DE102021205872A1 - Method for monitoring an energy store in a motor vehicle - Google Patents

Abstract

The invention relates to a method for monitoring an energy store in a motor vehicle, with the energy store (10) supplying at least one consumer, preferably for a safety-related driving function, with at least one age-dependent variable (SOHest(t)) of the energy store (10) predicting a future point in time is determined, a predicted remaining service life (RULest) being determined at least as a function of the predicted age-dependent variable (SOHest), with at least one confidence interval for the predicted remaining service life (ΔRULest) being determined.

Description

The invention relates to a method for monitoring an energy store in a motor vehicle according to the generic type of the independent claim.

State of the art

From the DE 102018220494 A1 a method for monitoring an energy store in a vehicle electrical system of a motor vehicle is already known, in which at least one current operating variable of the energy store is determined and this at least one operating variable is passed on to a forecast model and this forecast model uses the current value for the at least one operating variable to determine future values , wherein the future value is given to a voltage predictor, which calculates an expected minimum voltage of the energy store for a selected function. Battery condition detection software determines or measures the current capacity and internal resistance of the battery. These are passed on to a forecast model. The prognosis model calculates the future values of the capacity and the internal resistance with the help of representative load collectives and a load-carrying capacity model, on which the calculation of the expected minimum voltage is based.

The object of the invention is to further simplify the prediction of a specific parameter of the electrical energy store. This object is solved by the features of the independent claim.

Disclosure of Invention

By determining a confidence interval for the predicted remaining service life, a statement can be made about the quality of the prediction. Depending on the quality of the forecast, appropriate countermeasures can be taken in a targeted manner. Furthermore, the failure rate of the energy store, which is particularly relevant for safety-critical applications, can be further reduced and availability can be increased by means of predictive maintenance or predictive health management.

In an expedient development, it is provided that the confidence interval for the predicted remaining service life is determined using a large number, in particular more than 2000, of reference curves of the age-dependent variable. In this way, more precise confidence intervals for the prognosis of the remaining service life can be determined without the restriction of theoretically derived calculation formulas or recourse to extensive measurement data. Due to the more precise confidence intervals, maintenance intervals can be planned more precisely or extended using predictive energy management, which reduces maintenance costs, which is particularly advantageous for vehicle fleet operators.

In an expedient development, the reference curves of the age-dependent variable are determined by previously determined characteristic measurement curves of the energy store and/or by a simulation, in particular a Monte Carlo simulation. As a result, relatively accurate forecasts of the remaining service life can be made without much effort. Especially in a large number of simulations that reflect the variance of the influencing variables on the aging of the energy storage device in reality, an empirical formula for calculating the confidence interval of the predicted remaining service life as a function of a particular one-step prediction error and a predicted remaining service life can be derived. This further simplifies the calculation, particularly preferably using an approximation formula.

In an expedient development, it is provided that the confidence interval for the predicted remaining service life is determined as a function of a measure of a prediction error between the predicted age-dependent variable and the actual age-dependent variable. Since the forecasts are becoming more and more accurate towards the end of life, the quality of the prediction of the remaining life is further increased by this influencing factor.

The predicted age-dependent variable is particularly expediently determined as a function of the actual age-dependent variable that is generated as a function of a measurement of a characteristic variable of the energy store. As a result, the data that actually occurs during operation is used as a basis for the further prediction of the remaining service life, so that the prediction is further improved. The predicted remaining service life is particularly preferably reached when when the predicted age-dependent variable reaches a specific limit value. In this way, desired minimum requirements can be easily mapped through suitable specification of the limit value.

In an expedient development, it is provided that the confidence interval for the predicted remaining service life is calculated using the approximation formula and for this purpose at least the predicted remaining service life and/or a standard deviation of the prediction error between the predicted age-dependent variable and the actual age-dependent variable is used as the input variable for the approximation formula or become. This consideration further increases the quality of the prediction.

In an expedient development, it is provided that an approximation formula for the confidence interval for the age-dependent variable as a function of a remaining service life predicted based on at least one reference value of the age-dependent variable and/or as a function of a measure of a prediction error, in particular standard deviation, of a prediction error between the predicted age-dependent variable and a reference value of the age-dependent variable is determined. The particularly simulated reference curves of the age-dependent variable thus form a large database for the offline creation of the approximation formula, which further simplifies the online determination of the prediction.

In an expedient development, it is provided that at least one histogram is created to determine the confidence interval limits of age-dependent value at the end of life for further use in the approximation formula. Corresponding series of measurements can thus be made available for the determination of the approximation formula depending on the desired confidence level in each case.

In an expedient development, it is provided that for the determination of the approximation formula, the predicted remaining service life is respectively predicted based on reference values of the aging-dependent variable at different points in time and/or a prediction error, in particular a standard deviation of a prediction error, between the predicted aging-dependent variable for the different points in time and the reference value of the age-dependent variable is determined. In this way, the determination of the approximation formula can be based on different points in time, which are representative for later courses, precisely on the basis of the simulation data. This improves later predictions of the remaining service life.

