Battery lifetime prediction is crucial in industrial applications. However, the lack of diversity in training data often poses challenges regarding the robustness and generalization of lifetime predictions for batteries from different batches. Motivated by the early cycle data from lithium-ion batteries, this article proposes a robust transfer learning method by employing a model average framework, where the weights are determined based on the distance between the source domain and the target domain. Kernel regression is used to build the prediction of battery lifetime using early cycle data, and transfer component analysis is utilized to transfer knowledge between different domains. The case study on lithium-ion phosphate/graphite cells demonstrates that the proposed method can mitigate the impact of negative transfer and has superior performance compared to traditional methods.

Simulations are often used to investigate the flow structures and system dynamics of complex natural phenomena and systems, which are significantly harder to obtain from experiments or theoretical analyses. Surrogate models are employed to mimic the results of simulations by reducing computational costs. In order to reduce the amount of computational time consumed, a novel framework for building efficient surrogate models is proposed in this work. The novelty lies in that the new framework runs simulations using the different simulation time spans for different inputs and builds a comprehensive surrogate model through the fusion of non-homogeneous spatio-temporal data by integrating the temporal and spatial correlations in parametric space. This differs from the existing works in the literature, which only consider the situation of spatio-temporal data with a consistent time span during simulations under different inputs. Some simulation studies and real data analysis concerning the pollution of the river in the Sichuan Province of China are used to demonstrate the superior performance of the proposed methods.