Method and device for predicting latency time at a charging station

Abstract

The invention relates to a device (101) for forecasting latency at a charging station (200) having n charging posts (201) for carrying out n charging processes, where n > = 1. The device (101) is designed to determine status data relating to the occupancy of the n charging posts (201) and m additional waiting positions at the charging station (200) at an initial time t0, wherein m > = 1. Furthermore, the device (101) is designed to predict a waiting time for carrying out a charging process at the n charging posts (201) at a prediction time t1 from an occupancy model (210) of the n charging posts (201) and the m waiting positions.

Description

Technical Field

[0001] The invention relates to a method for predicting waiting times at a charging station and a corresponding device. Background Art

[0002] Vehicles that are at least partially electrically driven have an electrical energy storage device that must be charged at a charging station when necessary. Due to the relatively long charging time for the charging process, waiting times may occur at the charging station before a free charging point or free charging post is available for the vehicle. Summary of the invention

[0003] The technical task addressed here is to predict the expected waiting time at a charging station in an efficient and precise manner, in particular in order to adjust, in particular optimize, the route guidance of the vehicle based thereon.

[0004] This task is solved by each of the independent claims. Advantageous embodiments are further described in the dependent claims. It should be pointed out that the additional features of the claims dependent on the independent claims can form their own invention independent of the combination of all the features of the independent claims without the features of the independent claims or in combination with only a subset of the features of the independent claims, which can become the technical solution of the independent claims, divisional applications or subsequent applications. This applies in the same way to the technical teachings described in the specification, which can form inventions independent of the features of the independent claims.

[0005] According to one aspect, a device for predicting a waiting time at a charging station is described, the charging station having n charging posts for carrying out n charging processes, where n≥1. Usually only exactly one charging process can be carried out at each charging post at the same time. The waiting time can indicate the time you have to wait at the charging station until a free charging post is available for a charging process.

[0006] The device is configured to determine status data about the (current) occupancy (Belegung) of n charging posts and m additional waiting positions (each for a waiting vehicle) at the charging station at an initial time t0, where m≥1. The status data may indicate the number of charging posts occupied at the initial time t0 and / or the number of waiting positions occupied. The device may be configured to query status data from a server (external to the vehicle) in which status data about a plurality of different charging stations are recorded. Alternatively or additionally, status data, in particular status data about m additional waiting positions, may be estimated. For this purpose, for example, the behavior of forward movement (Aufrück) at each charging post of the charging station may be analyzed. For example, it may be determined how quickly a charging post that becomes idle is occupied again for a subsequent charging process. Status data about m additional waiting positions may be estimated from the duration for reoccupying (Nachbesetzung) the charging post. The status data may indicate the number of charging posts currently occupied, and if necessary (if all charging posts are occupied), the number of waiting positions currently occupied.

[0007] In addition, the device is configured to predict the waiting time for performing a charging process at n charging posts (in particular, exactly one of the n charging posts) at a predicted time t1 based on an occupancy model of n charging posts and m additional waiting positions. The occupancy model may include a Markov chain model (Markov Ketten Modell). Here, the occupancy model may include n+1 different states for different numbers (0, 1, 2, ..., n) of charging posts to be occupied. In addition, the occupancy model may include m different states for different numbers (1, 2, ..., m) of waiting positions to be occupied. The different states of the occupancy model can be arranged along a chain here, in particular so that the n+1 different states for different numbers of charging posts to be occupied follow one another in increasing numbers, and are followed by m different states for increasing numbers of waiting positions to be occupied.

[0008] The occupancy model may depend on a charge request rate λ for requesting to perform a charging process and / or on a charge completion rate μ for completing a charging process. In particular, the duration and / or rate of state transitions between different states of the occupancy model may depend on the charge request rate λ and / or on the charge completion rate μ. For example, the duration and / or rate of transitions to a state with a higher occupancy number may depend on the charge request rate λ. On the other hand, the duration and / or rate of transitions to a state with a lower occupancy number may depend on the charge completion rate μ.

[0009] The charging request rate λ and / or the charging completion rate μ are typically time-dependent. The device may be configured to determine the charging request rate λ and / or the charging completion rate μ based on measurement data regarding the actual occupancy of n charging posts and / or m waiting positions in the past. Alternatively or additionally, the device may be configured to read the charging request rate λ and / or the charging completion rate μ for predicting the time t1 from a digital map, in which the charging stations are recorded as points of interest (POI). For this purpose, the charging request rate λ and / or the charging completion rate μ can be learned (regularly if necessary) and updated in the digital map (e.g. in the form of map attributes).