In an expedient development, it is provided that the reference curves of the age-dependent variable are determined using a degradation model. Such a model can be used to simulate the load situations of the energy store that are to be expected in a particularly realistic manner, so that particularly realistic reference curves of the age-dependent variable can be generated. The quality of the prediction of the remaining service life can thus be further improved.

Further expedient developments result from further dependent claims and from the description.

character list

• 1 ein Blockschaltbild für eine Offline-Bestimmung der Approximationsformel des Konfidenzintervalls einer alterungsabhängigen Größe, insbesondere Gesundheitszustand SOH,
• 2 ein Blockschaltbild der Online-Berechnung des Konfidenzintervalls der Restlebensdauer bei einem auf der alterungsabhängigen Größe basierenden Lebensende-Kriterium,
• 3 ein Blockschaltbild für eine Offline-Bestimmung der Approximationsformel des Konfidenzintervalls einer weiteren alterungsabhängigen Größe, insbesondere Funktionszustand SOF,
• 4 ein Blockschaltbild der Online-Berechnung des Konfidenzintervalls der Restlebensdauer bei einem auf der weiteren alterungsabhängigen Grö-ße basierenden Lebensende-Kriterium,
• 5 ein Vergleich der berechneten Konfidenzintervalle der Restlebensdauer mit der Referenz für ein bestimmtes Konfidenzlevel,
• 6 ein Schema des Degradationsmodells.

Show it

• 1 a block diagram for an offline determination of the approximation formula of the confidence interval of an age-dependent variable, in particular the state of health SOH,
• 2 a block diagram of the online calculation of the confidence interval of the remaining service life for an end-of-life criterion based on the age-dependent variable,
• 3 a block diagram for an offline determination of the approximation formula of the confidence interval of a further age-dependent variable, in particular functional state SOF,
• 4 a block diagram of the online calculation of the confidence interval of the remaining service life for an end-of-life criterion based on the further age-dependent variable,
• 5 a comparison of the calculated confidence intervals of the remaining lifetime with the reference for a certain confidence level,
• 6 a scheme of the degradation model.

Embodiments of the invention

The invention is shown schematically in the drawings using embodiments and is described in detail below with reference to the drawings.

By way of example, a battery or accumulator is described in the exemplary embodiment as a possible energy store 10 . Alternatively, however, other energy stores suitable for this task, for example on an inductive or capacitive basis, fuel cells, capacitors or the like can also be used.

Mit m = RUL erhält man den geschätzten SOH Prädiktionsfehler bei Lebensende EOL: $$\mathrm{\sigma }\text{SOHRUL}=\surd \text{RUL}\ast {\mathrm{\sigma }}_{\text{SOH1}}$$

Für einen gegebenen Konfidenzlevel von z.B. 95% ergibt sich damit das SOH Konfidenzintervall bei Lebensende EOL: $$\Delta {\text{SOH}}_{\text{EOL}}=+/-1.96*{\mathrm{\sigma }}_{\text{SOHRUL}}$$

Das Konfidenzintervall bzgl. der Restlebensdauer RUL erhält man daraus durch Berechnung der RUL Prognose an den Intervallgrenzen der SOH Prädiktion, also z.B. für die EOL-Bedingungen SOH_EOL= 80% +/- |ΔSOH_EOL|.To calculate the confidence intervals of service life forecasts for electrical energy storage devices 10, a one-step prediction error is usually used, ie the prediction error up to the next measured sample T (e.g. 1 day) of the age-dependent variables (SOH) such as capacity or internal resistance up to the end of life or End of Life (EOL ) point in time extrapolated (-> multi-step SOH prediction error).
For example, assuming a normally distributed and uncorrelated one-step SOH prediction error with the standard deviation σSOH1 for the m-step SOH prediction error: $$\mathrm{\sigma }\text{SOHm}=\surd \text{m}\ast {\mathrm{\sigma }}_{\text{SOH1}}$$

With m = RUL, the estimated SOH prediction error at the end of life EOL is obtained: $$\mathrm{\sigma }\text{SOHRUL}=\surd \text{RUL}\ast {\mathrm{\sigma }}_{\text{SOH1}}$$

For a given confidence level of 95%, for example, this results in the SOH confidence interval at the end of life EOL: $$\Delta {\text{SOH}}_{\text{EOL}}=+/-1.96*{\mathrm{\sigma }}_{\text{SOHRUL}}$$

The confidence interval with regard to the remaining service life RUL is obtained by calculating the RUL forecast at the interval limits of the SOH prediction, ie for the EOL conditions SOH _EOL = 80% +/- |ΔSOH _EOL |.

With the following procedure, more accurate confidence intervals of the prediction of the remaining service life RUL for electrical energy stores 10 can be achieved.

