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Parameterization of a Battery Simulation Model Using Numerical Optimization Methods
Technical Paper
2009-01-1381
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Typically, battery models are complex and difficult to parameterize to match real-world data. Achieving a good generalized fit between measured and simulated results should be done using a variety of laboratory data. Numerical optimizations can ensure the best possible fit between a simulation model and measured data, given a set of constraints.
In this paper, we propose a semi-automated process for parameterizing a lithium polymer battery (LiPB) cell simulation model that is able to satisfy constraints on the optimized parameters. This process uses a number of measured data sets under a variety of conditions. An iterative numerical optimization algorithm using Simulink Parameter Estimation was implemented to estimate parameter values by minimizing error between measured and simulated results.
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Citation
Jackey, R., Plett, G., and Klein, M., "Parameterization of a Battery Simulation Model Using Numerical Optimization Methods," SAE Technical Paper 2009-01-1381, 2009, https://doi.org/10.4271/2009-01-1381.Also In
References
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- Plett, G. “Sigma-Point Kalman Filters for Battery Management Systems of LiPB-Based HEV Battery Packs-Parts 1-2,” Journal of Power Sources 152 2 2006 1356 84
- Plett, G. Klein, M. “Advances in HEV Battery Management Systems,” CD-ROM Proc. SAE Convergence 2006 Detroit, MI October 2006
- Plett, G. “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs-Parts 1-3,” Journal of Power Sources 134 2 2004 252 92
- Jackey, Robyn A. “A Simple, Effective Lead-Acid Battery Modeling Process for Electrical System Component Selection,” SAE Paper 2007-01-0778 SAE International Warrendale, PA 2007