CN114545269A - Method and device for determining a state variable of an electrical energy store - Google Patents
Method and device for determining a state variable of an electrical energy store Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000001514 detection method Methods 0.000 claims abstract description 83
- 238000009434 installation Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims description 6
- 230000001419 dependent effect Effects 0.000 claims description 5
- 238000003745 diagnosis Methods 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims 1
- 230000032683 aging Effects 0.000 description 39
- 238000011156 evaluation Methods 0.000 description 12
- 238000007599 discharging Methods 0.000 description 4
- 238000004146 energy storage Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 239000000446 fuel Substances 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
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- 230000003247 decreasing effect Effects 0.000 description 2
- 230000014759 maintenance of location Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
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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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- 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
- G01R31/005—Testing of electric installations on transport means
- G01R31/006—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks
- G01R31/007—Testing of electric installations on transport means on road vehicles, e.g. automobiles or trucks using microprocessors or computers
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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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- Combustion & Propulsion (AREA)
- Electric Propulsion And Braking For Vehicles (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to a computer-implemented method and a device for determining a state variable of an electrical energy store of a technical installation, in particular of an installation battery (41), on the basis of at least one course of change of at least one operating variable (F) by means of a state model, having the following steps: -detecting (S1) at least one course of variation of at least one operating variable (F) characterizing the operation of the electrical energy store at a predefined normal detection frequency; -detecting (S3-S10) at least one course of variation of the at least one operating variable (F) at a predefined high detection frequency or a predefined low detection frequency upon determination of an event; -providing (S13, S16) at least one course of change of the at least one operating variable (F) to the state model to determine the state variable.
Description
Technical Field
The present invention relates generally to the characterization of the system state of an electrical energy storage in an electrically operable device, such as a motor vehicle, in particular an electric vehicle or a hybrid vehicle, and also to measures for determining the state of the electrical energy storage, such as a device battery or a fuel cell system.
Background
In order to supply energy to the technical installation without connection to the power grid, an electrical energy store, for example a battery pack, is usually used. An energy storage in the sense described below also comprises an energy converter, for example a fuel cell system. For example, the energy supply of an electrically drivable motor vehicle is effected by means of, for example, a vehicle battery or a fuel cell system. This provides electrical energy for operation of the vehicle systems and in particular the drive system.
The aging state of an electrical energy store can deteriorate over its service life, which can lead to a reduction in the performance of the energy store, for example as the maximum storage capacity of the battery pack continues to decrease. The degree of ageing of the device battery pack depends on the individual load of the device battery pack, i.e. on the usage behaviour of the user and the type of device battery pack.
Although the instantaneous aging state can be determined on the basis of the history of the operating variable change process by means of purely physical aging models, the models are often inaccurate. This inaccuracy of conventional aging models makes it difficult to predict the aging state change process. However, for example, the prediction of the aging behavior of the vehicle battery pack is an important technical variable, since the residual value of the vehicle battery pack can be evaluated economically using this variable.
Other states of the vehicle battery pack (e.g., state of charge or other degradation states) are typically determined with high accuracy using conventional physical models.
In the case of a vehicle battery pack, for example, the aging state of the electrical energy store may be dependent on, for example, the current throughput through the battery, the number and depth of charge and discharge cycles, the maximum charge and discharge current, the thermal circuit, the operating temperature, etc. These influencing factors may be used to determine the aging state, corresponding to the aging state model.
Since the operating variables are detected relatively closely for determining the current aging state, the aging state model cannot be implemented in the vehicle control unit on account of the large data volume, but rather has to be implemented outside the vehicle, for example in a central unit (cloud), since there a high level of computing power is available to process the time series of the operating variables.
In order to determine state variables of an electrical energy store, for example the state of aging of a vehicle battery, accurately, it is therefore necessary to process a large amount of data which must be continuously recorded during the service life of the device, either during operation or during inactive operating phases. These operating variables are therefore temporarily stored and transmitted to the central unit in the form of data packets at predetermined times or periodically.
