The　state　of　health　(SOH)　and　remaining　useful　life　(RUL)　of　lithium-ion　batteries　are　crucial　for　health　management　and　diagnosis.　However,　most　data-driven　estimation　methods　heavily　rely　on　scarce　labeled　data,　while　traditional　transfer　learning　faces　challenges　in　handling　domain　shifts　across　various　battery　types.　This　paper　proposes　an　enhanced　vision-transformer　integrating　with　semi-supervised　transfer　learning　for　SOH　and　RUL　estimation　of　lithium-ion　batteries.　A　depth-wise　separable　convolutional　vision-transformer　is　developed　to　extract　local　aging　details　with　depth-wise　convolutions　and　establishes　global　dependencies　between　aging　information　using　multi-head　attention.　Maximum　mean　discrepancy　is　employed　to　initially　reduce　the　distribution　difference　between　the　source　and　target　domains,　providing　a　superior　starting　point　for　fine-tuning　the　target　domain　model.　Subsequently,　the　abundant　aging　data　of　the　same　type　as　the　target　battery　are　labeled　through　semi-supervised　learning,　compensating　for　the　source　model＇s　limitations　in　capturing　target　battery　aging　characteristics.　Consistency　regularization　incorporates　the　cross-entropy　between　predictions　with　and　without　adversarial　perturbations　into　the　gradient　backpropagation　of　the　overall　model.　In　particular,　across　the　experimental　groups　13–15　for　different　types　of　batteries,　the　root　mean　square　error　of　SOH　estimation　was　less　than　0.66　%,　and　the　mean　relative　error　of　RUL　estimation　was　3.86　%.　Leveraging　extensive　unlabeled　aging　data,　the　proposed　method　could　achieve　accurate　estimation　of　SOH　and　RUL.　©　2024　The　Authors