Phillip J. Kollmeyer

5.0k total citations · 3 hit papers
89 papers, 3.9k citations indexed

About

Phillip J. Kollmeyer is a scholar working on Automotive Engineering, Electrical and Electronic Engineering and Control and Systems Engineering. According to data from OpenAlex, Phillip J. Kollmeyer has authored 89 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Automotive Engineering, 69 papers in Electrical and Electronic Engineering and 14 papers in Control and Systems Engineering. Recurrent topics in Phillip J. Kollmeyer's work include Advanced Battery Technologies Research (77 papers), Advancements in Battery Materials (36 papers) and Electric and Hybrid Vehicle Technologies (35 papers). Phillip J. Kollmeyer is often cited by papers focused on Advanced Battery Technologies Research (77 papers), Advancements in Battery Materials (36 papers) and Electric and Hybrid Vehicle Technologies (35 papers). Phillip J. Kollmeyer collaborates with scholars based in Canada, United States and Italy. Phillip J. Kollmeyer's co-authors include Ali Emadi, Ephrem Chemali, Matthias Preindl, Carlos Vidal, Ryan Ahmed, Pawel Malysz, Mina Naguib, Thomas M. Jahns, Oliver Groß and R. D. Lorenz and has published in prestigious journals such as Journal of Power Sources, IEEE Transactions on Industrial Electronics and Applied Energy.

In The Last Decade

Phillip J. Kollmeyer

85 papers receiving 3.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries

2017 632 citations Ephrem Chemali, Phillip J. Kollmeyer et al. IEEE Transactions on Industrial Electronics profile →
State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

2018 591 citations Ephrem Chemali, Phillip J. Kollmeyer et al. Journal of Power Sources profile →
Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art

2020 326 citations Carlos Vidal, Phillip J. Kollmeyer et al. IEEE Access profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Phillip J. Kollmeyer Canada	28	3.4k	3.2k	836	206	148	89	3.9k
Jinpeng Tian China	31	4.3k 1.3×	3.7k 1.2×	1.1k 1.3×	533 2.6×	185 1.3×	61	4.7k
Quanqing Yu China	34	4.8k 1.4×	4.1k 1.3×	1.6k 1.9×	436 2.1×	143 1.0×	83	5.3k
Zhongwei Deng China	35	3.6k 1.0×	3.0k 1.0×	1.0k 1.2×	545 2.6×	168 1.1×	86	4.0k
Guangzhong Dong China	31	3.0k 0.9×	2.6k 0.8×	998 1.2×	581 2.8×	103 0.7×	71	3.4k
Jinhao Meng China	40	4.6k 1.3×	4.0k 1.3×	1.5k 1.8×	539 2.6×	211 1.4×	176	5.2k
Yunlong Shang China	34	4.2k 1.2×	3.6k 1.1×	1.4k 1.6×	556 2.7×	151 1.0×	142	4.7k
Yunhong Che China	23	2.4k 0.7×	2.1k 0.7×	625 0.7×	375 1.8×	105 0.7×	56	2.7k
Ji Wu China	33	3.5k 1.0×	3.0k 0.9×	1.1k 1.4×	591 2.9×	115 0.8×	92	3.8k
Naxin Cui China	35	3.2k 0.9×	3.1k 1.0×	704 0.8×	115 0.6×	124 0.8×	146	3.7k
Quan Ouyang China	21	1.8k 0.5×	1.7k 0.5×	496 0.6×	249 1.2×	91 0.6×	51	2.2k

Countries citing papers authored by Phillip J. Kollmeyer

Since Specialization

Citations

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

Fields of papers citing papers by Phillip J. Kollmeyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Phillip J. Kollmeyer. 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 Phillip J. Kollmeyer. The network helps show where Phillip J. Kollmeyer may publish in the future.

