CN117546033A - Method for monitoring an energy store in a motor vehicle - Google Patents
Method for monitoring an energy store in a motor vehicle Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/16—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to battery ageing, e.g. to the number of charging cycles or the state of health [SoH]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2200/00—Type of vehicles
- B60L2200/40—Working vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2260/00—Operating Modes
- B60L2260/40—Control modes
- B60L2260/50—Control modes by future state prediction
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Abstract
The invention relates to a method for monitoring an energy store in a motor vehicle, wherein the energy store (10) supplies at least one electrical consumer, preferably for a safety-relevant driving function, wherein at least one aging-related parameter (SOHest (t)) of the energy store (10) is predicted at a future point in time, wherein a predicted remaining service life (RULest) is determined at least from the predicted aging-related parameter (SOHest), wherein at least one confidence interval (DeltaRULest) for the predicted remaining service life is determined.
Description
Technical Field
The invention relates to a method for monitoring an energy store in a motor vehicle according to the preamble of the independent claim.
Background
DE 102001020040994 A1 discloses a method for monitoring an energy store in a vehicle electrical system of a motor vehicle, wherein at least one current operating variable of the energy store is determined and forwarded to a predictive model, and the predictive model determines a future value from the current value for the at least one operating variable, wherein the future value is supplied to a voltage predictor which calculates an expected minimum voltage of the energy store for a selected function. The battery state identification software determines or measures the current capacitance and internal resistance of the battery. And forwarding the current capacitance and the internal resistance of the battery to an estimation module. The predictive model calculates future values of the capacitance and the internal resistance by means of a representative load complex curve and via a load-load capacity model, said future values being based on the calculation of the minimum voltage that can be expected.
Disclosure of Invention
The invention is based on the object of further simplifying the prediction of certain parameters of an electrical energy store. This object is achieved by the features of the independent claims.
By determining a confidence interval (konfidenczintervall) for the predicted remaining useful life, a statement can be made about the quality of the forecast. Corresponding countermeasures can be taken in a targeted manner depending on the quality of the forecast. In addition, the failure rate of the energy store, which is relevant in particular for safety-critical applications, can be further reduced by predictive maintenance or predictive health management and the availability can be increased.
In an expedient embodiment, a confidence interval for the predicted remaining service life is determined using a plurality of reference profiles, in particular more than 2000, of the aging-related variables. Thus, a more accurate confidence interval for the prediction of the remaining useful life can be determined without limiting the theoretically derived calculation formula or utilizing widely available measurement data. Based on the more accurate confidence interval, the maintenance interval can be planned more precisely or can be extended by means of predictive energy management, whereby the maintenance costs are reduced, which is advantageous in particular for the vehicle fleet operator.
In an expedient embodiment, the reference profile of the aging-related variable is determined by means of a previously determined characteristic measurement profile of the energy store and/or by means of a simulation, in particular a Monte Carlo simulation. Thus, a relatively accurate prediction of the remaining service life can be made without significant expense. In particular in the simulation of a plurality of differences reflecting influencing variables for the aging of the energy store in reality, an empirical formula can be derived from, in particular, the single-step prediction error and the predicted remaining service life, for calculating the confidence interval for the predicted remaining service life. This is particularly preferred in the case of using an approximation formula, thereby further simplifying the calculation.
In one advantageous embodiment, the confidence interval for the predicted remaining service life is determined from a measure for the prediction error between the predicted aging-related variable and the actual aging-related variable. As the prediction becomes more and more accurate, especially near the end of life, the quality of the prediction of the remaining useful life is further improved by this influencing factor.
In particular, it is expedient to generate an actual aging-related variable from the measurement of at least one characteristic variable of the energy store, and to determine the predicted aging-related variable from the actual aging-related variable. Thus, in order to further predict the remaining useful life, the prediction is further improved based on the data actually present at run-time. It is particularly preferred that the predicted remaining service life is reached when the predicted aging-related parameter reaches a defined limit value. Thus, by suitably predefining the limit values, the desired minimum requirements can be easily mapped.
In one advantageous embodiment, the approximation formula is used to calculate a confidence interval for the predicted remaining service life, and for this purpose at least the predicted remaining service life and/or a standard deviation of a prediction error between the predicted aging-related variable and the actual aging-related variable is used as an input variable for the approximation formula. With this consideration, the quality of the forecast (vorhage) is further increased.
In one advantageous embodiment, the approximation formula for the confidence interval of the aging-related parameter is determined from the residual service life predicted on the basis of at least one reference value of the aging-related parameter and/or from a measure for the prediction error between the predicted aging-related parameter and the reference value of the aging-related parameter, in particular a measure for the standard deviation of the prediction error. The aging-related parameters, in particular the simulated reference curves, thus form a large data base for the offline creation of the approximation formula, which further simplifies the online determination of the prediction.
