Andrew Burke

8.7k total citations · 7 hit papers
136 papers, 6.4k citations indexed

About

Andrew Burke is a scholar working on Automotive Engineering, Electrical and Electronic Engineering and Electronic, Optical and Magnetic Materials. According to data from OpenAlex, Andrew Burke has authored 136 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 110 papers in Automotive Engineering, 92 papers in Electrical and Electronic Engineering and 32 papers in Electronic, Optical and Magnetic Materials. Recurrent topics in Andrew Burke's work include Advanced Battery Technologies Research (73 papers), Electric Vehicles and Infrastructure (49 papers) and Electric and Hybrid Vehicle Technologies (49 papers). Andrew Burke is often cited by papers focused on Advanced Battery Technologies Research (73 papers), Electric Vehicles and Infrastructure (49 papers) and Electric and Hybrid Vehicle Technologies (49 papers). Andrew Burke collaborates with scholars based in United States, China and Canada. Andrew Burke's co-authors include Jingyuan Zhao, John R. Miller, Marshall Miller, Patrice Simon, Yubo Lian, Hengbing Zhao, Junbin Wang, Jonathan X. Weinert, Xuning Feng and Minggao Ouyang and has published in prestigious journals such as Advanced Energy Materials, Proceedings of the IEEE and Journal of Power Sources.

In The Last Decade

Andrew Burke

129 papers receiving 6.1k citations

Hit Papers

R&D considerations for the performance and applicatio... 2007 2026 2013 2019 2007 2008 2007 2020 2023 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Andrew Burke United States 39 4.5k 3.2k 2.9k 1.1k 618 136 6.4k
Michael Kintner‐Meyer United States 23 6.1k 1.4× 1.8k 0.6× 2.2k 0.8× 353 0.3× 641 1.0× 46 6.8k
Jay Whitacre United States 50 6.3k 1.4× 1.2k 0.4× 3.0k 1.1× 497 0.5× 977 1.6× 163 7.9k
Kai Wang China 58 5.1k 1.1× 2.0k 0.6× 3.2k 1.1× 1.1k 1.0× 1.8k 3.0× 248 9.3k
Lili Liu China 51 6.6k 1.5× 2.1k 0.7× 1.6k 0.6× 546 0.5× 1.3k 2.1× 235 8.1k
Andrew Cruden United Kingdom 29 3.2k 0.7× 1.3k 0.4× 1.4k 0.5× 387 0.4× 405 0.7× 138 4.0k
Billy Wu United Kingdom 42 6.1k 1.3× 808 0.3× 5.2k 1.8× 241 0.2× 448 0.7× 111 7.2k
Haresh Kamath United States 7 12.6k 2.8× 4.4k 1.4× 3.7k 1.3× 660 0.6× 2.1k 3.4× 12 13.3k
Wei He China 34 3.1k 0.7× 722 0.2× 1.4k 0.5× 189 0.2× 586 0.9× 102 4.1k
Jingyuan Zhao China 30 1.8k 0.4× 962 0.3× 1.3k 0.4× 264 0.2× 734 1.2× 97 3.0k
Zhao Li China 32 2.5k 0.6× 1.1k 0.4× 864 0.3× 217 0.2× 1.4k 2.2× 117 4.1k

Countries citing papers authored by Andrew Burke

Since Specialization
Citations

This map shows the geographic impact of Andrew Burke'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 Andrew Burke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Burke more than expected).

Fields of papers citing papers by Andrew Burke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andrew Burke. 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 Andrew Burke. The network helps show where Andrew Burke may publish in the future.

