Journal
JOURNAL OF POWER SOURCES
Volume 270, Issue -, Pages 367-378Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2014.07.110
Keywords
Parameter identification; Multi-objective genetic algorithm; Multi-physics model; Lithium-ion battery
Funding
- National Natural Science Foundation of China [51107021]
- Fundamental Research Funds for the Central Universities [HIT. NSRIF. 2014021]
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A multi-objective parameter identification method for modeling of Li-ion battery performance is presented. Terminal voltage and surface temperature curves at 15 degrees C and 30 degrees C are used as four identification objectives. The Pareto fronts of two types of Li-ion battery are obtained using the modified multi-objective genetic algorithm NSGA-II and the final identification results are selected using the multiple criteria decision making method TOPSIS. The simulated data using the final identification results are in good agreement with experimental data under a range of operating conditions. The validation results demonstrate that the modified NSGA-II and TOPSIS algorithms can be used as robust and reliable tools for identifying parameters of multi-physics models for many types of Li-ion batteries. (C) 2014 Elsevier B.V. All rights reserved.
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