In an expedient further development, it is provided that, as a function of the deviation or error of the aging-related parameter at the end of the service life relative to the aging-related parameter at the end of the service life at the time point of the predicted remaining service life and/or as a function of a confidence level, at least one histogram is created for determining a confidence interval limit for the aging-related parameter at the end of the service life, said confidence interval limit being used further in the approximation formula. Accordingly, a corresponding series of measurements may be provided for determining the approximation formula according to the respectively desired confidence level.
In one advantageous embodiment, it is provided that the predicted remaining service life is predicted on the basis of the reference values of the aging-related variables at different points in time, and/or that the prediction error, in particular the standard deviation of the prediction error, between the predicted aging-related variables and the reference values of the aging-related variables is determined for the different points in time. Thus, in particular from the simulation data, the approximation formula can be determined on the basis of different points in time, which are typical for later curves of variation. The later prediction of remaining useful life is thereby improved.
In an expedient embodiment, a reference profile of the aging-related variable is determined by means of a degradation model. By means of such a model, the expected load conditions of the energy store can be simulated in a particularly realistic manner, so that a reference curve can be produced which is particularly close to reality for the aging-related variables. Thus, the quality of the prediction of the remaining useful life may be further improved.
Further advantageous embodiments result from the further dependent claims and the description.
Drawings
The drawings show:
figure 1 shows a block diagram for the offline determination of an approximation formula for a confidence interval of an aging-related parameter, in particular a state of health SOH,
figure 2 shows a block diagram of an online calculation of a confidence interval for the remaining service life in the case of an end-of-life criterion based on aging-related parameters,
figure 3 shows a block diagram for the offline determination of an approximation formula for the confidence interval of a further aging-related parameter, in particular a functional state SOF,
figure 4 shows a block diagram of the online calculation of the confidence interval for the remaining service life in the case of an end-of-life criterion based on further age-related parameters,
figure 5 shows a comparison of the calculated confidence interval for the remaining useful life with a reference value for the determined confidence level,
fig. 6 shows a diagram of a degradation model.
Detailed Description
The present invention is schematically illustrated in the drawings according to embodiments, and is described in detail hereinafter with reference to the drawings.
Illustratively, in this embodiment, a battery or accumulator is described as a possible energy storage 10. Alternatively, however, other energy storages, for example based on inductance or capacitance, fuel cells, capacitors or the like, which are suitable for the task, can likewise be used.
In general, in order to calculate the confidence interval of the Life prediction of the electrical energy storage device 10, a single step prediction error, i.e. a prediction error until the next measured sample T (e.g. 1 day) of the aging related parameter (SOH), such as the capacitance or the internal resistance until the End of Life (EOL) time point (- > multi-step SOH prediction error), is extrapolated.
For example, assuming a normally distributed and uncorrelated single step SOH prediction error, it is obtained with the aid of the standard deviation σsoh1 for the m step SOH prediction error:
σSOHm＝√m*σ SOH1
with m=rul, the estimated SOH prediction error at end-of-life EOL is obtained:
σSOHRUL＝√RUL*σ SOH1
thus, for a given confidence level of, for example, 95%, the SOH confidence interval at end-of-life EOL is derived:
ΔSOH EOL ＝+/-1.96*σ SOHRUL
thus, by calculating the RUL predictions at the interval limits of SOH predictions, confidence intervals are obtained for the remaining useful life RUL, i.e. SOH is obtained for example for EOL conditions EOL ＝80％+/-|ΔSOH EOL |。
By means of the subsequent action, a more accurate confidence interval for the prediction of the remaining service life RUL of the electrical energy store 10 can be achieved.
Fig. 1 shows a confidence interval Δsoh for aging-related variables, in particular state of health SOH, at an end-of-life EOL with a predefined or predefinable confidence level 24 EOL A block diagram of an off-line determination of the approximation formula of (c). For this purpose, a block 14, for example a degradation model 14, is provided, in which a large number (n>2000 N different reference curves SOH (t). The reference curve SOH (t) of the aging-related variable is obtained, for example, by simulation, in particular by monte carlo simulation. The acquisition is described later by way of example in connection with fig. 6. Alternatively, however, the reference profile SOH (t) may also be provided in other ways, for example in the case of using a large number of measured profiles specific to the energy store 10. The characteristic curve of the aging-related variable can be obtained, for example, from the measurement series (Messreihen) on the test bench or from a similar actual measured value curve of the energy store 10. It is however important to provide a correspondingA large number n (e.g. n>2000 A) such reference profile SOH (t) that a subsequent statistical analysis process can be performed. By means of a block 14, a plurality of reference curves SOH (t) characteristic for the respective energy store 10 are generated or provided for the prediction 16 of the remaining service life RUL of the energy store 10.
