JPWO2020109108A5 - - Google Patents

Description

The present invention relates to a method for monitoring an energy store in the on-board electrical network of a motor vehicle and to a device for carrying out the method.

PRIOR ART In automotive applications, the on-board electrical network is understood to be the sum of all electrical components in the vehicle. This includes both electrical consumers and power sources, such as batteries. In this case, a distinction is made between an on-board energy network and an on-board communication network, but in the present specification, in particular, the on-board energy network responsible for supplying energy to the components of the motor vehicle will be mentioned. For controlling the vehicle electrical system, a microcontroller is usually provided which, in addition to the control functions, also carries out monitoring functions.

Note that in a motor vehicle, electrical energy can be supplied in such a way that the motor vehicle can be started at any time and is sufficiently powered during operation. However, even in the stopped state, the consumer must still be able to be driven for a suitable time range without impairing the next start.

The onboard electrical network has the task of supplying the electrical consumers with energy. In today's vehicles, when the energy supply fails due to errors or deterioration in the on-board electrical network or in the on-board electrical network components, important functions such as power steering are lost. On-board power grid failures are generally accepted in vehicles produced today, as the driver can be considered a fallback level since the vehicle's steering ability is not compromised, it is only sluggish.

The increasing electrification of equipment groups and the introduction of new driving functions are creating high demands on the safety and reliability of the electrical energy supply in motor vehicles.

Future highly automated driving functions, such as highway pilots, will allow a limited range of driver-involved actions. For this reason, until the end of the highly automated driving function, the human driver can only perceive the function to a limited extent as sensory, control-technical, mechanical and energetic fallback levels. or cannot be perceived at all. The power supply in highly automated driving therefore has a hitherto unknown safety importance in motor vehicles in order to ensure sensor-, control-technical and actuator fallback levels. Errors or degradations in the onboard electrical network must therefore be identified reliably and as completely as possible in the sense of product safety.

Reliability engineering approaches to vehicle component monitoring have been addressed to make component failures predictable. For this purpose, the on-board electrical network components are monitored during operation and their damage is calculated.

The publication DE 10 2013 203 661 A1 describes a method for operating a motor vehicle with an on-board electrical network comprising at least one semiconductor switch which is loaded during operation. In the method, the actual load state of the semiconductor switch is calculated based on previous load events.

The use of prior art battery sensors will now be described with reference to FIG. A battery state detection method is described in the publication DE 10 2016 211 898 A1. Here, a method for describing the SOH (Gesundheitszustand) of a battery is used by the reliability determination unit. A so-called load-capacity model is used here, which provides a description of the failure probability of a component.

The document DE 199 59 019 A1 discloses a method for identifying the state of an energy accumulator. The actual amount of the energy accumulator can be supplied to the estimation routine and the model-based parameter estimator and filter separated therefrom. The received parameterized quantities are supplied to a predictor that extrapolates the behavior of the energy store.

The publication EP 1 231 476 A1 describes a method for determining the state of health of a battery. In the method, quiescent voltage, internal resistance and internal voltage drop are estimated and used as input quantities for the model. The model is initialized and then simulated. A model is used to estimate the state of deterioration.

DE 102013203661 A1 DE 102016211898 A1 DE 19959019 A1 EP-A-1231476

DISCLOSURE OF THE INVENTION Against this background, a method for monitoring an energy accumulator, e.g. be done. Embodiments derive from the dependent claims and the description.

The proposed method is used for monitoring energy stores in the on-board electrical network of motor vehicles. In the following, in particular the monitoring of the battery as an energy store in the vehicle's electrical network will be discussed. However, the proposed method is not limited to battery monitoring only, but can also be applied to other energy stores, such as capacitors, especially high-power capacitors.

In a configuration of the above method , at least one operating quantity of the battery, such as the internal resistance, capacitance and/or polarization of the battery, is determined, and the at least one operating quantity is transferred to a predictive model, which predictive model: A current value of the working quantity is calculated and a future value of at least one working quantity is determined by a load-capacity model. The future value of at least one operating quantity is supplied to a voltage estimator, which calculates the expected minimum voltage of the battery for the selected function.

