The　state-of-charge(SOC)and　state-of-health(SOH)of　lithium-ion　batteries　affect　their　operating　per-formance　and　safety.The　coupled　SOC　and　SOH　are　dif-ficult　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　estima-tion　and　aging　information.The　open-circuit　voltages(OCVs)are　corrected　globally　by　the　temporal　convolu-tional　network　with　accurate　OCVs　in　time-sliding　win-dows.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　var-ious　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　℃　dynamic　stress　test　and　25　℃　constant　current　cycling　was　less　than　0.9％and　0.8％,respectively.Notably,the　pro-posed　method　exhibits　excellent　adaptability　to　varying　temperature　and　aging　conditions,accurately　estimating　SOC　and　SOH.