Method for monitoring the energy supply in a motor vehicle

Abstract

The invention relates to a method for monitoring the energy supply in a motor vehicle, wherein in a sub-onboard power system (30, 40) at least one energy storage device (32) supplies energy to a plurality of preferably safety-related electrical consumers (36, 46), wherein at least one measured variable (Ubatt, Ibatt; Uv, Iv) of the energy storage device (32) and / or at least one electrical consumer (36, 46) is detected, wherein at least one cable harness model (91) is provided, which describes the sub-onboard power system (30, 40), and a parameter estimator (104) is provided, which estimates at least one characteristic variable (Rb12) of the cable harness model (91) using the measured variables (Ubatt, Ibatt; Uv, Iv).

Description

Technical Field

[0001] The invention relates to a method for monitoring the energy supply in a motor vehicle, in particular a motor vehicle with automated driving operation, according to the preamble of the independent claim. Background Art

[0002] The onboard power supply has the task of supplying energy to electrical consumers. If the energy supply fails due to a fault or aging in the onboard power supply or onboard power supply components in today's vehicles, important functions such as servo steering will be lost. Since the steering ability of the vehicle is not impaired but only becomes sluggish, the failure of the onboard power supply is generally accepted in today's mass-produced vehicles because the driver is at the disposal as a backup force. In order to increase the availability, a dual-channel onboard power supply structure is proposed, such as in WO 2015 / 135729 A1. The dual-channel onboard power supply structure is required to power systems for highly or fully automated driving.

[0003] A method for monitoring an onboard power supply is known from DE 102015221725 A1. The onboard power supply comprises: at least one channel with at least one component; and an energy supply, wherein a terminal voltage at the component is measured and compared with a voltage providing the energy supply. In addition, other measurements are performed in other states with other power consumption and compared with the first measurement, and the transition resistance of the component is determined based on this, taking into account the current flowing into the component in the other state.

[0004] To determine the lead resistance, the measured variables required for the calculation (eg, battery voltage, other voltages in the leads, and battery current) need only be detected once. The resistance is then determined computationally in a second step using Ohm's law.

[0005] If the network is now expanded, for example by adding further power branches for further energy sources and electrical consumers, the number of unknown quantities (line resistances of the individual branches) and the number of boundary conditions (grid equations) increase. In this case, the system is underdetermined, that is to say the number of unknown quantities (e.g. line resistances) is one greater than the number of grid equations. Therefore, the described direct approach for determining the individual line resistances cannot be applied.

[0006] The object of the invention is to further increase the reliability of monitoring. This object is achieved by the features of the independent claims. Summary of the invention

[0007] By using parameter estimation methods according to the present invention to determine interesting characteristic variables of the onboard power supply, in particular transition resistances, the reliability of monitoring can be further increased. In this case, the determination of characteristic variables of the underdetermined system can be achieved by means of multiple measurements at different time points (time course), because the time correlation (characteristic variables of the onboard power supply, such as line resistance, remain approximately constant) leads to other equations for determining these characteristic variables or resistances. Now, according to the present invention, the combination of multiple measurements at different time points by parameter estimation methods is used to determine characteristic variables specific to the onboard power supply. Even in the case of a lack of measuring points, sufficient information for simultaneously estimating multiple characteristic variables (transition resistances) of the onboard power supply can be provided by making full use of the time correlation.

[0008] The use of such an estimation method also offers the advantage that it not only provides the estimated value itself, but also the estimation quality (e.g. the estimated variance), so that the reliability of the determined characteristic variable can be estimated here. This reliability can be used as a further criterion for enabling safety-related functions, for example safety-related functions in the case of automated driving operation.

[0009] The characteristic variables to be estimated, in particular the transition resistance or the resistance, can also be determined simultaneously and independently of one another. The only prerequisite is that a corresponding excitation is present in the respective branch.

[0010] In particular, by adding other additional measured variables, for example by using a central distributor / power distributor, this additional information can be used to increase the statistical reliability of the results, for example by selecting the most accurate measuring sensors or by fusing the measured data to increase the statistical reliability of the results. In addition, the failure probability of the system can be reduced by additional redundancy.

