Data-based flooding fault diagnosis of proton exchange membrane fuel cell systems using LSTM networks

Flooding fault diagnosis is critical to the stable and efficient operation of fuel cells, while the on-board embedded controller has limited computing power and sensors, making it difficult to incorporate the complex gas-liquid two-phase flow models. Then in fuel cell system for cars, the neural network modeling is usually regarded as an appropriate tool for the on-line diagnosis of water status. Traditional neural network classifiers are not good at processing time series data, so in this paper, Long Short-Term Memory (LSTM) network model is developed and applied to the flooding fault diagnosis based on the embedded platform. Moreover, the fuel cell auxiliary system statuses are adopted as the inputs of the diagnosis network, which avoids installing a large number of sensors in the fuel cell system, and contributes to reduce the total system cost. The bench test on the 92 kW vehicle fuel cell system proved that this model can effectively diagnose/pre-diagnose the fuel cell flooding, and thus help optimize the water management under vehicle conditions

PTEN-Regulated AID Transcription in Germinal Center B Cells Is Essential for the Class-Switch Recombination and IgG Antibody Responses

Class-switch recombination (CSR) and somatic hypermutation (SHM) occur during the differentiation of germinal center B cells (GCBs). Activation-induced cytidine deaminase (AID) is responsible for both CSR and SHM in GCBs. Here, we show that ablation of PTEN through the Cγ1-Cre mediated recombination significantly influences the CSR and SHM responses. The GCs fail to produce the IgG1 B cells, the high affinity antibodies and nearly lost the dark zone (DZ) in Ptenfl/flCγ1Cre/+ mice after immunization, suggesting the impaired GC structure. Further mechanistic investigations show that LPS- and interleukin-4 stimulation induced the transcription of Cγ1 in IgM-BCR expressing B cells, which efficiently disrupts PTEN transcription, results in the hyperphosphorylated AKT and FoxO1 and in turn the suppression of AID transcription. Additionally, the reduced transcription of PTEN and AID is also validated by investigating the IgM-BCR expressing GCBs from Ptenfl/flCγ1Cre/+ mice upon immunization. In conclusion, PTEN regulated AID transcription in GCBs is essential for the CSR and IgG antibody responses

Zn(II) porphyrin-based photocatalytic synthesis of Cu nanoparticles for electrochemical reduction of CO2

Copper nanoparticles frequently exhibit unique electrocatalytic activity for the electrochemical reduction of carbon dioxide. However, the synthesis of Cu nanoparticles in aqueous systems remains rare. Herein, we report the synthesis of copper nanoparticles by using a zinc(II) porphyrin-based photocatalytic method in the presence of polyacrylic acid sodium salt (PAA) in water under visible light irradiation, leading to a series of Cu nanoparticles with relatively small average sizes ranging from 69 to 97 nm. PAA molecules adsorbed on Cu nanoparticles were simply removed by washing with copious amount of water. The purified Cu nanoparticles show some activity toward the electrochemical reduction of carbon dioxide as evidenced by linear sweep voltammetry (LSV) and gas chromatography (GC)

Magnetic field induced synthesis of (Ni, Zn)Fe2O4 spinel nanorod for enhanced alkaline hydrogen evolution reaction

Recently, the introduction of external fields (light, thermal, magnetism, etc.) during electrocatalysis reactions gradually becomes a new strategy to modulate the catalytic activities. In this work, an external magnetic field was innovatively employed for the synthesis progress of (Ni, Zn)Fe2O4 spinel oxide (M-(Ni, Zn)Fe2O4). Results indicated the magnetic field (≤250 ​mT) would affect the morphology of catalyst due to the existing Fe ions, inducing the M-(Ni, Zn)Fe2O4 nanosphere particles to be uniform and coral-like nanorods. In addition, the electronic structure of the catalyst was regulated by the valence state of Fe, changing the bonding of metal to oxygen atoms in different spinel sites. The results manifested that the M-(Ni, Zn)Fe2O4 requires a lower overpotential of only 67 ​mV to deliver 10 ​mA ​cm−2 for hydrogen evolution reaction (HER) in alkaline electrolyte. Moreover, M-(Ni, Zn)Fe2O4 respectively as the anode and cathode electrode for the overall water splitting, the catalysis system requires a cell voltage of only 1.76 ​V to gain a current density of 50 ​mA ​cm−2, combining with an excellent discharging stability after 10 ​h. This work provides a facile synthesis strategy toward the design of efficient non-noble metal catalysts for alkaline HER and overall water splitting

Data-driven Fault Diagnosis for PEM Fuel Cell System Using Sensor Pre-Selection Method and Artificial Neural Network Model

International audienceFault diagnosis is a critical process for the reliability anddurability of proton exchange membrane fuel cells (PEMFCs). Due tothe complexity of internal transport processes inside the PEMFCs,developing an accurate model considering various failure mechanismsis extremely difficult. In this paper, a novel data-driven approachbased on sensor pre-selection and artificial neural network (ANN)are proposed. Firstly, the features of sensor data in time-domainand frequency-domain are extracted for sensitivity analysis. Thesensors with poor response to the changes of system states arefiltered out. Then experimental data monitored by the remainingsensors are utilized to establish the fault diagnosis model byusing the ANN model. Levenberg-Marquardt (LM) algorithm, resilientpropagation (RP) algorithm, and scaled conjugate gradient (SCG)algorithm are utilized in the training process, respectively. Theresults demonstrate that the diagnostic accuracy reaches 99.2% andthe recall reaches 98.3%. The effectiveness of the proposed methodis verified by comparing the diagnostic results in this work andthat by support vector machine (SVM) and logistic regression (LR).Besides, the high computational efficiency of the proposed methodsupports the possibility of online diagnosis. Meanwhile, timelyfault diagnosis can provide guidance for fault tolerant control ofthe PEMFCs system

A novel feature susceptibility approach for a PEMFC control system based on an improved XGBoost-Boruta algorithm

Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical phenomena. However, there is little research in this field detailing the pre-processing and selection of balance of plants (BOP) features for the input layer of system performance prediction at different current densities. Furthermore, most of the previous research applies neural networks based on simulation data rather than real-time bench or vehicle operation datasets which leads to low robustness and unreliable practical results. This paper details the application of a novel algorithm denoted XGBoost-Boruta, which utilises the combination of an ensemble learning approach and a wrapping approach, to improve the robustness of feature selection and to increase the accuracy and robustness of PEMFC system performance prediction. By introduction of the Z score and shadow features to eliminate the randomness of conventional ensemble learning methods, seven key controllable BOP variables of the hydrogen anode, air cathode and cooling subsystems are selected as the original input variables to determine their dependency on the stack voltage. Two case studies are presented for verification and validation of the proposed algorithm based on the real-time dataset of bench experimental data and data obtained from heavy truck operation at current densities ranging from 100 to 1500 mA/cm2. The feature selection strategy, based on the proposed XGBoost-Boruta algorithm, largely decreases the RMSE by 23.8% and 14.1% and the R2 increases by 0.06 and 0.04 of both the bench experimental and the heavy truck validation datasets respectively