Expertise
Areas of Specialization
Research Clusters
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Assistant Professor
Electrical & Computer Engineering
Overview
Battery energy storage is on the cusp of dramatic growth, poised to reshape our transportation systems and the way we generate and consume energy. This growth multiplies many of the ever-present challenges for battery energy storage, increasing the need for ways to create longer lasting, faster charging, and more cost effective and sustainable energy storage systems. At the same time, more energy and resource efficient electric drive systems are needed so that ground, marine, and air born vehicles can travel further with each kilowatt hour of energy.
My research aims to make fundamental improvements to how battery energy storage systems are designed and managed, helping to bring down cost while improving functionality. To achieve this, my group is investigating a number of strategies including co-optimized design of the battery cells and packs, applying machine learning and standardized testing to improve battery management systems, and aging sensitive control to better manage batteries from beginning to end of life. To support this work, our laboratory includes: (1) 48 cell cycler channels (testing up to 600 A), (2) Three module/pack cycler channels (up to 800V, 240 kW), (3) Eight micro and four 8+ cubic foot thermal chambers, (4) Liquid chillers with 1 kW and 20 kW capacity, (5) Thermal imaging camera, battery management systems, cell tab welder, and high voltage assembly and safety equipment.
My group is also developing world-class capabilities for modeling, design, prototyping, and laboratory testing of high voltage (800 V), high power (250+ kW), electric traction systems. This work aims to accurately quantify the benefits of a number of technologies including wide bandgap semiconductor inverters, multi-speed gearboxes, and different electric traction machines and powertrain configurations.
Phillip Kollmeyer received the BS (2006), MS (2011), and PhD (2015) degrees in electrical engineering from the University of Wisconsin-Madison. In July of 2023 he started as an assistant professor at McMaster University, where he was a Senior Principal Research Engineer from 2019 to 2023 and a Postdoctoral Research Associate from 2016 to 2019. His research is focused (1) in the battery area – with topics including state estimation, modeling, aging, ultra-fast charging, and thermal management – and (2) on optimizing electric drivetrain efficiency via multi-speed gearboxes, wide bandgap power electronics, and power split control algorithms. Phil has authored and coauthored more than 60 publications, supervised more than a dozen graduate students, and created several widely utilized open-source battery datasets and algorithms. From 2018 to 2023, Phil served on the senior organizing committee of the IEEE Transportation Electrification Conference (ITEC) and he was General Chair of the conference in 2023.
- BSc University of Wisconsin-Madison
- MSc University of Wisconsin-Madison
- PhD University of Wisconsin-Madison
Open Source Data Sets and Example Algorithms
“Tesla Model 3 2170 Li-ion Cell Dataset and Battery SOC Estimation Blind Modeling Tool”, Borealis Data, 2023. https://doi.org/10.5683/SP3/ZVTR4B
“Fifteen minute fast charge aging dataset – Samsung 30T cells”, Borealis Data, 2023. https://doi.org/10.5683/SP3/UYPYDJ
“Convolutional neural network battery SOH estimation example code”, Borealis Data, 2023, https://doi.org/10.5683/SP3/6AGUAW
“Panasonic 18650PF Li-ion Battery Data and Example FNN and LSTM Neural Network SOC Estimator Training Script”, Mendeley Data, 2021. https://data.mendeley.com/datasets/xf68bwh54v/1
“State of Charge Estimation Function based on Kalman Filter”, MathWorks File Exchange, 2021. https://www.mathworks.com/matlabcentral/fileexchange/90381-state-of-charge-estimation-function-based-on-kalman-filter#version_history_tab
“Samsung INR21700 30T 3Ah Li-ion Battery Data”, Mendeley Data, 2020. https://data.mendeley.com/datasets/9xyvy2njj3/1
“LG 18650HG2 Li-ion Battery Data”, Mendeley Data, 2020. http://dx.doi.org/10.17632/b5mj79w5w9.2
“LG 18650HG2 Li-ion Battery Data and Example Deep Neural Network xEV SOC Estimator Script”, Mendeley Data, 2020. http://dx.doi.org/10.17632/cp3473x7xv.3
“Turnigy Graphene 5000mAh 65C Li-ion Battery Data”, Mendeley Data, 2020. http://dx.doi.org/10.17632/4fx8cjprxm.1
“Panasonic 18650PF Li-ion Battery Data”, Mendeley Data, 2018. http://dx.doi.org/10.17632/wykht8y7tg.1
Journal
A. Allca-Pekarovic et al., “Loss Modeling and Testing of 800 V DC Bus IGBT and SiC Traction Inverter Modules,” in IEEE Transactions on Transportation Electrification, 2023.
