Method for monitoring the charging operation of electrically powered vehicles in a fleet.

Abstract

The invention relates to a method for monitoring the charging operation of electrically powered vehicles (1) of a fleet, wherein the vehicles (1) are parked at a charging station (5) at a first time and are charged with electrical energy by it at a second time, wherein the charging of the vehicles (1) is controlled by a vehicle-external fleet management system (3).In a method that can also manage the charging operation of electrically powered vehicles that are not parked at a common logistics location, the geographically distributed vehicles (1) send their position data to the fleet management system (3) after being parked at a charging station (5), wherein the position data of all vehicles (1) are continuously processed by an artificial intelligence (15), the artificial intelligence (15) determines a preferred charging station (5) of the respective vehicle (1) from various position data of each vehicle (1), which is used by the fleet management system (3) to coordinate the charging processes of the different vehicles (1).

Description

[0001] The invention relates to a method for monitoring the charging operation of electrically powered vehicles of a fleet, wherein the vehicles are parked at a charging station at a first time and are charged with electrical energy from this at a second time, wherein the charging of the vehicles is controlled by a vehicle-external fleet management system.

[0002] DE 10 2022 001 716 A1 discloses a method for ensuring the charging function of an electric vehicle. The vehicle is connected to a charging station at a first time in order to charge the vehicle at a second, later time. When the vehicle's ignition is switched off, a check is made to determine whether the vehicle is within a predefined distance from a charging station suitable for charging. If this is the case, the vehicle's readiness for charging is checked. If this is not the case, a warning signal is issued to a vehicle backend or the vehicle user. If the vehicle user uses the same charging station at least three times, its position is stored in the vehicle's control unit as the vehicle's typical charging position.

[0003] The object of the invention is to provide a method for monitoring the charging operation of electrically powered vehicles of a fleet association, with which method the charging operation of electrically powered vehicles that are not parked at a common logistics location can also be managed.

[0004] The invention is based on the features of the independent claims. Advantageous developments and refinements are the subject of the dependent claims. Further features, possible applications, and advantages of the invention will become apparent from the following description and the explanation of exemplary embodiments of the invention illustrated in the figures.

[0005] The problem is solved by the subject matter of patent claim 1.

[0006] In the method explained at the outset for monitoring the charging operation of electrically powered vehicles in a fleet, wherein the vehicles are parked at a charging station at a first time and charged with electrical energy from this station at a second time, wherein the charging of the vehicles is controlled by a fleet management system external to the vehicle, the geographically distributed vehicles send their position data to the fleet management system after parking, where the position data of all vehicles are continuously processed by artificial intelligence, wherein the artificial intelligence determines a preferred charging station for the vehicle from various position data of each vehicle, which is used by the fleet management system to coordinate the individual charging processes of the various vehicles.Artificial intelligence is understood to mean computer science technologies that mimic cognitive skills previously only possessed by humans, such as strategic thinking. The use of artificial intelligence methods allows for the rapid processing of large quantities of individual data with high computing power and storage capacity.

[0007] In addition to the position data, the artificial intelligence can process other data, such as vehicle identification, ignition status, charge level, etc., and creates a cluster of position data for all vehicles in the fleet. This can reduce inaccurate positioning, especially in locations with poor GPS reception.

[0008] In one embodiment, the determined preferred charging station is transmitted from the fleet management system to a vehicle control unit, which saves it as the default charging station. The default charging station can be incorporated into the vehicle's route management system and taken into account during route planning.

[0009] In a further embodiment, the artificial intelligence links the position data of the preferred charging station of the respective vehicle with a geofence area surrounding the vehicle. The vehicle's control unit checks subsequent position data of the vehicle to determine whether the vehicle is within the geofence of the preferred charging station. This makes it easy to estimate the distance of the vehicle to the preferred charging station. The artificial intelligence can incorporate backend data from other vehicles into the calculation and differentiate between the geofence areas of neighboring vehicles.

[0010] In a further embodiment, the vehicle's readiness for charging is checked when the vehicle is parked within the geofence of the preferred charging station. This check determines whether a reliable electrical connection exists between the vehicle and the charging station and whether the vehicle's charging process can be initiated.

[0011] In a further embodiment, the artificial intelligence sends a warning message to the fleet management system and / or a mobile device of the vehicle user if the vehicle is parked within the geofence of the preferred charging station or is not ready to charge. The warning can be transmitted over a longer distance, allowing the user to return to the vehicle and, for example, check that the charging cable is properly connected. The fleet management system can send a suitable person to the vehicle and the charging station to verify its proper functionality.

[0012] In a further embodiment, the vehicle's position data is transmitted to the fleet management system and / or the vehicle's control unit when the vehicle's ignition is switched off. Since the ignition status is transmitted to the fleet management system and the artificial intelligence as a digital signal, they can easily distinguish whether a particular vehicle should be considered when planning the fleet's charging processes. This reduces the data volume that the artificial intelligence must process.

[0013] In a further development, the fleet management system evaluates the energy infrastructure of each vehicle's individual geofence and takes this into account when coordinating the individual charging processes of each vehicle. This allows the charging time to be determined individually, taking into account the energy available within the geofence as well as the energy costs. In particular, weather conditions can also be taken into account, making charging with renewable energy sources such as solar or wind particularly advantageous.