[0010] Thus, a device is described which enables the prediction of waiting times at one or more charging stations in an efficient and reliable manner based on an occupancy model which in each case also includes a certain number of waiting positions.

[0011] Furthermore, the device can be configured to generate measures for route guidance of the vehicle as a function of the determined waiting time. In particular, a driving route for a vehicle that is at least partially electrically driven can be determined as a function of the determined waiting time. In this way, the comfort of an electrically driven vehicle can be increased in an efficient and reliable manner.

[0012] The duration and / or rate of the state transition to a reduced number of occupied waiting positions can in particular depend on n*μ or equivalent to n*μ in the occupancy model (where the operator "*" corresponds to multiplication). Thus, the waiting time can be determined in a particularly efficient and precise manner.

[0013] The apparatus may be arranged to solve the following matrix differential equation for the occupancy model,

[0014]

[0015] In order to determine the waiting time. Here, P0(t),...,P n (t) can be the probability of n+1 different states for different numbers of charging piles being occupied at time t. n+1 (t),...,P n+m (t) may be the probabilities of m different states for different numbers of waiting places being occupied at the time t. The above-mentioned matrix differential equation enables a prediction of the waiting time in a particularly accurate manner.

[0016] The device can be configured, for example, to determine, based on an occupancy model (in particular based on the above-mentioned matrix differential equation), the probability P that at the prediction time t1 all n charging posts are occupied but no waiting position is occupied nFurthermore, the probability P for m different states that different numbers of waiting positions are occupied at the prediction time t1 can be determined based on the occupancy model (in particular based on the above-mentioned matrix differential equation): n+1 (t1),...,P n+m (t1) (wherein, in the state, all n charging posts are occupied). Then, based on the probability P n (t1) and P n+1 (t1), ..., P n+m (t1) The waiting time is determined in a particularly precise manner.

[0017] The device can be configured in particular to determine (based on the charging completion rate μ) a single waiting time for a state (211) in which all n charging posts are occupied but no waiting positions are occupied, and to determine a single waiting time for each of m different states in which a different number of waiting positions are occupied. The single waiting time for a state in which i waiting positions are occupied can depend on (i+1) / n·μ (or an equivalent value), where i=0, ..., m.

[0018] The waiting time can then be determined in a particularly precise manner based on the individual waiting times and based on the probabilities, in particular as an expected value or median of the individual waiting times.

[0019] According to a further aspect, a (road) motor vehicle, in particular a passenger car or a truck or a bus or a motorcycle, is described, which comprises a device as described in this context.

[0020] According to a further aspect, a method for forecasting a waiting time at a charging station is described, the charging station having n charging posts for carrying out n charging processes, where n≥1. The method comprises determining status data about the (current) occupancy of the n charging posts and m additional waiting positions at the charging station at an initial time t0, where m≥1. The method further comprises predicting a waiting time for carrying out a charging process at one of the n charging posts at a predicted time t1 (which is after the initial time) based on an occupancy model of the n charging posts and the m waiting positions.

[0021] According to a further aspect, a software (SW) program is described which can be configured to be executed on a processor (for example on a control unit of a vehicle or on a central processing unit) and thus to execute the method described in this context.

[0022] According to another aspect, a storage medium is described. The storage medium may include a SW program which is configured to be executed on a processor and thereby to execute the method described herein.

[0023] It should be noted that the methods, devices and systems described in this document can be used not only alone, but also in combination with other methods, devices and systems described in this document. In addition, any aspects of the methods, devices and systems described in this document can be combined with each other in various ways. In particular, the features of the claims can be combined with each other in various ways. In addition, the features listed in brackets should be understood as optional features. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The present invention is described in more detail below based on examples.

[0025] Here it is shown:

[0026] Figure 1 Exemplary components of a vehicle are shown;

[0027] Figure 2a An exemplary charging station with multiple charging points is shown;

[0028] Figure 2b An exemplary model of the occupancy status of a charging station is shown;

[0029] Figure 3 A flow chart of an exemplary method for forecasting waiting time at a charging station is shown. DETAILED DESCRIPTION

[0030] As stated at the beginning, this paper studies the efficient and accurate prediction of the expected waiting time of a vehicle at a charging station. In this regard, Figure 1 An example vehicle 100 is shown with a position sensor 102 which is configured to determine position data (e.g. GNSS (Global Navigation Satellite System) coordinates) regarding the current position of the vehicle 100. The position data can be evaluated (by a (control) device 101 of the vehicle 100) in conjunction with a digital map of the road network traveled by the vehicle 100 in order to determine the position of the vehicle 100 within the road network.