Disclosure of Invention
According to the invention, a method for determining the state of aging on the basis of operating variables of a technical installation according to claim 1 and a corresponding device and technical system according to the parallel independent claims are provided.
Further designs are specified in the dependent claims.
According to a first aspect, a computer-implemented method for determining a state variable of an electrical energy store of a plant on the basis of at least one course of change of at least one operating variable by means of a state model is provided, having the following steps:
-detecting at least one course of change of at least one operating variable characterizing the operation of the electrical energy store at a predefined normal detection frequency;
-detecting at least one course of change of the at least one operating variable at a predefined high detection frequency or a predefined low detection frequency upon determination of an event;
-providing at least one course of change of the at least one operating variable to the state model for determining the state variable.
In order to determine the state variables of the electrical energy store, the operating variables from which the state variables are to be determined by means of the state model must first be detected. Depending on the state variable to be determined, the time grid of the detection is very small and may typically be between 1Hz and 100Hz, in particular between 5Hz and 50Hz, for example 10 Hz. Up to now, the operating variables were detected according to a fixed predetermined time grid, so that correspondingly large data volumes were generated even in time ranges in which the operating variables did not change significantly, in order to map the time series of the operating variables. Especially in cases where the storage space is limited (such as is often the case in mobile devices) or where the transmission capacity of the communication connection to the central unit or other means implementing the state model is limited, it is desirable to reduce the amount of data without affecting the accuracy of determining the state variables.
The method described above therefore provides for the operating variables to be detected or sampled on a variable time grid. The time grid is adapted depending on the event to thereby take into account the operating dynamics of the electrical energy store. Thus, if the evaluation demand increases or there is high dynamics, the detection frequency can be increased to a predetermined high detection frequency, which is higher than the normal detection frequency. If there is less evaluation requirement or low dynamics, the detection frequency can also be reduced to a predetermined low detection frequency, which is lower than the normal detection frequency.
In this way, the memory space required for temporarily storing the operating variables and the bandwidth of possible transmissions to the central unit can be reduced. Since the time grid for detecting the operating variables changes only temporarily, the required memory space can be optimized without affecting the accuracy of the determination of the state variables. By defining the event that leads to the detection of the time grid switch, the operating conditions can be independently verified, so that the detected and/or transmitted data volume can be adapted.
The above method suggests that the time grid for detecting the operating variables is adapted temporarily if a specific event is present. Such events may be known errors, certain high model uncertainty ranges defined by the state model, high dynamic operating state ranges in case of fast changes of state variables, and ranges in which the operating variables do not change or change only slightly. In the simplest case, the detection time grid can be switched to another detection frequency, i.e. a higher or lower frequency.
Furthermore, the state model can be implemented in a central unit outside the device, wherein at least one course of change of the at least one operating variable or of a variable derived therefrom is transmitted to the central unit, wherein the state model is implemented in the central unit to determine the state variable from the at least one course of change of the at least one operating variable.
Provision may be made for the event to comprise a determination of an error determined in the diagnosis, wherein the detection frequency is increased or set to a predetermined high detection frequency when the error is determined.
Alternatively or additionally, it may be provided that the event comprises a determination of a state range of the energy store in which the state model has a high modeling uncertainty, wherein the detection frequency is increased or set to a predetermined high detection frequency in the case of a high modeling uncertainty.
Alternatively or additionally, it can be provided that the event comprises a determination of an operating range with too high dynamics, wherein the detection frequency is increased or set to a predetermined high detection frequency when an operating range with too high dynamics is determined.
In particular, a high dynamics can be determined if the load-dependent input variable has a gradient which is numerically above a predefined gradient threshold.
Alternatively or additionally, it can be provided that the event comprises a determination of an excessive change in the modeled state variable, wherein the detection frequency is increased or set to a predetermined high detection frequency when an excessive change in the modeled state variable is determined.
Furthermore, if the detection frequency corresponds to a high detection frequency and a normalization condition is determined, the at least one course of change of the at least one operating variable is detected at a normal detection frequency.