The prediction 16 estimates or predicts the predicted remaining useful life RULest (t) at different prediction time points t. Furthermore, the estimate 16 finds the standard deviation σ of the single step prediction up to these time points t SOH1 (t)。
The prediction 16 of the remaining service life RUL uses a defined end-of-life criterion (EOL criterion) and then, when the predicted aging-related parameter SOHest reaches a defined limit SOH EOL When the determined end-of-life criteria is given. The limit value SOH EOL It may be, for example, that the aging-related parameter SOH only reaches 80% of the starting value SOH0, for example in the case of a new energy store 10, which is defined as end-of-life EOL. Furthermore, the prediction 16 determines a predicted aging-related parameter SOHest (t), which is based on the model of the energy store 10.
In fig. 1, a plurality of reference profiles SOH (t), for example, of the aging-related variables stored in block 14, are used as input variables for the prediction 16, preferably for different reference profiles SOH1 (t), SOH2 (t), … SOHn (t) at different points in time t0, t1, t 2. The associated reference values SOH1 (t 0), SOH2 (t 0), … SOHn (t 0), SOH1 (t 1), SOH2 (t 1), SOHn (t 1) of the aging-related variables are supplied as input variables to the prediction 16, as shown in fig. 1. These reference values are used to simulate the actual aging-related variable SOHist (t), as is provided later on in the online operation according to fig. 2, i.e. during the actual vehicle operation, to the same prediction 16.
By means of these input variables, i.e. the respective reference values SOHn (t) of the aging-related variables at different points in time (t=t0, t1, t2, … tm), and if appropriate by means of the reference values of the aging-related variables SOHn preceding these points in time, the predicted aging-related variable or the predicted time profile SOHest (t) of the aging-related variable is predicted 16. The predicted change curves SOHest (t) of the aging-related variables are each initiated at different future time points (t 0, t1, t2, tm). This can now be done for each reference curve SOHn (t) of the n reference curves of the aging-related parameter. That is, therefore, m predicted change curves sohnred (t) of the aging-related parameter are provided for each of the reference change curves SOHn (t) according to the number m of different time points.
If the predicted aging-related parameter SOHest (t) reaches a limit value SOH corresponding to the end-of-life EOL EOL This point in time defines the predicted remaining service life RULest of the energy store 10. Alternatively, it is also possible to determine the associated predicted aging-related parameter SOHest (t+1) and/or to determine the associated predicted aging-related parameter and the limit value SOH at the next point in time (for example, the second day) EOL A comparison is made. If the limit SOH has not been reached EOL Then, the next forecast time T1 (t1=t0+t) with the associated reference value SOH (T1) is considered and is equal to the limit value SOH EOL A comparison is made. This is done until the reference value SOH (t) reaches the limit value. The corresponding time point t is end of life t=eol. Alternatively, if, for example, a defined confidence period is reached, for example, 100 days, the comparison can be interrupted, for which reliable predictions can still be made according to the invention, or further future information is distinguished by high uncertainty because the user is not interested in said information.
In parallel with this, for each reference curve SOH (t) at each of m different time points, a corresponding estimated RULest (t) of the remaining service life is now performed as input variable, in particular for each next forecast time point t.
That is, therefore, in the case of using the prediction 16, n predicted remaining service lives RULest (t), that is, m×n predicted remaining service lives RULest, are provided at m different time points, respectively, for n >2000 reference change curves of the aging-related parameter.
Furthermore, a measure for the prediction error is determined for each of the predicted remaining useful lives RULest, which are preferred. In the case of this measure, as mentioned at the outset, for example, the standard deviation σ can be taken into account for the case of normally distributed and uncorrelated prediction errors SOH Or the standard deviation sigma may be considered in the case of single step prediction error SOH1 . Thus consider that the accuracy of the prediction from time point t2 that has approached the end-of-life EOL that is expected is greater than the accuracy of the prediction at time point t 0: this point in time is also very far from the expected end-of-life EOL.
