Method and device for predicting the waiting time at a charging station

Abstract

The invention relates to a device (101) for predicting a waiting time at a charging station (200) which comprises n charging posts (201) for carrying out n charging processes, where n ≥ 1. The device (101) is designed to determine state data regarding the occupancy of the n charging posts (201) and of m additional waiting positions at the charging station (200) at an initial point in time t 0, where m ≥ 1. The device (101) is further designed to predict a waiting time for carrying out a charging process at the n charging posts (201) at a prediction point in time t 1 on the basis of an occupancy model (210) of the n charging posts (201) and the m waiting positions.

Description

[0001] Method and device for predicting the waiting time at a

[0002] Charging station

[0003] The invention relates to a method and a corresponding device for predicting the waiting time at a charging station.

[0004] A vehicle that is at least partially electric has an electrical energy storage device that must be charged at a charging station when needed. Due to the relatively long charging times for a single charging process, there may be a waiting time at the charging station before a free charging point or charging station becomes available for the vehicle.

[0005] This document deals with the technical task of predicting the expected waiting time at a charging station in an efficient and precise manner, in particular in order to adapt, and in particular optimize, the route guidance of a vehicle based on this.

[0006] The problem is solved by each of the independent claims. Advantageous embodiments are described, among other things, in the dependent claims. It should be noted that additional features of a patent claim dependent on an independent patent claim can form a separate invention, independent of the combination of all features of the independent patent claim, without the features of the independent patent claim or only in combination with a subset of the features of the independent patent claim, which invention can be made the subject of an independent claim, a divisional application, or a subsequent application. This applies equally to technical teachings described in the description, which can form an invention independent of the features of the independent patent claims.According to one aspect, a device for predicting the waiting time at a charging station is described, which has n charging columns for performing n charging processes, where n ≥ 1. Typically, only exactly one charging process can be performed at each charging column at a time. The waiting time can indicate the time that must be waited at the charging station until a free charging column is available for a charging process.

[0007] The device is configured to determine status data relating to the (current) occupancy of the n charging stations and m additional waiting positions (each for a waiting vehicle) at the charging station at an initial time t0, with m ≥ 1. The status data can indicate the number of occupied charging stations and / or the number of occupied waiting positions at the initial time t0. The device can be configured to query the status data from a (vehicle-external) server in which status data relating to a plurality of different charging stations is recorded. Alternatively or additionally, the status data, in particular the status data relating to the m additional waiting positions, can be estimated. For this purpose, for example, the approach behavior at the individual charging stations of the charging station can be analyzed. For example, it can be determined how quickly a vacated charging station is occupied again for a subsequent charging process.From the time it takes to refill a charging station, the status data for the m additional waiting positions can be estimated. The status data can indicate the number of currently occupied charging stations and, if all charging stations are occupied, the number of currently occupied waiting positions.

[0008] The device is further configured to predict the waiting time for carrying out a charging process at the (in particular at exactly one of) the n charging stations at a prediction time G based on an occupancy model of the n charging stations and the m additional waiting positions. The occupancy model can comprise a Markov chain model. The occupancy model can comprise n + 1 different states for different numbers (0, 1, 2, . . . , n) of occupied charging stations. Furthermore, the occupancy model can comprise m different states for different numbers (1, 2, . . ., m) of occupied waiting positions.The different states of the occupancy model can be arranged along a chain, in particular such that the n + 1 different states for the different numbers of occupied charging stations follow one another with increasing numbers, and are followed by the m different states for increasing numbers of occupied waiting positions.

[0009] The occupancy model may depend on a load request rate λ of requests to perform load operations and / or on a load completion rate p of completions of load operations. In particular, durations and / or rates of state transitions between the different states of the occupancy model may depend on the load request rate λ and / or the load completion rate p. For example, the duration and / or rate of a state transition to a higher number of occupancies may depend on the load request rate λ. On the other hand, the duration and / or rate of a state transition to a lower number of occupancies may depend on the load completion rate p.

[0010] The charging request rate λ and / or the charging end rate p are typically time-dependent. The device can be configured to determine the charging request rate λ and / or the charging end rate p based on measured data relating to the actual occupancy of the n charging stations and / or the m waiting positions in the past. Alternatively or additionally, the device can be configured to determine the charging request rate λ and / or the charging end rate p for the prediction time from a digital map in which the charging station is listed as a point of interest (POI). For this purpose, the charging request rate λ and / or the charging completion rate p can be re-learned (if necessary regularly) and updated in the digital map (e.g., in the form of a map attribute).

