State　of　Charge　and　State　of　Health　are　critical　indicators　for　evaluating　the　operational　status　of　lithium-ion　batteries.　Accurate　estimation　of　these　parameters　is　essential　for　ensuring　safe　operation　and　optimizing　charging-discharging　performance.　This　study　presents　a　novel　method　for　estimating　SOC　and　SOH　based　on　ultrasonic　characteristics.　A　pouch　battery　is　selected　as　the　experimental　object,　with　ultrasonic　waves　extracted　in　real-time　during　battery　operation.　Acoustic　parameters　are　obtained　via　signal　processing　to　establish　a　training　dataset　for　SOC　estimation.　Additionally,　the　impact　of　varying　discharge　rates　on　battery　capacity　degradation　is　analyzed　to　support　battery　aging　experiments.　During　the　aging　experiments,　real-time　acoustic　parameters　are　collected　to　develop　a　training　dataset　for　SOH　estimation.　The　results　indicate　a　strong　correlation　between　the　acoustic　parameters　and　the　SOH.　Moreover,　the　acoustic　parameters　serve　as　the　characteristic　parameters,　and　a　hybrid-model　combining　the　sparrow　search　algorithm　and　relevance　vector　machine　is　employed　to　accurately　estimate　the　state　parameters.　The　maximum　relative　error　for　the　SOC　estimation　is　1.51　%,　while　the　maximum　absolute　error　for　the　SOH　estimation　is　0.79　%.　The　research　results　provide　a　new　technical　support　for　the　non-destructive　quantitative　characterization　of　lithium-ion　battery　state　parameters.　©　2025　Elsevier　B.V.