Abstract: Accurate state estimation and prediction of lithium-ion battery are crucial for ensuring operational performance and safety. Data-driven state estimation algorithms are prone to the distribution shift between training data and testing data, limiting their generalization capabilities. Transfer-learning-based cross-domain state estimation algorithms are proposed to address these issues. This paper discusses around three common application scenarios: state of charge estimation, state of health estimation, and remaining useful life estimation. While comparing the differences between methods across various scenarios, the review also reveals their commonalities. From a technical perspective, this paper categorizes commonly used transfer methods into three types: finetuning-based transfer, metric-based transfer, and adversarial training-based transfer. Based on these technical approaches, this paper provides a comprehensive and clear summary of recent cross-domain lithium-ion battery state estimation methods.