EP1941290A1

EP1941290A1 - Method for predicting the power capacity of an electrical energy store

Info

Publication number: EP1941290A1
Application number: EP06807394A
Authority: EP
Inventors: Eberhard Schoch
Original assignee: Robert Bosch GmbH
Current assignee: Robert Bosch GmbH
Priority date: 2005-10-21
Filing date: 2006-10-19
Publication date: 2008-07-09
Also published as: WO2007045673A1; US20090306915A1; DE102005050563A1; KR20080068659A; JP2009512845A

Abstract

A method and corresponding devices for predicting the power capacity of an electrical energy store are disclosed, for example, for a battery in a vehicle, wherein the status values and parameters for the energy store are continuously adapted using a mathematical model of the energy store and hence a charge and discharge power capacity estimated and predicted.

Description

13.10.2005 Bü / Bo

ROBERT BOSCH GMBH, 70442 Stuttgart

Method for predicting the performance of electrical energy storage

The invention relates to a method for predicting the performance of electrical energy storage devices having the features of claim 1. In particular, variables of an electrical energy or power storage device for a motor vehicle are determined.

State of the art

For the electrical energy management in vehicles as accurate information about the current, maximum available discharge capacity of the battery is important. This is especially true in electric and hybrid vehicles and vehicles with

Start / stop function and Rekuperationseingriff in which the current, maximum available Entladeleistung the intended for the engine start, electric drive and other electrical consumers electrical energy storage and the currently available maximum charging power of recovery of braking energy used electrical energy storage is crucial.

There are already known various methods for determining the performance of electrical energy storage. Usually, most methods are limited to determining the available discharge capacity. In DE 103 01 823, for example, the discharge capacity is predicted on the basis of a model

Rated voltage response to a given load current profile. However, this approach does not yet provide an answer to the question as to which maximum power the energy storage device can deliver at a predefined, minimum permissible vehicle electrical system voltage. For the evaluation of the Rekuperationsfähigkeit, the charging power capability or the charge acceptance of an energy storage methods are proposed, based on maps depending on state of charge and temperature and / or the impedance of the energy storage. Such processes are described for example in DE 198 49 055. Other limiting factors for charging performance such as

Polarization, acid stratification or icing of the energy storage device, in particular a lead-acid battery, are not considered.

Advantages of the invention

The inventive method with the features of claim 1 allows over the prior art, an improved determination of the relevant sizes of the energy storage. This advantage is achieved by means of a mathematical model of the energy storage device, whose state variables and parameters are continuously adapted. Thus, an accurate prediction of the maximum charging and discharging power of the electric energy storage, in particular of a lead accumulator used in a motor vehicle by taking into account all relevant factors such as temperature, state of charge, Ohm 'shear internal resistance, polarization, acid stratification, aging and icing is possible.

Further advantages of the invention are made possible by the measures specified in the dependent claims. The associated methods advantageously enable the model-based prediction of the current maximum charging and discharging power of an electrical energy store, taking into account in particular the permissible maximum charging voltage or the minimum vehicle electrical system voltage. In an advantageous embodiment, further specifications for the permissible maximum charge and / or discharge current and / or the minimum or maximum charge state can be taken into account.

In addition to the prediction of the currently available charging or discharging power, the method according to the invention is also able, in a particularly advantageous manner, to determine the charging or discharging power to be expected at arbitrarily predeterminable temperatures and states of charge. For example, the available charging power of a battery with SOC (State of Charge) = 50% for cold start at -18 ° C can be determined. The loading temperature, which is fixed at a fixed temperature and or discharge capacity can also be used as a measure of the battery aging SOH (State of Health).

drawing

The invention is illustrated in the drawing and will be explained in more detail in the following description. 1 shows an equivalent circuit diagram for lead accumulators, and FIG. 2 shows a structural image of the power prediction. FIG. 3 shows a flow chart for the prediction of the discharge power, and FIG. 4 shows a flow chart of the prediction of the charging power.