[0011] Particularly expediently, the parameter estimation is carried out in a targeted manner only when it can be ensured that the current for exciting the corresponding branch has a sufficient intensity. For this purpose, for example, a pre-filtering device, such as a logic circuit, may be embedded, which then analyzes the measured current intensity and compares it with a threshold value. If the measured current of the onboard power supply branch exceeds the threshold value, the parameter estimation of the corresponding characteristic variable can be started. In this way, the quality of the diagnosis result can be further improved. If the corresponding threshold value should not be exceeded, the updating of the estimated value within the time step is stopped. In this way, the safety of the device is further improved.

[0012] Particularly expediently, a specially optimized stimulus for improving the estimation quality can be provided. This is advantageously possible in particular when the stimulus does not impair the operation of the onboard power supply. In this case, it is particularly preferred to provide redundant channels or onboard power supply branches for this purpose.

[0013] In a suitable embodiment, the measured variables used to determine the characteristic variable are synchronized, in particular by means of a filter, preferably a fractional delay filter, a Lagrange filter, a Farrow filter or a similar method. This can reduce distortions of the diagnostic result due to possible asynchronization of the measured variables, for example due to asynchronization of the clock generator.

[0014] Particularly expediently, the filter determines a delay factor for all measured variables to be delayed for time synchronization of these measured variables. Particularly expediently, the delay factor of the filter is also determined by the parameter estimator. Thus, a high signal quality of the delayed signal can be achieved with low computational effort for estimating the delay factor.

[0015] Further expedient embodiments result from the disclosure of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A possible on-board electrical network for an autonomous vehicle is shown;

[0017] Figure 2 The power supply and data structure of two safety-related channels for onboard power supply diagnostics are shown;

[0018] Figure 3 An equivalent circuit diagram or model of the accumulator is shown;

[0019] Figure 4 shows the structural construction of the parameter estimator; and

[0020] Figure 5 The structural construction of a filter structure for time synchronization is shown. DETAILED DESCRIPTION

[0021] The invention is schematically illustrated in the drawings based on exemplary embodiments and is described in detail below with reference to the drawings.

[0022] By way of example, in this exemplary embodiment, a battery or accumulator is described as a possible energy storage device. Alternatively, however, other energy storage devices suitable for the task, for example based on inductance or capacitance, fuel cells, capacitors, etc., can be used equally.

[0023] Figure 1 A possible topology of an energy supply system consisting of a basic onboard power supply system 22 is shown, which supplies at least one basic electrical consumer 24, which is shown by way of example. Alternatively, an energy storage device or a battery pack with associated (battery pack) sensors and / or a starter, and / or a plurality of non-safety-related convenience electrical consumers, which may be protected or controlled by means of electrical load distribution, may also be provided in the basic onboard power supply system 22. The basic onboard power supply system 22 has a lower voltage level than the high-voltage onboard power supply system 10, and may be, for example, a 14V onboard power supply system. A DC voltage converter 20 is arranged between the basic onboard power supply system 22 and the high-voltage onboard power supply system 10. The high-voltage onboard power supply system 10 comprises, by way of example: a high-voltage energy storage device 16, such as a high-voltage battery pack, which may have an integrated battery pack management system; non-safety-related loads 18 or convenience electrical consumers, such as air conditioning devices etc., which are supplied with an increased voltage level, which are shown by way of example; and an electric machine 12. The energy storage device 16 can be switched on via the switching device 14 for supplying the high-voltage vehicle electrical system 10. In this context, a voltage level that is higher than the voltage level of the basic vehicle electrical system 22 is understood to be a high voltage. Thus, for example, a 48-volt vehicle electrical system is possible. Alternatively, even higher voltage levels are possible, especially in the case of vehicles with electric drives. Alternatively, the high-voltage vehicle electrical system 10 can be completely omitted, wherein components such as the starter, the generator and the energy storage device are then assigned to the basic vehicle electrical system.

