Quality testing costs hinder the large-scale production of PEM fuel cell systems due to long testing times and high safety measures for hydrogen.While eliminating both issues,electrochemical impedance spectroscopy at ...Quality testing costs hinder the large-scale production of PEM fuel cell systems due to long testing times and high safety measures for hydrogen.While eliminating both issues,electrochemical impedance spectroscopy at low hydrogen concentrations can provide valuable insights into fuel cell processes.However,the influence of high anode stream dilutions on PEM fuel cell performance is not yet completely understood.This study presents a new equivalent circuit model to analyze impedance spectra at low hydrogen partial pressures.The proposed model accurately describes the impedance response and explains the performance decrease at low hydrogen concentrations.First,the reduced availability of hydrogen at the anode leads to rising reaction losses from the hydrogen side.Further,the resulting losses lead to potential changes also influencing the cathode processes.The findings indicate that impedance spectroscopy at low hydrogen partial pressure might provide a reliable fuel cell quality control tool,simplifying production processes,reducing costs,and mitigating risks in fuel cell production.展开更多
Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a cr...Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.展开更多
在中国城市客车行驶工况条件下,运用车载排放测试技术(portable em ission measurement sys-tem,简称PEMS)研究了某重型混合动力客车在怠速运转时采取停机和不停机两种不同控制策略下的实际排放因子和燃油消耗率.试验研究结果表明,停机...在中国城市客车行驶工况条件下,运用车载排放测试技术(portable em ission measurement sys-tem,简称PEMS)研究了某重型混合动力客车在怠速运转时采取停机和不停机两种不同控制策略下的实际排放因子和燃油消耗率.试验研究结果表明,停机模式比不停机模式可提高该车燃油经济性4.6%,但CO排放因子却增加了53.6%;怠速不停机时,HC+NOx排放增加20.6%.同时说明对于混合动力车辆的油耗和排放,特别是排放的评价,应当采取基于整车的评价方法.展开更多
文摘Quality testing costs hinder the large-scale production of PEM fuel cell systems due to long testing times and high safety measures for hydrogen.While eliminating both issues,electrochemical impedance spectroscopy at low hydrogen concentrations can provide valuable insights into fuel cell processes.However,the influence of high anode stream dilutions on PEM fuel cell performance is not yet completely understood.This study presents a new equivalent circuit model to analyze impedance spectra at low hydrogen partial pressures.The proposed model accurately describes the impedance response and explains the performance decrease at low hydrogen concentrations.First,the reduced availability of hydrogen at the anode leads to rising reaction losses from the hydrogen side.Further,the resulting losses lead to potential changes also influencing the cathode processes.The findings indicate that impedance spectroscopy at low hydrogen partial pressure might provide a reliable fuel cell quality control tool,simplifying production processes,reducing costs,and mitigating risks in fuel cell production.
基金supported by the Chinese Scholarship Council(Nos.202208320055 and 202108320111)the support from the energy department of Aalborg University was acknowledged.
文摘Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.
文摘在中国城市客车行驶工况条件下,运用车载排放测试技术(portable em ission measurement sys-tem,简称PEMS)研究了某重型混合动力客车在怠速运转时采取停机和不停机两种不同控制策略下的实际排放因子和燃油消耗率.试验研究结果表明,停机模式比不停机模式可提高该车燃油经济性4.6%,但CO排放因子却增加了53.6%;怠速不停机时,HC+NOx排放增加20.6%.同时说明对于混合动力车辆的油耗和排放,特别是排放的评价,应当采取基于整车的评价方法.