[0011] A device is thus described which makes it possible to predict the waiting times at one or more charging stations in an efficient and reliable manner using an occupancy model which also includes a certain number of waiting positions.

[0012] The device can further be configured to initiate a measure related to route guidance of a vehicle depending on the determined waiting time. In particular, a route for an at least partially electrically powered vehicle can be determined depending on the determined waiting time. This allows the comfort of an electrically powered vehicle to be increased efficiently and reliably.

[0013] The durations and / or the rate of a state transition to a reduced number of occupied waiting positions can, in particular, depend on n · μ in the occupancy model or correspond to n · μ (where the operator corresponds to a multiplication). This allows the waiting time to be determined particularly efficiently and precisely.

[0014] The device can be configured to solve the following matrix differential equation of the occupancy model, to determine the waiting time. Here, P0(t),. —.., P n (t)

[0015] Probabilities for the n + 1 different states for different numbers of occupied charging stations at time t. P n+1 (t), ... , P n+m(t) can be probabilities for m different states for different numbers of occupied waiting positions at time t. The above-mentioned matrix differential equation allows the waiting time to be predicted in a particularly precise manner.

[0016] The device can, for example, be set up to calculate the probability P based on the occupancy model (in particular based on the above-mentioned matrix differential equation). n (t1) for the condition that at the prediction time all n charging stations but no waiting position are occupied. Furthermore, using the occupancy model (in particular using the matrix differential equation mentioned above), probabilities P n+ i(t1), ... , P n+m(t1) for m different states for different numbers of occupied waiting positions at the prediction time (where all n charging stations are occupied in each of the states). The waiting time can then be calculated in a particularly precise manner based on the probabilities P n (ti) and P n+1 (t1), ... , P n+m (t1) can be determined.

[0017] The device can be configured in particular (based on the charging end rate p) to determine an individual waiting time for the state (211) that all n charging stations but no waiting position are occupied, and to determine an individual waiting time for each of the m different states for the different numbers of occupied waiting positions. The individual waiting times for the state with i occupied waiting positions, for i = 0, ... , m, can be dependent on (i + 1) / n . μ be (or equal to this value).

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

[0020] According to a further aspect, a method is described for predicting the waiting time at a charging station having n charging columns for carrying out n charging processes, with n > 1. The method comprises determining state data relating to the (current) occupancy of the n charging columns and m additional waiting positions at the charging station at an initial time t0, with m > 1. The method further comprises predicting, based on an occupancy model of the n charging columns and the m waiting positions, the waiting time for carrying out a charging process at one of the n charging columns at a prediction time (which follows the initial time).

[0021] According to a further aspect, a software (SW) program is described. The SW program can be configured to be executed on a processor (e.g., on a vehicle control unit or on a central processing unit) and thereby to carry out the method described in this document.

[0022] According to a further aspect, a storage medium is described. The storage medium can comprise a software program which is configured to be executed on a processor and thereby to carry out the method described in this document. It should be noted that the methods, devices, and systems described in this document can be used both alone and in combination with other methods, devices, and systems described in this document. Furthermore, any aspects of the methods, devices, and systems described in this document can be combined with one another in a variety of ways. In particular, the features of the claims can be combined with one another in a variety of ways. Furthermore, features listed in parentheses are to be understood as optional features.

[0023] The invention will be described in more detail below using exemplary embodiments.

[0024] Figure 1 shows exemplary components of a vehicle;

[0025] Figure 2a shows an exemplary charging station with a plurality of charging points;

[0026] Figure 2b shows an exemplary model of the occupancy states of a charging station; and Figure 3 shows a flowchart of an exemplary method for predicting the waiting time at a charging station.

[0027] As stated at the outset, this document deals with the efficient and precise prediction of the expected waiting time of a vehicle at a charging station. In this context, Fig. 1 shows an exemplary vehicle 100 with a position sensor 102 configured to determine position data (e.g., GNSS (Global Navigation Satellite System) coordinates) relating to 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 for the road network traveled by the vehicle 100 in order to determine the position of the vehicle 100 within the road network. The digital map can include information relating to charging stations for charging vehicle batteries. The information relating to a charging station can include

[0028] • the position of the charging station (within the road network); and

[0029] • the number of different charging points (where one vehicle can be charged at each charging point).