Description:

Mathematical model of energy storage

FIG. 1 shows the equivalent circuit diagram of a lead-acid battery used for power prediction. The counting direction of the battery current I _Batt was chosen to be positive for charging and negative for discharging.

voltages:

Ußatt ⁼ terminal voltage of the battery

U _R1 = ohmic voltage drop

Uco ⁼ no-load voltage (~ average acid concentration in the battery, measure of the state of charge)

U _k = concentration polarization (~ deviation of the acid concentration at the reaction site from the mean value in the battery)

U _D (W, T _B att, Uco) = stationary penetration polarization, depending on battery current and acid temperature and in loading case additionally from the open circuit voltage - A -

Equivalent circuit components:

Ri (Uco, U _k , T _Ba tt) = ohmic internal resistance, depending on

Quiescent voltage, concentration polarization and acid temperature Rk (Uco, T _Ba tt) ⁼ acid diffusion resistance, depending on

Quiescent voltage and acid temperature ^ _k ⁼ R _k * C _k = time constant of acid diffusion (is considered to be constant on the order of

10 minutes accepted)

RD _; Entiaden (lBatt, T _B att) = Ström u. temperature dependent resistance of

Transverse polarization at discharge RD, charging (lBatt, T _B att, Uco) = Ström-, temperatur- u. Low-voltage-dependent resistance of

Transverse polarization on charge

Maps and parameters:

Ohmic internal resistance:

R _I (Uc _θ , U _k , T _Bat ,) = R _{I θ} (T _Bat ,) * (I + R _{I; fak} , * (Uc _θma χ-Uc _θ ) / (Uc _θ + U _k -U _{e; border} ))

With

R ₁₀ (Ta *) = R, 025 / (1 + TK _Lfakt * (T _B atr25 ° C))

Ue ₎ limit ⁼ niax (UcO, limit, Ucθ, ice (T _Ba tt))

Ucθ, ice (T _Ba tt) ⁼ UcO.EisO + Cl _; Ice * T _Ba tt + C2 _; Ice * T _Ba tt + C3 _; Ice * T _Ba tt

R ₁ 0 ₂ 5 ⁼ ohmic internal resistance at full charge and

T _Batt = 25 ° C

TK _Lf a _k t = temperature coefficient of the battery conductance Ri, fact ⁼ map parameter

Uco _max ⁼ maximum open circuit voltage of the fully charged battery

U _{e; grenz} ⁼ minimum open circuit voltage at discharge end Uco, _grenz ⁼ minimum open circuit voltage at discharge deadline without consideration of the

Baking icing Uco _{; E} i _s (T _Batt ) = temperature-dependent quiescent voltage limit for

Baking icing (icing characteristic) UCO, E _I SO, Ci _{; E} is, C _2; E _I S, c _{3; E} i _S = parameters of the icing characteristic

Acid diffusion resistance

Rk (Ucθ, T _B att) ⁼

Rkθ (TBatt) * (l + Rk, faktl * (Ucθmaχ-Ucθ) + Rk, fakt2 * (Ucθmaχ-Ucθ))

With

=

R _M2 5 * exp (- (E _Rk0 /J)/8.314*(l/(273.15 + TW ° C) -1 / 298.15)) (Arrhenius Approach)

R _kO25 ⁼ acid diffusion resistance at full charge and T _Batt ⁼ 25 ° CE _RJJ0 = activation energy

Rk, fakti, Rk _; fact2 = polynomial coefficients

Stationary penetration polarization

unloading:

UD, Ela (lBatt, TBatt) ⁼ UD0, Ela (TBatt) * ln (lBatt / lD0, Ela),

With lOO.Ela ⁼ "1 A, Ißatt ^< lDO, Ela

UDO, Ela (TBatt) ⁼

U _D0 25, Ela * (1 + ΩC _U D01 * (T _Ba -25 ^O C) + + ...

