Ming Kuang
- Automotive Engineering top 0.2%
- Electrical and Electronic Engineering top 5%
- Control and Systems Engineering top 5%
- Mechanical Engineering
- Fluid Flow and Transfer Processes top 5%
- Co-authors
- Anthony M. PhillipsIlya KolmanovskyHoseinali BorhanArdalan VahidiStefano Di CairanoRyan McGeeYi Lu MurpheyJungme Park
- Topics
- Electric and Hybrid Vehicle Technologies (31 papers)Electric Vehicles and Infrastructure (17 papers)Advanced Battery Technologies Research (16 papers)
- Cited by
- Automotive EngineeringFluid Flow and Transfer ProcessesElectrical and Electronic Engineering
- Journals
- Scientific ReportsIEEE Transactions on Vehicular TechnologyIEEE Transactions on Intelligent Transportation Systems
- Partner nations
- United StatesChinaFrance
In The Last Decade
Ming Kuang
51 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 51
- Automotive Engineering 1.6k
- Electrical and Electronic Engineering 1.2k
- Control and Systems Engineering 363
- Mechanical Engineering 151
- Fluid Flow and Transfer Processes 139
Countries citing papers authored by Ming Kuang
This map shows the geographic impact of Ming Kuang'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 Ming Kuang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Kuang more than expected).
Fields of papers citing papers by Ming Kuang
This network shows the impact of papers produced by Ming Kuang. 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 Ming Kuang. The network helps show where Ming Kuang may publish in the future.
Co-authorship network of co-authors of Ming Kuang
This figure shows the co-authorship network connecting the top 25 collaborators of Ming Kuang. A scholar is included among the top collaborators of Ming Kuang 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 Ming Kuang. Ming Kuang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 12 | |
| 6 | 40 | |
| 7 | 3 | |
| 8 | 4 | |
| 9 | 4 | |
| 10 | 5 | |
| 11 | 1 | |
| 12 | 51 | |
| 13 | 10 | |
| 14 | 148 | |
| 15 | 8 | |
| 16 | MPC-Based Energy Management of a Power-Split Hybrid Electric Vehiclebreakdown → | 589 |
| 17 | 110 | |
| 18 | 179 | |
| 19 | 14 | |
| 20 | 24 |
About Ming Kuang
Ming Kuang is a scholar working on Automotive Engineering, Transportation and Control and Systems Engineering, having authored 53 papers that have together received 1.9k indexed citations. Recurring topics across this work include Electric and Hybrid Vehicle Technologies (31 papers), Electric Vehicles and Infrastructure (17 papers) and Advanced Battery Technologies Research (16 papers). The work is most often cited by research in Automotive Engineering (1.6k citations), Fluid Flow and Transfer Processes (139 citations) and Electrical and Electronic Engineering (1.2k citations). Ming Kuang has collaborated with scholars based in United States, China and France. Frequent co-authors include Anthony M. Phillips, Ilya Kolmanovsky, Hoseinali Borhan, Ardalan Vahidi, Stefano Di Cairano, Ryan McGee, Yi Lu Murphey, Jungme Park, Hai Yu and Fazal Syed. Their work appears in journals such as Scientific Reports, IEEE Transactions on Vehicular Technology and IEEE Transactions on Intelligent Transportation Systems.
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.