1 shows a block diagram for offline determination of the approximation formula of a confidence interval ΔSOH _EOL of an age-dependent variable at the end of life EOL, in particular state of health SOH, with a predetermined or specifiable confidence level 24. For this purpose, a block 14 such as a degradation model 14 is provided, in which a large number (n > 2000) of n different reference curves SOH(t) of the age-dependent variable SOH. The reference curves SOH(t) of the age-dependent variable are obtained, for example, by a simulation, in particular by a Monte Carlo simulation. Obtaining will be exemplified later in connection with 6 described. Alternatively, however, the reference curves SOH(t) can be made available in a different way, for example using numerous measurement curves specific to the energy store 10 . The characteristic curves of the age-dependent variable SOH(t) could be obtained, for example, using series of measurements on test benches or real measured value curves of comparable energy stores 10 . However, it is essential to provide a correspondingly large number n (eg n>2000) of such reference curves SOH(t) in order to be able to carry out the subsequent statistical evaluation. Block 14 is used to generate or make available a large number of reference curves SOH(t) that are characteristic of the respective energy store 10 for a prognosis 16 of a remaining service life RUL of the energy store 10 .

The forecast 16 estimates or predicts the predicted remaining service life RULest(t) at different forecast times t. In addition, the prognosis 16 determines a standard deviation σ _SOH1 (t) of the one-step prediction up to these points in time t.

The prognosis 16 of the remaining service life RUL makes use of a specific end-of-life criterion (EOL criterion), which is given when the predicted age-dependent variable SOHest one certain limit SOH _EOL reached. This limit value SOH _EOL could consist, for example, in the age-dependent variable SOH only reaching 80% of a starting value SOH0, for example in the case of a new energy store 10, which is defined as end-of-life EOL. In addition, the prognosis 16 determines the predicted age-dependent variable SOHest(t), which is based on a model of the energy store 10, for example.

The input variables for the prognosis 16 are: 1 the numerous reference curves of the age-dependent variable SOH(t) as stored in block 14, preferably at different points in time t0, t1, t2 for different reference curves SOH1(t), SOH2(t), . . . SOHn(t). The associated reference values of the age-dependent quantity SOH1(t0), SOH2(t0),...SOHn(t0), SOH1(t1), SOH2(t1), SOHn(t1) are supplied to the prognosis 16 as input quantities as in 1 shown. These reference values are used to simulate the actual age-dependent variable SOHact(t), as per online operation, ie during actual vehicle operation 2 be supplied to the same prognosis 16 later.

With these input variables, namely the respective reference values of the age-dependent variable SOHn(t) at different points in time (t=t0, t1, t2, aging-dependent quantity or the course over time of the predicted aging-dependent quantity SOHest(t). The course of the predicted age-dependent variable SOHest(t) starts at the different points in time (t0, t1, t2, tm) in the future. This can now be done for each of the n reference curves of the age-dependent variable SOHn(t). Thus, depending on the number m of different points in time, m predicted curves of the age-dependent variable SOHnpred(t) are available for each of the reference curves SOH(t).

If the predicted age-dependent variable SOHest(t) reaches the limit value SOH _EOL corresponding to the end of life EOL, then this point in time defines the predicted remaining service life RULest of the energy store 10. Alternatively, the associated predicted age-dependent variable could always be at the next point in time (for example for the next day). SOHest(T+1) can be determined and/or compared with the limit value SOH _EOL . As long as the limit value SOH _EOL has not yet been reached, the next prediction time t1 (t1=t0+T) with the associated reference value SOH(t1) is used and compared with the limit value SOH _EOL . This continues until the reference value SOH(t) reaches the limit value. The corresponding point in time t is the end of life t = EOL. Alternatively, the comparison can be terminated if, for example, a certain confidence period has been reached, for example 100 days, for which experience has shown that reliable predictions are still possible or because the user is not interested in information reaching even further into the future, which is also characterized by a high level of uncertainty is.

In parallel, the corresponding forecast of the remaining service life RULest(t) is now carried out as an input variable for each reference curve SOH(t) at each of the m different points in time, in particular for each next forecast point in time t.

This means that for the n > 2000 reference curves of the age-dependent variable SOHact(t) using the prognosis 16, n predicted remaining lifetimes RULest(t) are available at m different points in time, i.e. m*n predicted remaining lifetimes RULest.

In addition, a measure for a prediction error is preferably determined for each of the predicted remaining lifetime RULest. As stated at the outset, the standard deviation σ _SOH can be used for this measure in the case of a normally distributed and uncorrelated prediction error, or the standard deviation σ _SOH1 in the case of a one-step prediction error. This takes into account that a prognosis based on a point in time t2, which is already close to the expected end of life EOL, is made with greater accuracy than a prognosis at a point in time t0, which is still very far away from the expected end of life EOL.

