METHOD AND APPARATUS FOR THE USER-DEPENDENT SELECTION OF A BATTERY OPERATED TECHNICAL DEVICE DEPENDING ON A USER USAGE PROFILE

Abstract

A computer-implemented method, for associating a user with a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types. In one example, the method includes providing a usage behavior of the user; associating a usage category with the user's usage behavior; determining a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a mode of operation of the technical device affecting a load on the device battery; simulating a predicted ageing state profile for the predicted usage parameter profile for a predetermined duration for each type of device belonging to a plurality of device types to determine a predicted ageing state at a predetermined end of a useful life period; and selecting a device type depending on the predicted ageing state.

Claims

1. A computer-implemented method, for associating a user with a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types, said method comprising the following steps: obtaining (S1), via a computer, a usage behavior of the user; associating (S2), via the computer, a usage category with the usage behavior of the user; determining (S3), via the computer, a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a parameter indicative of a mode of operation of the technical device affecting a load on the device battery; simulating (S5), via the computer, a predicted ageing state profile for the predicted usage parameter profile for a predetermined amount of time for each device type belonging to a plurality of device types in order to determine a predicted ageing state at a predetermined end of useful life period; and selecting (S6), via the computer, a device type for the user depending on the predicted ageing state.

2. The method according to claim 1, wherein the usage behavior of the user is continuously detected based on at least one usage parameter, wherein the usage behavior is aggregated into usage characteristics, wherein the usage categories are determined by characteristics of the usage characteristics.

3. The method according to claim 2, wherein the usage characteristics comprise an average load during operation, a service duration relative to the calendar age, and a frequency of use, wherein in vehicles acting as technical devices, the usage characteristics comprise a predicted annual mileage, a number and type of charging cycles, a temperature range, and an average load range.

4. The method according to claim 1, wherein simulating the predicted ageing state profile for the predicted usage parameter profile initially includes predicting at least one load parameter profile for the device battery, which profile corresponds to at least one operational parameter profile for the device battery, by means of a predetermined useful life period operational model for each device type, then predicting the ageing state profile by means of an ageing state model, wherein the ageing state model is based on a differential equation system which determines the ageing state by means of a chronological integration method.

5. The method according to claim 4, wherein at least one additional operational parameter is determined by means of a battery performance model dependent on the at least one load parameter profile, wherein, for the simulation, the model parameters of the battery performance model are adjusted with respect to the respective predicted ageing state (S4).

6. The method according to claim 1, wherein selecting a device type for the user is performed at the end of the predetermined useful life period, depending on the predicted state of ageing, so that a remaining potential use of the device battery is at a maximum.

7. An apparatus for performing a method according to claim 1.

8. A computer-readable storage medium comprising instructions that, when executed by computer cause the computer to obtain (S1) a usage behavior of the user; associate (S2) a usage category with the usage behavior of the user; determine (S3) a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a parameter indicative of a mode of operation of the technical device affecting a load on the device battery; simulate (S5) a predicted ageing state profile for the predicted usage parameter profile for a predetermined amount of time for each device type belonging to a plurality of device types in order to determine a predicted ageing state at a predetermined end of useful life period; and select (S6) a device type for the user depending on the predicted ageing state.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Embodiments are described in greater detail hereinafter with reference to the accompanying drawings. Shown are:

[0047] FIG. 1 a flow chart illustrating a method of associating a vehicle belonging to a plurality of vehicle types with a user having a predetermined usage profile;

[0048] FIG. 2 a chart of possible usage categories for various driver behaviors;

[0049] FIG. 3 a graph of the profiles of the ageing state of vehicle batteries of various vehicles over time.

DETAILED DESCRIPTION

[0050] In the following, the method according to the invention is described based on vehicles of a vehicle pool as an example of technical devices of various device types using vehicle batteries as device batteries. The method is performed in a data processing apparatus, in which the method described below is implemented as software and/or hardware. The example is representative of a plurality of stationary or mobile devices with off-grid power supplies, e.g., equipment, machine tools, domestic devices, Internet of Things (IoT) devices, and the like.

[0051] FIG. 1 illustrates, using a flow diagram, a method of associating a specific type of vehicle with a user. This scenario can be part of assigning a leased vehicle to a user wherein, e.g., according to a target, a residual value of the vehicle is intended to be the maximum possible. Other targets can be set at a predetermined residual value after a predetermined lease period, i.e., useful life period wherein the residual value/potential use is largely determined by an ageing state then reached by the vehicle battery. This method also makes it possible determine the leasing rate for the user of each of the vehicles.