P. G. Anselma et al., “Economic Payback Time of Battery Pack Replacement for Hybrid and Plug-In Hybrid Electric Vehicles,” in IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1021-1033, March 2023.
M. Naguib, P. Kollmeyer and A. Emadi, “Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature Estimation Under Driving and Fast Charge Conditions,” in IEEE Transactions on Transportation Electrification, vol. 9, no. 1, pp. 1153-1165, March 2023, doi: 10.1109/TTE.2022.3200225.
J. Lempert, P. J. Kollmeyer, M. He, M. Haußmann, J. Cotton, and A. Emadi, “Cell Selection and Thermal Management System Design for a 5C-Rate Ultrafast Charging Battery Module,” Journal of Power Sources, Volume 550, 2022.
E. Chemali, P. J. Kollmeyer, M. Preindl, Y. Rahmy, and A. Emadi, “A Deep Learning Approach for State-of-Health Estimation of Li-ion Batteries”, Energies, 2022, 15, 1185.
C. Vidal, P. Malysz, M. Naguib, A. Emadi, P.J. Kollmeyer, “Estimating battery state of charge using recurrent and non-recurrent neural networks,” Journal of Energy Storage, 2021, 103660, ISSN 2352-152X.
F. A. Machado, P. J. Kollmeyer, D. G. Barroso and A. Emadi, “Multi-Speed Gearboxes for Battery Electric Vehicles: Current Status and Future Trends,” IEEE Open Journal of Vehicular Technology, vol. 2, pp. 419-435, 2021.
S. Feraco, P.G. Anselma, A. Bonfitto, P.J. Kollmeyer, “Robust data-driven battery state of charge estimation for hybrid electric vehicles,” SAE International Journal of Electrified Vehicles, accepted June 2021.
M. Naguib, P. Kollmeyer and A. Emadi, “Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review,” IEEE Access, vol. 9, pp. 50570-50582, 2021.
P.G. Anselma, P.J. Kollmeyer, J. Lempert, Z. Zhao, G. Belingardi, A. Emadi, “Battery State-of-Health Sensitive Energy Management of Hybrid Electric Vehicles: Lifetime Prediction and Ageing Experimental Validation”, Applied Energy, Volume 285, 2021.
I. Aghabali, J. Bauman, P. Kollmeyer, Y. Wang, B. Bilgin and A. Emadi, “800V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends,” IEEE Transactions on Transportation Electrification, vol. 7, no. 3, pp. 927-948, Sept. 2021 Recipient of First Place Prize Paper Award for 2022 in the IEEE Transactions on Transportation Electrification
Conference
R. N. Vieira, P. Kollmeyer, M. Naguib and A. Emadi, “Feedforward and NARX Neural Network Battery State of Charge Estimation with Robustness to Current Sensor Error,” 2023 IEEE Transportation Electrification Conference & Expo (ITEC), Detroit, MI, USA, June 2023.
J. Chen, P. Kollmeyer, F. Chiang and A. Emadi, “Lithium-ion Battery State-of-Health Estimation via Histogram Data, Principal Component Analysis, and Machine Learning,” 2023 IEEE Transportation Electrification Conference & Expo (ITEC), Detroit, MI, USA, June 2023.
M. Naguib, P. J. Kollmeyer and A. Emadi, “State of Charge Estimation of Lithium-Ion Batteries: Comparison of GRU, LSTM, and Temporal Convolutional Deep Neural Networks,” 2023 IEEE Transportation Electrification Conference & Expo (ITEC), Detroit, MI, USA, June 2023.
J. Chen et al., “A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation,” 2023 IEEE Transportation Electrification Conference & Expo (ITEC), Detroit, MI, USA, June 2023.