The prediction error, in particular a one-step prediction error, is determined based on the next time interval T (for example one day or another suitable variable) of the age-dependent variable SOH. At time t0, the respective associated reference value SOH(t0) is determined for the reference curve of age-dependent variable SOH(t) to be considered as an input variable of prognosis 16 for the prediction of the next predicted age-dependent variable SOHest(t0+T). In the next step, the associated reference value SOH(t0+T) of the corresponding curve is determined in block 14 for the point in time t0+T, quasi as a simulated actual value. The one-step prediction error ΔSOH(t0) is determined by forming the difference between the predicted age-dependent variable SOHest(t0+T) and the predicted one Time to+T and the associated reference value SOH(t0+T) at time t0+T as stored in block 14. This is done for each of the n reference values SOH(t0+T) and/or for each of the possibly different starting times t0, t1, t2. The associated standard deviation σSOH1(t0) of the one-step prediction error is determined from the n*m one-step prediction errors ΔSOH(t0, t1, t2). For each next time step t1, t2 after the period T (t1-t0 or t2-t1) has elapsed, the associated one-step prediction error ΔSOH(t) and/or the associated standard deviation σSOH1(t) of the one-step prediction error is updated.

Furthermore, the one-step prediction error can be extrapolated into the future, in particular up to the time of the remaining service life RUL or end of life EOL.

In addition, the remaining service life RULest(t) predicted by the prognosis 16 is again made available to the degradation model 14 as an input variable. From this, the associated age-dependent variable at the time of the remaining service life SOH(RULest(t)) is determined. Block 14 provides a summation point 20 with the associated age-dependent variable at the time of the remaining term SOH(RULest(t)). In the summation point 20, the difference is formed from the age-dependent variable at the time of the remaining running time SOH(RULest(t)) and the limit value SOH _EOL . The difference is a measure of the error in the age-dependent quantity ΔSOH _EOL at the end of life EOL. For the different n reference curves of the age-dependent variable SOH(t) and in this case different m points in time t0, t1, t2 of the prediction as the basis for determining the predicted remaining running time RULest form the database for creating corresponding error histograms 22.

A prediction error of the age-dependent variable ΔSOH _EOL of the forecast 32 of the remaining service life RUL at the end of life EOL (end of life) for the forecast times t is determined with the aid of the reference curves SOH(t) of the age-dependent variable. For this purpose, the associated percentiles (for example 2.5% and 97.5%) at the confidence interval boundaries are read for each prediction time from an associated histogram 22 of the cumulative probability of the prediction error at the end of life EOL and a given confidence level 24 (for example 95%).

The prediction error ΔSOH _EOL as the output variable of the histograms 22 is supplied to the block 18 for determining the approximation formula for the confidence interval ΔSOH _EOLest . Depending on the respective confidence level 24 (for example 95%, 97% etc.), the associated limits of the confidence interval _ΔSOH EOL can be made available to the block 18 via the frequency distribution of the corresponding ΔSOH _EOL errors.

Die Parameter a1 und a2 werden beispielsweise mittels Optimierungsverfahren bestimmt und können für obere und untere Intervallgrenze auch unterschiedlich sein. Über diese Kurve wird der Zusammenhang zwischen dem Konfidenzintervall ΔSOH_EOL , der prädizierten Restlebensdauer RULest und der Standardabweichung σ_SOH1 für den Einschritt-Prädiktionsfehler für die Vielzahl der Messwerte in einfacher Art und Weise hergestellt.The course of the confidence interval limits determined in this way for the prediction of the aging-dependent parameter SOH over the remaining service life RUL can be approximated by the following empirical formula depending on the standard deviation σ _SOH1 of the one-step prediction error: $$\Delta {\text{SOH}}_{\text{EOHest}}={\text{a}}_1*{\text{RUL}}_{\text{est}}{}^{\text{a2}}*{\mathrm{\sigma }}_{\text{SOH1}}$$

The parameters a1 and a2 are determined, for example, using optimization methods and can also be different for the upper and lower interval limits. The relationship between the confidence interval ΔSOH _EOL , the predicted remaining service life RULest and the standard deviation σ _SOH1 for the one-step prediction error for the large number of measured values is produced in a simple manner via this curve.

2 shows a block diagram for the online calculation of the confidence interval of the predicted remaining service life ΔRUL _est , with the remaining service life RUL being determined using the predicted age-dependent variable SOHest. Corresponding 2 may be provided online on the basis of Block 18 1 Offline determined approximation formula for each prediction time t, the confidence interval of the predicted remaining service life ΔRUL _est be determined from the confidence interval of the age-dependent variable ΔSOH _EOL of the prediction of the age-dependent variable SOH at the end of life EOL.

The sensor 12 detects characteristic measured values U, I, T of the energy store 10 and uses them to determine the actual age-dependent variable SOHactual(t). From this, the prognosis 16 determines the predicted course of the age-dependent variable SOHest(t). This can be done using a model of the energy store 10, for example. The predicted course of the age-dependent variable SOHest(t) is compared with a limit value SOH _EOL , which defines the end of life EOL. This is the point in time at which the predicted profile of the age-dependent variable SOHest(t) reaches the limit value SOH _EOL End of life RUL reached. A confidence interval for the predicted remaining service life ΔRUL _est is then determined. The procedure is explained in more detail below.

An energy storage device 10 is shown schematically. A sensor 12 detects specific measured variables of the energy storage device 10, such as current I, voltage U, temperature T. A state detection system, for example, is implemented in the sensor 12.