The prediction error, in particular a single-step prediction error, is determined with respect to the next time interval T (for example one day or another suitable parameter) of the aging-related parameter SOH. At time T0, for the reference profile SOH (T) to be observed for the aging-related variable, the associated reference value SOH (T0) is determined as the input variable of the prediction 16 for predicting the next predicted aging-related variable SOHest (t0+t). In a further step, for the time t0+t, the associated reference value SOH (t0+t) of the corresponding curve in block 14 is determined, approximately as the actual value simulated. A single-step prediction error Δsoh (T0) is determined by determining the difference between the predicted aging-related variable SOHest (t0+t) at the predicted time t0+t and the associated reference value SOH (t0+t) at the time t0+t, which is stored in block 14. This is achieved for each of the n reference values SOH (t0+t) and/or for each of the possibly different starting time points T0, T1, T2. The standard deviation σsoh1 (t 0) of the single step prediction error is determined from n×m single step prediction errors Δsoh (t 0, t1, t 2). For each next time step T1, T2 after the time span T (T1-T0 or T2-T1), the associated single-step prediction error Δsoh (T0, T1, T2) and/or the associated standard deviation of the single-step prediction error is updated if necessary.
In addition, the single step prediction error may be extrapolated to the future, especially up to the point in time of the remaining useful life RUL or end-of-life EOL.
Furthermore, the remaining service life RULest (t) predicted by the prediction 16 is supplied to the degradation model 14 again as an input variable. The associated aging-related parameter SOH (RULest) at the time point of the remaining operating time is determined. Block 14 supplies the sum point 20 with the associated aging-related variable SOH (RULest) at the time point of the remaining operating time. At the summing point 20, the aging-related variables SOH (RULest (t)) and the limit SOH at the time points of the remaining operating time are compared EOL And (5) obtaining a difference. The difference is the error ΔSOH at end of life EOL for the aging related parameter EOL Is a measure of (a). For the different n reference curves SOH (t) of the aging-related variables and the different m time points t0, t1, t2 predicted here, as a basis for determining the predicted remaining run time RULest, a data basis for creating the corresponding error histogram 22 is formed.
By means of the reference curve SOH (t) of the aging-related parameter, for the prediction time t, a prediction error Δsoh of the aging-related parameter of the prediction 32 of the remaining service life RUL at the end of life EOL (end of life) is determined EOL . For this purpose, for each forecast time point, the percentiles (e.g., 2.5% and 97.5%) of the assignment at the confidence interval limits are read from the histogram 22 of the assignment of the cumulative probability of the prediction error at the end-of-life EOL and the given confidence level 24 (e.g., 95%).
Estimation error ΔSOH EOL The output parameters as histogram 22 are provided to block 18 for determining an approximation formula Δsoh for the confidence interval EOLest . According to the corresponding confidence level 24 (e.g., 95%, 97%, etc.), the corresponding ΔSOH may be passed EOL The frequency distribution of the error may provide the block 18 with the associated limit Δsoh of the confidence interval EOLdem 。
The curve of the predicted confidence interval limit value of the aging-related characteristic parameter SOH over the remaining service life RUL, determined in this way, can be determined by the following empirical formula from the standard deviation σ of the single-step prediction error SOH1 To approximate:
ΔSOH EOLest ＝a 1 *RUL est a2 *σ SOH1
the parameters a1 and a2 are determined, for example, by means of an optimization method and can also be different for the upper and lower interval limits. By means of this curve, the confidence interval Δsoh is established in a simple manner for a plurality of measured values EOL The predicted remaining useful life RULest and the standard deviation sigma for single step prediction error SOH1 And an association between them.
FIG. 2 shows a confidence interval ΔRUL for the predicted remaining useful life est Wherein the remaining service life RUL is determined from the predicted aging-related parameter SOHest. According to fig. 2, for each forecast time t, the confidence interval Δsoh of the predicted aging-related parameter SOH at the end-of-life EOL can be calculated online from the approximation formula determined offline in block 18 according to fig. 1 EOL Confidence interval DeltaRUL for determining predicted remaining useful life est 。
The sensor 12 detects a characteristic measured value U, I, T of the energy store 10 and determines therefrom the actual aging-related variable SOHist (t). The prediction 16 thus determines a predicted curve SOHest (t) of the aging-related parameter. This can be done, for example, using a model of the energy store 10. The predicted curve SOHest (t) of the aging-related variable is compared with the limit value SOH EOL The limit value defines the end of life EOL for comparison. End-of-life EOL is reached at the following time points: at this point in time, the predicted curve SOHest (t) of the aging-related variable reaches the limit value SOH EOL . Next, a confidence interval Δrul for the predicted remaining service life is determined est . The manner of this process is described in more detail below.