For the safety-relevant functioning of the consumer, it has been found that in each channel the clamping voltage at the consumer is decisive. Such clamping voltages are obtained from a combination of a voltage source, eg a battery or a transmission chain including a DC voltage converter, the cable tree resistance in the corresponding sub-branches and the load current of the individual components.

It is also recognized that the lack of the minimum supply voltage required for each operating case causes failure of the corresponding components. In safety-related scenarios, this could lead to failure of safety goals or limit the availability of automated driving functions.

Such shortfalls in the minimum supply voltage can occur due to deterioration of the energy store, eg the battery. In order to achieve countermeasures and the highest possible functional availability, a predictive diagnostic function is required for the battery, based on which predictive maintenance (English: Predictive Maintenance) or installation One of the measures in power grid energy management (English: Predictive Health Management) is taken.

Ex-ante failure prediction based on function and boundary conditions has a significantly higher quality of prediction compared to known functions. This is because it is possible to predict under what conditions the battery will no longer be able to adequately support the on-board electrical network and thus a failure will occur.

The method described predicts energy storage, e.g., battery, failures based on prior usage conditions and associated system functions in order to introduce countermeasures in a timely manner, thereby reducing the function utilization Increased chances.

The proposed method, at least in some embodiments, offers the following set of advantages:
- improved functionality availability, e.g. improved start-stop functionality and/or automated driving functionality;
maintenance assistance without additional breakdowns and the resulting maintenance interval maximization, thereby maximizing vehicle availability for fleet operators;
・Cost reduction by avoiding immobility, for example, reduction of rescue costs, etc.
・Improve safety by avoiding immobility in unforeseen circumstances,
have

The proposed device is used to carry out the above method and can be used, for example, in connection with battery sensors.

Further advantages and configurations of the invention can be obtained from the description and the accompanying drawings.

It is to be understood that the features mentioned above and those to be mentioned below can be used not only in the combinations presented respectively, but also in other combinations or individually, without departing from the scope of the invention.

1 is a block diagram showing a prior art battery sensor; FIG. 2 is an equivalent circuit diagram showing a battery; FIG. FIG. 4 is a diagram showing a technique for calculating SOF (State of Function); 4 is a flow chart showing the execution of the proposed method;

MODES FOR CARRYING OUT THE INVENTION The invention is schematically illustrated in the drawings by means of embodiments and will be explained in greater detail below with reference to the drawings.

The following embodiments describe applications of the proposed method in relation to batteries. The proposed method is not limited to this application, but in connection with any suitable energy store, for example in connection with capacitors, in particular high power capacitors, such as supercaps or ultracapacitors. It is viable.

A prior art battery sensor, generally designated 10, is shown in FIG. The input quantities to the unit 12, in particular the measuring unit, are the temperature T14 and the current I16, and the output quantity is the voltage U18.

２８、状態変数

３０、及び、モデルパラメータ

３２が出力される。 At block 20, parameter and state estimation is performed. Here, a feedback unit 22, a battery model 24 and a parameter adaptation 26 are provided. where the variable

28, state variables

30 and model parameters

32 is output.

２８を計算し、これを実際の電圧Ｕ１８によって補償する。偏差がある場合、バッテリモデル２４は、フィードバックユニット２２により補正される。 Node 29 is used to adapt battery model 24 to the battery. The current I16 is directly input to the battery model 24 and the temperature T14 is indirectly input to the battery model 24 . The battery model 24 is

28 and compensate it by the actual voltage U18. If there are deviations, the battery model 24 is corrected by the feedback unit 22 .

Furthermore, a block 40 is provided for sub-algorithms. The block 40 includes a battery temperature model 42 , a static voltage calculator 44 , a peak current measurer 46 , an adaptive starting current predictor 48 and a battery charge detector 50 .

In addition, a charge characteristic 60 for inputting the predicted amount to block 62 is also provided. The charge characteristics are a charge prediction amount 64 , a voltage prediction amount 66 and a deterioration prediction amount 68 . Outputs from block 62 are SOC 70 , current 72 and voltage 74 waveforms, and SOH 76 .

