A mathematical framework that provides practical guidelines for user adoption
is proposed for fuel cell performance evaluation. By leveraging the mathematical
framework, two measures that describe the average and worst-case performance
are presented. To facilitate the computation of the performance measures in a
practical setting, ...
A mathematical framework that provides practical guidelines for user adoption
is proposed for fuel cell performance evaluation. By leveraging the mathematical
framework, two measures that describe the average and worst-case performance
are presented. To facilitate the computation of the performance measures in a
practical setting, we model the distribution of the voltages at different current
points as a Gaussian process. Then the minimum number of samples needed
to estimate the performance measures is obtained using information-theoretic
notions. Furthermore, we introduce a sensing algorithm that finds the current
points that are maximally informative about the voltage. Observing the voltages
at the points identified by the proposed algorithm enables the user to estimate
the voltages at the unobserved points. The proposed performance measures
and the corresponding results are validated on a fuel cell dataset provided by
an industrial user whose conclusion coincides with the judgement from the fuel
cell manufacturer.