The energy store 10 is schematically shown. The sensor 12 detects certain measured variables of the energy store 10, such as current I, voltage U, temperature T. State detection is implemented in the sensor 12, for example. By this state detection, the time profile SOHist (t), SOFist (t) of at least one aging-related variable SOH, SOF or (of the past) aging-related variable of the energy store 10 can be determined using the detected measured value I, U, T. For example, the current aging-related variable of energy store 10 is determined as SOH (State of Health or gesundheitszuhand). For example, the internal resistance Ri, the capacitance C0, the breakdown polarization or the like can be used as the aging-related parameter SOH of the energy store 10. The aging-related variables SOH are distinguished in that they vary as a function of the life (Alter) of the energy store 10. Preferably, the aging-related variable SOH can be detected by the sensor 12 via the measured variable U, I, T and calculated by state detection. Preferably, the state recognition works continuously. The actual aging-related variable SOHist (t) provided by the sensor 12 may be transmitted, together with further predictive variables or diagnostic variables, via a communication interface (bus system, for example LIN, CAN) to a higher-level system, not shown, for further processing, for example for the purpose of predictive maintenance and/or predictive or preventive health management of the energy store 10.
For example, the actual aging-related variable SOHist (t) determined by the sensor 12 or other device is provided to the prediction 16 of the remaining service life RUL and/or to the confidence interval Δrul of the remaining service life est Is calculated 26 of (c). The prediction 16 of the remaining useful life RUL is identical to the prediction 16 described in connection with fig. 1. However, unlike fig. 1, instead of the reference profile SOH (t) of the aging-related parameter SOH from the predictive model 14, the actual aging-related parameter SOHist (t) based on the measured variable U, I, T of the sensor 12 is now used as the input variable.
In the prediction 16, a future, predicted change curve SOHest (t) of the aging-related variable is determined. The predicted curve SOHest (t) of the aging-related parameter is compared with a limiting value SOH of the aging-related parameter, which represents the end-of-life EOL EOL A comparison is made. When the predicted aging-related parameter SOHest reaches a defined limit value SOH EOL When the end-of-life EOL is reached, at the time of the determinationIn the case of a defined limit value, the proper operation of the energy store 10 can no longer be ensured reliably. The prediction 16 is a confidence interval ΔRUL for the remaining useful life in addition to the remaining useful life RULest est The calculation 26 of (2) provides the standard deviation σsoh1 (t) of the single step prediction. The calculation 26 uses an approximation formula that is taken off-line in fig. 1. In order to determine the standard deviation of the single-step prediction error σsoh1, only the corresponding last value (SOHpre (t 0)) of the aging-related parameter predicted for the time point t0 and the current actual value SOHist (t 0) of the aging-related parameter at the time point t0 are used. Relying only on the single-step prediction error σsoh1 simplifies the method and reduces memory requirements. The principle of action of the prediction 16 according to fig. 1 corresponds to the principle of action of the prediction according to fig. 2. Only the input parameters are different. The typical curve SOH (t) of the aging-related variable, which is simulated in fig. 1 or is determined off-line, is used as an input variable for the prediction 16, while according to fig. 2, the prediction 16 uses the actual value SOHist (t) of the aging-related variable, which is provided on-line as a function of the measured value U, I, T of the sensor 12 and the state detection.
The confidence interval Δrul for the remaining service life is achieved by means of the currently available variable of the predicted remaining service life RULest, the standard deviation of the single-step prediction error σsoh1 or the assignment of the single-step prediction error σsoh1 and the actual value SOHist (t) of the aging-related variable provided by the sensor 12 est Is calculated 26 of (c). In this case, it is particularly preferred to use the formula determined as described in connection with fig. 1:
ΔSOH EOLest ＝a 1 *RUL est a2 *σ SOH1 or delta SOH EOLest ＝a 1 *(RUL est ) a2 *σ SOH1
(RULest a2 )
Error tolerance Δsoh estimated by SOH at EOL EOLest Calculating a confidence interval DeltaRUL of a predicted useful life est By calculating the RUL estimate separately for the upper and lower tolerance limits taking into account the tolerance in the EOL standard (block 16 is reused at block 16): s is SOH＝SOH EOL +/-ΔSOH EOLest . As already described herein, here, the SOH is EOL Tolerance limit and lower SOH EOL The tolerance limits may be different (different parameterizations of the approximation formula).
When the determination of the approximation formula for the confidence interval is performed offline in block 18 or prior to normal operation, the determined confidence level 24 (e.g., 95%) is included. In the block 18, the respective parameters a1, a2 have been determined offline for the confidence level 24. From this confidence level 24, a confidence interval limit soh=soh is determined by the above formula EOL +/-ΔSOH EOLest . Based on the respective confidence interval limits of the aging-related variables, the associated remaining service life confidence interval limit Δrul can be determined by using the predicted aging-related variables SOHest at the respective confidence interval limits est . The lower confidence interval limit, in particular for the remaining service life RUL, can now be used as the following limit value: when the limit value is reached, measures are taken, such as activating a warning prompt, finding a workshop or the like. Block 26 uses the RUL estimate of block 16 to calculate RUL tolerances. Accordingly, SOHist is also required as input for this.
