This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Comparative Study between Equivalent Circuit and Recurrent Neural Network Battery Voltage Models
Technical Paper
2021-01-0759
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Event:
SAE WCX Digital Summit
Language:
English
Abstract
Lithium-ion battery (LIB) terminal voltage models are investigated using two modelling approaches. The first model is a third-order Thevenin equivalent circuit model (ECM), which consists of an open-circuit voltage in series with a nonlinear resistance and three parallel RC pairs. The parameters of the ECM are obtained by fitting the model to hybrid pulse power characterization (HPPC) test data. The parametrization of the ECM is performed through quadratic-based programming. The second is a novel modelling approach based on long short-term memory (LSTM) recurrent neural networks to estimate the battery terminal voltage. The LSTM is trained on multiple vehicle drive cycles at six different temperatures, including −20°C, without the necessity of battery characterization tests. The performance of both models is evaluated with four automotive drive cycles at each temperature. The results show that both models achieve acceptable performance at all temperatures. However, the LSTM performs better in 92% of the cases, especially at lower temperatures, where it has as much as two-thirds lower error than the ECM approach.
Authors
Citation
Naguib, M., Vidal, C., Kollmeyer, P., Malysz, P. et al., "Comparative Study between Equivalent Circuit and Recurrent Neural Network Battery Voltage Models," SAE Technical Paper 2021-01-0759, 2021, https://doi.org/10.4271/2021-01-0759.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
- Emadi , A. Advanced Electric Drive Vehicles Boca Raton CRC Press, Taylor & Francis 2017 10.1201/9781315215570
- Emadi , A. Transportation 2.0 IEEE Power and Energy Magazine 9 18 29 2011
- Dubarry , M. , Nicolas , V. Bor , L. From Li-Ion Single Cell Model to Battery Pack Simulation 2008 IEEE International Conference on Control Applications , 2008 10.1109/CCA.2008.4629598
- Hu , Y. , Yurkovich , S. , Guezennec , Y. , and Yurkovich , B. A Technique for Dynamic Battery Model Identification in Automotive Applications Using Linear Parameter Varying Structures Control Engineering Practice 17 10 1190 1201 2009 10.1016/j.conengprac.2009.05.002
- Andre , D. , Meiler , M. , Steiner , K. , Walz , H. et al. Characterization of High-Power Lithium-Ion Batteries by Electrochemical Impedance Spectroscopy. II: Modelling Journal of Power Sources 196 12 5349 5356 2011 10.1016/j.jpowsour.2010.07.071
- Kollmeyer , P. , Andreas , H. , and Emadi , A. Li-Ion Battery Model Performance for Automotive Drive Cycles with Current Pulse and EIS Parameterization 2017 IEEE Transportation Electrification Conference and Expo (ITEC) 2017 10.1109/ITEC.2017.7993319
- Ryan , A. et al. Reduced-Order Electrochemical Model Parameters Identification and SOC Estimation for Healthy and Aged Li-Ion Batteries Part I: Parameterization Model Development for Healthy Batteries IEEE Journal of Emerging and Selected Topics in Power Electronics 2 3 659 677 2014 10.1109/jestpe.2014.2331059
- Ran , G. , Malysz , P. , Yang , H. , and Emadi , A. On the Suitability of Electrochemical-Based Modeling for Lithium-Ion Batteries IEEE Transactions on Transportation Electrification 2 4 417 431 2016 10.1109/tte.2016.2571778
- Hu , X. , Shengbo , L. , and Huei , P. A Comparative Study of Equivalent Circuit Models for Li-Ion Batteries Journal of Power Sources 198 359 367 2012 10.1016/j.jpowsour.2011.10.013
- Ruxiu , Z. , Kollmeyer , P. , Lorenz , R. , and Jahns , T. 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 2019 10.1109/tia.2018.2874588
- Chemali , E. , Kollmeyer , P. , Preindl , M. , Rya , A. et al. Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-Ion Batteries IEEE Transactions on Industrial Electronics 65 8 6730 6739 2018 10.1109/tie.2017.2787586
- Dai , H. et al. A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain IEEE Transactions on Industrial Electronics 66 10 7706 7716 2018 10.1109/tie.2018.2880703
- Vidal , C. , Pawel , M. , Kollmeyer , P. , and Emadi , A. Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art IEEE Access 8 52796 52814 2020 10.1109/access.2020.2980961
- Larry , J. , Zhao , R. , Jahns , T. et al The Impact of DC Bias Current on the Modeling of Lithium Iron Phosphate and Lead-Acid Batteries Observed Using Electrochemical Impedance Spectroscopy 2014 IEEE Energy Conversion Congress and Exposition (ECCE) 2014 10.1109/ecce.2014.6953745
- Weizhong , W. , Malysz , P. , Khan , K. , Gauchia , L. et al Modeling, Parameterization, and Benchmarking of a Lithium Ion Electric Bicycle Battery 2016 IEEE Energy Conversion Congress and Exposition (ECCE) 2016 10.1109/ecce.2016.7855266
- Wang , W. Modeling, Parameterization, and Benchmarking of a Lithium Ion Electric Bicycle Battery 2016
- Khaled , A. , El-Sayed , M. , and Mohammed , S. Evaluation of Bidirectional LSTM for Short-and Long-Term Stock Market Prediction 2018 9th International Conference on Information and Communication Systems (ICICS) 2018 10.1109/iacs.2018.8355458
- Mike , H. et al A Novel Multi-Mode Adaptive Energy Consumption Minimization Strategy for P1-P2 Hybrid Electric Vehicle Architectures 2019 IEEE Transportation Electrification Conference and Expo (ITEC) 2019 10.1109/itec.2019.8790525
- Pascanu , R. , Mikolov , T. , Bengio , Y. On the Difficulty of Training Recurrent Neural Networks Proceedings of the 30th International Conference on Machine Learning 2012 10.5555/3042817.3043083
- Colah’s blog Understanding LSTM Networks https://colah.github.io/posts/2015-08-Understanding-LSTMs/ Jul. 2020
- IMR Batteries Specification Specification of Product - IMR Batteries https://www.imrbatteries.com/content/samsung_25r_2.pdf Jul. 2020
- Kollmeyer , P. and Skells , M. Samsung INR21700 30T 3Ah Li-ion Battery Data, V1 Mendeley Data 2020 10.17632/9xyvy2njj3.1
- Vidal , C. et al. XEV Li-Ion Battery Low-Temperature Effects—Review IEEE Transactions on Vehicular Technology 68 5 4560 4572 2019 10.1109/tvt.2019.2906487
- Vidal , C. et al. Robust XEV Battery State-of-Charge Estimator Design Using a Feedforward Deep Neural Network SAE Technical Paper Series 2020 https://doi.org/10.4271/2020-01-1181
- Kollmeyer , P. , Vidal , C. , Naguib , M. , and Skells , M. LG, 18650HG2 Li-Ion Battery Data and Example Deep Neural Network xEV SOC Estimator Script, V3 Mendeley Data 2020 10.17632/cp3473x7xv.3