Classifications

• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/01—Detecting movement of traffic to be counted or controlled
  • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
  • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
  • G08G1/012—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/01—Detecting movement of traffic to be counted or controlled
  • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
  • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
• • G—PHYSICS
  • G08—SIGNALLING
  • G08G—TRAFFIC CONTROL SYSTEMS
  • G08G1/00—Traffic control systems for road vehicles
  • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
  • G08G1/205—Indicating the location of the monitored vehicles as destination, e.g. accidents, stolen, rental
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2200/00—Type of vehicles
  • B60L2200/26—Rail vehicles
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B60—VEHICLES IN GENERAL
  • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
  • B60L2240/00—Control parameters of input or output; Target parameters
  • B60L2240/70—Interactions with external data bases, e.g. traffic centres
  • B60L2240/72—Charging station selection relying on external data
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T10/00—Road transport of goods or passengers
  • Y02T10/60—Other road transportation technologies with climate change mitigation effect
  • Y02T10/72—Electric energy management in electromobility
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/14—Plug-in electric vehicles
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
  • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
  • Y02T90/10—Technologies relating to charging of electric vehicles
  • Y02T90/16—Information or communication technologies improving the operation of electric vehicles

Definitions

• the invention relates to a method and to a device for deter ⁇ mining a position for a charging station.
• Electric vehicles are expected to play an increasing part in future transportation scenarios.
• EVs require charging, which consumes more time compared to filling up a tank of a vehicle with petrol.
• providing an efficient charging service with sufficient capacity for EVs is a major goal and challenge in electronic mobility scenarios.
• One solution might be to deploy charging stations (also re ⁇ ferred to as Electric Vehicle Supply Equipment, EVSE) on var ⁇ ious locations to allow the user of the EV to plug in his car and recharge it.
• Another solution might be providing replace- able batteries that could be quickly swapped, e.g., at cer ⁇ tain locations (e.g., petrol stations could be used for that purpose) .
• the system handles the movement of the user, i.e. the mobile terminal, within a cov ⁇ erage area set up by several base stations or cells.
• the com ⁇ munication network receives and generates several events based on state or activity (e.g., start communication, han ⁇ dover, [periodic] location update) of the mobile terminal. Some of these events are tagged with a unique identification of a serving base station and/or a cell. Matching the Cell- IDs to the geographical location of the base station allows locating the user at the time of the event.
• EVs can play an in ⁇ creasingly important role in municipal areas.
• a suitable provision of EVSEs is an important task to achieve suitable level of the acceptance and usability of EVs.
• EV users may prefer charging their EVs at home, housing density in cities often force people to park (and charge) on the street or in public parking places.
• the li- mited range of the EV may require charging at places where people work, play or shop.
• a normal parking lot may need to change into a parking lot supporting charging the EV.
• Municipal areas (especially historical ci ⁇ ties) parking space is a severely limited resource and any transition of parking space is not a trivial task.
• a lot of factors need to be considered by city plan ⁇ ners in order to provide a suitable EVSE network that sup ⁇ ports and enables the EV experience, yet keeping the overall usage of parking space at an efficient level.
• the problem to be solved is to provide a solution for efficiently setting up EVSEs to enable an attractive an effi ⁇ cient EV scenario.
• the solution presented can in particular be used in an EVSE planning system.
• a position for a charging station is determined.
• the position for a charging station can be a position of an additional charging station as well. It is even possible that such position for a charging station leads to an extension or an upgrade of an already existing charging station: For exam- pie, a charging station may already be deployed providing a capability for charging a certain number of electric ve ⁇ hicles. The result of this method reveals that this spot would be a suitable place for at least one (additional) charging station; hence, the capability of the charging sta ⁇ tion can be increased to supply charging further electric ve ⁇ hicles at this location.
• the charging station mentioned (also re ⁇ ferred to as EVSE, Electric Vehicle Supply Equipment) can be any kind of installation or deployment to be used for charging an electronic vehicle.