That is, the estimated quality of the predicted remaining useful life RULest is estimated by block 26. The estimated quality is represented by the corresponding confidence interval limit. The method is generally applicable to: the smaller the predicted remaining useful life RULest, the more accurate the prediction of the predicted remaining useful life, and therefore, the confidence interval DeltaRUL of the remaining useful life est The smaller, within the confidence interval, a determined forecast of the remaining service life RUL can be achieved at the determined confidence level 24. This is also mapped accordingly by the above formula.
The embodiment according to fig. 3 and 4 differs in that instead of the state of health SOH, an (additional) aging-related parameter of the power state SOF (State of Function) is used. However, the principle processing mode is not different. When the SOF EOL criterion is applied (function-specific load is based on the determination of a predictive operating variable, for example, the vehicle electrical system voltage or the like), a suitable operating variable is compared with a limit value, for example, the minimum voltage Ubattmin.
There is further provided a degradation model 14 or block 14 in which a number (n>=2000) different reference curves SOH (t). A plurality of random reference curves SOH (t) for the prediction 32 of the remaining service life RUL of the energy store 10 are generated, for example by means of a simulation of the degradation model 14, in particular by means of a monte carlo simulation of the degradation model 14. However, as an end-of-life (EOL) criterion, the functional state SOF is used as a basis for the determination of the predicted remaining service life RUL. The prediction 32 estimates or predicts the predicted remaining useful life RULest at different prediction time points t. Furthermore, the estimate 32 finds the standard deviation σ of the single step prediction up to these time points t SOH1 (t)。
The input variables of the predictor 28 for the further aging-related variables SOF are measured or estimated time curves which describe the energy store 10, for example the current state of health (=soh (t)), or aging-related variables, for example the capacitance C0, the internal resistance Ri, the polarization, etc., of the energy store 10 that are determined continuously, for example from the state detection of the sensor 12, in particular of the battery sensor. For example, a mathematical model of the accumulator 10 is stored in the predictor 28. For example, the predictor 28 determines a predicted operating variable of the energy store 10, for example, a predictable voltage dip with respect to a predefined application-specific load curve (for example, a current load curve).
In the case of the use of the predicted operating variables of the energy store 10, it is checked whether or not the determined edge conditions, for example for the application described by the load curve, are less than the required limit value, for example the minimum voltage, or when they are less than this limit value. The predicted remaining service life RULest is then defined at a point in time that is less than a limit value for the corresponding function in an end-of-life (EOL) standard defined by the limit value.
In an alternative circuit diagram or model of the energy store 10, which 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 breakdown polarization, etc., of the different electrodes, and corresponding parameters (e.g., the internal resistance Ri of the electrodes, the alternative capacitance C0, the resistance and capacitance of the acid diffusion or the resistance and capacitance of the double layers of the electrodes, etc.), may be used.
An additional aging-related variable SOF (RULest) at the predicted point in time of the remaining service life reaches the summing point 20 as an output variable of the predictor 28. In the summing point 20, the difference between the further age-related parameter SOF (RULest) at the predicted point in time of the remaining service life and the further age-related parameter SOF at the end of life is provided as a corresponding error to the further histogram 30. From a histogram 30 of the prediction error of the further aging-related parametric SOF at the end-of-life (EOL) time point, the standard deviation sigma of the single-step prediction error of the further aging-related parametric SOF is calculated SOF1 The following empirical formula is derived:
ΔSOF EOLest ＝b 1 *RUL est b2 *σ SOF1
the parameters b1 and b2 can also be determined here, for example, by an optimization method and can be different for the upper and lower interval limits.