TKunos ^ T _Batt ^ SX) ³ )

U _D o25, Eia = stationary transmission voltage at I _Ba tt = e * I _D o, Eia and T _Ba tt ⁼ 25 ^o C lD0, Eia ⁼ passage current for U _D = 0V

TK _UDO I, TKUD02, TKUD03 ⁼ temperature coefficients 1st, 2nd and

3rd order of penetration polarization

Load:

UD, Lad (lBatt, TBatt, Ucθ) ⁼

UDO ₎ Lad (TBatt) * Sqrt (lBatt / lDO ₎ Lad * (Ucθmaχ-Ucθmm) / (Ucθmaχ-Ucθ))

With

IA, I _Bat t> 0A

UDO, Lad (TBatt) ⁼ UD025, Lad * • • sqrt (exp (- (E _UD o _{, La} d / J) /8.314* (l / 298.15-l / (273.15 + T _B at, / ⁰ C) ))))

U _D o25, Lad ⁼ steady-state transmission voltage at TBatt ⁼ 25 ° C and Ucθ ⁼ Ucθmm

EuDo, Lad ⁼ activation energy

Uco _mm ⁼ minimum rest voltage of the fully discharged battery

Required state variables u. Parameter for the prediction of the unloading u. charging power

With the help of the model equations given above, assuming that the state variables and parameters of the prediction model correspond to those of the real battery, the currently available unloading u. Charging power to be predicted. The desired quantities can be determined, for example, by adjusting the model on the basis of the measured quantities current, voltage and temperature with the aid of a Kalman filter.

For the prediction of the current discharge / charging power must always be the current

State variables of the prediction model, ie the rest voltage Uco and the concentration polarization U _{k be} known. The state estimator must therefore at least determine the state vector x = [Uco, UJ. An improvement of the performance prediction is possible with additional estimation of the penetration polarization U _D.

Furthermore, at least the strongly aging-dependent parameters of the prediction model must be adapted. These are the characteristic parameters _l02 R 5 and _{Uco; gr} e _nz of the ohmic internal resistance and the acid diffusion resistance R _k ₂ O 5 at full charge and T _Batt ⁼ 25 ° C. The prediction can be further improved by additional adaptation of the characteristic parameters U _D o ₂ 5, _E ia and U _D o ₂ 5, _L ad of the Durchtrittspolarisation. This is the maximum of the parameter vector

above sea level. = [RiO25, boundary, RkO25, UD025, Ela, UD025, Lad]

to be determined by means of suitable parameter estimation methods.

Prediction of the maximum available unloading u. Charging power (power predictor)

FIG. 2 shows the basic structure of the power prediction. A state & u. Parameter estimators (e.g., Kalman filters) continuously estimate the current ones for the

Performance prediction required state variables and parameters of the electrical energy storage with which the prediction model is initialized. Subsequently, with the aid of the model equations and the specifications for the duration of the discharge / charge pulse, the permissible minimum and maximum values can be determined. maximum battery voltage, the maximum permissible discharge Charging current and the minimum u. maximum state of charge the available discharge

/ Charging power are calculated.

If, according to the available charging / discharging power at other temperatures (eg cold start temperature -18 ° C or nominal temperature 25 ° C) and / or charging states (eg full charge) is required as the current, T _Ba tt and the rest voltage Uco in the Performance predictor initialized with the corresponding default values T _Batt o and Xo instead of the current values. At the same time, these services also provide a measure of battery aging (SOH = State of Health).

The following prerequisites and assumptions are used for the determination of the unloading and.

