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FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries

Author

Listed:
  • Mattia Stighezza

    (Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy)

  • Valentina Bianchi

    (Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy)

  • Ilaria De Munari

    (Department of Engineering and Architecture, University of Parma, 43121 Parma, Italy)

Abstract

Monitoring the State of Charge (SoC) in battery cells is necessary to avoid damage and to extend battery life. Support Vector Machine (SVM) algorithms and Machine Learning techniques in general can provide real-time SoC estimation without the need to design a cell model. In this work, an SVM was trained by applying an Ant Colony Optimization method. The obtained trained model was 10-fold cross-validated and then designed in Hardware Description Language to be run on FPGA devices, enabling the design of low-cost and compact hardware. Thanks to the choice of a linear SVM kernel, the implemented architecture resulted in low resource usage (about 1.4% of Xilinx Artix7 XC7A100TFPGAG324C FPGA), allowing multiple instances of the SVM SoC estimator model to monitor multiple battery cells or modules, if needed. The ability of the model to maintain its good performance was further verified when applied to a dataset acquired from different driving cycles to the cycle used in the training phase, achieving a Root Mean Square Error of about 1.4%.

Suggested Citation

  • Mattia Stighezza & Valentina Bianchi & Ilaria De Munari, 2021. "FPGA Implementation of an Ant Colony Optimization Based SVM Algorithm for State of Charge Estimation in Li-Ion Batteries," Energies, MDPI, vol. 14(21), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7064-:d:667251
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    References listed on IDEAS

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    1. Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
    2. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2018. "A Study on the Open Circuit Voltage and State of Charge Characterization of High Capacity Lithium-Ion Battery Under Different Temperature," Energies, MDPI, vol. 11(9), pages 1-17, September.
    3. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    4. Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Tan, Kang Miao & Mithulananthan, N., 2015. "A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 365-385.
    5. Ruifeng Zhang & Bizhong Xia & Baohua Li & Libo Cao & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang, 2018. "State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles," Energies, MDPI, vol. 11(7), pages 1-36, July.
    6. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
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