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

Shaojun Wang, Datong Liu, Jianbao Zhou, Bin Zhang and Yu Peng
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Shaojun Wang: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
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

Energies, 2016, vol. 9, issue 8, 1-19

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.

Keywords: field programmable gate array; relevance vector machine; lithium-ion battery; remaining useful life (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: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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