[0031] The digital map may include information about charging stations for charging the vehicle battery. The information about the charging stations may include,

[0032] The location of charging stations (on the road network); and

[0033] The number of different charging points (at each of which a vehicle can be charged).

[0034] The vehicle 100 may comprise a communication unit 104 which is arranged to communicate with a unit external to the vehicle (eg with a server) via a (wireless) communication link (eg 3G, 4G, 5G etc.) in order to receive, for example, current information about the charging station.

[0035] Furthermore, the vehicle 100 may include a user interface 103 for interaction with a user of the vehicle 100. It is possible to plan a driving route through the road network for the user (starting from a current position to a target position). In this case, one or more stops at one or more corresponding charging stations may also be planned along the driving route in order to charge the electrical energy storage device of the vehicle 100. Route guidance along the planned driving route may be generated via the user interface 103 of the vehicle 100.

[0036] Figure 2a An exemplary charging station 200 with a plurality of different charging points or charging posts 201 is shown.

[0037] In particular, during rush hour, it may happen that all charging points 201 of a charging station 200 are occupied, which results in a waiting time for starting a charging process. A relatively long waiting time at a charging station 200 may result in the vehicle 100 not driving to this charging station 200, but to another charging station 200 (along the driving route to the destination). Therefore, when planning a driving route, the (expected) waiting times at different charging stations 200 may be taken into account in order to reduce, in particular minimize, the effective travel time of the driving route (including the time for performing one or more charging processes).

[0038] Some measures are described herein, with which the expected waiting time at charging station 200 can be predicted in an efficient and accurate manner.

[0039] As explained above, information can be provided for the charging station 200 in a digital map (e.g. as a map attribute and / or as a point of interest (POI)). The type of the map attribute can be POItype=Laden (POI type=charging). Such a POI (i.e. such a charging station 200) can have n charging piles (i.e. charging points) 201, wherein the n charging piles form a charging battery. Therefore, the POI 200 can have n+1 states or filling levels, in particular the following states: "no position in the pool is occupied", "one position in the pool is occupied", ..., "all positions in the pool are occupied".

[0040] The exact number of available charging posts 201 at POI 200 may change over time due to approaching and departing vehicles and is generally unknown. The instantaneous number of available charging posts 201 can be modeled by the vector P(t)=(P0(t), ..., P n (t)) T To achieve. i (t) represents the probability that the i charging posts 201 of the POI 200 are occupied at time t, where ∑Pi (t)=1.

[0041] The following time-related parameters can be defined

[0042] The charging request rate λ (the frequency of requesting a charging process at the charging battery 200); and / or

[0043] Charging completion rate μ (the inverse of the average charging duration per vehicle at the charging battery 200 ).

[0044] The above parameters can be estimated based on detected occupancy data from the past. Here, the value of the parameter is usually time-dependent. In particular, the value of the parameter can depend on,

[0045] Time of day

[0046] The day of the week;

[0047] Date type (holiday or weekday); and / or

[0048] School holidays.

[0049] The values ​​of parameters λ, μ can be determined online or in advance based on detected occupancy data from the past and, if necessary, entered into a digital map as a property for charging station 200 (and thus read out if necessary).

[0050] Figure 2b An exemplary birth-death Markov chain model 210 is illustrated that can be used to determine the expected waiting time at a charging station 200. Figure 2b The model 210 shown in FIG. 2 is applicable to a charging station 200 with n=3 charging posts 201 and includes a node or state 211 for each possible occupancy state of the charging posts 201 (0, 1, 2 or 3 occupied charging posts 201). In addition, the model 210 includes a node or state 212 for an additional waiting position (node ​​212 numbered "4"). In general, the model 210 may include m nodes or states 212 for m waiting positions (e.g., for one or more, or more, or three or more waiting positions).

[0051] The state transition 213 between the nodes 211, 212 depends on the above parameters. In this case, the occupancy of the charging posts 201 and the waiting positions increases according to the charging request rate λ. On the other hand, the occupancy of the charging posts 201 and the waiting positions decreases according to the charging completion rate μ. It should be taken into account here that, in the case of n charging posts 201 being occupied, it is sufficient to complete the charging process at one of the n occupied charging posts 201 to obtain a free charging post 201 (so that the rate for the corresponding state transition is n*μ). In a corresponding manner, in the case of n-1 charging posts 201 being occupied, it is sufficient to complete the charging process at one of the n-1 occupied charging posts 201 to only have n-2 charging posts 201 (so that the rate for the corresponding state transition is (n-1)*μ), etc. In addition, in the case of n charging posts 201 being occupied, it is sufficient to complete the charging process at one of the n occupied charging posts 201 to reduce one waiting position (so that the rate for the corresponding state transition is n*μ).