At least one aging-dependent variable SOH, SOF of the energy store 10 or a time profile of the (past) aging-dependent variable SOHactual(t), SOFactual(t) can be determined via the state detection with recourse to the measured values I, U, T recorded. For example, the current age-dependent variable of the energy store 10 is determined as the SOH (state of health). For example, the internal resistance Ri, the capacitance C0, transmission polarization or the like can be used as age-dependent variables SOH of the energy store 10 . The age-dependent variables SOH are characterized in that they change depending on the age of the energy store 10 . The aging-dependent variables SOH can preferably be detected by the sensor 12 via the measured variables U, I, T and calculated via the state detection. The status detection preferably works continuously. The actual age-dependent variable SOHact(t) made available by the sensor 12 is possibly transmitted together with other forecast or diagnostic variables via a communication interface (a bus system such as LIN, CAN) to higher-level systems (not shown) for further processing, for example for the purpose of predictive maintenance and/or predictive or preventive health management of the energy storage device 10.

The actual age-dependent variable SOHact(t) as determined by the sensor 12 or another device is fed to the prognosis 16 of the remaining service life RUL and/or to a calculation 26 of the confidence interval of the remaining service life ΔRUL _est . The prediction 16 of the remaining lifetime RUL is identical to that associated with 1 described prognosis 16. In contrast to 1 however, instead of a reference curve of an age-dependent variable SOH(t) from the prognosis model 14, the actual age-dependent variable SOHactual(t) based on the measured variables UIT of the sensor 12 is now used as the input variable.

In the prognosis 16, the future, predicted course of the age-dependent variable SOHest(t) is determined. The predicted course of the age-dependent variable SOHest(t) is compared with a limit value of the age-dependent variable SOH _EOL , which represents the end of life EOL. The end of life EOL is reached when the predicted age-dependent variable SOHest reaches a specific limit value SOH _EOL at which proper operation of the energy store 10 can no longer be reliably guaranteed. In addition to the remaining service life RULest, the prognosis 16 also provides the standard deviation σSOH1(t) of the one-step prediction for the calculation 26 of the confidence interval ΔRUL _est for the remaining service life. Calculation 26 accesses the in 1 approximation formula determined offline. To determine the standard deviation of the one-step prediction error σSOH1, only the last value of the age-dependent variable that was predicted for the time t0 (SOHpre(t0)) and the current actual value of the age-dependent variable SOHist(t0) at the time t0 are used. Recourse to only the one-step prediction error σSOH1 simplifies the method and reduces the memory requirement. The functioning of the prognosis 16 according to 1 corresponds to that according to 2 . Only the input sizes differ. while at 1 simulated or offline determined typical curves of the aging-dependent variable SOH(t) are used as input variables for the prognosis 16 applies 2 the prognosis 16 of the actual values of the age-dependent quantity SOHist(t) as provided online using the measured values U, I, T and a state detection of the sensor 12.

(RULest hoch a2).The calculation 26 of the confidence interval of the remaining lifetime ΔRUL _est . Here is particularly preferred in connection with 1 explained calculated formula used: $$\Delta {\text{SOH}}_{\text{EOHest}}={\text{a}}_1*{\text{RUL}}_{\text{est}}{}^{\text{a2}}*{\mathrm{\sigma }}_{\text{SOH1}}\text{or}\Delta {\text{SOH}}_{\text{EOLest}}={\text{a}}_1*{\left({\text{RUL}}_{\text{est}}\right)}^{\text{a2}}*{\mathrm{\sigma }}_{\text{SOH1}}$$

(RULest to the power of a2).

The confidence interval of the predicted lifetime ΔRUL _est is calculated from the estimated error tolerance of the SOH at EOL ΔSOH _EOLest by reusing the RUL forecast (block 16 in block 26) taking into account this tolerance in the EOL criterion: SOH = SOH _EOL +/- ΔSOH _EOLest is calculated separately for upper and lower tolerance limits. As already described in the text, the upper and lower SOH _EOL tolerance limits can be different (different parameterization of the approximation formula).

A certain confidence level 24 (for example 95%) was included in the determination of the approximation formula for the confidence interval carried out in block 18 offline or before normal operation. The corresponding parameters a1, a2 have already been determined offline for this confidence level 24 in this block 18 . Depending on this confidence level 24, the confidence interval limits SOH=SOH _EOL +/- ΔSOH _EOLest are determined using the above formula. Depending on the respective confidence interval limits of the aging-dependent variables, the associated remaining lifetime confidence interval limits ΔRUL _est can be determined by using the predicted aging-dependent variables SOHest at the respective confidence interval limits. In particular, the lower confidence interval limit for the remaining service life RUL can now be used as a limit value which, when it is reached, initiates certain measures such as activating a warning notice, visiting a workshop or the like. Block 26 uses the RUL prediction from block 16 to calculate the RUL tolerances. Accordingly, SOHist is required as an input for this.

The quality of the prediction of the predicted remaining service life RULest is thus estimated via block 26 . The quality of the prognosis is represented by the corresponding confidence interval limits. In general, the following applies: the lower the predicted remaining service life RULest, the more accurate the prediction of this predicted remaining service life, thus the confidence interval of the remaining service life ΔRUL _est , within which a specific prediction of the remaining service life RUL is possible at a specific confidence level 24, is smaller. This is also represented accordingly by the formula described above.