[0024] For example, the basic onboard power supply 22 is connected to two safety-related onboard power sub-power supplies 30, 40. The first safety-related onboard power sub-power supply 30 is connected to the basic onboard power supply 22 via an isolating element 28. The other safety-related onboard power sub-power supply 40 is connected to the basic onboard power supply 22 via another isolating element 26. The first safety-related onboard power sub-power supply 30 can be supplied with energy via an energy storage device 32. A characteristic variable of the energy storage device 32 is detected by a sensor 34. Preferably, the sensor 34 is arranged adjacent to the energy storage device 32. The first safety-related onboard power sub-power supply 30 supplies power to a safety-related electrical consumer 36. The safety-related electrical consumer 36 is shown only as an example. If necessary, other safety-related electrical consumers 36 are also supplied via the onboard power sub-power supply 30, as will be explained in detail later.

[0025] The safety-related onboard power subsystem 40 can also be supplied with power via the other energy storage device 42. The other sensor 44 detects a characteristic variable of the other energy storage device 42. The other sensor 44 is arranged adjacent to the other energy storage device 42. The safety-related onboard power subsystem 40 supplies power to at least one other safety-related electrical consumer 46. If required, other safety-related electrical consumers 46 can also be supplied to the safety-related onboard power subsystem 40. In the safety-related onboard power subsystem 40, a fault situation 50 is indicated. This fault situation can lead to an increase in the cable harness resistance 48.

[0026] As will be explained in greater detail below, a cable harness diagnosis is carried out using parameter estimator 104 , which reliably identifies such a fault situation 50 .

[0027] Redundant, in particular functionally redundant, safety-related electrical consumers 36, 46 which can be supplied via the two safety-related onboard electrical subsystems 30, 40 are such electrical consumers that are required, for example, in the event of a critical fault, to switch the vehicle from an automated driving mode (without requiring driver intervention) to a safe state. As will be described in detail below, this may involve stopping the vehicle, either immediately or only at the edge of the lane or at the next rest area, etc.

[0028] By way of example, the following safety-related electrical consumers 36, 46 are provided. In this case, at least the braking system 60 is involved. The braking functionality can either be realized by a first component or electrical consumer 62 (e.g. a so-called electronic stability program, which can stop the vehicle), which is powered via the onboard sub-system 30. Alternatively, a redundant braking functionality is realized via another component or another electrical consumer 64 (e.g. a so-called iBooster, which generates brake pressure electromechanically). The other electrical consumer 64 is powered via another safety-related channel 40. A redundant energy supply is possible via the two safety-related onboard sub-systems 30, 40, so that even in the event of a failure of one of the onboard sub-systems 30, 40, a reliable braking of the vehicle can be achieved as before.

[0029] For example, a steering system 66 is provided as a further safety-related component. The steering system 66 consists of a first component or consumer 68, for example in the form of an electric steering device, which is supplied with power via the onboard electrical subsystem 30. The steering system 66 also includes a further component or consumer 70 (also, for example, an electric steering device), which can also steer the vehicle in a desired manner independently of the first component 68. The further component or consumer 70 is supplied with energy by the further safety-related onboard electrical subsystem 40.

[0030] An automatic driving function 78 is provided as a further safety-related and redundantly implemented component. Thus, the associated first computing unit 80 and the first sensor unit 74 are supplied with energy by the first channel 30. The automatic driving function 78 can be implemented redundantly by a further computing unit 82 and one or more sensor units 76, which are supplied with power by a further safety-related channel 40. In the computing units 80, 82, trajectory planning is performed, for example, using the associated control values ​​for the required actuators. The sensor units 74, 76 each provide current surrounding information of the vehicle for trajectory planning.

[0031] In addition, according to Figure 2 In the exemplary embodiment of the present invention, data connections 54, 56 are also shown. The data connections 54, 56 forward detected data, in particular current and / or voltage values ​​at the isolation elements 26, 28 and / or at the energy storage devices 32, 42, such as determined via sensors 34, 44, and / or current and / or voltage values ​​at safety-related electrical consumers 62, 64, 68, 70, 74, 76, 80, 82, to the onboard power supply diagnosis 58. For example, the first data connection 54 forwards data of those components that are supplied by the onboard power supply subsystem 30 to the onboard power supply diagnosis 58. Another data transmission 56 forwards data of those components that are supplied with energy by another onboard power supply subsystem 40 to the onboard power supply diagnosis 58, for example. The data connections 54, 56 can be suitable bus systems, such as a LIN bus or a CAN bus.

