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Battery Model Identification Approach for Electric Forklift Application

Author

Listed:
  • 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)

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.

Suggested Citation

  • Cynthia Thamires da Silva & Bruno Martin de Alcântara Dias & Rui Esteves Araújo & Eduardo Lorenzetti Pellini & Armando Antônio Maria Laganá, 2021. "Battery Model Identification Approach for Electric Forklift Application," Energies, MDPI, vol. 14(19), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6221-:d:646203
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    References listed on IDEAS

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    1. Ngoc-Tham Tran & Abdul Basit Khan & Thanh-Tung Nguyen & Dae-Wook Kim & Woojin Choi, 2018. "SOC Estimation of Multiple Lithium-Ion Battery Cells in a Module Using a Nonlinear State Observer and Online Parameter Estimation," Energies, MDPI, vol. 11(7), pages 1-14, June.
    2. Théophile Paul & Tedjani Mesbahi & Sylvain Durand & Damien Flieller & Wilfried Uhring, 2020. "Sizing of Lithium-Ion Battery/Supercapacitor Hybrid Energy Storage System for Forklift Vehicle," Energies, MDPI, vol. 13(17), pages 1-18, September.
    3. Fotouhi, Abbas & Auger, Daniel J. & Propp, Karsten & Longo, Stefano & Wild, Mark, 2016. "A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1008-1021.
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    Cited by:

    1. Lluís Trilla & Lluc Canals Casals & Jordi Jacas & Pol Paradell, 2022. "Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries," Energies, MDPI, vol. 15(19), pages 1-14, September.
    2. Cynthia Thamires da Silva & Bruno Martin de Alcântara Dias & Rui Esteves Araújo & Eduardo Lorenzetti Pellini & Armando Antônio Maria Laganá, 2023. "Two-Outputs Nonlinear Grey Box Model for Lithium-Ion Batteries," Energies, MDPI, vol. 16(5), pages 1-15, February.

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