The embodiment according to 3 and 4 differs in that the functional status SOF (state of function) is used as a (further) age-dependent variable instead of the health status SOH. However, the basic procedure does not differ. When using a SOF EOL criterion (function-specific load is used as a basis for determining a predictive operating variable such as vehicle electrical system voltage or the like), a suitable operating variable is compared with a limit value, for example a minimum voltage Ubattmin.

Again, the degradation model 14 or the block 14 is provided, in which a large number (n>=2000) of reference curves SOH(t) of the age-dependent variable SOH are stored. For example, a simulation, in particular a Monte Carlo simulation, of the degradation model 14 generates a large number of random reference curves SOH(t) for a prognosis 32 of a remaining service life RUL of the energy store 10 . However, the functional state SOF is now used as the end-of-life (EOL) criterion as the basis for determining the predicted remaining service life RULest. The forecast 32 estimates or predicts the predicted remaining service life RULest at different forecast times t. In addition, the prognosis 32 determines a standard deviation σ _SOH1 (t) of the one-step prediction up to these points in time t.

The input variables of the predictor 28 for the further aging-dependent variable SOF are measured or estimated time curves that describe the energy store 10, such as the current state of health (=SOH(t)) or aging-dependent variable or variables describing this, such as capacity C0, Internal resistance Ri, polarizations, etc. of the energy store 10 under consideration, which are continuously determined, for example, by the status detection of the sensor 12, in particular the battery sensor. A mathematical model of the energy store 10 is stored in the predictor 28, for example. The predictor 28 determines, for example, a predicted operating variable of the energy store 10 such as the voltage drop to be expected with regard to a predefined application-specific load profile such as a current load profile.

Using the predicted operating variable of the energy store 10, it is checked whether, for example, for certain framework conditions for the application described via the load profile, a required limit value, such as a minimum voltage, is undershot or when this limit value is undershot. The point in time at which the limit value is not reached then defines the predicted remaining service life RULest for the end-of-life (EOL) criterion defined by the limit value for the respective function.

In the equivalent circuit diagram or model of the energy store 10, as may be implemented in the predictor 28, certain operating variables such as current I, voltage U, temperature T and state variables such as the open-circuit voltage, the concentration polarization, the passage polarization of the various electrodes etc. as well as corresponding parameters (e.g Internal resistance Ri, equivalent capacity C0, resistance and capacity of the acid diffusion or resistance and capacity of the double layer of the electrodes, etc.) can be used.

Die Parameter b1 und b2 können auch hier beispielsweise durch Optimierungsverfahren bestimmt werden und für obere und untere Intervallgrenze unterschiedlich sein.As an output variable of the predictor 28, the further aging-dependent variable SOF(RULest) arrives at the summation point 20 at the predicted point in time of the remaining service life fed to a further histogram 30 as a corresponding error at the end of life. The following empirical formula is derived from the histograms 30 of the prediction error of the additional age-dependent variable SOF at the end of life (EOL) time, depending on the standard deviation σ _SOF1 of the one-step prediction error of the additional age-dependent variable SOF: $$\Delta {\text{SOH}}_{\text{EOHest}}={\text{b}}_1*{\text{RUL}}_{\text{est}}{}^{\text{b2}}*{\mathrm{\sigma }}_{\text{SOF1}}$$

Here, too, the parameters b1 and b2 can be determined, for example, by optimization methods and can be different for the upper and lower interval limits.

$$\Delta {\text{SOF}}_{\text{EOLest}}={\text{b}}_1*{\text{RUL}}_{\text{est}}{}^{\text{b2}}*{\mathrm{\sigma }}_{\text{SOF1}}$$

5 zeigt ein Beispiel für die mittels der empirischen formelberechneten Konfidenzintervall im Vergleich zur Referenz aus der Monte Carlo Simulation für ein Konfidenzlevel 28 von 95 % zusammen mit dem Median der geschätzten Restlebensdauer des prädizierten Verlaufs der Restlebensdauer RULest. Die beschriebenen Formeln approximieren die Referenzwerte sehr gut. So ist die Restlebensdauer RUL in Tagen auf der x-Achse, die Konfidenzintervallgrenzen in Tagen auf der y-Achse aufgetragen. Kurve 1 beschreibt die Referenz-Restlebensdauer RUL, Kurve 2 den Median der prädizierten Restlebensdauer RUL, Kurve 3 prädizierte Restlebensdauer RULest bei einem Konfidenzlevel von 2,5 % in Tagen, Kurve 4 die prädizierte Restlebensdauer RULest bei einem Konfidenzlevel von 97,5 % in Tagen, Kurve 5 die (tatsächliche) Restlebensdauer RUL bei einem Konfidenzlevel von 2,5 % in Tagen sowie Kurve 6 die (tatsächliche) Restlebensdauer RUL bei einem Konfidenzlevel von 97,5 % in Tagen.Corresponding 4 the confidence interval can thus be calculated online at each prediction time t by calculating a corresponding prediction of the predicted remaining service life RULest at the limits of the confidence interval of the further age-dependent variable SOF. In turn, the sensor 12 uses the measured variables U, I, T of the energy store 10 to determine curves of the age-dependent variable, in particular the state of health SOH(t). On the one hand, the age-dependent variable SOH arrives at the prognosis 32, whose end-of-life (EOL) criterion SOF _EOL is based on the further age-dependent variable SOF. The forecast 32 estimates or predicts the predicted remaining service life RULest at different forecast times t. In addition, the prognosis 32 determines a standard deviation σ _SOH1 (t) of the one-step prediction up to these points in time t. These variables reach the calculation 36 of the confidence interval for the remaining service life ΔRULest(t). The calculation 36 accesses the block 34 of 3 approximation formula determined offline: $$\text{SOF}={\text{SOH}}_{\text{EOL}}+/-\Delta {\text{SOF}}_{\text{EOLest}}\text{With}$$

