This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
A Comparative Study of Recurrent Neural Network Architectures for Battery Voltage Prediction
Technical Paper
2021-01-1252
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation. This paper presents a comparative study on the battery voltage predication using all three neural network architectures. In this study, all neural network architectures use common pulse data for training and validation. The selected pulse data covers the voltage response in not only dynamic events but also during relaxation. Then the predictions are made in severe battery operation conditions such as high current, low temperature, and long duration. The results indicate the LSTM has the best prediction accuracy across various temperatures and current pulse sizes. All three neural network structures show the robustness at -30°C. But the standard RNN and GRU show the prediction error increases when the pulse size is higher.
Authors
Citation
Cho, G., ZHU, D., and Campbell, J., "A Comparative Study of Recurrent Neural Network Architectures for Battery Voltage Prediction," SAE Technical Paper 2021-01-1252, 2021, https://doi.org/10.4271/2021-01-1252.Data Sets - Support Documents
| Title | Description | Download |
|---|---|---|
| Unnamed Dataset 1 | ||
| Unnamed Dataset 2 | ||
| Unnamed Dataset 3 | ||
| Unnamed Dataset 4 | ||
| Unnamed Dataset 5 |
Also In
References
- Boston University Institute for Sustainable Energy Will Autonomous Vehicles be Electric? https://www.bu.edu/ise/2018/08/27/will-autonomous-vehicles-be-electric/
- Pritchard , E. , Mackey , L. , Zhu , D. , Gregory , D. , et al. Modular Electric Generator Rapid Deployment DC Microgrid Presented at 2017 IEEE Second International Conference on DC Microgrids (ICDCM) Germany 2017 10.1109/ICDCM.2017.8001030
- Sonnenberg , M. , Pritchard , E. , and Zhu , D. Microgrid Development Using Model-Based Design Presented at 2018 IEEE Green Technologies Conference (GreenTech), USA 2018 10.1109/GreenTech.2018.00019
- Li , W. , Lee , S. , and Manthiram , A. High-Nickel NMA: A Cobalt-Free Alternative to NMC and NCA Cathodes for Lithium-Ion Batteries Advanced Materials 32 33 2020 2002718 10.1002/adma.202002718
- Zhu , D. and Pritchard , E. NCSU Year Three Final Technical Report SAE Technical Papers 2014-01-2907 2014 https://doi.org/10.4271/2014-01-2907
- Zhu , D. , Pritchard , E. , Dadam , S.R. , Kumar , V. et al. Optimization of Rule-Based Energy Management Strategies for Hybrid Vehicles Using Dynamic Programming Combustion Engines 184 1 2021 3 10 10.19206/CE-131967
- Zhu , D. , Pritchard , E.G.D. , and Silverberg , L.M. New System , A. Development Framework Driven by a Model-Based Testing Approach Bridged by Information Flow IEEE Systems Journal 12 3 2917 2924 2016 10.1109/JSYST.2016.2631142
- Capizzi , G. , Bonanno , F. , and Tina , G.M. Recurrent Neural Network-Based Modeling and Simulation of Lead-Acid Batteries Charge-Discharge IEEE Transactions on Energy Conversion 26 2 2011 435 443 10.1109/tec.2010.2095015
- Eddahech , A. , Briat , O. , and Vinassa , J.M. Adaptive Voltage Estimation for EV Li-Ion Cell Based on Artificial Neural Networks State-of-Charge Meter Presented at 2012 IEEE International Symposium on Industrial Electronics China 2012 10.1109/isie.2012.6237281
- Bonanno , F. , Capizzi , G. , and Napoli , C. Some Remarks on the Application of RNN and PRNN for the Charge-Discharge Simulation of Advanced Lithium-Ions Battery Energy Storage Presented at International Symposium on Power Electronics Power Electronics, Electrical Drives, Automation and Motion Italy 2012 10.1109/speedam.2012.6264500
- Zhao , R. , Kollmeyer , P.J. , Lorenz , R.D. , and Jahns , T.M. A Compact Unified Methodology via a Recurrent Neural Network for Accurate Modeling of Lithium-Ion Battery Voltage and State-of-Charge Presented at 2017 IEEE Energy Conversion Congress and Exposition (ECCE) USA 2017 10.1109/ecce.2017.8096879
- Hong , J. , Wang , Z. , and Yao , Y. Fault Prognosis of Battery System based on Accurate Voltage Abnormity Prognosis using Long Short-Term Memory Neural Networks Applied Energy 251 2019 113381 10.1016/j.apenergy.2019.113381
- Li , D. , Zhang , Z. , Liu , P. , Wang , Z. et al. Battery Fault Diagnosis for Electric Vehicles based on Voltage Abnormality by Combining the Long Short-Term Memory Neural Network and the Equivalent Circuit Model IEEE Transactions on Power Electronics 36 2 2020 1303 1315 10.1109/tpel.2020.3008194
- Géron , A. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Second Edition Sebastopol, O'Reilly Media, Inc. 2019
- Graves , A. , Liwicki , M. , Fernández , S. , Bertolami , R. et al. A Novel Connectionist System for Unconstrained Handwriting Recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 31 5 2009 855 868 10.1109/tpami.2008.137