. Physica C Superconductivity. 493. 52–54. 4 indexed citations

13.

Ma, Guang-Tong, J.S. Wang, Qing Lin, et al.. (2009). Lateral restorable characteristics of the high-Tc superconducting maglev vehicle above the permanent magnet guideway. Physica C Superconductivity. 469(21). 1954–1957. 5 indexed citations

14.

Liu, Weiqun, et al.. (2008). Levitation performance of YBCO bulk in different applied magnetic fields. Physica C Superconductivity. 468(13). 974–977. 31 indexed citations

15.

Song, Honghai, et al.. (2005). Evaluation of inhomogeneities of bulk YBCO superconductors using pulsed field magnetization. Physica C Superconductivity. 420(1-2). 51–55. 4 indexed citations

16.

Wang, J.S., Youwen Zeng, Honghai Song, et al.. (2005). Design Consideration of a High Temperature Superconductor Maglev Vehicle System. IEEE Transactions on Applied Superconductivity. 15(2). 2273–2276. 9 indexed citations

17.

Wang, Xiaorong, et al.. (2003). Levitation force and guidance force of YBaCuO bulk in applied field. Physica C Superconductivity. 386. 536–539. 24 indexed citations

18.

Wang, J.S., Zunsong Ren, He Jiang, et al.. (2003). Experiment results of high temperature superconducting Maglev vehicle. Physica C Superconductivity. 386. 431–437. 13 indexed citations

19.

Wang, J.S., et al.. (2001). Levitation force of multi-block YBaCuO bulk high temperature superconductors. IEEE Transactions on Applied Superconductivity. 11(1). 1808–1811. 31 indexed citations

20.

Wang, J.S., et al.. (2001). Levitation force of a YBaCuO bulk high temperature superconductor over a NdFeB guideway. IEEE Transactions on Applied Superconductivity. 11(1). 1801–1804. 50 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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