• the solution presented in particular bears the advantage that based on mobility information of users (users with mobile phones and/or electric vehicles), derived, e.g., from mobili ⁇ ty events of different kinds, movement profiles can be deter ⁇ mined. In particular based on a large number of movement pro ⁇ files suitable locations for charging stations can be deter- mined. This approach allows considering various kinds of mo ⁇ bility events.
• mobility in this case also comprises loca tions where the users remain for a certain amount of time, e.g., park the electric vehicle, work, play, stay at home, etc .
• mobility events of at least one electric vehicle are determined, wherein the electric vehicle compris es means for communicating via a radio communication system.
• the electric vehicle may be connected to the communication system via an on-board unit having (communication module) , e.g., a SIM-card.
• communication module e.g., a SIM-card.
• At least one electric vehicle is associated with at least one mobile phone
• the movement profiles are determined based on the mo ⁇ bility events of the mobile phone and of the electric vehicle .
• the at least one electric vehicle associated with the at least one mobile phone via a trusted third party.
• the mobility events can be tagged with identities.
• An electric vehicle users' da ⁇ tabase can be provided that can be searched for such identi- ties.
• the movement profile is determined on at least one of the following information:
• At least one of the movement profiles can be determined by assessing additional information.
• All kinds of information available via different resources can be consi ⁇ dered, e.g., Internet, local data in the electric vehicle, local data in the mobile phone, input made by the user, traf ⁇ fic data from a central unit or from a traffic component, building information, charging service information (e.g., oc- cupied or free charging stations), routing information (comprising, e.g., a path and/or alternative routes to reach a destination), information from public transportation, etc.
• charging service information e.g., oc- cupied or free charging stations
• routing information comprising, e.g., a path and/or alternative routes to reach a destination
• information from public transportation etc.
• the movement profile of the mobile phone may comprise a move ment of the electric vehicle, wherein particular mobility events or a succession of events may indicate that the user is moving within his electric vehicle.
• Further parameters can be used to derive that the user is moving in the electric vehicle, e.g., GPS-data (speed above a given threshold; path on a road (that may not be accessible to pedestrians)), status information (the user may have indi ⁇ cated manually that he is in the car; also this kind of sta ⁇ tus information can be set automatically, e.g., by near field communication when the user enters the car or the status of the car can be conveyed to the mobile phone, e.g., via Blu ⁇ etooth) or the like.
• GPS-data speed above a given threshold; path on a road (that may not be accessible to pedestrians)
• status information the user may have indi ⁇ cated manually that he is in the car; also this kind of sta ⁇ tus information can be set automatically, e.g., by near field communication when the user enters the car or the status of the car can be conveyed to the mobile phone, e.g., via Blu ⁇ etooth
• the movement profile is deter- mined based on transportation data, comprising at least one of the following:
• the movement profiles can, e.g., be correlated with a trans ⁇ portation database, in particular a database comprising or compiling data of public transportation means.
• a trans ⁇ portation database e.g., a database comprising or compiling data of public transportation means.
• geographical coordinates of transportation e.g., train sta- tion, bus terminal, routes of trains and busses
• correlating movement profiles of a plurality of users enables detecting public transportation means: If hundreds of users travel the same route at substan- tially the same time, this may indicate that they are all on the same train.
• profiles of transportation means can be compiled for, e.g., trains, planes, busses, etc. This allows differentiating (with certain likelihood) the us ⁇ er from traveling in his EV or on a bus.
• the electric vehicle has an embedded, SIM- equipped communication module mapping the IMSI of the elec ⁇ tric vehicle to the IMSI of the user and tracking both allows detecting the deviation of the location between the user and the electric vehicle.
• a certain threshold e.g. 1 km
• the movement profile is determined based on a correlation of individual profiles.
• single users can be utilized to determine at least one movement profile.
• car-sharing and/or car-pooling concepts can be utilized for determining movement profiles.