[0030] The vehicle 100 may include a communication unit 104 configured to communicate with a vehicle-external unit (such as a server) via a (wireless) communication connection (e.g., 3G, 4G, 5G, etc.), e.g., to receive current information relating to a charging station.

[0031] Furthermore, the vehicle 100 can include a user interface 103 for interaction with a user of the vehicle 100. A user can be enabled to plan a route through the road network (starting from the current position to a destination position). One or more stops at one or more corresponding charging stations along the route can also be planned to charge the electrical energy storage device of the vehicle 100. Route guidance along the planned route can be initiated via the user interface 103 of the vehicle 100.

[0032] Fig. 2a shows an exemplary charging station 200 with a plurality of different charging points or charging columns 201.

[0033] Particularly during peak times, it may happen that all charging points 201 of a charging station 200 are occupied, resulting in a waiting time before a charging process can begin. The presence of a relatively long waiting time at a charging station 200 may result in the vehicle 100 not approaching this charging station 200 and instead approaching another charging station 200 (along the route to the destination position). The (expected) waiting times at the various charging stations 200 can thus be taken into account when planning a route in order to reduce, in particular minimize, the effective travel time of the route (including the time required to complete one or more charging processes).

[0034] This document describes measures that can be used to predict the expected waiting time at a charging station 200 in an efficient and precise manner.

[0035] As explained above, information for a charging station 200 can be provided in the digital map (e.g., as a map attribute and / or as a point of interest (POI)). The map attribute can be of the type POItype=Charging. Such a POI (i.e., such a charging station 200) can have n charging stations (i.e., charging points) 201, wherein the n charging stations form a charging pool. Thus, a POI 200 can assume n+1 states or fill levels, in particular the states: "no space within the pool occupied," "one space in the pool occupied," . . . , all spaces in the pool occupied.

[0036] The exact number of available charging stations 201 at the POI 200 can change over time due to vehicles arriving and departing and is generally unknown. The current number of available charging stations 201 can be modeled using a vector P(t) = (P0(t), — > Pn(t)). T P i(t) denotes the probability that at time t, i charging stations 201 of the POI 200 are occupied, where for all times

[0037] The following time-dependent parameters can be defined

[0038] • Charging request rate λ, (frequency with which 200 charging processes are requested at the charging pool); and / or

[0039] • Charging completion rate p (reciprocal of the average charging time per vehicle at the charging pool 200). The above parameters can be estimated based on recorded occupancy data from the past. The parameter values ​​are typically time-dependent. In particular, the parameter values ​​can depend on:

[0040] • the time of day;

[0041] • the day of the week;

[0042] • the type of day (holiday or weekday); and / or

[0043] • School holidays.

[0044] The values ​​of the parameters λ , p can be determined online or in advance based on the recorded occupancy data from the past, and if necessary, included in the digital map as attributes for the charging station 200 (and thus read out if required).

[0045] Fig. 2b illustrates an exemplary Birth-Death Markov Chain model 210 that can be used to determine the expected waiting time at a charging station 200. The model 210 shown in Fig. 2b applies to a charging station 200 with n = 3 charging stations 201, and includes a node or state for each possible occupancy state of the charging stations 201.

[0046] 211 (0, 1, 2 or 3 occupied charging stations 201). Furthermore, the model 210 includes a node or state 212 for an additional waiting position (node

[0047] 212 with the number "4"). In general, the model can have 210 m nodes or states 212 for m waiting positions, e.g., for one or more, or two or more, or three or more waiting positions.

[0048] The state transitions 213 between the nodes 211, 212 depend on the above-mentioned parameters. The occupancy of the charging stations 201 and the waiting positions increases according to the charging request rate λ. On the other hand, the occupancy of the charging stations 201 and the waiting positions decreases according to the charging completion rate p. It should be noted that, with n occupied charging stations 201, it is sufficient for the charging process to be terminated at one of the n occupied charging stations 201 to create a free charging station 201 (so that the rate for the corresponding state transition is n · p). Similarly, with n — 1 occupied charging stations 201, it is sufficient that the charging process is terminated at one of the n — 1 occupied charging stations 201 in order to have only n — 2 charging stations 201 (so that the rate for the corresponding state transition is (n — 1) · μ), etc.Furthermore, with n occupied charging stations 201, it is sufficient that the charging process is terminated at one of the n occupied charging stations 201 in order to reduce a waiting position (so that the rate for the corresponding state transition is n · p).