[0032] In detail, the data connection 54 forwards the current and voltage values ​​(Udcdc, Idcdc) of interest of the isolating or coupling device 28. The sensor 34, in particular the battery sensor, detects the voltage Ubatt at the energy storage device 32 and / or the current Ibatt of the energy storage device 32 and / or other states, parameters of the model of the energy storage device 32. In addition, the current current Iv at the individual electrical consumers 62, 64, 68, 70, 74, 76, 80, 82 or the current voltage Uv (terminal voltage) of the individual electrical consumers 62, 64, 68, 70, 74, 76, 80, 82 at the corresponding terminals of these electrical consumers 62, 64, 68, 70, 74, 76, 80, 82 is available for forwarding to the onboard power supply diagnosis 58.

[0033] exist Figure 3 In FIG. 1 , a cable harness model 91 based on a typical onboard power subsystem 30 , 40 is depicted as an example. In this case, the configuration is as follows Figure 1or 2 and a sub-onboard power supply 30, 40 with associated onboard power supply components, which consists of a DC voltage converter (isolating element 26, 28), an energy storage device 32, 42 with a sensor 34, 44, a cable harness branch with a positive-side lead resistor (positive-side resistor, index 1 Rx1: R11, R21, R31) including a line resistor 51 and a ground-side lead resistor (ground-side resistor, index 2 Rx2: R12, R22, R32) including a contact resistor 55, and various safety-related electrical consumers 36, 46, characterized by associated load currents Iv1, Iv2, Iv3 and terminal voltages Uv1, Uv2, Uv3 for, for example, three electrical consumers 36, 46. Electrical consumers 36, 46 are controlled by a power distributor 52. This structure forms the basis of a cable harness model 91. The cable bundle resistance (positive side line resistance including contact resistance 51, ground side lead resistance including contact resistance 55) is estimated by a parameter estimation method as described in detail later. The required current Iv and voltage Uv for calculating the cable bundle resistance (positive side line resistance including contact resistance 51, ground side lead resistance including contact resistance 55) are provided by the component containing the measuring device via the described communication network (data connection 54, 56).

[0034] The resistors Rb1, Rb2, etc. are the lead resistances 51 to the corresponding components including the contact resistances 55. According to the invention, the positive-side (index 1) and ground-side (index 2) resistances Rxy, 51, 55 are estimated as a sum, that is to say, for example, for the transition resistance Rb1 of the energy storage device 32 on the positive side or the transition resistance Rb2 on the ground side as follows:

[0035] R B1,B2 = R B1 + R B2

[0036] Thus, a fault occurring in a branch can be identified on both lines, but cannot be distinguished. The corresponding situation applies to the lead resistances of the isolation element 26 (Rdc1+Rdc2=Rdc1,dc2; R11+R12=R11,12; R21+R22=R21,22; R31+R32=R31,32). The resistances described above are the corresponding characteristic variables of the cable harness model 92 and are subsequently combined in the state vector Xk.

[0037] The voltage and current present are measured as U BATT ,I BATT etc. are drawn into the equivalent circuit diagram.

[0038] The following system of equations results, which describes the onboard electrical system model or the cable harness model 91 of the corresponding onboard electrical system subsystem 30 , 40 :

[0039] U Batt = U DCDC – I DCDC ·R DC1,DC2 + I Batt ·R B1,B2

[0040] U DCDC = U Batt – I Batt ·R B1,B2 + I DCDC ·R DC1,DC2

[0041] U V1 = U Batt + I Batt ·R B1,B2 – I V1 ·R 11,12

[0042] U V2 = U Batt + I Batt ·R B1,B2 – I V2 ·R 21,22

[0043] U V3 = U Batt + I Batt ·R B1,B2 – I V3 ·R 31,32

[0044] According to Figure 3 Each of the three consumers 36 , 46 , energy stores 34 , 44 and separating elements 26 , 28 shown by way of example and fed by the power distribution unit 52 .

