Li Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Li Zhang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Kaixiang Peng
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China
1 shared publication - Yanyan Hu
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
1 shared publication - Yunpeng Jiang
SPIC Digital Technology Co., Ltd., Beijing 100080, China
1 shared publication - Zengwang Jin
Anhui University
1 shared publication
Latest Publications
- Open Access
A Hybrid Method for Performance Degradation Probability Prediction of Proton Exchange Membrane Fuel Cell
The proton exchange membrane fuel cell (PEMFC) is a promising power source, but the short lifespan and high maintenance cost restrict its development and widespread application. Performance degradation prediction is an effective technique to extend the lifespan and reduce the maintenance cost of PEMFC. This paper proposed a novel hybrid method for the performance degradation prediction of PEMFC. Firstly, considering the randomness of PEMFC degradation, a Wiener process model is established to describe the degradation of the aging factor. Secondly, the unscented Kalman filter algorithm is used to estimate the degradation state of the aging factor from monitoring voltage. Then, in order to predict the degradation state of PEMFC, the transformer structure is used to capture the data characteristics and fluctuations of the aging factor. To quantify the uncertainty of the predicted results, we also add the Monte Carlo dropout technology to the transformer to obtain the confidence interval of the predicted result. Finally, the effectiveness and superiority of the proposed method are verified on the experimental datasets.
Open AccessA Hybrid Method for Performance Degradation Probability Prediction of Proton Exchange Membrane Fuel Cell
The proton exchange membrane fuel cell (PEMFC) is a promising power source, but the short lifespan and high maintenance cost restrict its development and widespread application. Performance degradation prediction is an effective technique to extend the lifespan and reduce the maintenance cost of PEMFC. This paper proposed a novel hybrid method for the performance degradation prediction of PEMFC. Firstly, considering the randomness of PEMFC degradation, a Wiener process model is established to describe the degradation of the aging factor. Secondly, the unscented Kalman filter algorithm is used to estimate the degradation state of the aging factor from monitoring voltage. Then, in order to predict the degradation state of PEMFC, the transformer structure is used to capture the data characteristics and fluctuations of the aging factor. To quantify the uncertainty of the predicted results, we also add the Monte Carlo dropout technology to the transformer to obtain the confidence interval of the predicted result. Finally, the effectiveness and superiority of the proposed method are verified on the experimental datasets.