Thus, according to fig. 4, at any forecast time t, the confidence interval can be calculated online by calculating a corresponding prediction of the predicted remaining service life RULest at the limit of the confidence interval of the further aging-related parameter SOF. The sensor 12 in turn determines a change in the aging-related variable, in particular a change in the state of health SOH (t), from the measured variable U, I, T of the energy store 10. The aging-related parameter SOH reaches an estimated 32, which is an end-of-life (EOL) standard SOF EOL Based on a further aging-related parameter SOF. The prediction 32 estimates or predicts the predicted remaining useful life RULest at different prediction time points t. Furthermore, the estimate 32 finds the standard deviation of the single step prediction up to these time points tσ SOH1 (t). These parameters arrive at the calculation 36 of the confidence interval Δrulest (t) for the remaining service life. The calculation 36 uses an approximation formula taken offline according to block 34 of fig. 3:
SOF＝SOF EOL +/-ΔSOF EOLest wherein
ΔSOF EOLest ＝b 1 *RUL est b2 *σ SOF1
Fig. 5 shows an example of a comparison of the confidence interval calculated by means of an empirical formula with the median of the estimated remaining useful life of the predicted change curve RULest of the remaining useful life combined with the reference from the monte carlo simulation for the 95% confidence level 28. The formula described approximates the reference value very well. Thus, on the x-axis is the remaining useful life RUL in days, and on the y-axis is plotted the confidence interval limit in days. Curve 1 depicts the median of the predicted remaining useful life RUL with reference to the remaining useful life RUL, curve 2 depicts the predicted remaining useful life RULest in days with a confidence level of 2.5%, curve 4 depicts the predicted remaining useful life RULest in days with a confidence level of 97.5%, curve 5 depicts the (actual) remaining useful life RUL in days with a confidence level of 2.5%, and curve 6 depicts the (actual) remaining useful life RUL in days with a confidence level of 97.5%.
Fig. 6 discloses a block diagram of a calculation scheme of a degradation model 14 for providing the electrical energy store 10 with a suitable data basis in block 14 for generating a random reference profile SOH (t) of aging-related variables, in particular the state of health SOH of the energy store 10.
First, for each sampling step, based on the measurement data and/or expert knowledge, a normally distributed aging rate 40 is generated in a range that is true for the type of the energy storage 10 when the vehicle is in use (step 1). In block 40 of fig. 6, the daily aging rate (e.g., between 0-1%/day or pdf (Δsoh/day)) is shown in a semi-normal distribution.
The ageing rate 40 randomly generated in the first step is recorded over a uniformly distributed duration of, for example, 1-7 days, to thereby produce a phase with stronger or smaller absolute ageing (block 42; second step). In block 42 of fig. 6, the daily aging rate is shown in a uniform distribution (e.g., between 0-1%/day or pdf (Δsoh/day)).
Aging due to use is taken into account by specifying a use profile (nutzungspprofile) 46, in particular a weekly use profile 46, which has a description of the percentage use of the vehicle and thus of the percentage use of the energy store per day of the week (for example 100% for taxi operation or 10% for commute), and multiplying the aging due to use by the aging rate 42 of the respective date, in particular in the case of the multiplier 44.
The accelerated aging with increasing ambient temperature is taken into account by the temperature profile 48, in particular the annual temperature profile, weighted by the arrhenius function 50, of the following regions: the vehicle is operated in the region. The output parameter 45 of the multiplier 44 and the output parameter 50 of the arrhenius function 50 are supplied to a further multiplier 52. The output parameter 53 of the further multiplier is an input parameter for a summer 54. The output variable of the summer 54 is supplied to a summing point 56, to which, in addition, an initial value SOH0 of the aging-related variable is supplied as a further variable. By summing the daily calculated aging rates starting from a predefined initial value SOH0, a corresponding reference profile SOH (t) of the aging-related variables is derived.
If query 46 concludes that the remaining useful life RUL has been reached, countermeasures may be taken in block 50. Accordingly, a corresponding warning cue for the driver, vehicle operator, shop floor, etc. may be generated. Predictive maintenance may be initiated. Certain security-related functions may also be blocked or no longer allowed. In the same way, in the case of particularly serious malfunctions, corresponding countermeasures can be taken, such as driving to the next parking lot, parking on a road shoulder, etc.
The described method is particularly suitable for monitoring the energy store 10 in a motor vehicle, in particular during autonomous driving, for safety-relevant applications, such as for example the supply of a safety-relevant electrical consumer. However, the application is not limited thereto.
Claims (15)
1. Method for monitoring an energy store in a motor vehicle, wherein the energy store (10) supplies at least one electrical consumer, preferably for a safety-relevant driving function, wherein at least one aging-related parameter (SOHest (t)) of the energy store (10) is predicted at a future point in time, wherein a predicted remaining service life (RULest) is determined at least from the predicted aging-related parameter (SOHest), characterized in that at least one confidence interval (DeltaRULest) for the predicted remaining service life is determined.
2. Method according to claim 1, characterized in that a confidence interval (Δrulest) for the predicted remaining service life is determined using a plurality of aging-related variables, in particular more than 2000 reference curves (SOH (t)).
3. Method according to any of the preceding claims, characterized in that a reference profile (SOH (t)) of the aging-related parameter is determined by means of a previously determined characteristic measured profile of the energy store (10) and/or by means of a simulation, in particular a monte carlo simulation.
4. Method according to any of the preceding claims, characterized in that a confidence interval (Δrulest) for the predicted remaining service life is determined using an approximation formula.