[0045] The system of equations describing the above-mentioned onboard power subsystems 30, 40 can be described in the form of interesting characteristic variables of the onboard power subsystems 30, 40, i.e. the sum of the corresponding positive-side lead resistance including the contact resistance 51 and the ground-side lead resistance including the contact resistance 55 in the corresponding branch can be used as parameter vector x k The parameter vector x k When using the parameter estimator 104, the parameters combined in the vector z k The corresponding measurement values ​​in are updated for each new time step.

[0046] Subsequently, the corresponding parameters are combined in the corresponding vectors or matrices as described:

[0047]

[0048]

[0049] In order to recursively solve the system of equations, in this embodiment, a so-called extended Kalman filter (EKF) is used as a parameter estimator 104, such as Figure 4 Alternatively, other parameter estimators 104 may be used, such as (recursive) least squares, or other state estimators, such as standard / unscented Kalman filters, particle filters or similar estimation / optimization methods.

[0050] According to Figure 4 The parameter estimator 104 of the embodiment of includes at least one prediction 106. The state parameter x k Initial estimate of and the error covariance P k The initial estimate P k-1 The input variables are received by the prediction 106. In addition, in the steady state, the currently determined output values ​​from the correction 108, ie the current parameter vector x k And the current error covariance P k The prediction 106 arrives as input variables.

[0051] In the prediction 106, an update or time update is performed. In this case, the state variable x is calculated in the form of the following equation: k Status prediction:

[0052]

[0053] In addition, in the prediction 106, the error covariance matrix P is calculated in the form of the following equation: k Prediction:

[0054]

[0055] in

[0056] Ak: The Jacobi matrix of

[0057] Wk: System matrix of system noise

[0058] Q k-1 : Variance of system noise

[0059] Output parameters as well as As input parameters, it arrives at the block correction 108. In the correction 108, the estimated value is updated according to one or more measurements. First, the so-called Kalman gain K is updated using the following formula: k Calculation:

[0060]

[0061] in

[0062] Hk: The Jacobian matrix

[0063] Vk: System matrix of measurement noise

[0064] Next, in the correction 108, the estimation is updated according to the following formula based on the measurement:

[0065]

[0066] Finally, in the correction 108, the error covariance Pk is updated according to the following formula:

[0067]

[0068] where I corresponds to the identity matrix.

[0069] Therefore, the estimated characteristic parameters In the first step of the filtering process, a temporally prior estimate of the state dynamics is made in order to obtain a prediction for the current point in time. These predictions are corrected in block correction 108 using new information about the current measured values ​​and result in the sought current estimate.

[0070] Measurement data fusion / selection

[0071] By adding further additional measured variables, for example by using (other) central distributors / power distributors 52, this additional information can be used to increase the statistical reliability of the results, for example by selecting the most accurate measuring sensors or by fusing the measured data. In addition, the failure probability of the system can be reduced by additional redundancy. and to adapt the parameter estimates.

[0072] Selectivity Estimation Method

[0073] A prerequisite for a good diagnostic result is that the excitation current has a sufficient amplitude. It is therefore advantageous that the parameter estimation 104 is carried out in a targeted manner only when it can be ensured that the current Iv used to excite the corresponding branch has a sufficient intensity. This can be done, for example, by embedding a pre-filter device such as a logic circuit, which then analyzes the measured current intensity Iv and compares it with a threshold value (for example 15A). If the measured current Iv of the onboard power supply branch is, for example, 20A and therefore exceeds this threshold value, the parameter estimation 104 of the corresponding line resistance 51 can be started. Otherwise, the estimation of the resistance 51 of the current measurement test is stopped. For this purpose, the matrix of the extended Kalman filter for the corresponding measurement equation is not updated within this time step.