Accordingly, the battery sensor 10 calculates a current SOC (State of Charge) 70 and a current SOH 76 (State of Health, ratio of capacitance loss to initial state) of the battery. The predictive quantities 64, 66, 68 allow the battery sensor 10 to predict SOC 70 and SOH 76 according to multiple predefined load scenarios. The load scenario can here also be adapted for automated driving or the respective application case.

The predictive quantities 64, 66, 68 can also simulate the engine starting process under current battery conditions to calculate its effect on SOC 70, SOH 76 and State of Function (SOF). If an engine start in the simulation results in a lower excursion of a pregiven limit value, the start-stop operation is inhibited.

FIG. 2 shows the equivalent circuit diagram of the battery, generally referenced 100 . The equivalent circuit diagram includes an internal resistance R _i 102, a first capacitance C _D 104, a second capacitance C _k 106, a resistance R _k 108 connected in parallel to the second capacitance C _k 106, a 3 capacitance C _Dp 110 , a resistor R _Dp 112 connected in parallel to the third capacitance C _Dp 110 and a further resistor R _Dn 114 .

FIG. 3 shows the operating method of SOF (State of Function) calculation. A first graph 150 plotting time t on the horizontal axis 152 and voltage u(t) on the vertical axis 154 plots the waveform of voltage 156 against past time 160 . A second graph 170 plotting time t on the horizontal axis 172 and current i(t) on the vertical axis 174 plots the current 176 waveform against past time 160 . For a future time 162, the characteristic current waveform 182 for a given driving maneuver and the voltage waveform 180 expected or predicted by the predictor are shown. Additionally, a voltage U190 is shown which represents the output point of the SOF calculation. U190 is typically the current measurable operating voltage, but it can also be viewed as the theoretically expected minimum voltage available for worst case prediction. Characteristic current waveform 182 represents a hypothetical current characteristic i(t) according to platform or customer specifications, e.g., for predicting battery voltage droop during engine warm start for stop/start applications. Battery current characteristics occurring during starting.

The predicted minimum voltage for a given current characteristic i(t) is used as the SOF (State of Function; a measure of the battery's output capability to satisfy a given vehicle function, e.g. engine warm start), and in the following: , shall be used to determine the availability of a given feature.

FIG. 4 shows a flowchart of an exemplary use of the proposed method. In a first step, the battery status identification software 200 calculates or measures the current capacitance and internal resistance of the battery. The current capacitance and internal resistance are forwarded to predictive model 202 . The predictive model 202 uses a representative load set (RLK; expected future load characteristics of the battery) as an aid to predict the future value of capacitance (C_pred(t)) and the future value of internal resistance through a load-capacity model. Calculate the value (Ri_pred(t)).

Predictive models may be based on load-capacity models, physical models, machine learning-based models, regression, or spline extrapolation.

Each of the above values is forwarded to voltage predictor 204 . Voltage estimator 204 calculates the minimum expected voltage of the battery for a given function, similar to the SOF operating scheme, by way of an electrical equivalent circuit diagram such as that shown in FIG. 2 by way of example. A load characteristic 206 for current I, starting voltage U and temperature T is used for this purpose. The set current characteristic can in this case originate from any function, for example a start-stop maneuver or a safe-stop maneuver for automated driving.

In the next step 208, the predicted minimum voltage (U_pred(t)) is compared with a limit value below which the onboard electrical network will fail. Time t corresponds to the remaining life of the battery if the limit value is reached or is exceeded. Otherwise, the time step t is increased by Δt and the future load model 210 calculates a new representative load set (RLK). The representative load set is based on past battery loads , eg, in the form of changes in state of charge, current, voltage, temperature, average ampere hours, etc., and simulates expected future battery loads . In this case, for example, various boundary conditions, such as time of day, driving segments, etc., are also distinguished. The representative load set is then fed to the forecast model to calculate new values for C_pred(t) and Ri_pred(t). These iterations are performed until the remaining life (RUL) is calculated by reaching the minimum predicted voltage limit. This information is transferred in a next step to the control unit 212, from which the control unit 212 derives, for example, predictive parts replacement (Predictive Maintenance) or control measures to extend the service life (Predictive Health Management). Derive measures.

