A fusion prognostics strategy for fuel cells operating under dynamic conditions

Wang, Chu; Dou, Manfeng; Li, Zhongliang; Outbib, Rachid; Zhao, Dongdong; Liang, Bin

doi:10.1016/j.etran.2022.100166

Cited by 26 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, compared to other prognostic methods, such as stacked LSTM and PF, the proposed prognostic strategy achieved higher accuracy and required less computation time. Wang et al [98] presented a fusion prognostics approach for fuel cells that are operating in dynamic scenarios. Their strategy involved identifying the system dynamics using an electrochemical mechanism model and extracting degradation indicators based on the identified model parameters.…”

Section: Hybrid Strategymentioning

confidence: 99%

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Nguyen

Lee

2023

Energies

View full text Add to dashboard Cite

Proton exchange membrane fuel cells (PEMFCs) are an alternative power source for automobiles that are capable of being cleaner and emission-free. As of yet, long-term durability is a core issue to be resolved for the mass production of hydrogen fuel cell vehicles that requires varied research in the range from sustainable materials to the optimal operating strategy. The capacity to accurately estimate performance degradation is critical for developing reliable and durable PEMFCs. This review investigates various PEMFC performance degradation modeling techniques, such as model-based, data-driven, and hybrid models. The pros and cons of each approach are explored, as well as the challenges in adequately predicting performance degradation. Physics-based models are capable of simulating the physical and electrochemical processes which occur in fuel cell components. However, these models tend to be computationally demanding and can vary in terms of parameters between different studies. On the other hand, data-driven models provide rapid and accurate predictions based on historical data, but they may struggle to generalize effectively to new operating conditions or scenarios. Hybrid prediction approaches combine the strengths of both types of models, offering improved accuracy but also introducing increased computational complexity to the calculations. The review closes with recommendations for future research in this area, highlighting the need for more extensive and accurate prediction models to increase the reliability and durability of PEMFCs for fuel cell electric vehicles.

show abstract

Section: Hybrid Strategymentioning

confidence: 99%

A Comprehensive Review of Degradation Prediction Methods for an Automotive Proton Exchange Membrane Fuel Cell

Nguyen

Lee

2023

Energies

View full text Add to dashboard Cite

show abstract

“…Based on this model, the developed life prediction and estimation algorithm achieves high accuracy and strong robustness [10]. Some scholars have proposed a fusion prediction strategy, which uses a degradation model to deal with the dynamic operating conditions of the PEMFC and have extracted the degradation index for the prediction [11]. The establishment and application of degradation indicators could also promote the development of more life prediction methods, which are broadly classified into three categories [12,13]: data-driven approaches, model-driven approaches, and hybrid approaches, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Review of Life Prediction Methods for PEMFCs in Electric Vehicles

Tang

Yao

et al. 2022

Sustainability

View full text Add to dashboard Cite

The proton-exchange membrane fuel cell (PEMFC) has the advantage of high energy conversion efficiency, environmental friendliness, and zero carbon emissions. Therefore, as an attractive alternative energy, it is widely used in vehicles. Due to its high nonlinearity, strong time variation, and complex failure mechanisms, it is extremely difficult to predict PEMFC life in electric vehicles. The uncertainty of life predictions for the PEMFC limits its wide application. Since it is particularly important to accurately carry out PEMFC life predictions, significant research efforts are directed toward tackling this issue by adopting effective methods. In this paper, a number of PEMFC life prediction methods for electric vehicles are reviewed and summarized. The goal of this review is to render feasible and potential solutions for dealing with PEMFC life issues considering dynamic vehicle conditions. Based on this review, the reader can also easily understand the research status of PEMFC life prediction methods and this review lays a theoretical foundation for future research.

show abstract