[0074] Active diagnosis with specially optimized stimuli for improving the estimation quality is also conceivable, provided that these stimuli do not impair the operation of the onboard electrical system. Redundant channels or onboard electrical system subsystems 30 , 40 are suitable for this.

[0075] Online estimation and compensation of systematic measurement errors

[0076] In principle, measurement errors, that is to say errors in the actual measurement process, consist of systematic (epidemic) and random (accidental) errors. The latter originate from random physical processes and are not affected if the physical measurement principle does not change. Systematic errors, however, are based on deterministic relationships. If these relationships can now be estimated online, that is to say during operation, this source of error can be eliminated.

[0077] For this purpose, the previous estimation model can be extended by a so-called disturbance variable model. The disturbance variable model describes the influence of unknown states on the measurement result, here the systematic measurement errors. In order to compensate for the systematic measurement errors, these unknown parameters must be estimated in addition to the line resistance 51. This is possible if there are enough measuring points (observability).

[0078] The model commonly used in practice for systematic measurement error is the linear association

[0079] .

[0080] In this case, m is the physical quantity to be measured, for example, the current Iv or the voltage Uv in the vehicle electrical system 30, 40. Correspondingly, are generally different measurements. The parameters a, b correspond to the gain and offset (constant superposition). A perfect sensor exists when a=1, b=0. If a, b can now be estimated (also called calibration), systematic measurement errors can be compensated.

[0081] In the vehicle electrical system, there are usually a plurality of sensors 34, 44 and corresponding systematic measurement errors. However, for the estimation of the lead and contact resistances 51, 55, only the cumulative effect of these errors on the estimation of the resistances 51, 55 is relevant. Thus, the additional parameters to be estimated can be greatly reduced, which makes calibration mostly possible. In the case of an estimation of, for example, three resistances 51, 55 and corresponding measuring points, the voltage U1 in the first branch is obtained as

[0082]

[0083] If we now adopt the above model for systematic measurement errors, we obtain:

[0084]

[0085] Therefore, instead of biases b1, b2, and b3, only the cumulative bias is estimated , in order to compensate for the offset error. Depending on the availability of the measuring points, the same situation is possible for the gain error. The same approach applies here. In addition, a combination of these two calibration schemes is possible, provided that there is a suitable stimulus. The estimation of the systematic measurement error can also be used for the diagnosis of the measuring points and these measuring points are identified as erroneous in the case of large errors. This additional information may be essential for sufficient ASIL qualification, in which the diagnosability of these measuring points is also explored.

[0086] If an estimation method is used that also takes into account random measurement errors, the measurement equation can be extended with the random parameter , for example, is expanded to . Other parameters can be recorded as and to adapt the parameter estimates.

[0087] An estimation method, for example a Kalman filter, then provides estimates of the line and contact resistances 51 , 55 and possibly cumulative parameters of the measurement equation with minimal variance, ie with the highest possible estimation quality, based on the probability.

[0088] Time Synchronization:

[0089] The structural construction of the filter structure for time synchronization is Figure 5. The input variable u(k) reaches the parameter estimator 116 for the delay D, reaches the physical system 112 and reaches the filter 114, in particular the so-called fractional delay filter. The detected measurement variable z(k) is not only supplied to the parameter estimator 104 but also to the parameter estimator 116 for the delay D. The delay D determined by the parameter estimator 116 also reaches the filter 114. The filter 114 determines the time-synchronized output variables u(k + D*ts), which are supplied to the parameter estimator 104. The parameter estimator 104 determines the estimated parameters Rb12 of the cable harness model 91 based on the supplied time-synchronized input variables u(k + D*ts) and the measurement variables z(k). These parameters Rb12 estimated by the parameter estimator 104 also arrive as input variables at the parameter estimator 116 for the delay D.

[0090] The above-mentioned methods for sensor fusion and measurement error calibration presuppose synchronized measurement data. In this context, synchronization means that the start time of the measurement is the same for all measured variables (U v (k) I v (k) U BATT (k) I BATT (k) etc.).

