Next Article in Journal
Maximum Power Point Tracking Sensorless Control of an Axial-Flux Permanent Magnet Vernier Wind Power Generator
Previous Article in Journal
Analytical Calculation of D- and Q-axis Inductance for Interior Permanent Magnet Motors Based on Winding Function Theory
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Energies 2016, 9(8), 572;

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

Department of Automatic Test and Control, Harbin Institute of Technology, Harbin 150080, China
Department of Computing, Imperial College London, London SW7 2BZ, UK
College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA
Author to whom correspondence should be addressed.
Academic Editor: Shengshui Zhang
Received: 21 May 2016 / Revised: 13 July 2016 / Accepted: 15 July 2016 / Published: 25 July 2016
Full-Text   |   PDF [2808 KB, uploaded 25 July 2016]   |  


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. View Full-Text
Keywords: field programmable gate array; relevance vector machine; lithium-ion battery; remaining useful life field programmable gate array; relevance vector machine; lithium-ion battery; remaining useful life

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Wang, S.; Liu, D.; Zhou, J.; Zhang, B.; Peng, Y. A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis. Energies 2016, 9, 572.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top