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Machine Learning Based Optimal Energy Storage Devices Selection Assistance for Vehicle Propulsion Systems
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
This study investigates the use of machine learning methods for the selection of energy storage devices in military electrified vehicles. Powertrain electrification relies on proper selection of energy storage devices, in terms of chemistry, size, energy density, and power density, etc. Military vehicles largely vary in terms of weight, acceleration requirements, operating road environment, mission, etc.
This study aims to assist the energy storage device selection for military vehicles using the data-drive approach. We use Machine Learning models to extract relationships between vehicle characteristics and requirements and the corresponding energy storage devices.
After the training, the machine learning models can predict the ideal energy storage devices given the target vehicles design parameters as inputs. The predicted ideal energy storage devices can be treated as the initial design and modifications to that are made based on the validation results. In the training phase, 80% of vehicle’s data borrowed from the literature were used, and the remaining 20% was used for validation. Results obtained from the proposed design predict the battery size and peak power with mean errors of 3.14% and 8.17%, respectively.
CitationXu, B., Rizzo, D., and Onori, S., "Machine Learning Based Optimal Energy Storage Devices Selection Assistance for Vehicle Propulsion Systems," SAE Technical Paper 2020-01-0748, 2020, https://doi.org/10.4271/2020-01-0748.
Data Sets - Support Documents
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- Kramer, D.M. and Parker, G.G. , “Current State of Military Hybrid Vehicle Development,” Army Tank Automotive Research Development and Engineering Center, Warren, MI, 2011.
- Zhang, L., Hu, X., Wang, Z., Sun, F. et al. , “Multiobjective Optimal Sizing of Hybrid Energy Storage System for Electric Vehicles,” IEEE Transactions on Vehicular Technology 67:1027-1035, 2018.
- Schaltz, E., Khaligh, A., and Rasmussen, P.O. , “Influence of Battery/Ultracapacitor Energy-Storage Sizing on Battery Lifetime in a Fuel Cell Hybrid Electric Vehicle,” IEEE Transactions on Vehicular Technology 58:3882-3891, 2009.
- Schaltz, E. and Rasmussen, P.O. , “Design and Comparison of Power Systems for a Fuel Cell Hybrid Electric Vehicle,” in 2008 IEEE Industry Applications Society Annual Meeting, 2008, 1-8.
- Douglas, H. and Pillay, P. , “Sizing Ultracapacitors for Hybrid Electric Vehicles,” in 31st Annual Conference of IEEE Industrial Electronics Society, 2005 (IECON 2005), 2005, 6.
- Negarestani, S., Fotuhi-Firuzabad, M., Rastegar, M., and Rajabi-Ghahnavieh, A. , “Optimal Sizing of Storage System in a Fast Charging Station for Plug-In Hybrid Electric Vehicles,” IEEE Transactions on Transportation Electrification 2:443-453, 2016.
- Mamun, A., Liu, Z., Rizzo, D., and Onori, S. , “An Integrated Design and Control Optimization Framework for Hybrid Military Vehicle Using Lithium-Ion and Supercapacitor,” IEEE Transactions on Transportation Electrification 5(1):239-251, 2019.
- Rahman, M.L.H.A., Hudha, K., Kadir, Z.A., Amer, N.H. et al. , “Modelling and Validation of a Novel Continuously Variable Transmission System Using Slider Crank Mechanism,” International Journal of Engineering Systems Modelling and Simulation 10:49-61, 2018.
- Arunachalam, H. and Onori, S. , “Full Homogenized Macroscale Model and Pseudo-2-Dimensional Model for Lithium-Ion Battery Dynamics: Comparative Analysis, Experimental Verification and Sensitivity Analysis,” Journal of The Electrochemical Society 6(8):1380-1392, 2019.
- Hannan, M.A., Lipu, M.H., Hussain, A., and Mohamed, A. , “A review of Lithium-Ion Battery State of Charge Estimation and Management System in Electric Vehicle Applications: Challenges and Recommendations,” Renewable and Sustainable Energy Reviews 78:834-854, 2017.
- Burbidge, R., Trotter, M., Buxton, B., and Holden, S. , “Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis,” Computers & Chemistry 26:5-14, 2001.
- Catenaro, E., Rizzo, D., and Onori, S. , “Eperimental Analysis and Analytical Modeling of an Enhanced Ragone Plot,” Environmental Science and Technology, in preparation, 2020.
- Budde-Meiwes, H., Drillkens, J., Lunz, B., Muennix, J. et al. , “A Review of Current Automotive Battery Technology and Future Prospects,” Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 227:761-776, 2013.
- He, K., Zhang, X., Ren, S., and Sun, J. , “Delving Deep into Rectifiers: Surpassing Human-Level Performance on Imagenet Classification,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, 1026-1034.
- Young, T., Hazarika, D., Poria, S., and Cambria, E. , “Recent Trends in Deep Learning Based Natural Language Processing,” IEEE Computational Intelligence Magazine 13:55-75, 2018.
- Fang, S.-H., Tsao, Y., Hsiao, M.-J., Chen, J.-Y. et al. , “Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach,” Journal of Voice, 2018.
- Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R. et al. , “The Limits and Potentials of Deep Learning for Robotics,” The International Journal of Robotics Research 37:405-420, 2018.
- Wold, S., Esbensen, K., and Geladi, P. , “Principal Component Analysis,” Chemometrics and Intelligent Laboratory Systems 2:37-52, 1987.
- Breiman, L. , “Random Forests,” Machine Learning 45:5-32, 2001.
- Zhang, D., Xu, B., and Wood, J. , “Predict Failures in Production Lines: A Two-Stage Approach with Clustering and Supervised Learning,” in IEEE International Conference on Big Data, 2016, 2070-2074.
- Xu, B., Rathod, D., Yebi, A., and Filipi, Z. , “Real-Time Realization of Dynamic Programming Using Machine Learning Methods for IC Engine Waste Heat Recovery System Power Optimization,” Applied Energy 262:114514, 2020.
- Xu, B., Zhang, D., and Tang, S. , “Malware Classification Utilizing Supervised Learning in Autonomous Driving Applications,” in SAE - 19th Asian Pacific Automotive Engineering Conference, Shanghai, China, 2017.