[0091] However, this does not exist in decentralized systems such as the on-board power grid. The measurement is distributed to multiple subcomponents, which each use an internal, independent clock generator. For this reason, the diagnostic results are strongly influenced by the asynchrony of the measured variables.

[0092] However, if the asynchrony can be determined and eliminated, this has a positive effect on the diagnostic result. For the subsequent synchronization design, time-discrete measured variables are assumed, which are present at the same sampling rate but have a time offset of D·t relative to each other. s (t s is the sampling time of the samples, and D is the delay factor between these signals as a linear factor with respect to the sampling time).

[0093] Thus, of the above-mentioned measured variables, the following measured values ​​are available at sampling time k (assuming that the time offset of each measured variable of a particular component is the same):

[0094] U v (k) I v (k) U BATT (k+D·t s ), I BATT (k+D·t s )

[0095] In order to determine the delay factor D, a filtering is performed for all measured variables u(k) to be delayed, which filtering results in a time shift by the initial factor D. This can be achieved by a so-called fractional delay filter 114. Subsequently, an implementation by a first-order Lagrangian filter is described.

[0096]

[0097] Therefore, the estimated output parameters are calculated according to the method described above .

[0098] Next, an estimate of the new delay factor can be calculated as follows:

[0099]

[0100] k: sampling time point

[0101] z: measured output parameter

[0102] h: Output parameter estimated from u by the model equation

[0103] If this measurement equation is implemented in the parameter estimator 116, the delay D can be estimated with the aid of the noisy measurement variables u(k) and z(k).

[0104] For parameter estimation of the lead and contact resistance of the cable bundle, the Alternatively, an additional higher-quality filtering (e.g. a higher-order Lagrangian filter) can be implemented with the aid of the calculated factor D, the output variable of which is then supplied to the parameter estimation. This has the advantage that a high signal quality of the delayed signal can be achieved with low computational effort for estimating the factor D.

[0105] The parameter estimator 104 may be placed, for example, in Figure 2 The block shown in FIG. 1 is in the vehicle power supply diagnosis 58 or can be part of the vehicle power supply diagnosis 58 . Figure 2 , the corresponding measured values ​​arrive at the onboard power supply diagnosis 58 .

[0106] The described method for determining the relevant characteristic variables of the onboard power subsystem 30, 40 in a motor vehicle is particularly suitable for systems in which a high accuracy of prediction is required, such as in the case of autonomous driving operation. In any case, corresponding countermeasures should be initiated quickly and reliably in the case of a possible fault. With the proposed parameter estimator 104, a fast and reliable diagnosis is possible, and it is therefore particularly suitable for this. In this way, the determined characteristic variables Rb12 or xk are made available to the superordinate onboard power system diagnostic function in order to ensure a reliable onboard power supply for the selected systems (e.g. braking system, steering device, surrounding environment recognition device, control computer). If there is a significant deviation of the characteristic variables of the cable harness model 91, a fault message is generated. This fault message is provided to the superordinate onboard power system diagnosis. If necessary, in the presence of this fault message, countermeasures are initiated, such as carrying out a safe stop of the motor vehicle, etc. Alternatively, when the vehicle is not in autonomous driving operation, this operation may be prohibited. Other information about maintenance or diagnosis can also be initiated. If necessary, the determined characteristic variable can be corrected or determined as a function of certain systematic measurement errors, in particular the error covariance Pk, the gain a or the offset b, and / or a fault message can be generated as a function of the systematic measurement errors.

Claims

1. A method for monitoring the energy supply in a motor vehicle, wherein in an onboard electrical subsystem (30, 40) at least one energy storage device (32) supplies energy to a plurality of safety-related electrical consumers (36, 46), wherein at least one measured variable of the energy storage device (32) and / or of at least one electrical consumer (36, 46) is detected, characterized in that At least one cable harness model (91) is provided, which depicts the onboard power subsystem (30, 40); and a parameter estimator (104) is provided, which estimates at least one characteristic variable (Rb12) of the cable harness model (91) using the measured variables, wherein a state vector is used as the characteristic variable, the state vector comprising a plurality of contact resistances and / or lead resistances in different branches of the onboard power subsystem (30, 40).

