Battery Models for Battery Powered Applications: A Comparative Study

Nicola Campagna, Vincenzo Castiglia, Rosario Miceli, Rosa Anna Mastromauro, Ciro Spataro, Marco Trapanese and Fabio Viola
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Nicola Campagna: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Vincenzo Castiglia: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Rosario Miceli: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Rosa Anna Mastromauro: Department of Information Engineering (DINFO), University of Florence, 50139 Florence, Italy
Ciro Spataro: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Marco Trapanese: Department of Engineering, University of Palermo, 90133 Palermo, Italy
Fabio Viola: Department of Engineering, University of Palermo, 90133 Palermo, Italy

Energies, 2020, vol. 13, issue 16, 1-26

Abstract: Battery models have gained great importance in recent years, thanks to the increasingly massive penetration of electric vehicles in the transport market. Accurate battery models are needed to evaluate battery performances and design an efficient battery management system. Different modeling approaches are available in literature, each one with its own advantages and disadvantages. In general, more complex models give accurate results, at the cost of higher computational efforts and time-consuming and costly laboratory testing for parametrization. For these reasons, for early stage evaluation and design of battery management systems, models with simple parameter identification procedures are the most appropriate and feasible solutions. In this article, three different battery modeling approaches are considered, and their parameters’ identification are described. Two of the chosen models require no laboratory tests for parametrization, and most of the information are derived from the manufacturer’s datasheet, while the last battery model requires some laboratory assessments. The models are then validated at steady state, comparing the simulation results with the datasheet discharge curves, and in transient operation, comparing the simulation results with experimental results. The three modeling and parametrization approaches are systematically applied to the LG 18650HG2 lithium-ion cell, and results are presented, compared and discussed.

Keywords: e-mobility; electric vehicles; battery electric vehicles; battery model; parameter identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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