Selection of mother wavelet and decomposition level for energy management in electrical vehicles including a fuel cell

Fault diagnosis can help extend the lifetime of fuel cells and has been widely explored for externally humidified fuel cells. However, fault detection in self-humidified fuel cells is scarce, despite its importance. This paper explores various wavelet-based descriptors for identifying hydration levels in self-humidified stacks. Wavelet-based techniques are nonintrusive and provide concurrent examinations in time and frequency. Thus, they encapsulate health related data that can be used as health monitoring features. The specific wavelet-based techniques used here are (i) impedance obtained through the wavelet-based fast electrochemical impedance spectroscopy (EIS) and (ii) changes in wavelet energy through wavelet and wavelet packet decomposition. Results are compared based on their trends under dehydration con-ditions and the robustness of those trends across different currents and sampling rates. They are then assessed based on their predictive ability using RReliefF (Regression-ReliefF)-a regression based feature ranking tool. Based on the qualitative and quantitative analysis, descriptors are assessed in the context of creating a short-term health monitoring system for self-humidified fuel cells. While a modified version of the wavelet energies provides the best results for differentiating faults, fast EIS opens the possibility for analysis of more complex fault modes and is shown to be more robust.

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“…The levels of decomposition are based on the frequencies of interest [0 ,f c ] and the sampling frequency f s and can be found using Eq. :

true n = [\frac{\log (\frac{f_{s}}{f_{c}})}{\log (2)} - 1]

…”

Section: Wavelet Theory and Rrelieffmentioning

confidence: 99%

A Comparative Study of Wavelet‐based Descriptors for Fault Diagnosis of Self‐Humidified Proton Exchange Membrane Fuel Cells

Sethi

Verstraete

2020

Fuel Cells

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“…A Daubechies4 wavelet is chosen for its energy conservation properties and the decomposition level j is defined according to Eq. .

j = [\frac{\log (\frac{f_{s}}{f_{c}})}{\log (2)} - 1]

…”

Section: Diagnosis Principlementioning

confidence: 99%

“…About the frequency f c for the fuel cells, the frequency band is comprised between [10 −4 ; 10 −2 ] according to Refs. , . It is common and preferable to use the fuel cell in this frequency range to avoid degradations.…”

Section: Diagnosis Principlementioning

confidence: 99%

A Non‐Intrusive Signal‐Based Method for a Proton Exchange Membrane Fuel Cell Fault Diagnosis

et al. 2016

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This paper presents an experimental study dealing with a non‐intrusive signal‐based method for a fault diagnosis of a proton exchange membrane fuel cell (PEMFC). The aim of this tool is to define a specific signature of different operating conditions. First, a healthy state has to be defined: an operating condition with fixed parameters values for current, temperature, pressure is used as a reference state. When the operating conditions divert from this state of functioning, a fault is considered. Thanks to the wavelet transform of different signals from the fuel cell system, a signature can be extracted and linked to the considered operating conditions. The energy contents of the wavelet transform is used as an indicator of the state‐of‐health (SoH) of the fuel cell system with the purpose to detect deviations from healthy state operating conditions and to detect the fault related to. The diagnosis tool presented in this paper is based on existing signals acquired on the fuel cell system, in order to minimize the number of actual sensors. The other goals are to minimize the number of extracted parameters from the fuel cell system and to be as less intrusive as possible in the fuel cell system.

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“…The basic idea of FD-based methods is to decouple and redistribute the required load power in the frequency domain according to the load characteristics of each power source [18][19][20]. For example, an FD-based method is designed to divide power demand into high-and low-frequency components, and then distributes them to the battery and fuel cell, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…For example, an FD-based method is designed to divide power demand into high-and low-frequency components, and then distributes them to the battery and fuel cell, respectively. Li et al and Ibrahim et al proposed a wavelet-transform-and fuzzy-logic-based FD method in which low-positive-frequency components of the required power are distributed to the fuel cell and high-frequency components are distributed to the auxiliary energy source [18,19]. Marzougui et al used a first-order low-pass filter to separate the required power for the FD but the filtering-hysteresis problem made it difficult for the fuel cell to follow the low-frequency trend of the load power [20].…”

Section: Introductionmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles' hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle's running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV's hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

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Selection of mother wavelet and decomposition level for energy management in electrical vehicles including a fuel cell

Cited by 38 publications

References 21 publications

A Comparative Study of Wavelet‐based Descriptors for Fault Diagnosis of Self‐Humidified Proton Exchange Membrane Fuel Cells

A Comparative Study of Wavelet‐based Descriptors for Fault Diagnosis of Self‐Humidified Proton Exchange Membrane Fuel Cells

A Non‐Intrusive Signal‐Based Method for a Proton Exchange Membrane Fuel Cell Fault Diagnosis

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

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