• any type or kind of correlating information of individual users can be used for determining movement profiles.
• the speed can be used to de- termine groups of people moving together in a particular type of vehicle.
• mobility events of various kinds can be considered, in particular mobility events of individual profiles that are not attached to an electronic vehicle.
• This allows determining promising locations for charging stations based, e.g., on a number of people commuting at a certain time between certain spots: For example, parking areas in the vicinity of train stations could be identified as promising locations for deploying or upgrading charging stations, in particular considering charging stations that have already been deployed. This enables reaching the train station by electric vehicle to an additional number of users who did not yet utilize an electric vehicle for that purpose, but perce- ive this as an opportunity because of the additional or available charging resources. This approach may thus help ac ⁇ cepting electric vehicles as being a convenient means to com ⁇ mute .
• a location or an area is determined at which the mobile phone and/or the electric vehicle remains for at least a pre ⁇ determined period of time.
• the mobile phone and/or the electric vehicle can be determined whether or not the mobile phone and/or the electric vehicle is on the move. It is in particular possible to determine whether the vehicle is parked and does not move at all; such parking position can be a suitable candidate for deploying a charging station.
• mobility data may be aggregated for a longer time period, e.g., for several days or weeks, in order to ob tain a sufficient data basis.
• the aggregated mobility data can be analyzed for movement patterns in order to determine likelihood for the user (mobile phone and/or electric ve ⁇ hicle) to spend said amount of time at one place considered long enough for at least partially charging the vehicle's battery .
• the mobility events can be fil- tered.
• Mobility events can be collected and aggregated from many mo ⁇ bile phones and/or electric vehicles and be filtered. For ex ⁇ ample, sporadic events can be automatically filtered out (when being the exception to the rule) thereby increasing the overall accuracy. It is noted, however, that filtering can be achieved in various ways, e.g., an exception can be identi ⁇ fied by at least one event occurring less than a pre-defined number of times within a large set of aggregated data.
• Excep- tion rules and filters can be set up for each mobile phone and/or electric vehicle. As an alternative, such rules or filters may apply for a set of mobile phones and/or electric vehicles .
• the position for a charging station is determined based on at least one already existing charging station.
• the mobility events are determined by at least one of the following:
• the mobility events comprise at least one of the following: - a call event;
• the event may refer to an incoming or an outgoing event.
• the event can be triggered by a movement of the mobile phone, by time, by a message or call arriving at or departing from the mobile phone.
• the electric vehicle may comprise an interface towards the mobile communication system and thus be able to provide mobility events as well that could be con ⁇ sidered for compiling the movement profiles.
• the elec ⁇ tric vehicle in this scenario acts as a mobile user.
• mobility events may use network locations asso ciated with location area codes and/or Cell-IDs.
• a Cell-ID database can be used to convert a network location into a geographical position or an area (e.g., depending on the accuracy of the system) .
• a charging station based on the position for a charging station, a charging station is deployed, installed and/or set up at such position or in the vicinity of such po sition .
• a forecast is provided regarding a utili zation of charging stations.
• a forecast (or simulation) can be provided regard ⁇ ing already existing and otherwise added or projected charg ⁇ ing stations.
• it can be determined how an additional charging station or an upgrade of an existing charging sta- tion affects the overall charging service.
• the mobility events are deter ⁇ mined by intercepting data via interfaces of a radio network, in particular of a standardized mobile communication network.
• a device for de ⁇ termining a position for a charging station comprising a processing unit that is arranged
• processing unit can comprise at least one, in particular several means that are arranged to execute the steps of the method described herein.
• the means may be logically or physically separated; in particular sev ⁇ eral logically separate means could be combined in at least one physical unit.
• Said processing unit may comprise at least one of the follow ⁇ ing: a processor, a microcontroller, a hard-wired circuit, an ASIC, an FPGA, a logic device.
• said device is a charging station planning system or it is utilized by a charging station planning system.