The event may include a determination of a low-dynamic operating range in which the current change in the current of the electrical energy store is constant or changes only within a predefined current range, wherein the detection frequency is reduced or set to a predefined low detection frequency when the low-dynamic operating range is determined.
The event queries may be performed in an order corresponding to priority, with the highest priority query being the first query and the other queries performed in descending order of priority.
According to a further aspect, an apparatus is provided for determining a state variable of an electrical energy store of a plant on the basis of at least one course of change of at least one operating variable by means of a state model, wherein the apparatus is designed to:
-detecting at least one course of change of the at least one operating variable at a normal detection frequency;
-detecting at least one course of change of the at least one operating variable at a predefined high detection frequency or a predefined low detection frequency when an event is determined;
-providing at least one course of change of the at least one operating variable to the state model for determining the state variable.
Drawings
Embodiments are explained in more detail below based on the drawings.
Fig. 1 shows a schematic diagram of a system for a fleet with a large number of motor vehicles and with a central unit for providing state variables on the basis of a process of change of operating variables.
Fig. 2 shows a flow chart illustrating a method for determining the aging state of a vehicle battery of a motor vehicle.
Detailed Description
The method according to the invention is described below on the basis of a vehicle battery as an electrical energy store in a large number of motor vehicles as devices of the same type. In these motor vehicles, a data-based aging state model for the respective vehicle battery pack can be implemented in the control unit. The aging state model represents, for example, a state model which characterizes internal states of the energy store which cannot be measured directly or are difficult to measure directly. The aging status model may be continuously updated or retrained in the central unit based on operating variables of the vehicle battery packs from the fleet.
The above examples represent a large number of stationary or mobile devices with a grid-independent energy supply, such as vehicles, facilities, internet of things devices, energy supply facilities, autonomous robots, etc., which are connected to a central unit (cloud) via a corresponding communication connection (e.g. LAN, internet). These devices have strongly fluctuating load characteristics, by means of which the degree of the device state of the energy store is significantly influenced. The state variable represents a variable which cannot be determined in a simple manner with high accuracy and at the same time on a model-by-model basis in the same type of installation, but can only be determined by complex calculations, in particular by destructive measures or after a predefined operating cycle of the installation.
Fig. 1 shows a system 1 for providing fleet data of motor vehicles 4 of a fleet 3 in a central unit 2. In the central unit 2, the course of the change of the aging state of the vehicle battery packs of the respective motor vehicles 4 of the fleet 3 should be predicted on the basis of the fleet data.
One of the motor vehicles 4 is shown in more detail in fig. 1. The motor vehicle 4 has a vehicle battery pack 41 as a rechargeable electric energy storage, an electric drive motor 42 and a control unit 43, respectively. The control unit 43 is connected to a communication module 44 suitable for transmitting data between the respective motor vehicle 4 and the central unit (cloud). The control unit 43 is connected to a sensor unit 45, which sensor unit 45 has one or more sensors to continuously detect the operating variables.
The central unit 2 has a data processing unit 21 in which the method described below can be carried out, and the central unit 2 has a database 22 for storing operating variables and aging states of the vehicle battery packs, which are determined in a large number of vehicles 4 of the fleet 3.
The motor vehicle 4 transmits to the central unit 2 an operating variable F that specifies at least the variable on which the aging state of the vehicle battery pack depends. In the case of a vehicle battery, the operating variable F may account for instantaneous battery current, instantaneous battery voltage, instantaneous battery temperature, and instantaneous State of Charge (SOC). The operating variables F are detected in a rapid time grid between 1Hz and 100Hz, wherein the course of the change of these operating variables is periodically transmitted to the central unit 2 in uncompressed and/or compressed form.
The operating characteristics relating to the evaluation time period can be generated already in the central unit 2 or in the case of other embodiments in the respective motor vehicle 4, for example, from the operating variables F. To determine the state of aging, the evaluation period may be several hours (e.g., 6 hours) to several weeks (e.g., one month). The usual value for the evaluation period is one week.
