The　state-of-charge　(SOC)　and　state-of-health　(SOH)　of　lithium-ion　batteries　affect　their　operating　performance　and　safety.　The　coupled　SOC　and　SOH　are　difficult　to　estimate　adaptively　in　multi-temperatures　and　aging.　This　paper　proposes　a　novel　transformer-embedded　lithium-ion　battery　model　for　joint　estimation　of　state-of-charge　and　state-of-health.　The　battery　model　is　formulated　across　temperatures　and　aging,　which　provides　accurate　feedback　for　unscented　Kalman　filter-based　SOC　estimation　and　aging　information.　The　open-circuit　voltages　(OCVs)　are　corrected　globally　by　the　temporal　convolutional　network　with　accurate　OCVs　in　time-sliding　windows.　Arrhenius　equation　is　combined　with　estimated　SOH　for　temperature-aging　migration.　A　novel　transformer　model　is　introduced,　which　integrates　multiscale　attention　with　the　transformer＇s　encoder　to　incorporate　SOC-voltage　differential　derived　from　battery　model.　This　model　simultaneously　extracts　local　aging　information　from　various　sequences　and　aging　channels　using　a　self-attention　and　depth-separate　convolution.　By　leveraging　multi-head　attention,　the　model　establishes　information　dependency　relationships　across　different　aging　levels,　enabling　rapid　and　precise　SOH　estimation.　Specifically,　the　root　mean　square　error　for　SOC　and　SOH　under　conditions　of　15　degrees　C　dynamic　stress　test　and　25　degrees　C　constant　current　cycling　was　less　than　0.9%　and　0.8%,　respectively.　Notably,　the　proposed　method　exhibits　excellent　adaptability　to　varying　temperature　and　aging　conditions,　accurately　estimating　SOC　and　SOH.