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A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis

Author

Listed:
  • Shaojun Wang

    (Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
    Department of Computing, Imperial College London, London SW7 2BZ, UK)

  • Datong Liu

    (Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China)

  • Jianbao Zhou

    (Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China)

  • Bin Zhang

    (College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA)

  • Yu Peng

    (Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China)

Abstract

As safety and reliability critical components, lithium-ion batteries always require real-time diagnosis and prognosis. This often involves a large amount of computation, which makes diagnosis and prognosis difficult to implement, especially in embedded or mobile applications. To address this issue, this paper proposes a run-time Reconfigurable Computing (RC) system on Field Programmable Gate Array (FPGA) for Relevance Vector Machine (RVM) to realize real-time Remaining Useful Life (RUL) estimation. The system leverages state-of-the-art run-time dynamic partial reconfiguration technology and customized computing circuits to balance the hardware occupation and computing efficiency. Optimal hardware resource consumption is achieved by partitioning the RVM algorithm according to a multi-objective optimization. Moreover, pipelined and parallel computation circuits for kernel function and matrix inverse are proposed on FPGA to further accelerate the computation. Experimental results with two different battery data sets show that, without sacrificing the RUL prediction performance, the embedded RC platform significantly reduces the computation time and the requirement of hardware resources. This demonstrates that complex prognostic tasks can be implemented and deployed on the proposed system, and it can be extended to the embedded computation of other machine learning algorithms.

Suggested Citation

  • Shaojun Wang & Datong Liu & Jianbao Zhou & Bin Zhang & Yu Peng, 2016. "A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis," Energies, MDPI, vol. 9(8), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:572-:d:74686
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    References listed on IDEAS

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    1. Renxin Xiao & Jiangwei Shen & Xiaoyu Li & Wensheng Yan & Erdong Pan & Zheng Chen, 2016. "Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods," Energies, MDPI, vol. 9(3), pages 1-15, March.
    2. Datong Liu & Hong Wang & Yu Peng & Wei Xie & Haitao Liao, 2013. "Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction," Energies, MDPI, vol. 6(8), pages 1-15, July.
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    1. Vincenzo Conti & Leonardo Rundo & Giuseppe Dario Billeci & Carmelo Militello & Salvatore Vitabile, 2018. "Energy Efficiency Evaluation of Dynamic Partial Reconfiguration in Field Programmable Gate Arrays: An Experimental Case Study," Energies, MDPI, vol. 11(4), pages 1-22, March.

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