[0014] Further advantages, features, and details will become apparent from the following description, which describes at least one exemplary embodiment in detail. Described features may form the subject matter of the invention alone or in any meaningful combination, possibly even independently of the claims, and may, in particular, also be the subject matter of one or more separate applications.

[0015] Showing: Fig. 1 an embodiment of a system for carrying out the method according to the invention, Fig. 2 an embodiment of the method according to the invention.

[0016] In Fig. 1 shows an embodiment of a system for carrying out the method according to the invention. A plurality of vehicles 1 of a vehicle fleet, for example of a service provider, are controlled by a fleet management system 3 in the form of a vehicle-external backend. The vehicles 1 are electrically powered vehicles which, after completing their orders, are parked by the user at a charging station 5 and electrically connected to it via a charging cable 7. Each of these vehicles 1 is located within a geofence area 11 extending around the charging station 5. Each vehicle comprises a control unit 13 which communicates wirelessly with the fleet management system 3 and exchanges data. The data of all vehicles 1 of the vehicle fleet is processed in an artificial intelligence 15.

[0017] In the following, Fig.2, the case can be considered that the geographically distributed electrically powered vehicles 1 are to be charged individually at a later time, for example at night, via the charging station 5 at which they were parked at an initial time. The respective time is controlled for each vehicle 1 based on the results of the artificial intelligence 15 by the fleet management system 3, which outputs corresponding data to the respective vehicle 1. In step 100, the vehicles 1 continuously output data to the vehicle management system 3. This data includes the current position of the vehicle 1, which is determined using a GPS signal. Furthermore, the vehicle identification number of the vehicle 1 is transmitted to identify the vehicle 1.Further transmitted data relates to the status of the vehicle, such as information about whether the vehicle is being charged or not and further information about whether the ignition of vehicle 1 is on or off. With a good GPS signal, only a few data points are collected per vehicle 1, whereas with a poor GPS signal, considerably more data points are collected. In step 110, the vehicle management system 3 only forwards data from vehicles 1 whose ignition is switched off to the artificial intelligence 15. In step 120, the artificial intelligence 15 learns from the data available for each vehicle 1 which charging station 5 it prefers to use. The charging station 5 determined as preferred is output to the vehicle management system 3 and / or the control unit 13 of the vehicle 1 in step 130.

[0018] Once the preferred charging position of vehicle 1 has been determined, artificial intelligence 15 checks in step 140 whether the vehicle has been parked in the geofence area 11 of charging station 5 for longer than a specified period of time. If this is the case, step 150 checks whether vehicle 1 is reliably connected to charging station 5 via charging cable 7 and is therefore ready to charge. If this is not the case, the fleet management system 3 receives corresponding information from artificial intelligence 15 to either inform the user of vehicle 1 via their mobile phone or to dispatch service personnel to the affected vehicle 1 to restore vehicle 1's charging readiness.If vehicle 1 is ready to be charged, the artificial intelligence 15 adds information on the availability of electrical energy and the current energy costs in the geofence area 11 of the respective vehicle 1 and transfers this charging readiness information to the fleet management system 3 for individual control of the charging process for each vehicle 1. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2022 001 716 A1

[0002]

Claims

[1] Method for monitoring the charging operation of electrically powered vehicles (1) of a fleet, wherein the vehicles (1) are parked at a charging station (5) at a first time and are charged with electrical energy by the latter at a second time, wherein the charging of the vehicles (1) is controlled by a vehicle-external fleet management system (3), characterized by that the geographically distributed vehicles (1) send their position data to the fleet management system (3) after being parked at a charging station (5), wherein the position data of all vehicles 1 are continuously processed by an artificial intelligence (15), wherein the artificial intelligence (15) determines a preferred charging station (5) of the respective vehicle (1) from various position data of each vehicle (1), which is used by the fleet management system (3) to coordinate the individual charging processes of the various vehicles (1). [2] Method according to claim 1, characterized by that the determined preferred charging station (5) is transmitted from the fleet management system (3) to a control unit (13) of the vehicle (1), which stores it as a standard charging station. [3] Method according to claim 1 or 2, characterized by that the artificial intelligence (15) links the position data of the preferred charging station (5) of the respective vehicle (1) with a geofence area (11) surrounding the vehicle (1), wherein the control unit (13) of the vehicle (1) checks later position data of the vehicle (1) to determine whether the vehicle (1) is located in the geofence area (11) of the preferred charging station (5). [4] Method according to claim 3, characterized by that the readiness of the vehicle (1) to be charged is checked when the vehicle (1) is parked in the geofence area (11) of the preferred charging station (5). [5] Method according to claim 3 or 4, characterized bythat the artificial intelligence (15) issues a warning message to the fleet management system (3) and / or a mobile terminal of the user of the vehicle (1) if the vehicle (1) is parked within the geofence area (11) of the preferred charging station (5) or if it is not ready to charge. [6] Method according to at least one of the preceding claims, characterized by that the position data from the vehicle (1) are output to the fleet management system (3) and / or the control unit (13) of the vehicle (1) if the ignition of the vehicle (1) is switched off for a longer than a predetermined period of time. [7] Method according to at least one of the preceding claims, characterized by that the fleet management system (3) evaluates the energy infrastructure of the individual geofence area (11) of each vehicle (1) and takes it into account when coordinating the charging processes of the individual vehicles (1).

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