[0052] In step S1, a usage behavior of the user is provided for this purpose. This usage profile indicates a travel and charging behavior of the user, which can be obtained, e.g., by recording user trips using a current or former vehicle. Alternatively, the usage behavior can also be created by providing information about the user.

[0053] The usage behavior can be taken from the user's previous uses of vehicles and provide information about an estimated annual mileage, a ratio between the frequencies of fast charging and normal charging, an average charge stroke per charging operation, a driving style, in particular as an average and/or maximum acceleration during acceleration paths, an average battery temperature during operation of the vehicle, and the like.

[0054] In step S2, the usage behavior is categorized into a usage category. User categories can categorize various usage behaviors based on characteristic ranges of usage characteristics, which are shown by way of example in the table in FIG. 2. Various usage patterns are recognizable based on usage categories of usage that can be associated with a user. The usage categories differ by belonging to the above characteristics of the usage properties. The usage characteristics of correspond to aggregated usage parameters, which each indicate a parameter that characterizes a mode of operation of the technical device affecting a load on the device battery.

[0055] Each of the usage categories is associated with a usage parameter profile that indicates an artificial travel profile, i.e., a predicted usage parameter profile, for a predetermined usage duration. The predicted usage parameter profile is generally designed so that, in reality, it would lead to ageing of the vehicle battery that corresponds to the actual ageing of the vehicle battery when used by the user. For example, this predicted usage parameter profile can comprise a predicted speed profile at a high temporal resolution during the entire usage time period. The predicted usage parameter profile can also indicate charging times, the type of charging process, and a temperature profile during the period of use, etc.

[0056] Using a functional drive model as a data-based, mathematically based, or physically based operational model, the usage parameter profile, i.e., the trajectories of travel movements, charging times, and idle times, e.g., in the form of a speed profile and temperature profile, is then converted into a corresponding profile of the battery power and the battery temperature in step S3. In this case, taking into account the weight of the vehicle, the rated battery voltage, the frictional moments, and the reduction of the reduction gear for predetermined acceleration and deceleration times, corresponding motor load moments are determined and the motor current profile determined thereby. By means of the drive train model, a battery current profile is thus determined based on the usage parameter profile, and a resulting battery temperature profile is determined from, e.g., climate models using forecasted ambient temperatures.

[0057] By means of a battery performance model a corresponding battery voltage profile and state of charge profile is modeled from the battery current profile and the battery temperature profile in step S4. The change in the ageing state can be continuously taken into account in the battery performance model, in particular by parameter adjustment, in order to take into account the influence of the increasing ageing of the vehicle battery. The vehicle battery retains an operational parameter profile until the end of the useful life period or lease duration.

[0058] Using a chronological integration-based ageing state model, a profile of the ageing state for the respective vehicle and in particular an ageing state for the end of the useful life period can be predicted based on the operational parameter profile during step S5. As a result, based on the predetermined user category, an ageing state is provided for each of the available vehicle types, which state indicates determination of a remaining ageing state at the end of the useful life period.

[0059] By means of a suitable rule-based selection criterion, e.g., selecting the type of vehicle causing the predetermined user category to have the least battery ageing over the overall lease period or, correspondingly, a minimum depreciation of the vehicle over the useful life period, wherein the depreciation of the vehicle depends on the ageing state at the end of the lease term, a selection of the type of vehicle can be made for the specific driver in step S6.

[0060] The respective vehicle is then provided to the user.

[0061] For example, FIG. 3 indicates possible ageing state profiles for a specific driver usage profile for various types of vehicles.

[0062] If the type of vehicle is associated with a driver, then, while using the vehicle in real-world operation, it can be verified whether the actual ageing matches the simulated ageing previously performed for selecting the type of vehicle. In this case, a verification can first be performed in order to determine whether the assumed driver usage parameter profile deviates from the associated usage category, e.g., based on the annual mileage and other usage parameters in the table shown in FIG. 2. If a classification is made into another use category, then a new simulation can be performed as previously described.

[0063] If the simulated operational parameter profiles, e.g., simulated battery temperatures, deviate from the real-world measured operational parameters, an irregularity can be determined if a deviation of the actual ageing state from the previously simulated ageing state profile occurs. In addition, predictive maintenance can be planned and performed based on the irregularities previously determined.