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Phillip J. Kollmeyer

Observed names: Kollmeyer, Phillip, Kollmeyer, Phillip J., P.J. Kollmeyer, Phil Kollmeyer, Phillip J. Kollmeyer, Phillip Kollmeyer
Institution: McMaster University
ORCID: 0000-0003-3576-672X
Observed ORCID: 0000-0003-3576-672X
OpenAlex ID: A5064174882
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
2017 · Ephrem Chemali, Phillip J. Kollmeyer, et al. · IEEE Transactions on Industrial Electronics · 901
State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
2018 · Ephrem Chemali, Phillip J. Kollmeyer, et al. · Journal of Power Sources · 818
Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art
2020 · Carlos Vidal, Pawel Malysz, et al. · IEEE Access · 507
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800-V Electric Vehicle Powertrains: Review and Analysis of Benefits, Challenges, and Future Trends
2020 · Iman Aghabali, Jennifer Bauman, et al. · IEEE Transactions on Transportation Electrification · 435
Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review
2021 · Mina Naguib, Phillip Kollmeyer, et al. · IEEE Access · 220
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Panasonic 18650PF Li-ion Battery Data
2018 · Kollmeyer, Phillip · Data Archiving and Networked Services (DANS) · 215
xEV Li-Ion Battery Low-Temperature Effects—Review
2019 · Carlos Vidal, Oliver Gross, et al. · IEEE Transactions on Vehicular Technology · 187
Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation
2021 · Pier Giuseppe Anselma, Phillip Kollmeyer, et al. · Applied Energy · 97
Robust xEV Battery State-of-Charge Estimator Design Using a Feedforward Deep Neural Network
2020 · Carlos Vidal, Phillip Kollmeyer, et al. · SAE International Journal of Advances and Current Practices in Mobility · 96
Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning
2019 · Carlos Vidal, Phillip Kollmeyer, et al. · IEEE Transportation Electrification Conference and Expo · 93
Multi-Speed Gearboxes for Battery Electric Vehicles: Current Status and Future Trends
2021 · Fabricio Machado, Phillip Kollmeyer, et al. · IEEE Open Journal of Vehicular Technology · 89
A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles
2022 · Ephrem Chemali, Phillip J. Kollmeyer, et al. · Energies · 82
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Estimating battery state of charge using recurrent and non-recurrent neural networks
2021 · Carlos Vidal, Pawel Malysz, et al. · Journal of Energy Storage · 80
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A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries
2018 · Ruxiu Zhao, Phillip J. Kollmeyer, et al. · IEEE Transactions on Industry Applications · 75
Li-ion battery model performance for automotive drive cycles with current pulse and EIS parameterization
2017 · Phillip Kollmeyer, Andreas Hackl, et al. · IEEE Transportation Electrification Conference and Expo · 75
A Comparative Study between Physics, Electrical and Data Driven Lithium-Ion Battery Voltage Modeling Approaches
2022 · Yang Liang, Ali Emadi, et al. · SAE technical papers on CD-ROM/SAE technical paper series · 72
Application of Deep Neural Networks for Lithium-Ion Battery Surface Temperature Estimation Under Driving and Fast Charge Conditions
2022 · Mina Naguib, Phillip Kollmeyer, et al. · IEEE Transactions on Transportation Electrification · 63
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Investigation of the influence of superimposed AC current on lithium-ion battery aging using statistical design of experiments
2017 · Larry W. Juang, Phillip J. Kollmeyer, et al. · Journal of Energy Storage · 61
A Convolutional Neural Network for Estimation of Lithium-Ion Battery State-of-Health during Constant Current Operation
2023 · Junran Chen, Manjula Manivanan, et al. · IEEE Transportation Electrification Conference & Expo (ITEC) · 55
3D FEA Thermal Modeling with Experimentally Measured Loss Gradient of Large Format Ultra-Fast Charging Battery Module Used for EVs
2022 · Ziyu Zhao, Satyam Panchal, et al. · SAE technical papers on CD-ROM/SAE technical paper series · 55
A compact unified methodology via a recurrent neural network for accurate modeling of lithium-ion battery voltage and state-of-charge
2017 · Ruxiu Zhao, Phillip J. Kollmeyer, et al. · IEEE Energy Conversion Congress and Exposition (ECCE) · 48
Comparison of IGBT and SiC Inverter Loss for 400V and 800V DC Bus Electric Vehicle Drivetrains
2020 · Alexander Allca-Pekarovic, Phillip J. Kollmeyer, et al. · IEEE Energy Conversion Congress and Exposition (ECCE) · 47
A Kalman Filter Based Battery State of Charge Estimation MATLAB Function
2021 · Fauzia Khanum, Eduardo Louback, et al. · IEEE Transportation Electrification Conference & Expo (ITEC) · 46
Sequence Training and Data Shuffling to Enhance the Accuracy of Recurrent Neural Network Based Battery Voltage Models
2024 · Junran Chen, Phillip Kollmeyer, et al. · SAE technical papers on CD-ROM/SAE technical paper series · 44
Experimental Results of Battery Power Capability Measurement on Cells with Different State of Health Levels
2024 · Junran Chen, Phillip Kollmeyer, et al. · IEEE Transportation Electrification Conference and Expo · 40
Works count: 115
Citation count: 5,521
H-index: 32
I10-index: 69
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