An environmental crisis due to an indiscreet act of fossil fuels gives rise to the necessity to obtain some renewable solution such as hydrogen fuel cells, geothermal, tidal, solar, biomass, and wind energy. Above all things, an interest in fuel cell has been more increased in academic and industry fields. Among them, the proton exchange membrane (PEM) fuel cell has been recognized as the optimal solution because of having advantages such as low-operating temperature, high potential for low volume and cost, fast start-up ability and high current density, etc. After selection of the PEM fuel cell, the next step is to establish the fuel cell management system (FCMS) for a reliable and an efficient operation. Among several techniques in the FCMS, an equivalent electrical-circuit modeling (EECM) is absolutely indispensable to know fuel cell’s electrochemical and dynamic characteristics. According to more sophisticated EECM, it is possible to predict the fuel cell’s performance in detail. The performance on the EECM is definitely connected with achieving an elaborate terminal voltage. Namely, if the EECM-based estimated- and the measured-voltages are approximately same, it can be judged that the well-designed EECM is accomplished. Nevertheless, it is not easy to obtain an elaborate terminal voltage because unanticipated and instantaneous sensing of noisy may be included in the output voltage. (This output voltage is named as output terminal voltage; OTV). Therefore, it is required to construct the noise suppressed methodology and should be applied as de-noised OTV in the EECM. Little solution for noise suppression methodologies have been known, moreover these have been provided no definitive result.

Wavelet transform (WT)-based noise suppression is preferentially selected as the basic methodology in this research. The OVT is considered as the non-stationary signal with different time interval and transient phenomena, and is used as an input signal in the WT. These rules basically implemented the multi-resolution analysis (MRA) that have a vigorous function of both time and frequency localization. Based on this MRA, an original OVT (with noise) is divided into two components such as low- and high-frequency component, A_n and D_n. Two components have respectively corresponding coefficients, approximate- a_j,k and detailed coefficients d_j,k, for implementation of the decomposition and reconstruction processes in the MRA. Among two coefficients, because of abrupt change during a short period of time of noise, the detailed coefficient d_j,k should be adjusted to suppress noise included in an original OVT. In general, the performance on noise suppression is intimately linked with decomposition and reconstruction levels in the MRA. Specifically, this research different from previous studies is to show the clear comparison of the performance on noise suppression based on two rules of the multi-level wavelet transform (MLWT) and single connected-unit level wavelet transform (SC-ULWT) from the OVT of a PEM fuel cell. For MLWT-based noise suppression, without use of detail component D_n, only approximation component decomposed at the previous level is considered as an input at the next decomposition level. The reconstruction process is vice versa. On the contrary, the SC-ULWT performed the MRA and noise suppression at every unit decomposition and reconstruction levels. As it were, the noise suppressed OVT at the previous unit level is used an input at the next unit level. This rule applied n unit level WT that suppressed sensing of noisy at every steps and connected in series. This research compared the performance on noise suppression between the MLWT and SC-ULWT by the signal-to-noise (SNR) ratio calculation. The comparative analysis on noise suppression between two rules clearly showed in this research. For reference, the PEM fuel cell for this research was made by the ‘Materials and Electro-Chemistry Laboratory’ in Inha University.