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.