[0052] The probability vector P(t1) at time t1 can be calculated as an initial value problem of the following matrix differential equation taking into account the previous state P(t0) and represents an estimate of the state at any future time:

[0053]

[0054] For modeling of waiting time, the above model can be extended by introducing occupancy levels n+1, n+2, ..., n+m representing m waiting positions. Therefore, the vehicle 100 at the waiting position can be represented as a charging battery 200 that virtually extends an additional charging post 201. It should be noted here that the charging request rate λ applies to the transition from state n to n+1, while the charging completion rate nμ applies to the transition from state n+1 to n, that is, when the car leaves the charging station, because all n charging stations 201 are still occupied.

[0055] The solution of the above matrix differential equation (which can be obtained, for example, by matrix exponentialization) provides the following result:

[0056] · The probability P0(t1), ..., P of different occupancy levels of the charging pile 201 at the (next) time t1 n (t1), and the probability P of occupancy of m different waiting positions n+1 (t1),...,P n+m (t1).

[0057] The speed at which the occupancy level of the charging posts 201 and / or waiting positions changes. Since all statistical state transitions 213 are known by means of λ and μ, the time of any state transition 213 within the extended pool 200 can be read from the model 210. In particular, for the case where all charging positions 201 are occupied, the expected waiting time can be determined. For example, the expected waiting time for the vehicle 100 in Figure 2b In the example of the waiting position, the waiting time t is calculated. wait =1 / 3μ+1 / 3μ=2 / 3μ.

[0058] Therefore, it can be determined that

[0059] a probability vector of the state at the requested time instant; and / or

[0060] ·t wait : The expected waiting time at the requested time.

[0061] Thus a system is described which calculates estimates of availability and waiting time based on occupancy data of charging station 200. Other input data may be real-time information from the vehicle.

[0062] The components of the system may be:

[0063] Static geographic location data for charging battery 200;

[0064] Historical availability data of the individual charging stations 200 up to the present moment as input into the system (POI data of the charging station operator);

[0065] A computing unit for summarizing the availability of the individual charging stations into a report about the pool (i charging posts in a segment are occupied);

[0066] a computing unit for processing the incoming data and preparing the required charging information; and / or

[0067] An interface for calling and outputting prediction data.

[0068] Vehicle users generally want to reach their destination safely and without unplanned waiting times. A forecast showing the availability and waiting times at charging stations 200 enables planning of routes that avoid or minimize waiting times. Transparency about waiting times enables waiting times to be planned in advance and, if necessary, to be better utilized.

[0069] Figure 3A flow chart of an exemplary (possibly computer-implemented) method 300 for forecasting a waiting time at a charging station 200 having n charging posts 201 for carrying out n charging processes, where n≥ 1. Method 300 may be carried out by device 101 of vehicle 100 (for example within the scope of route planning).

[0070] The method 300 comprises determining 301 status data about the occupancy of n charging posts 201 and m additional waiting positions at the charging station 200 at an initial time t0, where m≥1. For example, the status data may be directly queried from the charging station 200. The initial time may correspond to the current time.

[0071] The method 300 also includes predicting 302 a waiting time for performing a charging process at one of the n charging posts 201 at the (next) prediction time t1 based on an occupancy model 210 of the n charging posts 201 and the m waiting positions. The occupancy model 210 can be, for example, a Markov chain model. Alternatively or additionally, the occupancy model 210 can include a matrix differential equation (as described herein). Here, the occupancy model 210 can depend on a (statistically determined) charging request rate λ for requesting to perform a charging process and / or on a (statistically determined) charging completion rate μ for completing the charging process.

[0072] By means of the measures described in this document, the expected waiting time for carrying out a charging process can be estimated in an efficient and precise manner, whereby, for example, the route guidance of vehicle 100 can be optimized.

[0073] The present invention is not limited to the embodiments shown. It should be noted in particular that the description and drawings are intended only to illustrate the principles of the proposed methods, devices and systems by way of example.