The operating characteristics may include, for example, characteristics relating to an evaluation period and/or cumulative characteristics and/or statistical variables determined over the service life so far. In particular, the operating characteristics may include, for example: state of charge variation process, temperature, battery voltage, histogram data on battery current (especially histogram data related to battery temperature distribution over state of charge, charging current distribution over temperature and/or discharging current distribution over temperature), accumulated total charge (Ah), average capacity increase during charging (especially for charging processes with a charge increase above a threshold fraction (e.g. 20%) of total battery capacity), maximum differential capacity (dQ/dU: charge change divided by battery voltage change), etc.
Further statements can be taken from the operating characteristics: time load patterns such as charging and driving periods, determined by the usage pattern (e.g. rapid charging or strong acceleration at high current intensities or braking processes with recuperation), the usage time of the vehicle battery, the charge accumulated during the operating time and the discharge accumulated during the operating time, the maximum charging current, the maximum discharging current, the charging frequency, the average charging current, the average discharging current, the power throughput during charging and discharging, (in particular the average) charging temperature, the (in particular average) dispersion of the charging state, etc.
The State of aging (SOH) is a key variable that accounts for the remaining battery capacity or the remaining battery charge. The state of aging is a measure of the aging of the vehicle battery pack or the battery module or the battery cell, and can be described as a Capacity Retention Rate (SOH-C) or as an internal resistance increase (SOH-R). The capacity retention rate SOH-C is illustrated as the ratio of the measured instantaneous capacity to the initial capacity of a fully charged battery. The relative change in internal resistance SOH-R increases as the battery pack ages.
A state model, in particular an aging state model, which determines the state variables on the basis of the course of changes and/or operating characteristics of the operating variables, can be implemented in the central unit 2. The state model may be constructed based on data.
Fig. 2 shows a flow chart illustrating a method for determining the aging state as a state variable in the central unit 2. The method is carried out in the control unit 43 of the vehicle concerned and in the data processing unit 21 of the central unit 2 and can be implemented there as software and/or hardware.
In step S1, operational variables are detected according to a normal detection frequency, i.e., a conventional detection time grid of, for example, 10Hz, and are collected and temporarily stored in a block manner in step S2.
In step S3 it is checked whether an error has been diagnosed in the vehicle, for example in the drive train or in the vehicle battery 41, and a DFC (diagnostic error code) is stored in the error memory of the control unit 43 or of the component control device of the vehicle 4. If an error is determined (alternative: yes), the detection frequency of sampling of the operational variable is increased or the detection time grid of detecting the operational variable is decreased in step S4. From the moment of error occurrence, it is important to record the operating variables at a higher frequency until the end of the driving cycle or charging cycle, in order to be able to carry out further error diagnosis-related calculations, for example time-sensitive, safety-related calculations, in the central unit. All relevant operating variable data are thus recorded at high frequency immediately after an error in the driving or charging mode. The detection frequency can be increased to a predetermined higher frequency, i.e. a predetermined high detection frequency. Alternatively, the detection frequency may also be increased to a frequency depending on the type of error. If no error is identified (alternative: no), the method continues with step S5.
In step S5, it is checked whether the vehicle battery pack 41 is in a state range in which the state model has a high modeling uncertainty. This can be communicated from the central unit 2 to the control unit 43. The model uncertainty is derived from the state model on the basis of the evaluation points, in particular if the state model is designed as a probabilistic regression model or the like. In this regard, a description is received from the central unit 2 in the vehicle about the state range in which the model evaluation is performed. If the model uncertainty is signaled to exceed the predefined uncertainty threshold (alternative: yes), the detection frequency of the operating variables is increased in step S6, in particular to a high detection frequency. This is used to enable accounting for edge effects of range/history data, as they can affect tag generation. The recorded higher detection frequency is maintained until the model uncertainty in the state range has decreased by a predetermined threshold. The evaluation of the operating variables can then be improved by generating training data to improve the state model. If the model uncertainty is below a predefined uncertainty threshold (alternative: no), the method continues with step S7.