2. The method according to claim 1, characterized in that: The characteristic parameter (Rb12) of the cable harness model (91) is used to generate fault information.

3. The method according to claim 2, characterized in that A characteristic variable (Rb12) of the wiring harness model (91) is used to generate a fault message when the characteristic variable (Rb12) deviates from a limit value and / or when other characteristic variables determined based on the characteristic variable of the wiring harness model (91) deviate from a limit value.

4. The method according to any one of claims 1 to 3, characterized in that Depending on the characteristic variable (Rb12) or the fault information, the vehicle is switched to a safe operating state or the autonomous driving operation is inhibited.

5. The method according to any one of claims 1 to 3, characterized in that The parameter estimator (104) includes at least one prediction component (106) and / or at least one correction component (108).

6. The method according to any one of claims 1 to 3, characterized in that The parameter estimator (104) updates the characteristic variable (Rb12) using the measured variable and / or the previous characteristic variable (Rb12).

7. The method according to any one of claims 1 to 3, characterized in that At least one load distribution device (52) is arranged in the onboard power subsystem (30, 40), wherein the load distribution device (52) is supplied with energy via the energy storage device (32), wherein the load distribution device (52) supplies energy to a plurality of electrical consumers (36, 46), wherein a measured variable at the load distribution device (52) is detected and supplied to the parameter estimator (104) for determining the characteristic variable (Rb12).

8. The method according to claim 7, characterized in that The load distribution device (52) supplies energy to a plurality of electrical consumers (36, 46) required for the autonomous driving operation.

9. The method according to any one of claims 1 to 3, characterized in that At least one isolating element (26) is provided in the onboard power subsystem (30, 40), wherein a measured variable is detected at the isolating element (26) and supplied to the parameter estimator (104) for determining the characteristic variable (Rb12).

10. The method according to claim 9, characterized in that The isolation element is a DC voltage converter.

11. The method according to any one of claims 1 to 3, characterized in that The transition resistance is used as the characteristic variable (Rb12).

12. The method according to claim 11, characterized in that A transition resistor between the energy storage device (32) and the load distribution device (52) is used as a characteristic variable (Rb12).

13. The method according to any one of claims 1 to 3, characterized in that The parameter estimator (104) determines a systematic measurement error and generates a fault message as a function of the systematic measurement error and / or corrects the characteristic variable (Rb12) as a function of the systematic measurement error.

14. The method according to claim 13, characterized in that The systematic measurement errors include error covariance (Pk), gain (a) or bias (b).

15. The method according to any one of the preceding claims 1 to 3, characterized in that The positive side contact resistance and the ground side contact resistance are estimated in total as characteristic variables of the cable harness model (91).

16. The method according to any one of the preceding claims 1 to 3, characterized in that The measured variables used to determine the characteristic variable (Rb12) are synchronized.

17. The method according to claim 16, characterized in that The measured variable used to determine the characteristic variable (Rb12) is synchronized via a filter (114).

18. The method according to claim 17, characterized in that The filter (114) is a fractional delay filter, a Lagrangian filter or a Faro filter.

19. The method according to any one of the preceding claims 1 to 3, characterized in that For the time synchronization of the measured variables, a filter (114) determines a delay factor (D) for all measured variables to be delayed.

20. The method according to claim 19, characterized in that The delay factor (D) of the filter is determined by a further parameter estimator (116).

21. The method according to any one of the preceding claims 1 to 3, characterized in that A state estimator is used, either as the parameter estimator (104) or as an additional parameter estimator (116).

22. The method according to claim 21, characterized in that The state estimator is a Kalman filter.

23. The method according to claim 22, characterized in that The Kalman filter is an extended Kalman filter (EKF) or an unscented Kalman filter.

24. The method according to claim 23, characterized in that The unscented Kalman filter is a recursive least squares or particle filter.

25. The method according to any one of the preceding claims 1 to 3, characterized in that Using future characteristic variables (Rb12), predictive diagnosis and / or maintenance advice and / or operating strategy optimization are performed.

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