• the solution provided herein further comprises a computer program product directly loadable into a memory of a digital computer, comprising software code portions for performing the steps of the method as described herein.
• a comput ⁇ er-readable medium e.g., storage of any kind, having comput ⁇ er-executable instructions adapted to cause a computer system to perform the method as described herein.
• Fig.l shows an example of a typical daily movement pattern of an EV user on a map driving between locations, wherein said locations can be determined based on
• Fig.2 shows a path on a map from a first location via a
• the second location to a third location, wherein the first location is associated with a home and the third location is associated with a workplace of the user;
• Fig.3 shows a schematic diagram comprising various exem ⁇ plary information sources to be utilized by a Charg ⁇ ing Station Planning System (CSPS);
• CSPS Charg ⁇ ing Station Planning System
• Fig.4 shows a diagram visualizing a possibility to provide an identify mapping utilizing information from an external identity provider
• Fig.5 shows an exemplary timeline visualizing how the CSPS aggregates data for each user (or EV) identity.
• An electric vehicle charging station planning system (CSPS) is suggested that enables an efficient electric vehicle (EV) charging station service by detecting areas for providing, e.g., deploying, setting up or installing new charging station equipment (i.e., Electric Vehicle Supply Equipment, EVSE) .
• CSPS Electric Vehicle Supply Equipment
• daily movement patterns of the EV users can be detected and used to discover areas where the users spend sufficient time that could potentially be utilized for (at least partially) charging the EV.
• the CSPS collects data from a communication network
• the data collected comprises mobility events of the mobile phone subscribers.
• PLMN Public Land Mobile Network
• MSS Mobile Switching Server
• RNC Radio Network Controller
• the PLMN maintains an approximate loca ⁇ tion of the mobile phone to serve calls; this is
• mobility events comprise, e.g.
• the location update may occur pe ⁇ riodically (e.g., after a pre-defined timeout the mo ⁇ bile phone may send a location report to the network) and/or it may occur irregularly (e.g., when the mobile phone crosses a location area boundary, which can be determined by a location area code change) ; - if the phone switches base stations during an active call, the core network is informed about the new serving base station (handover event) ;
• a location area may comprise a set of base stations that are grouped together to op ⁇ timize signaling. Typically, tens or even hundreds of base stations share a single Base Station Controller
• BSC Global System for Mobile Communications
• RNC Radio Network Controller
• the BSC handles allocation of radio channels, receives measurements from the mobile phones and con ⁇ trols handovers from base station to base station.
• a unique number called a "location area code" can be assigned to each location area.
• the location area code is broadcast by each base station, known as a "base tran ⁇ sceiver station" BTS in GSM, or a NodeB in UMTS, at regular intervals.
• Fig.l shows an example of a typical daily movement pat ⁇ tern of an EV user on a map driving from a location 101 to a location 102 and returns at the end of the work day back to the location 101.
• These locations 101, 102 can be determined based on PLMN coverage information.
• a driver of an EV leaves his car in a parking lot around hubs of public transpor ⁇ tation (e.g., train stations, etc.) to continue his journey via such public transportation.
• public transpor ⁇ tation e.g., train stations, etc.
• the location of the user (determined via the user's mobile phone) during, e.g., business hours does not represent a desired location for deploying an EV charging facility; instead, the park and ride parking lot may have to provide the required charging capacity. It is possible to automatically determine the user switching from his EV to public transportation by using information associated with the public transportation. For example, a database of public transportation means can be used and correlated with the user's movement pro ⁇ file.
• An EV is assigned to its user (via the user's mobile phone) ;
• - Mobility events of mobile phones are collected in a database (including on-board units of EV's that have a connection to the communication network, e.g., a SIM card being deployed with the EV) ;
• This allows building movement profiles for the users.
• the movement profiles are correlated with a public transportation database. For example, geographical coordinates of public transportation (e.g., train station, bus terminal, routes of trains and busses) can be matched against the coordinates of the user's mobile phone.
