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Comparison of Optimization Techniques for Lithium-Ion Battery Model Parameter Estimation
Technical Paper
2014-01-1851
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Due to rising fuel prices and environmental concerns, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been gaining market share as fuel-efficient, environmentally friendly alternatives. Lithium-ion batteries are commonly used in EV and HEV applications because of their high power and energy densities. During controls development of HEVs and EVs, hardware-in-the-loop simulations involving real-time battery models are commonly used to simulate a battery response in place of a real battery. One physics-based model which solves in real-time is the reduced-order battery model developed by Dao et al. [1], which is based on the isothermal model by Newman [2] incorporating concentrated solution theory and porous electrode theory [3].
The battery models must be accurate for effective control; however, if the battery parameters are unknown or change due to degradation, a method for estimating the battery parameters to update the model is required. A set of manufacturer recommended battery parameters were evaluated using a numerical sensitivity analysis to evaluate their identifiability. The parameters chosen to be identified were εp, εs and brugg. The optimization algorithms that were evaluated for parameter estimation were: Self-Adaptive Evolution, Efficient Global Optimization, Differential Evolution, and Simulated Annealing. These algorithms were evaluated based on how many simulation calls were required to converge to an accuracy of 1e-4. Differential Evolution was shown to have the best performance in estimating the parameters, requiring an average of 1485 simulations to converge.
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Ing, A., Masoudi, R., McPhee, J., and Dao, T., "Comparison of Optimization Techniques for Lithium-Ion Battery Model Parameter Estimation," SAE Technical Paper 2014-01-1851, 2014, https://doi.org/10.4271/2014-01-1851.Also In
References
- Dao , T.S. , Vyasarayani , C.P. , McPhee , J. Simplification and Order Reduction of Lithium-Ion Battery Model Based on Porous-Electrode Theory J. Power Sources. 198 329 337 2012 10.1016/j.jpowsour.2011.09.034
- Doyle , M. , Fuller , T.F. , Newman , J. Modeling of Galvanostatic Charge and Discharge of the Lithium/Polymer/Insertion Cell J. Electrochem. Society. 140 1526 1533 1993 10.1149/1.2221597
- Newman , J. , Tiedemann , W. Porous-Electrode Theory with Battery Applications J. AlChE. 21 1 24 41 1975 10.1002/aic.690210103
- Ning , G. , Popov , B.N. Cycle Life Modeling of Lithium-Ion Batteries J. Electrochem. Society 151 10 A1584 A1591 2004 10.1149/1.1787631
- Millner , A. Modeling Lithium Ion Battery Degradation in Electric Vehicles IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply (CITRES) 2010 10.1109/CITRES.2010.5619782
- Fairweather , A.J. , Foster , M.P. , Stone , D.A. Battery Parameter Identification with Pseudo Random Binary Sequence Excitation (PBRS) J. Power Sources 196 9398 9406 2011 10.1016/j.jpowsour.2011.06.072
- Samadi , M.F. , Mahdi Alavi S.M. , Saif , M. An Electrochemical Model-Based Particle Filter Approach for Lithium-ion Battery Estimation 51st IEEE Conference on Decision and Control 2012
- Domenico , D.D. , Stefanopoulou , A. , Fiengio , G. Lithium-ion Battery State of Charge and Critical Surface Charge estimation Using an Electrochemical Model-Based Extended Kalman Filter J. Dyn. Sys., Control 132 6 061302 2010 10.1115/1.4002475
- Schmidt , A.P. , Bitzer , M. , Imre A. W. , Guzella , L. Experiment-Driven Electrochemical Modeling and Systematic Parametrization for a Lithium-ion Battery Cell J. Power Sources 195 5071 5080 2010
- Subramanian , V.R. , Boovaragavan , V. , Ramadesigan , V. , Arabandi , M. Mathematical Model Reformulation for Lithium-Ion Battery Simulations: Galvanostatic Boundary Conditions J. Electromchem. Society. 156 4 A260 A271 2009 10.1149/1.3065083
- Subramanian , V.R. , Diwakar , V.D. , Tapriyal , D. Efficient Macro-Micro Scale Couple Modeling of Batteries J. Electrochem. Soc. 152 10 2005 10.1149/1.2032427
- Guo , S. The application of Genetic Algorithms to Parameter Estimation in Lead-Acid Battery Equivalent Circuit Models Master thesis School of Electronic Electrical & Computer Engineering, University of Birmingham 2010
- Yang , W.J. , Yu , D.H. , K , Y.B. Parameter Estimation of Lithium-ion Batteries and Noise Reduction Using an H Filter J. Mech. Sci. and Tech. 27 1 247 256 2013 10.1007/s12206-012-1203-z
- Optimus (Rev. 10.10), Computer Software Noesis Solutions Leuven, Belgium 2013
- Storn , R. Price , K. Differential Evolution - A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces J. Global Optimization 11 4 341 359 1997 10.1023/A:1008202821328
- Schwefel , H.P. Numerical Optimization of Computer Models John Wiley & Sons, Inc. New York 0471099880 1981
- Rao , S.S. Engineering Optimization John Wiley & Sons, Inc. New York 978-0470183526 1996