Battery Model Identification Approach for Electric Forklift Application

Cynthia Thamires da Silva, Bruno Martin de Alcântara Dias, Rui Esteves Araújo, Eduardo Lorenzetti Pellini and Armando Antônio Maria Laganá
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Cynthia Thamires da Silva: PEA—Polytechnic School (POLI-USP), University of São Paulo, 05508-010 São Paulo, Brazil
Bruno Martin de Alcântara Dias: PEA—Polytechnic School (POLI-USP), University of São Paulo, 05508-010 São Paulo, Brazil
Rui Esteves Araújo: INESC TEC, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Eduardo Lorenzetti Pellini: PEA—Polytechnic School (POLI-USP), University of São Paulo, 05508-010 São Paulo, Brazil
Armando Antônio Maria Laganá: PEA—Polytechnic School (POLI-USP), University of São Paulo, 05508-010 São Paulo, Brazil

Energies, 2021, vol. 14, issue 19, 1-26

Abstract: Electric forklifts are extremely important for the world’s logistics and industry. Lead acid batteries are the most common energy storage system for electric forklifts; however, to ensure more energy efficiency and less environmental pollution, they are starting to use lithium batteries. All lithium batteries need a battery management system (BMS) for safety, long life cycle and better efficiency. This system is capable to estimate the battery state of charge, state of health and state of function, but those cannot be measured directly and must be estimated indirectly using battery models. Consequently, accurate battery models are essential for implementation of advance BMS and enhance its accuracy. This work presents a comparison between four different models, four different types of optimizers algorithms and seven different experiment designs. The purpose is defining the best model, with the best optimizer, and the best experiment design for battery parameter estimation. This best model is intended for a state of charge estimation on a battery applied on an electric forklift. The nonlinear grey box model with the nonlinear least square method presented a better result for this purpose. This model was estimated with the best experiment design which was defined considering the fit to validation data, the parameter standard deviation and the output variance. With this approach, it was possible to reach more than 80% of fit in different validation data, a non-biased and little prediction error and a good one-step ahead result.

Keywords: battery models; battery management system; electric forklift; transfer function battery model; output error battery model; Hammerstein-Wiener battery model; nonlinear grey box battery model (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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