F. Machado, P.J. Kollmeyer, A. Emadi, “Chevrolet Bolt Electric Vehicle Model Validated with On-the-Road Data and Applied to Estimating the Benefits of a Multi-Speed Gearbox,” SAE World Congress, Detroit, MI, April 2023.
A. Allca-Pekarovic, P. J. Kollmeyer, A. Forsyth and A. Emadi, “Experimental Characterization and Modeling of a YASA P400 Axial Flux PM Traction Machine for Electric Vehicles,” IEEE Transportation Electrification Conference and Expo (ITEC), Anaheim, CA, June 2022.
P.J. Kollmeyer, F. Khanum, M. Naguib, A. Emadi, “A Blind Modeling Tool for Standardized Evaluation of Battery State of Charge Estimation Algorithms, IEEE Transportation Electrification Conference and Expo (ITEC), Anaheim, CA, June 2022.
J. Duque, P.J. Kollmeyer, A. Emadi, “Battery Dual Extended Kalman Filter State of Charge and Health Estimation Strategy for Traction Applications”, IEEE Transportation Electrification Conference and Expo (ITEC), Anaheim, CA, June 2022.
P. Anselma, P.J. Kollmeyer, A. Emadi, G. Belingardi, “Battery State-of-health Adaptive Energy Management of Hybrid Electric Vehicles”, IEEE Transportation Electrification Conference and Expo (ITEC), Anaheim, CA, June 2022.
M. Naguib, P.J. Kollmeyer, O. Gross, A. Emadi, “Microprocessor Execution Time and Memory Use of Battery State of Charge Estimation Algorithms”, SAE World Congress, Detroit, MI, April 2022.
F. Machado, P.J. Kollmeyer, A. Emadi “Chevrolet Bolt Electric Vehicle Model Validated with On-The-Road Data and Applied to Estimating the Benefits of a Multi-Speed Gearbox”, SAE World Congress, Detroit, MI, April 2022.
Z. Zhao, S. Panchal, P.J. Kollmeyer, O. Gross, D. Dronzkowski, A. Emadi, “3D FEA Thermal Modeling with Experimentally Measured Loss Gradient Function of Large Format Ultra-Fast Charging Battery Module used for EVs”, SAE World Congress, Detroit, MI, April 2022.
Y. Liang, M. Canova, C. Vidal, M. Naguib, S. Panchal, A. Emadi, P. Kollmeyer, O. Gross, “A comparative study between physics-based, electrical, and data-driven lithium-ion battery voltage modelling approaches”, SAE World Congress, Detroit, MI, April 2022.
M. Naguib, P.J. Kollmeyer, C. Vidal, A. Emadi, “Accurate Surface Temperature Estimation of Lithium-Ion Batteries Using Feedforward and Recurrent Artificial Neural Network Models,” IEEE Transportation Electrification Conference and Expo (ITEC), Virtual Conference, 2021.
F. Khanoum, E. Louback, F. Duperly, C. Jenkins, P.J. Kollmeyer, A. Emadi, ”A Kalman Filter Based Battery State of Charge Estimation MATLAB Function”, IEEE Transportation Electrification Conference and Expo (ITEC), Virtual Conference, 2021.
Z. Zhao, P.J. Kollmeyer, A. Emadi, “Experimental Comparison of Two Liquid Cooling Methods for Ultrafast Charging Lithium-Ion Battery Modules,” IEEE Transportation Electrification Conference and Expo (ITEC), Virtual Conference, 2021
P.G. Anselma, P.J. Kollmeyer, S. Feraco, A. Bonfitto, G. Belingardi, A. Emadi, N. Amati, A. Tonoli, “Assessing Impact of Heavily Aged Batteries on Hybrid Electric Vehicle Fuel Economy and Drivability,” IEEE Transportation Electrification Conference and Expo (ITEC), Virtual Conference, 2021.
M. Naguib, C. Vidal, P.J. Kollmeyer, P. Malysz, O. Gross, A. Emadi, “Comparative Study between Equivalent Circuit and Recurrent Neural Network Battery Voltage Models,” SAE World Congress, Detroit, MI, 2021.