Charging power with respect to a Konstantstromentlade- or charge pulse made:

Requirements:

Δt _E i _a = duration of the discharge pulse in s

Δt _Lad = duration of the charging pulse in s

U _E ia, _mm ⁼ minimum permissible vehicle voltage in V

U _L ad _{; m} a _x ⁼ maximum permissible battery (charge) voltage in V

l _E ia, _m a _x ⁼ maximum permissible discharge current in A l _L ad, _m a _x ⁼ maximum permissible charging current in A

SOC _mm = minimum permissible state of charge in%

SOC _max = maximum permissible state of charge in%

With the SOC definition of the quiescent voltage Uco:

SOC = 100 * (Uc0-Uc0 _; mm) / (Uc0 _; maχ-Uc0 _; mm)

Δt _E i _a and Δt _Lad are to be chosen so small that the charge state change is negligible by the current pulse (Uco ⁼ const) and so large that the Durchtrittspolarisation during the current pulse assumes its steady state value (order 1-1Os).

The sequence of the power prediction or the procedure for the prediction of the maximum available discharging and charging power in two flow diagrams is shown. The process is the

Leistungsprädiktion separated for unloading and charging power in Figures 3 and 4 indicated.

Prediction of the maximum available discharge capacity The discharging current I _E i _{; Pred} and the discharging power P _E i _{a; Pred} are determined by determining the zero of

! UBatt, model (lEla, pred) " ⁼ 0

determined, wherein the battery voltage UBatt _; Modeii (lEia _{; P} red) which is calculated at the end of the discharge current pulse of duration Δt _E i _a using the prediction model already described:

UBatt, model (lEla, pred) ⁼ UcO.pred + Uk, pred + •••

Ri (Ucθ, pred, Uk ₎ pred, TBatt) * lEla ₎ pred + Uk _{) P} red + UD, Ela (lEla, pred, TBatt)

With

Uco.pred ⁼ Uco ( ^=> charge state change neglected by current pulse)

Uk _{) P} red ⁼ Rk (Ucθ ₎ pred, TBatt) * lEla, pred + •••

(Uk "Rk _(UCO; pred, T _B att) * _LeLa; pred) * exp (-AtEla / Tk)

(=> Solution of the differential equation for the RC-element R _k || C _k )

After the above relations have been established, l _E i _{a; Pred} can be calculated. Due to the nonlinear function U _D; Eia (lEia, pred, TBatt) this is only possible numerically with the help of a zero-search method, eg secant method.

The maximum available discharge capacity is then:

^ Ela, pred UEla, mm J-Ela, pred

Before calculating the zero, it must still be checked whether there is any solution l _E i _{a; Pred} <OA. For this it is tested if the condition:

U _{Batt; model} (l _{Ela; pred} = OA, Δt _Ela = Os) = U _C 0 + U _k > U _{Ela; mm is} satisfied.

If not, I _E ia _{; P} red = 0A and P _E ia _{; P} red = OW are output. Further check whether the specifications for the maximum allowable discharge current l _e i _{a, max} and the minimum SOC _mm are observed.

If | I _e _la; pred | > | lEla, max | WlM I _E la _; pred = Iεla.max set And U _B att _; Model (lEla _; max) to calculate the maximum discharge capacity:

"Ela, pred ⁼ UBatt, model (lEla, max) ^ EIa ₁ IiIaX

results.

Compliance with the minimum state of charge is based on the condition:

SOC = I OO * (Uco-Uco, _mi n) / (Uco, _m a-Uco, _mi n)> SOC _1111n

checked. If the condition is not fulfilled, lEia _{; P} red ⁼ OA and PEia _{; P} red ⁼ OW are output.

It should be noted that:

SOC _1111n > must be specified (see

Formula for R ₁ in Section 2.4.1).

Prediction of the maximum available charging power

Equivalent to the determination of the maximum discharge current and the maximum discharge power, the maximum charging current Ie _{; P} red and the maximum charging power PLad _{; Pre} d are determined by determining the zero of

! UBatt, model (lLad _{) P} red) ^~ ULad, max ⁼ 0

determined, wherein the battery voltage U _B att _; Modeii (lLad _{; P} red), which is calculated at the end of the charging current pulse of duration Δt _Lad again calculated using the prediction model already described: UBatt, model (lLad, pred) ⁼