• correlating movement pro ⁇ files of a plurality of users enables detecting pub ⁇ lic transportation means: If hundreds of users travel the same route at substantially the same time this may indicate that they are all on the same train.
• profiles of transportation means can be compiled for, e.g., trains, planes, busses, etc. This allows differentiating (with certain likelihood) the user from traveling in his EV or on a bus.
• the location of this change of transportation can be used for EVSE planning pur- poses (rather than the mere position of the user determined via his mobile phone) .
• the accuracy of determining the location may affect the correlation results.
• the CSPS may utilize the GMLC/SMLC methods supported by the PLMN .
• the EV has an embedded, SIM-equipped communication module (e.g. an according OBU) mapping the IMSI of the EV to the IMSI of the user and tracking both allows detecting the deviation of the location between the user and the EV.
• SIM-equipped communication module e.g. an according OBU
• mapping the IMSI of the EV to the IMSI of the user and tracking both allows detecting the deviation of the location between the user and the EV.
• a certain threshold e.g. 1 km
• the CSPS may determine the areas where the user spends enough time for (at least partially) charging the EV. Since the mo ⁇ bility events can be scarce and not sufficient to imme ⁇ diately derive accurate results, the CSPS may aggregate mobility data for several days or weeks, in order to ob ⁇ tain a sufficient data basis. The aggregated mobility data can be analyzed for movement patterns in order to determine a likelihood for the user to spend said time amount at one place considered enough for charging the EV's battery.
• the CSPS may create daily schedules inserting the mobility event into a 24-hour timeline. After the aggregation the timeline can be analyzed and the involved Cell-IDs can be weighed according to the time spent within their cell coverage. The Cell-IDs of the highest weight may then represent typical locations of this user. Typical areas are the user's home and/or workplace. Home areas may be more or less relevant from a charging ser ⁇ vice planning' s point of view, but in particular target locations of EV users in the dense city center can be a key information for deciding on providing charging services.
• locations may be detected and/or differentiated.
• one example may be a user's home and another example may be the user's workplace. Any location at which the user stays for a given period of time and at a certain rate (e.g., how often during a week) may be used as a spot for an EVSE . It may even be of advantage to know that a user has already chosen an existing EVSE, which indicates that this user is willing to charge the EV at this location .
• a decision for deploying an EVSE at a location may be based on a location that is frequently chosen by several users .
• the CSPS can gather and then utilize da ⁇ ta regarding quantitative figures about a real number of the EV users.
• the EV user may not be able to charge the EV in the vicinity of one of the user's favorite loca ⁇ tion (i.e., home or workplace), because of a general lack of EVSEs or because of the EVSEs available are oc ⁇ cupied .
• the EV itself may be registered with the mobile network.
• the EV may have a SIM card (e.g., in an on-board unit) and communicate via the mobile network with a manufacturer or a service provider. This is a further possibility to collect mobility events for the EVs .
• the CSPS may be able to determine how the service of charging EVs can be improved. Based on the number of users or EVs and/or the locations where the users park their EVs, a decision can be made where to deploy EVSEs
• Fig.2 shows a path 201 on a map from a location 202 via a lo cation 203 to a location 204.
• a user with his EV may move from the location 202 (home) to the location 204 (work) .
• the EV is parked at the location 202 for the night and at the lo cation 204 throughout the day.
• the CSPS can determine that EVSEs 205 and 206 provided far from the location 204 (although the EVSEs 205 are deployed along the path 201) are most likely not be used for charging this particular EV travelling between locations 202 and 204.
• installing EVSEs 207 in the vicinity of the location 204 would increase the likelihood for this user to cope commuting with an EV even if this user has no possibility to charge the EV at the location 202.
• Fig.3 shows a schematic diagram comprising various exemplary information sources to be utilized by the CSPS.
• the CSPS may collect mobility data (mobility events 301) from the PLMN operator (s) 302.
• mobility data mobility events 301
• all or some local operators and/or local authorities may pro ⁇ vide this information.
