StateofHealth(SOH)estimationforlithium-ionbatteriesisacriticalaspectofbatterymanagementsystems(BMS).Itprovidesessentialinformationaboutthebattery'sagingandperformancedegradationovertime.AccurateSOHestimationhelpsinpredictingtheremainingusefullife(RUL)ofthebattery,ensuringoptimalperformance,safety,andreliabilityinvariousapplicationssuchaselectricvehicles,renewableenergystorage,andportableelectronics.SeveralfactorsinfluencetheSOHoflithium-ionbatteries,includingcharge-dischargecycles,temperaturevariations,andoperatingconditions.CommonmethodsforSOHestimationincludemodel-basedapproaches(e.g.,equivalentcircuitmodels,electrochemicalmodels),data-driventechniques(e.g.,machinelearning,neuralnetworks),andexperimentalmeasurements(e.g.,capacityfade,internalresistanceincrease).ReliableSOHestimationenablesbetterbatteryutilization,maintenancescheduling,andreplacementdecisions,ultimatelyimprovingtheefficiencyandlifespanofbattery-poweredsystems.Ongoingresearchfocusesonenhancingestimationaccuracy,reducingcomputationalcomplexity,andadaptingtoreal-worldoperatingconditions.