Co-authorship network of co-authors of Andrew Burke

This figure shows the co-authorship network connecting the top 25 collaborators of Andrew Burke. A scholar is included among the top collaborators of Andrew Burke 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 Andrew Burke. Andrew Burke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Qu, Xudong, Jingyuan Zhao, Hui Pang, Michael Fowler, & Andrew Burke. (2025). Challenges and prospects in real-world battery status prediction within Industry 4.0. Green Energy and Intelligent Transportation. 5(2). 100298–100298. 2 indexed citations
2.
Xie, Xinhui, et al.. (2025). Battery state estimation for electric vehicles: Translating AI innovations into real-world solutions. Journal of Energy Storage. 115. 116000–116000. 6 indexed citations
3.
Zhao, Jingyuan, et al.. (2025). A survey of transformer networks for time series forecasting. Computer Science Review. 60. 100883–100883.
4.
Zhao, Jingyuan, et al.. (2025). A Survey of Autonomous Driving from a Deep Learning Perspective. ACM Computing Surveys. 57(10). 1–60. 1 indexed citations
5.
Zhao, Jingyuan, et al.. (2024). Cross-material battery capacity estimation using hybrid-model fusion transfer learning. Journal of Power Sources. 625. 235674–235674. 19 indexed citations
6.
Zhao, Jingyuan, et al.. (2024). Artificial intelligence-driven real-world battery diagnostics. Energy and AI. 18. 100419–100419. 41 indexed citations
7.
Burke, Andrew, Jingyuan Zhao, & Lewis Fulton. (2024). Projections of the costs of light-duty battery-electric and fuel cell vehicles (2020–2040) and related economic issues. Research in Transportation Economics. 105. 101440–101440. 27 indexed citations
8.
Eze, Chika, et al.. (2024). Numerical heat generation analysis of the tabbed and novel tabless designs of cylindrical-type lithium-ion batteries. Applied Thermal Engineering. 246. 122879–122879. 10 indexed citations
9.
Zhao, Jingyuan, Xuning Feng, Quanquan Pang, et al.. (2023). Battery prognostics and health management from a machine learning perspective. Journal of Power Sources. 581. 233474–233474. 84 indexed citations
10.
Zhao, Jingyuan, Xuebing Han, Minggao Ouyang, & Andrew Burke. (2023). Specialized deep neural networks for battery health prognostics: Opportunities and challenges. Journal of Energy Chemistry. 87. 416–438. 71 indexed citations
12.
Burke, Andrew, Matthew S. J. Marshall, & Hengbing Zhao. (2012). Fast Charging Tests (up to 6C) of Lithium Titanate Cells and Modules: Electrical and Thermal Response. eScholarship (California Digital Library). 13 indexed citations
13.
Zhao, Hengbing, et al.. (2012). A First-Order Transient Response Model for Lithium-ion Batteries of Various Chemistries: Test Data and Model Validation. eScholarship (California Digital Library). 3 indexed citations
14.
Zhao, Hengbing, Andrew Burke, & Marshall Miller. (2011). Comparison of Hybrid Fuel Cell Vehicle Technology and Fuel Efficiency. eScholarship (California Digital Library). 1 indexed citations
15.
Burke, Andrew, et al.. (2009). Simulated Performance of Alternative Hybrid-Electric Powertrains in Vehicles on Various Driving Cycles. eScholarship (California Digital Library). 8 indexed citations
16.
Burke, Andrew, et al.. (2003). A Feasibility Study of the Hybrid Carbon/Lead Oxide Ultracapacitor: Analysis, Assembly, Testing, and Projection of Future Potential. 1 indexed citations
17.
Burke, Andrew. (1995). Ultracapacitors for Electric and Hybrid Vehicles - Performance Requirements, Status of the Technology, and R&D Needs. RePEc: Research Papers in Economics.
18.
Burke, Andrew. (1987). Status of electric vehicle simulation programs at the INEL: Summer 1987. STIN. 88. 22271. 1 indexed citations
19.
Burke, Andrew, et al.. (1982). COMPUTER AIDED DESIGN OF ELECTRIC AND HYBRID VEHICLES. International Journal of Vehicle Design. 4 indexed citations
20.
Burke, Andrew. (1967). Theoretical Studies of Continuum, Weakly Ionized Gas Flows Including Compressibility and Electron Energy Effects.. PhDT. 2 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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