METHOD FOR MONITORING AN ENERGY STORE IN A VEHICLE ELECTRICAL SYSTEM

Abstract

A method for monitoring an energy store in an on-board electrical system of a motor vehicle. At least one instantaneous parameter of the energy store is determined, and this at least one parameter is forwarded to a forecast model. The forecast model determines future values for the at least one parameter from the instantaneous value for the at least one parameter. The future value of the at least one parameter is provided to a voltage predictor which calculates a minimum voltage of the energy store to be expected for a selected function.

Claims

1-15. (canceled)

16. A method for monitoring an energy store in an on-board electrical system of a motor vehicle, the method comprising the following steps: determining an instantaneous value for at least one parameter of the energy store; forwarding the instantaneous value to a forecast model; determining, by the forecast model, a future value for the at least one parameter from the instantaneous value for the at least one parameter; providing the future value of the at least one parameter to a voltage predictor; and calculating, by the voltage predictor, a minimum voltage of the energy store to be expected for a selected function.

17. The method as recited in claim 16, wherein the forecast model is based on a load/capacity model, or a physical model, or a machine learning-based model, or a regression extrapolation, or a spline extrapolation.

18. The method as recited in claim 16, wherein the energy store is a battery, and a capacitance of the battery is determined as the parameter.

19. The method as recited in claim 16, wherein the energy store is a battery, and an internal resistance of the battery is determined as the parameter.

20. The method as recited in claim 16, wherein the energy store is a battery, and polarizations of the battery are determined as the parameter.

21. The method as recited in claim 16, wherein the forecast model is calculates the future value of the at least one parameter using a future estimated load.

22. The method as recited in claim 16, wherein the voltage predictor calculates the minimum voltage using an equivalent circuit diagram of the energy store.

23. The method as recited in claim 16, wherein load profiles for current, voltage and temperature are used during the calculation of the minimum voltage.

24. The method as recited in claim 16, wherein the calculated minimum voltage is compared to a limiting value.

25. The method as recited in claim 23, wherein it is ascertained via a limiting value shortfall whether it will still be possible in the future to carry out functions assigned to the used load profiles.

26. The method as recited in claim 16, wherein a remaining useful life of the energy store is ascertained.

27. The method as recited in claim 26, wherein a maintenance interval and/or a replacement of the energy store is regulated based on the remaining useful life.

28. The method as recited in claim 26, wherein measures in energy management for increasing the remaining useful life are taken based on the remaining useful life.

29. The method as recited in claim 28, wherein the measure includes: suspending and/or degrading functions, or changing a setpoint operating range of the energy store, or shifting a load between energy stores.

30. A system for monitoring an energy store in an on-board electrical system of a motor vehicle, the system configured to: determine an instantaneous value for at least one parameter of the energy store; forward the instantaneous value to a forecast model; determine, by the forecast model, a future value for the at least one parameter from the instantaneous value for the at least one parameter; provide the future value of the at least one parameter to a voltage predictor; and calculate, by the voltage predictor, a minimum voltage of the energy store to be expected for a selected function.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] FIG. 1 shows a battery sensor according to the related art in a block diagram.

[0029] FIG. 2 shows the equivalent circuit diagram of a battery.

[0030] FIG. 3 shows the procedure during the determination of the state of function (SOF).

[0031] FIG. 4 shows an execution of a method in a flowchart, in accordance with an example embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0032] The present invention is schematically shown in the figures based on specific embodiments and is described in greater detail hereafter with reference to the figures.

[0033] The following specific embodiments describe the use of the presented method in connection with a battery. The presented method is not limited to these applications and may be carried out in connection with all suitable energy stores, for example in connection with capacitors, in particular with high performance capacitors, such as for example supercapacitors (supercaps) or ultracapacitors.

[0034] FIG. 1 shows a battery sensor according to the related art, which is denoted overall by reference numeral 10. Input variables in a unit 12, in particular a measuring unit, are temperature T 14 and current I 16, and the output variable is voltage U 18.

[0035] In a block 20, parameters and states are estimated. A feedback unit 22, a battery model 24 and an adaptation 26 of the parameters are provided therein. A variable û 28, state variables {circumflex over ( )}x 30 and model parameters {circumflex over ( )}p 32 are output.

[0036] A node 29 is used to adapt battery model 24 to the battery. Current I 16 is incorporated directly, and temperature T 14 is incorporated indirectly in battery model 24. This calculates û 28 and compares this to real voltage U 18. In case of deviations, battery model 24 is corrected with the aid of feedback unit 22.

[0037] Moreover, a block 40 for sub-algorithms is provided. This includes a battery temperature model 42, an open circuit voltage determination 44, a peak current measurement 46, an adaptive starting current prediction 48 and a battery size detection 50.

[0038] In addition, charge profiles 60 are provided, which are incorporated in a block 62 including predictors. These are a charge predictor 64, a voltage predictor 66 and an aging predictor 68. Outputs of block 62 are an SOC 70, curves of current 72 and voltage 74 and an SOH 76.

[0039] Battery sensor 10 thus ascertains instantaneous SOC (state of charge) 70 of the battery and instantaneous SOH 76 (state of health, loss of capacitance compared to the initial state) of the battery. With the aid of predictors 64, 66, 68, battery sensor 10 is able to predict SOC 70 and SOH 76 according to multiple previously defined loading scenarios. These may now also be adapted to automated driving or to the respective application.

[0040] Predictors 64, 66, 68 are furthermore able to simulate an engine starting process at the present battery state and to ascertain its effects on SOC 70, SOH 76 and the state of function (SOF). If the engine start during the simulation causes a drop below certain limiting values, the start-stop operation is blocked.