• the system is also useful if partial information from a limited set of commu- nication services providers is available. It is an op ⁇ tion to extrapolate (missing) information.
• Data can be extracted from the MSS, e.g., using NSN's Traffica product.
• the BSC in case of GSM
• RNC in case of UMTS
• the MSC in case of GSM
• MSS in case of UMTS
• standardized GSM/3G in ⁇ terfaces can be intercepted to become aware of the events of interest.
• the CSPS may focus on EV users and/or EVs (having a con ⁇ nection to the mobile network) .
• the CSPS may in particu ⁇ lar utilize a database of effective EV users 304. A list of such users may be matched with phone subscribers 303 in order to determine a link between EV movements and mobility events of the respective car user's mobile phone .
• the identities of EV user and mobile subscribers can be handled by a trusted third party (an Identity Provider 403 as shown in Fig. .) .
• an Identity Provider 403 as shown in Fig. .
• different identities of users i.e. EV users and/or EV with SIM card 401 and mobile subscribers 402
• the mobility events can be tagged with identi ⁇ ties pursuant to the scope used by the CSPS.
• addi ⁇ tion the EV users' database can be searched for such identities .
• An owner of the EV (according to official databases), which may not necessarily be the driver of the EV.
• a person charging the EV (from the EVSE database) ; it may be a fairly correct assumption that whoever charges the EV is the actual driver.
• a driver i.e. the person who is moving with the EV from a starting location to a destination (e.g., when the EV has stopped for a considerable amount of time) . It is an option to check the mobility events of the EV (if it is connected to the mobile network) and/or this user's mobile phone (e.g., location up ⁇ dates that can be determined when changing location area) .
• the CSPS may collect and aggregate data, sporadic events (e.g., the owner's wife took the EV for shopping) can be automatically filtered out (when being the exception to the rule) without affecting the accuracy of the system.
• sporadic events e.g., the owner's wife took the EV for shopping
• Mobility events use network locations expressed in loca ⁇ tion area codes and Cell-IDs, etc.
• a Cell-ID DB 305 in ⁇ put source enables the CSPS to convert a network loca ⁇ tion into a geographical position.
• the accuracy of mo ⁇ bile events may be not as good as geographical position ⁇ ing (e.g. GPS), thus the locations of the mobile events can be transformed to areas (e.g. cell coverage) .
• the phone may select a single serving base station
• the serving base sta ⁇ tion is connected and the Cell-ID is recorded by the network (e.g., in the MSC) .
• the CSPS may have a table of cells with the base station's geographi ⁇ cal coordinates and the angle of the cell. Combined with location services (LCS) methods a (rough) position of the mobile phone can be determined.
• LCS location services
• the phone may use several NodeBs at the same time.
• a geome ⁇ tric centre of the Node Bs can be used as a more accu ⁇ rate position of the mobile phone.
• the CSPS may consid ⁇ er already existing EVSE installations 306.
• potential charging areas can be determined in a step 307.
• the charging areas on the map can be refined in a step 308.
• the EVSE service areas can be determined in a step 309.
• the CSPS may collect the events and information from the giv ⁇ en sources continuously or iteratively.
• mobility events 301 may comprise at least one of the following:
• tim ⁇ ing advance e.g., tim ⁇ ing advance, round-trip-time, etc.
• the mobile subscriber database 303 may comprise at least one of the following:
• the EV database 304 may comprise at least one of the follow ⁇ ing :
• the EV including its owner (referring, e.g., to a vehicle subscriber database), - the IMSI (if available, to be paired with mobility events ) ,
• the CSPS may be aware of the type of the EV, e.g., based on data from the EV database 304: For example, according to ISO/IEC 15118, the EVSE may communicate details regarding the type of the EV to a backend server. A battery size together with a battery load status could be used for determining a minimum charging time required. When the EV is connected to the EVSE, its charging time is known from the EVSE database.