In step S7, it is checked whether a highly dynamic operating range is present. In order to identify operating ranges with high dynamics as quickly as possible, it is necessary to react as early as possible to such load-value-dependent events. Operating variables in the highly dynamic operating range are important, for example, for calculating the state of aging as state variables, in particular when using electrochemical models. Thus, a trigger event, for example defined by an absolute magnitude of a gradient exceeding an input variable of a system or device, can be considered as a highly dynamic operating range. So that the gradient of the position of the accelerator pedal or brake pedal in the motor vehicle 4 can be used to identify high dynamics. If the gradient is above a predefined gradient threshold (alternative: yes), a dynamic switching process to a high detection frequency is carried out in step S8. The travel gradient of the accelerator pedal can be determined in the control unit 43 of the vehicle 4 and trigger the detection of the operating variable with a high detection frequency. Alternatively, in the case where there are a plurality of input variables monitored in this manner, the detection frequency may also be increased to a frequency depending on the respective input variables.
In addition to the accelerator pedal for detecting the upcoming dynamic acceleration, the actuation of the brake pedal can also be taken into account, since the recuperation process for providing electrical recuperation energy for charging the vehicle battery 41 is likewise a dynamic process for loading the vehicle battery 41.
In step S9, the feedback from the central unit 2 is queried again to determine whether a large jump change has occurred in the calculated state variable (i.e. for example the aging state), said change being determined on the basis of the normal detection frequency. This can be communicated from the central unit 2 to the control unit 43. To this end, the change in the state variable may be compared to a change threshold. If the change is above the change threshold, then there is a large jump change. If this is the case (alternative: yes), the detection frequency is increased to a high detection frequency in step S10. The variation threshold is derived from an applicable threshold parameter. If it is indicated that the change exceeds x% of the aging state, the aging state value must be verified so that data is recorded at a high detection frequency until the time when the aging state is next calculated.
If the gradient is below the predefined gradient threshold (alternative: no), the method continues with step S11.
After steps S4, S6, S8, and S10, the operating variable is determined at a high detection frequency in step S16. At a predetermined time, a time sequence of at least one operating variable F is transmitted to the state model in order to determine the state variable.
In step S17, it is checked whether a normalization condition exists. If so (alternative: YES), the detection frequency is again lowered to the normal detection frequency in step S18 and the process returns to step S1. Otherwise (alternative: no) the method continues with step S16.
The normalization conditions may be predefined by:
after an error in the drive train of the vehicle or in the vehicle battery 41 has been identified, the error flag is manually reset after the error has been eliminated,
-reducing the modeling uncertainty below an uncertainty threshold,
reducing the operating dynamics of the vehicle battery, for example by identifying that the vehicle battery is operating at constant current (current value within a predetermined bandwidth) for an applicable duration, and/or
-ending the evaluation period for determining the next aging state value.
According to one specific embodiment, it can be provided that the switching to the normal detection frequency takes place if a high detection frequency is selected as a result of an excessively high change (gradient) in the determined aging state and a previously determined aging state (based on the high detection frequency) is ascertained on the basis of the operating variables determined at the high detection frequency. However, if the value is not confirmed, an abnormal value may be assumed.
Inaccuracies due to highly dynamic operating states (in particular in the case of electrochemical models) which are not yet fully taken into account or inadequate state model parameterization due to the absence of labels in specific operating states are excluded by priority control.
In step S11, it is checked whether the vehicle is in the low-dynamic operation range. If low dynamics is determined (alternative: yes), a low detection frequency of, for example, 1Hz is set in step S12. For example, an operating state such as a CC charging process may serve as a trigger criterion for detecting an operating variable at a low detection frequency. Here, the current gradient is less than the applicable threshold or the range around the current mean is less than the applicable threshold. As long as the query criterion is fulfilled, the low detection frequency is maintained in step S13 and the operating variables are determined and transmitted to the central unit 2.
If it is determined in step S11 that the low-dynamics operating range has been left, it may return to step S1 and may switch to detection using the normal detection frequency.
Low detection frequency may also include detecting operational variable data, for example, hourly if, for example, only calendar-based aging needs to be considered to determine the aging status. Events may be prioritized by query order.
