인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Purpose: In hydrogen fuel cell stacks, degradation in one cell can accelerate degradation in neighboring cells. For this reason, accurately predicting the lifetime of a stack requires accounting for both individual cell data and spatial influences among adjacent cells. Therefore, this study aims to propose a degradation model that incorporates spatial correlation information.
Methods: The proposed approach applies a degradation model that explicitly incorporates spatial correlation among individual cells within the stack, employing exponential covariance functions based on Euclidean and Mahalanobis distances.
Results: The proposed method was applied to degradation data from 18 cells of a stack and demonstrated a lower prediction error compared to an independent cell model. Estimated correlations were stronger among physically closer cells, supporting the presence of localized inter-cell interactions and degradation propagation effects.
Conclusion: This study suggests that modeling spatial dependence among cells significantly improves the accuracy and reliability of lifetime predictions for hydrogen fuel cell stacks. Extending this approach to a full multivariate and potentially mixed-effects spatial framework across all cells is a key direction for building more robust prognostic systems.
Methods: The proposed approach applies a degradation model that explicitly incorporates spatial correlation among individual cells within the stack, employing exponential covariance functions based on Euclidean and Mahalanobis distances.
Results: The proposed method was applied to degradation data from 18 cells of a stack and demonstrated a lower prediction error compared to an independent cell model. Estimated correlations were stronger among physically closer cells, supporting the presence of localized inter-cell interactions and degradation propagation effects.
Conclusion: This study suggests that modeling spatial dependence among cells significantly improves the accuracy and reliability of lifetime predictions for hydrogen fuel cell stacks. Extending this approach to a full multivariate and potentially mixed-effects spatial framework across all cells is a key direction for building more robust prognostic systems.
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