Claims

1. A device (101) for predicting a waiting time at a charging station (200), the charging station having n charging posts (201) for performing n charging processes, wherein n≥1; wherein, The device (101) is configured to: - determining status data about the occupancy of n charging posts (201) and m additional waiting positions at the charging station (210) at an initial time t0, where m≥1; and - predicting the waiting time for performing a charging process at the n charging posts (201) at a prediction time t1 based on an occupancy model (210) of the n charging posts (201) and m additional waiting positions; wherein the occupancy model (210) depends on a charging request rate λ for requesting to perform a charging process and a charging completion rate μ for completing the charging process.

2. The device (101) according to claim 1, wherein - the occupancy model (210) comprises n+1 different states (211) for different numbers of charging posts (201) being occupied; - the occupancy model (210) comprises m different states (212) for different numbers of waiting positions being occupied; and - the duration and / or rate of state transitions (213) between different states (211, 212) of the occupancy model (210) depends on a charging request rate λ and / or on a charging completion rate μ.

3. The device (101) according to claim 2, wherein The different states (211, 212) of the occupancy model (210) are arranged along a chain, so that n+1 different states (211) for different numbers of occupied charging posts (201) follow one another in increasing numbers and are followed by m different states (212) for increasing numbers of occupied waiting positions; - the duration and / or rate of the transition to a state with a higher occupancy number (213) depends on the charge request rate λ; and The duration and / or rate of the transition to a state with a lower occupancy number (213) depends on the charging completion rate μ.

4. The device (101) according to any one of claims 2 to 3, wherein: The duration and / or rate of the state transition to a decrease in the number of occupied waiting positions (213) depends on n*μ.

5. The device (101) according to any of the preceding claims, wherein: The device (101) is configured to: - Determine the probability P of a state (211) in which all n charging posts (201) are occupied but no waiting position is occupied at the prediction time t1 according to the occupancy model (210) n (t1); - Determine, based on the occupancy model (210), the probability P of m different states (212) for different numbers of waiting places being occupied at the prediction time t1 n+1 (t1), ..., P n+m (t1); and -Based on probability P n (t1) and P n+1 (t1), ..., P n+m (t1) to determine the waiting time.

6. The device (101) according to claim 5, wherein The device (101) is configured to, in particular based on a charging completion rate μ, - determining a single waiting time for a state (211) in which all n charging posts (201) are occupied but no waiting position is occupied; - determining a single waiting time for each of the m different states (212) in which a different number of waiting positions are occupied; and - determining a waiting time based on the individual waiting times and based on the probabilities, in particular as an expected value or a median of the individual waiting times.

7. The device (101) according to claim 6, wherein The individual waiting time for the state (211, 212) in which i waiting positions are occupied depends on (i+1) / n·μ, where i=0, . . . , m.

8. The device (101) according to one of the preceding claims, wherein - the device (101) is arranged to solve the following matrix differential equation of the occupancy model (210), To determine the waiting time; -P0(t),...,P n (t) is the probability of n+1 different states (211) for different numbers of charging posts (201) being occupied at time t; and -P n+1 (t),...,P n+m (t) is the probability for m different states (212) for different numbers of waiting positions to be occupied at time t.

9. The device (101) according to any of the preceding claims, wherein: The device (101) is configured to: - generating measures for route guidance of the vehicle (100) as a function of the determined waiting time; and / or - determining a driving route for the at least partially electrically driven vehicle (100) as a function of the determined waiting time.

10. The device (101) according to one of the preceding claims, wherein - the charging request rate λ and / or the charging completion rate μ are time-dependent; - the device (101) is configured to determine a charging request rate λ and / or a charging completion rate μ based on measurement data of actual occupancy of n charging posts (201) and / or m waiting positions in the past; and / or The device (101) is configured to read a charging request rate λ and / or a charging completion rate μ for predicting the time t1 from a digital map, in which the charging station (200) is recorded as a point of interest.

11. The device (101) according to one of the preceding claims, wherein - the state data indicate the number of occupied charging posts (201) and / or the number of occupied waiting positions at the initial time t0; and / or The device (101) is configured to query status data from a server, in which status data about a plurality of different charging stations (200) are recorded.

12. A method (300) for predicting a waiting time at a charging station (200), the charging station having n charging posts (201) for performing n charging processes, wherein n≥1; wherein, The method comprises (300), - determining (301) status data about the occupancy of n charging posts (201) and m additional waiting positions at the charging station (200) at an initial time t0, where m≥1; and - predicting (302) a waiting time for performing a charging process at one of the n charging posts (201) at a predicted time t1 based on an occupancy model (210) of the n charging posts (201) and the m waiting positions; wherein the occupancy model (210) depends on a charging request rate λ for requesting to perform a charging process and a charging completion rate μ for completing the charging process.

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