A least-squares support vector machine method for modeling transient voltage in polymer electrolyte fuel cells

Wei Zou, Dieter Froning, Yan Shi and Werner Lehnert

Applied Energy, 2020, vol. 271, issue C, No S0306261920306048

Abstract: An investigation into the credibility and suitability of a transient voltage model that characterizes the dynamic behavior of polymer electrolyte fuel cells was carried out by means of quantitative and qualitative validations. The least squares support vector machine method was then used to construct a transient voltage model of a fuel cell in the first phase, including a validation based on experimental data obtained from a test rig. In the second phase, a thorough discussion of the effect of the fuel cell’s operating conditions and the exterior load changes on the model’s performance was implemented. For this phase, the influences of the sampling interval and ramp ratio are discussed and determined following a large number of tests under a variety of operating conditions. The results show that sampling with short time intervals is an effective way to improve the model’s performance, and a smoother change to the exterior load is more likely to be approximated by the least squares support vector machine model. Moreover, the voltage model is sensitive to the ramp value by comparison to the ramp time. Suggestions for future applications of the transient voltage models are also provided. For a given combination of load changes, the sampling interval should be managed within a range to reach the demand data that satisfies the voltage accuracy. On the other hand, for a determinate sampling interval, the dynamic change of the load should be restricted within a limit to ensure that the model error is lower than the demand value.

Keywords: Polymer electrolyte fuel cell; Data-driven method; Least squares support vector machine; Model validation; Model suitability (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261920306048
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:271:y:2020:i:c:s0306261920306048

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2020.115092

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().