5. Method according to any of the preceding claims, characterized in that a confidence interval for the predicted remaining useful life is determined from a measure for the prediction error between the predicted aging related parameter (SOHest) and the actual aging related parameter (SOHist)(DeltaRULest), wherein the actual aging-related parameter (SOHist (T)) is determined in particular from a measurement of at least one characteristic parameter (U, I, T) of the energy store (10), and/or the standard deviation (sigma) of the prediction error between the predicted aging-related parameter (SOHist (T)) and the actual aging-related parameter (SOHist (T)) is determined from the measurement of the actual aging-related parameter (SOHist (T)) SOH1 ) The actual aging-related parameter is determined.
6. Method according to any one of the preceding claims, characterized in that an actual aging-related parameter (SOHist (T)) is generated from the measurement of at least one characteristic parameter (U, I, T) of the energy store (10), and the predicted aging-related parameter (SOHest (T)) is determined from the actual aging-related parameter (SOHist (T)).
7. A method according to any of the preceding claims, characterized in that when the predicted aging-related parameter (SOHest) reaches a certain limit value (SOH) EOL ) At this time, the predicted remaining useful life (RULest) is reached.
8. Method according to any one of the preceding claims, characterized in that, using the approximation formula, a confidence interval (Δrulest) for the predicted remaining service life is calculated, and for this purpose at least the standard deviation of the prediction error between the predicted remaining service life (RULest) and/or the predicted aging-related parameter (SOHest) and the actual aging-related parameter (SOHist) is used as input parameter for the approximation formula.
9. Method according to any of the preceding claims, characterized in that an approximation formula for the confidence interval of the aging-related parameter is determined from the predicted remaining service life (RULest) based on at least one reference value (SOH (t)) of the aging-related parameter and/or from a measure of the prediction error, in particular the standard deviation of the prediction error, between the reference value (SOH (t 0)) for the predicted aging-related parameter (SOHest (t 0)) and the aging-related parameter (SOH (t 0)).
10. Method according to any of the preceding claims, characterized in that the aging-related parameter (SOH (ruiest (t)) at the point in time when the predicted remaining service life is determined is related to the aging-related parameter (SOH) at the end of life (EOL) EOL ) And/or creating at least one histogram (22, 30) for determining a confidence interval limit (delta SOH) of the aging-related parameter at the end of life (EOL) based on the confidence level (24) EOL ) The confidence interval limit is for further use in the approximation formula.
11. Method according to any one of the preceding claims, characterized in that for determining the approximation formula, the predicted remaining service life (RULest) is predicted on the basis of reference values (SOH (t 0, t1, t 2)) of the aging-related parameter (t 0, t1, t 2) at different points in time, respectively, and/or the predicted aging-related parameter (SOHest (t 0)) and the reference parameter (SOH (t 0)) are determined, in particular the standard deviation of the prediction error, respectively, for different points in time (t 0, t1, t 2).
12. The method according to any of the preceding claims, characterized in that a confidence interval (Δrulest) for the predicted remaining service life is determined using the following approximation formula:
confidence interval limits (Δsoh) of aging-related parameters at end-of-life EOLest )＝a 1 * (estimated Remaining Useful Life (RUL) est )) a2 * Standard deviation of prediction error (σ SOH1 ) Wherein a1 and a2 are parameters which can be determined in advance.
13. Method according to any of the preceding claims, characterized in that a reference curve of variation (SOH (t)) of the aging-related parameter is determined by means of a degradation model (14).
14. The method according to any of the preceding claims, characterized in that the degradation model (14) generates a plurality of reference curves of change (SOH (t)) of the aging-related parameter as a function of the aging rate (40) and/or as a function of the duration of the constant aging rate (42) and/or as a function of the usage profile (46), in particular as a function of the weekly usage profile (46) and/or as a function of the temperature profile (48), in particular as a function of the annual temperature profile and/or as a function of the aging function (50), in particular as a function of the temperature (50).
15. Method according to any of the preceding claims, characterized in that the aging (45) due to use is generated from a previously generated aging rate (40) for a uniformly distributed duration, in particular in the case of a daily range of aging rates (42), and/or that aging due to use is generated in the case of a use profile (46) of the energy store (10), in particular for explaining a one week daily percentage use of the energy store (10), and/or that the aging due to use (45) is generated by multiplying the aging rate (42) with a constant duration, in particular with days, and the use profile (46), and/or that the aging due to use (45) and/or the temperature dependent aging function (50), in particular the arrhenius function, are calculated from the initial values of the aging (45) and/or from the initial values of the aging (sot) calculated by calculating the sot (sot) and the sot (53).
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