$$\Delta {\text{SOF}}_{\text{EOLest}}={\text{b}}_1*{\text{RUL}}_{\text{est}}{}^{\text{b2}}*{\mathrm{\sigma }}_{\text{SOF1}}$$

5 shows an example of the confidence interval calculated using the empirical formula compared to the reference from the Monte Carlo simulation for a confidence level 28 of 95% together with the median of the estimated remaining service life of the predicted course of the remaining service life RULest. The formulas described approximate the reference values very well. The remaining service life RUL is plotted in days on the x-axis and the confidence interval limits in days on the y-axis. Curve 1 describes the reference remaining service life RUL, curve 2 the median of the predicted remaining service life RUL, curve 3 predicted remaining service life RULest with a confidence level of 2.5% in days, curve 4 the predicted remaining service life RULest with a confidence level of 97.5% in days , curve 5 the (actual) remaining service life RUL at a confidence level of 2.5% in days and curve 6 the (actual) remaining service life RUL at a confidence level of 97.5% in days.

6 discloses a block diagram of the calculation scheme of the degradation model 14 for providing a suitable database in block 14 for electrical energy storage devices 10 for generating random reference curves SOH(t) of the age-dependent variable, in particular the state of health SOH of the energy storage device 10.

First, normally distributed aging rates 40 are generated per sample step (for example per day) in a range that is realistic for the type of energy store 10 in vehicle use based on measurement data and/or expert knowledge (step 1). In block 40 of the 6 the daily aging rate (e.g. between 0-1%/day or pdf(ΔSOH/day) is shown in a semi-normal distribution.

The aging rate 40 randomly generated in the first step is recorded for an evenly distributed period of time of 1-7 days, for example, so that there are phases with greater or lesser absolute aging to generate (block 42; 2nd step). In block 42 of the 6 the duration of the constant aging rate (e.g. between 0-1%/day or pdf(ΔSOH/day) is shown in a uniform distribution.

The usage-related aging is taken into account by specifying a usage profile 46, in particular a weekly usage profile 46 specifying the percentage of vehicle and thus energy storage usage per day of the week (e.g. 100% for taxi operation or 10% for commuters) and multiplied by the aging rate 42 of the respective day, in particular using the multiplier 44.

The aging accelerated with increasing ambient temperature is taken into account via the temperature profile 48 weighted with an Arrhenius function 50, in particular the annual temperature profile, of the region in which the vehicle is operated. The output variable 45 of the multiplier 44 and the output variable 51 of the Arrhenius function 50 are fed to a further multiplier 52 . Its output variable 53 is the input variable for an adder 54. The output variable of the adder 54 is fed to a summation point 56, to which an initial value SOH0 of the age-dependent variable is also fed as a further variable. The corresponding reference curves of the age-dependent variable SOH(t) result from adding up the aging rates calculated per day, beginning with the specified initial value SOH0.

If query 46 reveals that the remaining service life RUL has been reached, countermeasures can be initiated in block 50 . Appropriate warnings to the driver, the vehicle operator, the workshop, etc. can thus be generated. Predictive maintenance could be initiated. Certain security-relevant functions can also be blocked or no longer permitted. It would also be possible to initiate appropriate countermeasures such as driving to the next parking lot, stopping at the hard shoulder, etc. in the event of particularly serious errors.

The method described is particularly suitable for monitoring energy storage devices 10 for safety-relevant applications such as, for example, for supplying safety-relevant consumers in a motor vehicle, in particular in the case of autonomous driving. However, the use is not limited to this.