• the CSPS can become aware of any occupation regarding existing EVSEs, e.g., from-to charging details may show when the re ⁇ spective EVSE is occupied or idle.
• the Cell-ID database 305 may comprise mappings from the Cell ID to a geographical position (exact info where the base sta tions are located) comprising, e.g., latitude, longitude, Cell-ID, angle, etc. This information can be used for calcu ⁇ lating the mobile device's position based on mobility events
• Fig.5 shows an exemplary timeline visualizing how the CSPS aggregates data for each user (or EV) identity.
• the timeline may comprise network locations (Cell-ID, SAC, LAC) of the us- er (and/or EV) at a given time of a day.
• the CSPS may then map the timeline data to the Cell-ID DB in order to convert the timeline into geographical coordinates.
• the SAC can be similar to the LAC, but the SAC refers to the packet-switched network of 3G. If there is no access to the actual Cell-ID, the SAC may be used for determining the loca ⁇ tion in the 3G network.
• the timeline may be analyzed thereby clustering the events. This allows the CSPS to determine areas where the user spends most time of a day. The CSPS may also match de ⁇ tected areas across the list of EV users and determine
• the system can be connected to the database of currently in ⁇ stalled EVSEs 306 to filter already served EV users from the ones that require charging (the EVSE database may provide geographical coordinates to find the area which they already support) . If the EVSE management system collects usage infor ⁇ mation, that data can be matched to the determined number of users to fine-tune the actual energy demands. For example, not every EV user needs charging during daytime every day, but for a long time period the average EVSE usage can be de- termined (e.g. need charge every 3 days) .
• the CSPS can determine (e.g., visualize) historical usage of data of the EVSE network (e.g., on a map showing where EVSE stations are already located and how well they are utilized throughout the day) .
• the CSPS can also determine (e.g., vi ⁇ sualize) target locations of the users (who already have an EV or of users that are promising candidates for obtaining an EV) .
• the mass of locations distributed statistically over time e.g., throughout the day(s) allows determining promis- ing spots for deploying (additional) EVSEs.
• the CSPS upon selection of additional EVSEs on a map may show an estimate as how such EVSEs may affect the overall network, e.g., how many users will utilize these EVSEs and which users are affected and how this affects al ⁇ ready existing EVSEs.
• One particular objective may be di ⁇ rected to determining whether additional EVSEs are well used and previous EVSEs remain occupied as well. Hence, a forecast can be provided regarding the overall EVSE utilization for a particular surrounding.
• the CSPS may in particular recommend locations to deploy fur- ther EVSEs for an optimized (overall) usage. It is noted, however, that locations may general be subject to certain re ⁇ strictions. For example, it may not be allowed installing EVSEs in security areas, flood-prone areas or the like. Such restrictions may have to be considered by setting up addi- tional EVSEs; in particular an optimized location can be determined also based on such restriction.
• EVSEs may be deployed in groups.
• the CSPS may thus recommend an (initial) number of EVSEs at a location or it may recom ⁇ mend expanding existing locations by adding EVSEs next to al ready existing ones.
• the CSPS may also provide different types of mappings. First, the CSPS may assign a list of users to every EV it is aware of based on the EVSE database. Next, for every such EV user, it may determine locations, at which the EV stays for a pre ⁇ defined period of time. Such location information can be used by the CSPS to determine a parking and/or charging need for a given area.
• the solution presented allows separating overall commuting traffic from the real EV users. Instead of pointing out the well-known high-traffic areas, the approach detects hotspots used by the EV users regardless of other traffic or people (getting there by other types of traffic) .
• the proposed solution does not require any action from the EV user to locate his possible charging positions. Hence, no ex ⁇ tra investment is required and it does not create any issue of data privacy.
• the CSPS may effectively reduce the required computational and storage capacity by focusing the movement pattern calculation to the list of EV users.

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• 2011
  • 2011-10-13 EP EP11769876.1A patent/EP2767105A1/de not_active Withdrawn
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