Uco.pred + Uk _{) P} red + Ri (Ucθ, pred, Uk ₎ pred, TBatt) * lLad ₎ pred + Uk _{) P} red + •• • UD, Lad (lLad, predjTBattjUcθ, pred)

With:

Uco.pred ⁼ Uco ( ^=> charge state change neglected by current pulse)

_{Uk) P} red ⁼ Rk (Ucθ, pred, T Batt) * Llad, pred + ••• (Uk - Rk _(UCO; pred, T _B att) * _Llad; pred) * exp (-At _L ad / Tk)

(=> Solution of the differential equation for the RC-element R _k || C _k )

After the relationships have been established l _{Lad; Pred} can be calculated. Due to the nonlinear function U _D; Lad (lLad _; pred, T _B att, Uco _; pred) is again only numerically with the help of a

Zero-searching method e.g. Secant procedure possible.

The maximum available charging power is then:

Before calculating the zero, it is still to be checked whether there is any solution l _{Lad; Pred} > OA. For this it is tested if the condition:

U _{Batt; Model} (l _{Lad; pred} = OA, Δt _Lad = Os) = U _C 0 + U _k <U _{Lad; max is} satisfied.

_Pred = 0A and P _{_Lad;;} If not, I is _Lad _pred = OW outputted.

Furthermore, it must be checked whether the specifications for the maximum permissible charging current I _Lad , _max and the maximum charging state SOC _{max are} complied with.

If | I _L ad _; pred | > | lLad _; max | WlM I _L ad _; pred = IlMmax set And U _B att _; Model (lAd _; max), so that the maximum charging power is: PLAD, pred ⁼ UBatt, model (Llad, max) Llad, max

Results.

Compliance with the maximum state of charge is based on the condition:

SOC = 100 * (Ucθ-Ucθ _; mm) / (Ucθ _; maχ-Ucθ _; mm) <SOC _max <100%

checked. If the condition is not fulfilled, I _L ad _{; P} red = OA and P _L ad _{; P} red = OW are output.

The described methods may optionally be modified as appropriate.

They preferably run in a suitably equipped control device, for example a control device for a battery state detection, to which the battery is connected, or an electrical system manager in a vehicle.

Another use is in an IBS (Intelligent Battery Sensor) and / or at

Body computers or as a software module under an electric

B attermanagements.

Claims

13.10.2005 Bü / BoROBERT BOSCH GMBH, 70442 Stuttgart claims

1. A method for predicting the performance of electrical energy storage, in particular a battery for a vehicle, characterized in that a mathematical model of the energy storage is formed, the state variables and parameters are continuously adapted and a prediction of the maximum charging and discharging power.

2. The method according to claim 1, characterized in that relevant factors such as temperature, state of charge, ohm shear internal resistance, polarizations,

Acid stratification, age and icing are taken into account.

3. The method of claim 1 or 2, characterized in that a state and parameter estimator is used for power prediction, which continuously estimates the current required state variables.

4. The method according to claim 3, characterized in that the state and parameter estimator is a Kalman filter.

5. The method according to any one of the preceding claims, characterized in that the maximum allowable charging voltage and / or the minimum vehicle electrical system voltage are taken into account.

6. The method according to claim 5, characterized in that further specifications for the maximum allowable charging and / or discharge and / or the minimum and / or maximum state of charge.

7. The method according to any one of the preceding claims, characterized in that the currently available charging and / or discharging power is determined.

8. The method according to claim 5, characterized in that are determined at any predetermined temperatures and / or states of charge expected charging and / or discharging.

9. The method according to any one of the preceding claims, characterized in that the reference to a fixed predetermined temperature determined charging and / or discharging power is used as a measure of battery aging.

10. A device for predicting the performance of electrical energy storage, in particular a battery for a vehicle, characterized in that a

Method according to one of the preceding claims is performed.

11. The device according to claim 10, characterized in that the device comprises at least one control device, in particular a control device or part of an intelligent battery sensor or a body computer or

Component of a software module for an electric battery management is ..