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• DE 102018220494 A1 [0002]

Claims (15)

Method for monitoring an energy store in a motor vehicle, the energy store (10) supplying at least one consumer, preferably for a safety-relevant driving function, with at least one age-dependent variable (SOHest(t)) of the energy store (10) being predicted for a future point in time, with at least a predicted remaining service life (RULest) is determined as a function of the predicted age-dependent variable (SOHest), characterized in that at least one confidence interval for the predicted remaining service life (ΔRULest) is determined. procedure after claim 1 , characterized in that the confidence interval for the predicted remaining service life (ΔRULest) is determined using a large number, in particular more than 2000, of reference profiles of the age-dependent variable (SOH(t)). Method according to one of the preceding claims, characterized in that the reference curves of the age-dependent variable (SOH(t) are determined by previously determined characteristic measurement curves of the energy store (10) and/or by a simulation, in particular a Monte Carlo simulation. Method according to one of the preceding claims, characterized in that the confidence interval for the predicted remaining service life (ΔRULest) is determined using an approximation formula. Method according to one of the preceding claims, characterized in that the confidence interval for the predicted remaining service life (ΔRULest) as a function of a measure for a prediction error between the predicted age-dependent variable (SOHest(t)) and the actual age-dependent variable (SOHist(t)) is determined, the actual age-dependent variable (SOHist(t)) being determined in particular as a function of a measurement of at least one characteristic variable (U, I, T) of the energy store (10) and/or a standard deviation (σ _SOH1 ) of a prediction error between the predicted age-dependent variable (SOHactual(t)) and the actual age-dependent variable (SOHactual(t)) is determined. Method according to one of the preceding claims, characterized in that the predicted age-dependent variable (SOHest(t)) as a function of the actual age-dependent variable (SOHist(t)) which is dependent on a measurement of at least one characteristic variable (U, I, T) of the energy store (10) is generated is determined. Method according to one of the preceding claims, characterized in that the predicted remaining service life (RULest) is reached when the predicted age-dependent variable (SOHest) reaches a specific limit value (SOH _EOL ). Method according to one of the preceding claims, characterized in that the confidence interval for the predicted remaining service life (ΔRULest) is calculated using the approximation formula and for this purpose at least the predicted remaining service life (RULest(t)) and/or a standard deviation of the prediction error between the predicted age-dependent variable (SOHest(t)) and the actual age-dependent variable (SOHist(t)) is or are used as input variable for the approximation formula. Method according to one of the preceding claims, characterized in that an approximation formula for the confidence interval for the age-dependent variable (ΔSOHest) as a function of a remaining service life (RULest) and/or in Depending on a measure of a prediction error, in particular standard deviation of a prediction error, between the predicted age-dependent variable (SOHest(t0)) and a reference value of the age-dependent variable (SOH(t0)) is determined. Method according to one of the preceding claims, characterized in that on the basis of deviations or errors in determining the age-dependent variable at the time of the predicted remaining service life (SOH(RULest(t)) based on the age-dependent variable (SOH _EOL ) at the end of life (EOL) and/or depending on a confidence level (24), at least one histogram (22, 30) is created to determine the confidence interval limits of the age-dependent variable (ΔSOH _EOL ) at the end of life (EOL) for further use in the approximation formula. Method according to one of the preceding claims, characterized in that for the determination of the approximation formula the predicted remaining service life (RULest) is respectively based on reference values of the age-dependent variable (SOH(t0, t1, t2)) at different points in time (t0, t1, t2). and/or a prediction error, in particular standard deviation of a prediction error, between the predicted age-dependent variable (SOHest(t0)) and the reference variable (SOH(t0)) is determined for the different points in time (t0, t1, t2). Method according to one of the preceding claims, characterized in that the confidence interval for the predicted remaining service life (ΔRULest) is determined using the following approximation formula: confidence interval limits of the age-dependent variable at the end of life (ΔSOH _EOLest ) = a ₁ * (predicted remaining service life (RUL _est )) ^a2 * Standard deviation of the prediction error (σ _SOH1 ), with a1, a2 as parameters to be determined in advance. Method according to one of the preceding claims, characterized in that the reference curves of the age-dependent variable (SOH(t)) are determined using a degradation model (14). Method according to one of the preceding claims, characterized in that the degradation model (14) generates the plurality of reference curves of the age-dependent variable (SOH (t)) as a function of an aging rate (40) and/or as a function of a period of a constant aging rate (42 ) and/or depending on a usage profile (46), in particular a weekly usage profile and/or depending on a temperature profile (48), in particular an annual temperature profile, and/or depending on an aging function (50), in particular a temperature-dependent aging function (50) such as an Arrhenius -Function. Method according to one of the preceding claims, characterized in that aging rates (42) are generated from the previously generated aging rates (40) for an evenly distributed period of time, in particular in the daily range, and/or that a usage-related aging is generated using a usage profile ( 46) of the energy store (10), in particular for specifying the percentage use of the energy store (10) per weekday and/or that the use-related aging (45) is generated by multiplying the aging rate (42) with a constant period of time, in particular days, and the use profile (46) and/or that the use-related aging (45) is corrected as a function of temperature, in particular using a temperature profile (48) and/or a temperature-dependent aging function (50), in particular an Arrhenius function, in particular by multiplying the use-related aging (45) by an output variable (51) of the temperature-dependent aging function tion (50) and/or that a reference curve (SOH(t)) of the age-dependent quantity takes place by summing up the aging rates (53) calculated per unit of time, in particular beginning with an initial value (SOH0).

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