Retired　batteries　should　undergo　rigorous　testing　and　evaluation　to　ascertain　their　performance　before　they　are　deployed　in　echelons　utilization　to　ensure　that　appropriate　application　scenarios　are　selected　based　on　the　performance　of　the　batteries.　An　accurate　assessment　of　the　State　of　Health　(SOH)　is　fundamental　to　determine　whether　a　power　battery　possesses　the　value　of　echelons　utilization.　In　view　of　the　low　accuracy　of　SOH　evaluation　of　retired　power　batteries,　the　relaxation　time　distribution　method　was　used　to　analyze　the　electrochemical　impedance　spectroscopy　in　this　study.　This　was　done　to　obtain　the　characteristic　frequency　that　can　accurately　reflect　the　health　state　of　the　battery.　The　impedance　data　corresponding　to　the　characteristic　frequency　was　used　as　the　characteristic　input　parameters,　and　the　extreme　learning　machine　model　optimized　using　the　sparrow　algorithm　served　as　input　to　realize　the　SOH　evaluation　of　decommissioned　power　batteries.　To　verify　the　effectiveness　of　the　evaluation　method,　seven　retired　prismatic　lithium-iron-phosphate　batteries　were　subjected　to　cyclic　aging　experiments,　and　electrochemical　impedance　spectroscopy　tests　were　performed　on　the　batteries　after　each　cycle.　Actual　electrochemical　impedance　spectroscopy　of　the　decommissioned　power　batteries　was　used　for　analysis　and　modeling　to　evaluate　the　SOH,　and　the　results　were　compared　with　actual　SOH　data　obtained　using　traditional　SOH　evaluation　methods.　Our　findings　demonstrate　that　the　Mean　Square　Error　(MSE)　and　Mean　Absolute　Percentage　Error　(MAPE)　associated　with　the　relaxation　time　distribution　method　are　lower　than　those　of　other　methods.　Compared　with　the　unoptimized　extreme　learning　machine　model,　the　values　of　the　MSE　and　MAPE　reduced　by　47.1%　and　60.5%,　respectively,　indicating　that　the　SOH　evaluation　method　employed　in　this　study　is　relatively　highly　accurate　and　less　prone　to　error,　indicating　its　applicability　in　practical　echelons　utilization.　©　2025　Editorial　office　of　Energy　Storage　Science　and　Technology.　All　rights　reserved.