Coupling a capacity fade model with machine learning for early prediction of the battery capacity trajectory

Li, Tingkai; Liu, Jinqiang; Thelen, Adam; Mishra, Ankush Kumar; Yang, Xiao-Guang; Wang, Zhaoyu; Hu, Chao

doi:10.1016/j.apenergy.2025.125703

Understanding the adaptive capacity of ecosystems to cope with change is crucial to management. However, unclear and often confusing definitions of adaptive capacity make application of this concept difficult. In this paper, we revisit definitions of adaptive capacity and operationalize the concept. We define adaptive capacity as the latent potential of an ecosystem to alter resilience in response to change. We present testable hypotheses to evaluate complementary attributes of adaptive capacity that may help further clarify the components and relevance of the concept. Adaptive sampling, inference and modeling can reduce key uncertainties incrementally over time and increase learning about adaptive capacity. Such improvements are needed because uncertainty about global change and its effect on the capacity of ecosystems to adapt to social and ecological change is high.

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