[0041] FIG. 2 shows the equivalent circuit diagram of a battery, which is denoted overall by reference numeral 100. This equivalent circuit diagram includes an internal resistance R.sub.i 102, a first capacitance C.sub.D 104, a second capacitance C.sub.k 106 in parallel to which a resistance R.sub.k 108 is connected, a third capacitance C.sub.Dp 110 in parallel to which a resistance R.sub.Dp 112 is connected, as well as a further resistance R.sub.Dn 114.

[0042] FIG. 3 shows the operating mode of the determination of the state of function. In a first graph 150, at whose abscissa 152 time t and at whose ordinate 154 voltage u(t) are plotted, a curve of voltage 156 for past 160 is plotted. In a second graph 170, at whose abscissa 172 time t and at whose ordinate 174 current i(t) are plotted, a curve of current 176 for past 160 is plotted. For future 162, a current curve 182 characteristic of a certain driving maneuver as well as a voltage curve 180 forecast or predicted by the predictor are plotted. Furthermore, a voltage U 190 is plotted, which represents the starting point for the calculation of the SOF. U 190 is typically the instantaneously measurable operating voltage, but it is also possible to use a theoretically expectable minimum voltage, which may be used for a worst case prediction. The characteristic current curve 182 represents a virtual current profile i(t) according to a platform or a customer specification, for example the battery current profile which results during an engine start, for the prediction of the battery voltage drop during the engine warm start for stop/start applications.

[0043] The minimum predicted voltage for a certain current profile i(t) is used as the state of function (SOF; measure of the performance capability of the battery for fulfilling a certain vehicle function, for example the warm start of the engine), and is used hereafter for the decision regarding the availability of a certain function.

[0044] FIG. 4 shows the flowchart of an exemplary implementation of a method in accordance with an example embodiment of the presnt invention. In a first step, the instantaneous capacitance and the internal resistance of the battery are determined or measured in a battery state detection software 200. These are forwarded to a forecast model 202. With the aid of representative load collectives (RLC; future load profile of the battery to be expected) and with the aid of a load/capacity model, forecast model 202 calculates the future values of the capacitance (C_pred(t)) and of the internal resistance (Ri_pred(t)).

[0045] The forecast model may be based on a load/capacity model, a physical model, a machine learning-based model, on regression or on a spline extrapolation.

[0046] These values are forwarded to a voltage predictor 204. It calculates the minimum voltage of the battery to be expected for a given function with the aid of an electrical equivalent circuit diagram, as is illustrated, for example, in FIG. 2, analogously to the operating mode of the SOF. For this purpose, load profiles 206 for current I, starting voltage U, and temperature T are used. The predefined current profile may stem from arbitrary functions, for example from a start-stop or safe stop maneuver for automated driving.

[0047] In next step 208, the predicted minimum voltage (U_pred(t)) is compared to the limiting value, which, upon a shortfall, would cause the vehicle electrical system to fail. If this limiting value is reached or fallen short of, point in time t corresponds to the remaining useful life of the battery. Otherwise, time step t is increased by a Δt, and new representative load collectives (RLC) are calculated with the aid of the future load model 210. These representative load collectives are based, for example, on the past load of the battery in the form of changes in the charge state, the current, the voltage, the temperature, the ampere hour throughput etc., and map the future load of the battery to be expected. In the process, a distinction is also made, for example, between different boundary conditions, such as the season, the route, etc. These representative load collectives are then provided to the forecast model, and new values are determined for C_pred(t) and Ri_pred(t). This iteration is carried out until the predicted minimum voltage reaches the limiting value, and thus the remaining useful life (RUL) is determined. In the next step, this information is forwarded to a control unit 212, which derives measures therefrom, such as the predictive component replacement (predictive maintenance) or control measures for increasing the useful life (predictive health management).

[0048] The method, thus provides, for the creation of a diagnostic model of a battery. In one example embodiment of the present invention, at least one battery variable, for example the voltage, current or temperature, is measured via a sensor. These battery variables are transmitted to the battery state detection software (BSD) 200, which determines variables describing the battery state. In the process, BSD 200 may be based on physical, statistical or artificial intelligence (AI) models. The state-describing variables, such as for example the internal resistance of the battery, the capacitance, etc., are forwarded to forecast model 202.

[0049] In a further model, the battery variables may be classified over the time to form, for example, representative load collectives of the load of the battery. In addition, further signals of the battery or from the system may be used to form the representative load collectives. These RLCs are also transmitted to forecast model 202.

[0050] Based on the RLCs and the instantaneously determined state-describing variables of the battery, forecast model 202 predicts the future progression of the state-describing variables of the battery. In the process, the forecast model may also again be a physical, statistical or AI model.

[0051] The extrapolated state-describing battery variables are used in an evaluation model to determine the point in time of failure of the battery. This may essentially take place in two different ways. The first option compares the extrapolated state-describing battery variables to a limiting value or a limiting value distribution starting at which the battery is no longer functional. The second option uses the extrapolated state-describing battery variables to simulatively establish the remaining useful life (RUL). Similarly to the SOF function, as is illustrated in FIG. 3, it is established in the process, based on the state-describing battery variables and a load profile for different functions, whether the voltage at the battery drops below a threshold value. A drop below this threshold value results in a system failure.

[0052] As was described above, the method may be used to ascertain a remaining useful life of the battery. Based on the remaining useful life, a maintenance interval and/or a replacement of the battery may then be regulated. Based on the remaining useful life, it is also possible to take measures in the energy management to increase the remaining useful life. These measures may be selected from a suspension and/or degradation of functions of a change in the setpoint operating range of the battery or, in the case of multiple energy stores, a shift in the load between these energy stores.