[0049] The probability vector P(t1) at time can be calculated taking into account a previous state P(t0) as an initial value problem of the following matrix differential equation and represents an estimator for the state at any future time:

[0050] To model waiting times, the above model can be extended by introducing occupancy levels n + 1, n + 2, ..., n + m, which represent m waiting positions. A vehicle 100 in a waiting position can thus be represented as a virtual extension of the charging pool 200 by an additional charging station 201. It should be noted that the charging request rate λ applies for the transition from state n to n + 1, and the charging end rate nμ applies for the transition from state n + 1 to n, namely when a car leaves the charging station, since all n charging stations 201 are still occupied.

[0051] The solution of the above-mentioned matrix differential equation, which can be obtained, for example, via the matrix exponent, offers the following results:

[0052] • The probabilities P0(t1), ... , Pn(t1) of the different occupancy levels of the charging stations 201 at the (preceding) time G, and the probabilities P n+1 (t1)> — > P n+m(t1) the occupancy of the different m waiting positions.

[0053] • The speed at which the occupancy levels of the charging stations 201 and / or the value positions change. Since all statistical state transitions 213 are known by means of λ and p, the times for any state transitions 213 within the extended pool 200 can be read from the model 210. In particular, the expected waiting time can be determined for the case that all charging stations 201 are occupied. For example, the waiting time for the case It can be calculated that the vehicle 100 in the example from Fig. 2b is in the waiting position.

[0054] It can thus be determined

[0055] • the probability vector for the states at a requested time; and / or

[0056] • t wait the expected waiting time at the requested time.

[0057] Thus, a system is described that calculates an estimate of availability and waiting time based on occupancy data from charging stations 200. Other input data can be real-time information from vehicles.

[0058] Components of the system can be: • Static geopositioning data for the charging pools 200;

[0059] • Historical availability data of individual charging stations 200 up to the current time as input into the system (POI data of the charging station operators);

[0060] • Calculation unit for summarizing individual charging station availabilities to make statements regarding the pools (i charging stations occupied in the segment);

[0061] • Computing unit for processing incoming data and preparing requested loading information; and / or

[0062] • Interface for calling and outputting forecast data

[0063] Vehicle users typically want to reach their destination safely and without unplanned waiting times. A forecast that provides information about the availability and waiting times at charging stations 200 makes it possible to plan routes that avoid or minimize waiting times. Transparency about waiting times allows waiting times to be planned in advance and, if necessary, utilized more effectively.

[0064] Fig. 3 shows a flowchart of an exemplary (possibly computer-implemented) method 300 for predicting the waiting time at a charging station 200 having n charging columns 201 for carrying out n charging processes, with n > 1. The method 300 can be carried out by a device 101 of a vehicle 100 (e.g., as part of route planning).

[0065] The method 300 comprises determining 301 state data relating to the occupancy of the n charging stations 201 and m additional waiting positions at the charging station 200 at an initial time t0, with m > 1. The state data can, for example, be queried directly from the charging station 200. The initial time can correspond to the current time. The method 300 further comprises predicting 302, based on an occupancy model 210 of the n charging stations 201 and the m waiting positions, the waiting time to carry out a charging process at one of the n charging stations 201 at a (preceding) prediction time t1. The occupancy model 210 can, for example, be a Markov chain model. Alternatively or additionally, the occupancy model 210 can comprise a matrix differential equation (as described in this document).The occupancy model 210 can depend on the (statistically determined) charging request rate λ of requests to carry out charging processes and / or on the (statistically determined) charging completion rate p of terminations of charging processes.

[0066] The measures described in this document allow the expected waiting time for carrying out charging processes to be estimated in an efficient and precise manner, which can, for example, optimize the route guidance of a vehicle 100.

[0067] The present invention is not limited to the embodiments shown. In particular, it should be noted that the description and figures are intended only to illustrate the principle of the proposed methods, devices, and systems by way of example.