Co-authorship network of co-authors of Phillip J. Kollmeyer

This figure shows the co-authorship network connecting the top 25 collaborators of Phillip J. Kollmeyer. A scholar is included among the top collaborators of Phillip J. Kollmeyer 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 Phillip J. Kollmeyer. Phillip J. Kollmeyer 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

Kollmeyer, Phillip J., et al.. (2025). Comprehensive Comparison of Machine Learning and Kalman Filter Battery State of Charge Estimators. IEEE Access. 13. 36321–36338. 5 indexed citations

Naguib, Mina, et al.. (2025). Thermal fault detection of lithium-ion battery packs through an integrated physics and deep neural network based model. Communications Engineering. 4(1). 79–79. 4 indexed citations

Kollmeyer, Phillip J., et al.. (2025). Battery state-of-health estimation using CNNs with transfer learning and multi-modal fusion of partial voltage profiles and histogram data. Applied Energy. 391. 125923–125923. 5 indexed citations

Yao, Qi, et al.. (2024). Review of Production Electric Vehicle Battery Thermal Management Systems and Experimental Testing of a Production Battery Module. SAE International Journal of Advances and Current Practices in Mobility. 7(2). 798–809. 1 indexed citations

Kollmeyer, Phillip J., et al.. (2024). Experimental Performance Analysis of LG E-66 Cells from a Fast-Charging Porsche Taycan Battery Module. 1–6. 1 indexed citations

Guirguis, John, et al.. (2024). Transformer-Based Deep Learning Strategies for Lithium-Ion Batteries SOX Estimation Using Regular and Inverted Embedding. IEEE Access. 12. 167108–167119. 1 indexed citations

Kollmeyer, Phillip J., et al.. (2023). Loss Modeling and Testing of 800-V DC Bus IGBT and SiC Traction Inverter Modules. IEEE Transactions on Transportation Electrification. 10(2). 2923–2935. 11 indexed citations

Kollmeyer, Phillip J., et al.. (2023). Experimental Characterization and Modeling of a YASA P400 Axial Flux PM Traction Machine for Performance Analysis of a Chevy Bolt EV. IEEE Transactions on Industry Applications. 60(2). 3108–3119. 12 indexed citations

Chemali, Ephrem, et al.. (2022). A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles. Energies. 15(3). 1185–1185. 54 indexed citations

10.

Naguib, Mina, Phillip J. Kollmeyer, & Ali Emadi. (2022). Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature Estimation Under Driving and Fast Charge Conditions. IEEE Transactions on Transportation Electrification. 9(1). 1153–1165. 36 indexed citations

11.

Anselma, Pier Giuseppe, Phillip J. Kollmeyer, Angelo Bonfitto, et al.. (2022). Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles. IEEE Transactions on Transportation Electrification. 9(1). 1021–1033. 7 indexed citations

12.

Naguib, Mina, Phillip J. Kollmeyer, & Ali Emadi. (2021). Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review. IEEE Access. 9. 50570–50582. 145 indexed citations

13.

Bauman, Jennifer, et al.. (2020). 800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends. IEEE Transactions on Transportation Electrification. 7(3). 927–948. 271 indexed citations

14.

Kollmeyer, Phillip J. & Thomas M. Jahns. (2019). Aging and performance comparison of absorbed glass matte, enhanced flooded, PbC, NiZn, and LiFePO4 12V start stop vehicle batteries. Journal of Power Sources. 441. 227139–227139. 12 indexed citations

15.

Kollmeyer, Phillip J., John Reimers, Daniel F. Opila, et al.. (2019). Real-Time Control of a Full Scale Li-ion Battery and Li-ion Capacitor Hybrid Energy Storage System for a Plug-in Hybrid Vehicle. IEEE Transactions on Industry Applications. 55(4). 4204–4214. 19 indexed citations

16.

Vidal, Carlos, Oliver Groß, Ran Gu, Phillip J. Kollmeyer, & Ali Emadi. (2019). xEV Li-Ion Battery Low-Temperature Effects—Review. IEEE Transactions on Vehicular Technology. 68(5). 4560–4572. 141 indexed citations

17.

Kollmeyer, Phillip J., et al.. (2018). A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries. IEEE Transactions on Industry Applications. 55(2). 1922–1931. 63 indexed citations

18.

Kollmeyer, Phillip J., et al.. (2018). A Comparison of the Performance and Thermal Management Requirements of Lithium-Ion Batteries During Ultra-Fast Charging. 675–680. 3 indexed citations

19.

Chemali, Ephrem, Phillip J. Kollmeyer, Matthias Preindl, Ryan Ahmed, & Ali Emadi. (2017). Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries. IEEE Transactions on Industrial Electronics. 65(8). 6730–6739. 632 indexed citations breakdown →

20.

Kollmeyer, Phillip J., Andreas Hackl, & Ali Emadi. (2017). Li-ion battery model performance for automotive drive cycles with current pulse and EIS parameterization. 486–492. 55 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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