BatteryManagement SystemsandBattery SOCEstimation
Battery Management Systems and Battery SOC Estimation
BatterySystems:TheBottleneckof HEV.PHEV,BEVMarketPenetrationMainChallengesforBEV:Price:Need to reach economicthresholdsManycostreductionpotentialsRange:MustbecomparabletoICEandPHEVInfrastructuresFastcharging stations(manypotential solutions)Life-cycle,replacement costs,vehicle resalevaluePerceptions:safety,reliability,etc
Battery Systems: The Bottleneck of HEV, PHEV, BEV Market Penetration Main Challenges for BEV: • Price: Need to reach economic thresholds Many cost reduction potentials • Range: Must be comparable to ICE and PHEV • Infrastructures Fast charging stations (many potential solutions) • Life-cycle, replacement costs, vehicle resale value • Perceptions: safety, reliability, etc
RolesofBatteryManagementSystems(BMS):Monitoring of operation status : SocChargeand dischargedecisions:When andhowCell balancingDiagnosisPrediction of remaininglife: SOHMainDifficulties:AccurateEstimationoftheStateofCharge(SOC)AccurateEstimationoftheBatteryKeyParametersAccurateEstimationof theStateof Health(SOH)DiagnosisduringOperation
Roles of Battery Management Systems (BMS): • Monitoring of operation status : SOC • Charge and discharge decisions: When andhow • Cell balancing • Diagnosis • Prediction of remaining life: SOH Main Difficulties: • Accurate Estimation of the State of Charge (SOC) • Accurate Estimation of the Battery Key Parameters • Accurate Estimation of the State of Health (SOH) • Diagnosis during Operation
WhyisSOC,SOH,parameterestimationcriticallyimportant?AccurateSOCestimationAccurateparameterestimation口Avoidovercharge/over-discharge(Safety)Lessconservativeoperationhigher effectivecapacity,longerrangeBetterqualityofservice (whentocharge)OptimalcoordinationwithotherpowersourcesPromptand reliablediagnosisOptimaloperationforlongercyclelives
Why is SOC, SOH, parameter estimation critically important? Accurate SOC estimation • Avoid overcharge/over-discharge (Safety) • Less conservative operation higher effective capacity, longerrange • Better quality of service (when to charge) • Optimal coordination with other powersources • Prompt and reliable diagnosis • Optimal operation for longer cyclelives Accurate parameterestimation
Implicationsof Real-TimeandIndividualized ModelingOff-Line ModelingReal-Time ModelingEquipmentCheap,Expensive,comprehensivesimpleHighLowData AccuracyData SizeLarge data can be storedDo not want to store large dataTimingData must be real-time processedData can be processed again andagainonthereal clockModel StructureMust use simplified andComplicatedmodelscanbeusedidentifiablemodelsOperating ConditionsControlledinalabenvironmentUncontrolledrealenvironmentAgingNewandold systemsTypicallynewsystemsPopulationOftenusetypical systemsMust deal with large variations inthepopulationChallengesNoteasy,Muchmore difficultNotmuchtimeavailabletobetime consumingconsumedControlForadaptive control,diagnosis,Forrobustandoptimalcontroand decision
Implications of Real-Time and IndividualizedModeling Off-Line Modeling Real-Time Modeling Equipment Expensive, comprehensive Cheap, simple Data Accuracy High Low Data Size Large data can be stored Do not want to store large data Timing Data can be processed again and again Data must be real-time processed on the real clock Model Structure Complicated models can be used Must use simplified and identifiable models Operating Conditions Controlled in a lab environment Uncontrolled real environment Aging Typically new systems New and old systems Population Often use typical systems Must deal with large variations in the population Challenges Not easy, time consuming Much more difficult, Not much time available to be consumed Control For robust and optimal control For adaptive control, diagnosis, and decision
FundamentalsonSOC Estimation:ObservabilityandObserverDesign
Fundamentals on SOC Estimation: Observability and Observer Design
State ObservabilityAx +Bun = the dimension of xCx + DuSuppose that the initial state x(O) is unknowny(t) - C ['e 4(-t) Bu(t)dt - Du(t) = Ce 4^ x(0)If we measure the input u(t) and output y(t), can weuniquely determine x(O) (then x(t) can be alsoderived)?
State Observability y x = Ax + Bu = Cx + Du Suppose that the initial state x(0) is unknown. 0 t At e Bu( A(t− ) y(t) −C )d − Du(t) = Ce x(0) If we measure the input u(t) and output y(t), can we uniquely determine x(0) (then x(t) can be also derived)? n = the dimension of x
CCAObservability Matrix: Wo =CAn-1The systemis observableif and onlyifthe observability matrixis full rank
C CAn−1 CA Observability Matrix: WO = The system is observable if and only if the observability matrix is full rank
Example:BatteryModelsVpRRpNocvThis is a linear circuit, but Vocv Is a nonlinearfunction of the SocVoev = f(s)
Rp R v i vocv + vp - + Cp - This is a linear circuit, but Vocv Is a nonlinear function of theSOC Vocv = f (s) Example: Battery Models
TheStateSpaceModelState equation is lineartRv(t) = Vocv+ Ri(t)+ v,(t) = f(s(t))+V,(t)+ Ri(t)Output equation is nonlinearThis model is mainlyused forthe SOC estimation:From the measured terminal voltage v(t) and the charge ordischarge current i(t), estimate the internal states, especially s(twhich is the state of charge (SOC)This is a nonlinear state observer or state estimationproblem
The State Space Model State equation is linear 1 1 Output equation is nonlinear Q s(t) = 1 i(t) vp (t) =− v p (t) + i(t) RpCp Cp v(t) = vocv + Ri(t)+ vp (t) = f (s(t)) + vp (t) + Ri(t) This model is mainly used for the SOC estimation: From the measured terminal voltage v(t) and the charge or discharge current i(t), estimate the internal states, especially s(t) which is the state of charge (SOC). This is a nonlinear state observer or state estimation problem