Claims

Claims 1) Device (101) for predicting a waiting time at a charging station (200) which has n charging points (201) for carrying out n charging processes, with n ≥ 1; wherein the device (101) is configured, - To determine status data regarding the occupancy of the n charging points (201) and of m additional waiting positions at the charging station (210) at an initial time t0, with m > 1; and - to predict a waiting time for carrying out a charging process at the n charging stations (201) at a prediction time based on an occupancy model (210) of the n charging stations (201) and the m additional waiting positions; wherein the occupancy model (210) depends on a charging request rate λ of requests to carry out charging processes and on a charging completion rate p of completions of charging processes. 2) Device (101) according to claim 1, wherein - the occupancy model (210) includes n + 1 different states (211) for different numbers of occupied charging stations (201); - the occupancy model (210) includes m different states (212) for different numbers of occupied waiting positions; and - Durations and / or rates of state transitions (213) between the different states (211, 212) of the occupancy model (210) depend on the load request rate λu and / or on the load discharge rate p. 3) Device (101) according to claim 2, wherein - the different states (211, 212) of the occupancy model (210) are arranged along a chain such that the n + 1 different states (211) are available for the different numbers occupied charging stations (201) follow one another in increasing number, and are followed by the m different states (212) for increasing numbers of occupied waiting positions; - the duration and / or rate of a state transition (213) to a higher number of occupancy depends on the load request rate λ; and - the duration and / or rate of a state transition (213) to a lower number of occupancy depends on the loading rate p. 4) Device (101) according to one of claims 2 to 3, wherein the duration and / or rate of a state transition (213) depends on a reduced number of occupied waiting positions of n . μ. 5) Device (101) according to one of the preceding claims, wherein the device (101) is configured, - using the occupancy model (210) a probability P n (t1) to determine for state (211) that at the prediction time all n charging stations (201) but no waiting position is occupied; - based on the occupancy model (210) probabilities P n+1 (t1), P n+m(t1) to determine different states for m (212) for different numbers of occupied waiting positions at the prediction time; and - the waiting time based on the probabilities P n (t1) and P n+1 (t1)> ..., P n+m to determine (t1). 6) Device (101) according to claim 5, wherein the device (101) is configured, in particular on the basis of the charging rate p, - to determine an individual waiting time for the condition (211) that all n charging stations (201) but no waiting position are occupied; - to determine an individual waiting time for each of the m different states (212) for the different numbers of occupied waiting positions; and - to determine the waiting time based on the individual waiting times and on the basis of the probabilities, in particular as the expected value or as the median of the individual waiting times. 7) Device (101) according to claim 6, wherein the individual waiting time for the state (211, 212) with i occupied waiting positions, for i = 0, ... , m, depends on. 8) Device (101) according to one of the preceding claims, wherein - the device (101) is set up, the following to solve the matrix differential equation of the allocation model (210), to determine the waiting time; - P0(t), ... , P n (t) Probabilities for n + 1 different states (211) for different numbers of occupied charging points (201) at time t are; and - P n+1 (t), ... , P n+m(t) Probabilities for m different states (212) for different numbers of occupied waiting positions at time t are. 9) Device (101) according to one of the preceding claims, wherein the device (101) is configured, - depending on the determined waiting time, to take action regarding the route guidance of a vehicle (100); and / or - to determine a route for a vehicle that is at least partially electrically powered (100) depending on the determined waiting time. 10) Device (101) according to one of the preceding claims, wherein - the charging request rate λ and / or the charging rate p are time-dependent; - the device (101) is set up to determine the charging request rate λ and / or the charging rate p based on measurement data relating to the actual occupancy of the n charging stations (201) and / or the m waiting positions in the past; and / or - the device (101) is set up to read the charging request rate u and / or the charging rate p for the prediction time from a digital map in which the charging station (200) is recorded as a point of interest. 11) Device (101) according to one of the preceding claims, wherein - display the status data, the number of occupied charging stations (201) and / or the number of occupied waiting positions at the initial time t0; and / or - the device (101) is configured to query the status data from a server containing status data relating to a multitude from different charging stations (200). ) Method (300) for predicting a waiting time at a charging station (200) which has n charging points (201) for carrying out n charging processes, with n ≥ 1; wherein the method (300) comprises, - Determining (301) state data regarding the occupancy of the n charging points (201) and of m additional waiting positions at the charging station (200) at an initial time t0, with m > 1; and - Predict (302), based on an occupancy model (210) of the n charging stations (201) and the m waiting positions, a waiting time to Performing a charging process at one of the n charging stations (201) at a prediction time where the occupancy model (210) depends on a charge request rate λ of requests to carry out charging operations and on a charge completion rate p of completions of charging operations.

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