The operating variables detected at the respective detection frequency are transmitted in the form of blocks to the central unit 2 and evaluated there. The evaluation of the central unit 2 is carried out by determining the operating variable characteristics for the respective evaluation period and using the aging state model for determining the aging state.
Claims (12)
1. A computer-implemented method for determining state variables of an electrical energy store of a technical installation, in particular of an installation battery (41), on the basis of at least one course of change of at least one operating variable (F) by means of a state model, having the following steps:
-detecting (S1) at least one course of variation of at least one operating variable (F) characterizing the operation of the electrical energy store at a predefined normal detection frequency;
-detecting (S3-S10) at least one course of variation of the at least one operating variable (F) at a predefined high detection frequency or a predefined low detection frequency upon determination of an event; and
-providing (S13, S16) at least one course of change of the at least one operating variable (F) to the state model to determine the state variable.
2. Method according to claim 1, wherein the state model is implemented in a central unit (2) external to the device, wherein at least one course of change of the at least one operating variable (F) or of a variable derived therefrom is transmitted to the central unit (2), wherein the state model is implemented in the central unit (2) to determine the state variable from the at least one course of change of the at least one operating variable (F).
3. The method according to claim 1 or 2, wherein the event comprises determining (S3) an error determined in a diagnosis, wherein the detection frequency is increased or set to a predefined high detection frequency when the error is determined.
4. Method according to any of claims 1 to 3, wherein the event comprises determining (S5) a state range of the energy store in which the state model has a high modeling uncertainty, wherein the detection frequency is increased or set to a predefined high detection frequency in the case of a high modeling uncertainty.
5. The method according to any one of claims 1 to 4, wherein the event comprises determining (S7) an operating range with too high dynamics, wherein the detection frequency is increased or set to a predefined high detection frequency when an operating range with too high dynamics is determined.
6. The method according to claim 5, wherein a high dynamics is determined if the load-dependent input variable has a gradient which is numerically above a predefined gradient threshold.
7. The method according to any one of claims 1 to 6, wherein the event comprises determining (S9) an excessive change of the modeled state variable, wherein the detection frequency is increased or set to a predefined high detection frequency when an excessive change of the modeled state variable is determined.
8. The method according to one of claims 1 to 7, wherein at least one course of change of the at least one operating variable is detected at a normal detection frequency if the detection frequency corresponds to a high detection frequency and a standardized condition is determined.
9. The method according to any one of claims 1 to 8, wherein the event comprises a determination of a low-dynamic operating range in which a current change of the current of the electrical energy store is constant or changes only within a predefined current range, wherein the detection frequency is reduced or set to a predefined low detection frequency when a low-dynamic operating range is determined.
10. An apparatus for determining a state variable of an electrical energy store of a plant on the basis of at least one course of change of at least one operating variable (F) by means of a state model, wherein the apparatus is configured to:
-detecting at least one course of variation of said at least one operating variable (F) at a normal detection frequency;
-detecting at least one course of change of the at least one operating variable (F) at a predefined high detection frequency or a predefined low detection frequency when an event is determined;
-providing at least one course of change of the at least one operating variable (F) to the state model for determining the state variable.
11. A computer program product comprising instructions which, when said program is executed by at least one data processing apparatus, cause said at least one data processing apparatus to carry out the steps of the method according to any one of claims 1 to 9.
12. A machine-readable storage medium comprising instructions which, when executed by at least one data processing apparatus, cause the at least one data processing apparatus to perform the steps of the method according to any one of claims 1 to 9.
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| Application Number | Priority Date | Filing Date | Title |
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| DE102020214180.0A DE102020214180A1 (en) | 2020-11-11 | 2020-11-11 | Method and device for determining a state variable of an electrical energy store |
| DE102020214180.0 | 2020-11-11 |
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| US9446678B2 (en) | 2014-03-05 | 2016-09-20 | Ford Global Technologies, Llc | Battery model with robustness to cloud-specific communication issues |
| DE102015205740A1 (en) | 2015-03-31 | 2016-10-06 | Bayerische Motoren Werke Aktiengesellschaft | Method for energy management of a motor vehicle |
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