Thus, the method provides for building a predictive model of the battery. In the present configuration, sensors measure at least one battery quantity, eg voltage, current, temperature. The battery quantity is sent to battery state identification software (BSD) 200, which calculates a quantity that describes the battery state. The BSD 200 may in this case be based on physical, statistical or AI models (AI: artificial intelligence). Quantities that describe the state, such as the battery's internal resistance, capacitance, etc., are transferred to the predictive model 202 .

In other models, for example, the battery charge is classified with respect to time to form a representative load set of battery loads . Additionally, other signals from the battery or other signals from the system can be used to form the representative load set. The RLK is also sent to predictive model 202 .

The predictive model 202 predicts the future waveform of the battery state describing quantity based on the RLK and the current calculated battery state describing quantity. The predictive model may again be based on physical, statistical or AI models.

The extrapolated quantity describing the state of the battery is used in a weighted model to calculate the point of failure of the battery. This can be done primarily in two different ways. A first measure is to compare the extrapolated quantity describing the state of the battery with the limit value or limit value distribution at which the battery no longer functions. A second approach is to determine the Remaining Useful Life (RUL) through simulation using extrapolated quantities that describe the state of the battery. Here, as in the case of the SOF function shown in FIG. 3, it is determined whether the voltage of the battery drops below a threshold based on quantities describing the state of the battery and the load characteristics for the various functions. be done. A downward excursion of the threshold causes a system failure.

As already mentioned, the method can be used to calculate the remaining life of the battery. Maintenance intervals and/or battery replacements can be adjusted based on remaining life. Also, based on the remaining life, energy management measures can be introduced to extend the remaining life. Such action can be selected from discontinuing and/or downgrading the battery's target operating area modification function, or moving the load between energy stores when multiple energy stores are provided.

Claims (13)

A method of monitoring an energy store in an on-board electrical network of a motor vehicle, comprising:
determining at least one current operating quantity of said energy store and transferring said at least one operating quantity to a predictive model (202);
The prediction model (202) calculates a future value of the at least one operating quantity from a current value of the at least one operating quantity, and predicts the future value of the at least one operating quantity to a voltage predictor (204). and the voltage estimator (204) calculates the expected minimum voltage of the energy storage for a given function, including start-stop maneuvers for autonomous driving or safe-stop maneuvers. ,
The method of claim 1, wherein the predictive model (202) calculates the future value of the at least one operating quantity according to a future estimated load . a battery (100) is monitored as an energy store and the ratio of the current capacitance loss to the initial state of said battery (100) is calculated as an operating quantity;
The method of claim 1 . A battery (100) is monitored as an energy store and an internal resistance (102) of said battery (100) is determined as an operating variable,
3. A method according to claim 1 or 2 . monitoring a battery (100) as an energy store and determining the polarization of said battery (100) as an operating variable;
4. A method according to any one of claims 1-3 . the voltage estimator (204) calculates the minimum voltage by an equivalent circuit of the energy storage;
5. A method according to any one of claims 1-4 . load characteristics for current, voltage and temperature are used in calculating the minimum voltage;
6. A method according to any one of claims 1-5 . the calculated minimum voltage is compared to a limit value;
7. The method of claim 6 . It is calculated whether the function corresponding to the load characteristic used is still feasible in the future, according to the lower exceeding of the limit value,
8. The method of claim 7 . The predictive model (202) calculates the remaining life of the energy storage from the time the minimum voltage reaches or falls below the limit .
9. A method according to any one of claims 1-8 . maintenance intervals and/or replacement of the energy storage are adjusted based on the remaining life;
10. The method of claim 9 . Based on the remaining life, action is taken in energy management to extend the remaining life.
11. A method according to claim 9 or 10 . Said measures are :
- changing the target operating area of the energy storage, or
the movement of the load between multiple energy stores, if multiple energy stores are provided;
is selectable from
12. The method of claim 11 . 13. Device for monitoring an energy accumulator in an on -board electrical network of a motor vehicle, the device being adapted to carry out the method according to any one of the preceding claims.