Method for positioning charging stations for electric vehicles in a predetermined space

Abstract

The invention relates to a method for positioning charging stations in a predetermined space, in which at least the following steps are performed: a) possible paths are defined (Traj); b) a route planner is used (Plan) to determine at least one route for each possible path, assuming the positions of potential charging stations, and the travel time and / or energy consumption, as well as the positions of the potential charging stations used, are determined; c) the predetermined space is discretized (Disc) into cells; d) a predefined number of cells with the highest number of potential charging station uses is determined; and e) the position (Pos) of the actual charging stations is defined. The invention also relates to a computer program product and a method for constructing charging stations. Figure 1 to be published

Description

Title of the invention: Method for positioning charging stations for electric vehicles in a predetermined space Technical field

[0001] The invention relates to a method for positioning charging stations for electric vehicles in a predetermined space.

[0002] It also relates to the construction of charging stations in the predetermined space as well as a computer program product.

[0003] Recent studies indicate that the needs of most single-car households would be met while requiring no behavioral changes more than 5% of the days. However, the concern about driving range is undoubtedly unavoidable for the user. In order to overcome this problem and avoid slowing down the sales and diffusion of electric vehicles, route planners are spreading rapidly these days.

[0004] While route optimization and charging strategy are important features for electric vehicle users, there are other levers to help users switch to electromobility and guide them in choosing the electric vehicle best suited to their needs. In particular, optimizing the placement of electric vehicle charging station infrastructure across the territory is a major tool for removing barriers to the adoption of electromobility.

[0005] Lack of knowledge about the consumption of electric vehicles in real use, as well as the still limited availability of charging stations in the territory, currently represent two major obstacles to the large-scale adoption of electric vehicles. Electric vehicles are often purchased based on the rare need for long journeys to be made during the year. In other words, users want the vehicle's battery to be large enough to allow the complete journey (for example from home to the holiday destination) with the minimum number of recharges. This leads to the purchase of vehicles with large batteries that are hardly suitable for everyday use and which have equivalent CO2 emissions (considering the entire life cycle of the vehicle) comparable to conventional vehicles.On the charging station infrastructure side, the incremental growth in the supply of charging stations is also not optimal because the locations for the installation of charging stations are limited and the installation decision is often driven by demand, which is higher near large cities. However, this does not cover the territory homogeneously or optimally, thus creating areas. white, which maintains the vicious circle of incentive to purchase vehicles with large batteries.

[0006] It is therefore necessary to reassure drivers about the dissemination of charging stations (also called “charging terminals”), and to suggest intelligent routes and charging sequences over long distances to reach the final destination while losing as little time as possible.

[0007] Other studies show that since the infrastructure of charging stations is not yet ubiquitous, proper route planning for electric vehicles is necessary. Prior art

[0008] The paper: “Liu, J., Peper, J., Lin, G., Zhou, Y., Awasthi, S., Li, Y., Rehtanz, C., 2021. A planning strategy considering multiple factors for electric vehicle charging stations along German motorways. International Journal of Electrical Power & Energy Systems, Volume 124” considers planning the location of charging station infrastructure to reduce user range anxiety. However, this method only provides for charging stations at motorway service areas and is therefore not suitable for all journeys. Furthermore, it uses a genetic algorithm that often yields inaccurate, far from optimal solutions. Furthermore, this method does not take into account geographical and temporal traffic data that can significantly influence travel time and energy consumption.The method is therefore limited to journeys mainly made by motorway, is not precise and is long and complex to implement.

[0009] Patent application US2016 / 300170 AA is also known, which seeks to position charging stations in a space. This method is based on the demand for charging distribution, which is collected by acquisition on electric vehicles. Thus, to develop charging stations, this assumes that certain vehicles make the journey. However, when no charging station is located on a certain route, there should be no electric vehicles on said route. Thus, the results of this method are biased by the journeys currently possible in electric vehicles.

[0010] Furthermore, the optimization carried out consists of finding a compromise between the cost of construction of the charging stations and the geographical coverage.

[0011] Thus, the technical problem that the present invention seeks to solve consists of optimizing the positioning of charging stations for electric vehicles in a certain area (department, country, state, region of the world) so that the greatest number of journeys by electric vehicle are possible, by optimizing the journey time and / or the energy consumption for each journey and this with a simple and precise. Summary of the invention

[0012] The invention relates to a method for positioning charging stations for electric vehicles in a given predetermined space comprising a road network for constructing charging stations in the predetermined space, the road network comprising road portions connected to each other by intersections, in which at least the following steps are carried out: a) we define possible paths in the predetermined space, each possible path starting from an origin and arriving at a destination, (b) a route planner is used to determine at least one possible route for each possible journey, each possible route being defined by a succession of road sections, and assuming that each intersection includes a potential charging station, and the travel time to cover the possible route by an electric vehicle and / or the energy consumption by the electric vehicle of each possible route and the position of the potential charging stations used by the electric vehicle on each possible route are determined, using the route planner; c) the predetermined space is discretized by discretization meshes, of predetermined width and / or length, and the number of uses of potential charging stations used for at least one possible journey, located in each discretization mesh, is identified; d) a predefined number of discretization meshes corresponding to the predefined number of discretization meshes having the greatest number of uses of potential charging stations is determined and; e) the position of the predefined number of effective charging stations is defined as a point of each discretization mesh of the predefined number of discretization meshes; and wherein at least steps b) to e) are implemented by computer means and preferably wherein all steps are implemented by computer means.

[0013] Preferably, in step a), a predetermined number of locations are determined in the predetermined space and the possible paths are defined, each possible path being defined by a path joining two distinct determined locations, one of the two distinct determined locations being the origin of the possible path considered and the other being the destination of the possible path considered.

[0014] Advantageously, data on the cities in the predetermined space are acquired to choose the locations, the data preferably being the total population of the cities and / or their hotel capacity, by means of an online registration system, to determine said locations.

[0015] According to a configuration of the invention, the different locations are separated two by two by at least a certain criterion, preferably at least 20 km, and more preferably, at least 40 km.

[0016] Advantageously, a defined number of locations is chosen, the defined number of locations being less than 200, and preferably less than 100.

[0017] Preferably, the route planner carries out at least the following steps: - a routing graph is constructed from the road network, the vertices of the routing graph representing the intersections of each road section and the arcs of the routing graph representing the road sections, - we position the potential charging stations on the routing graph, - the routing graph is extended by multiplying at least some arcs according to predefined parameters, the predefined parameters including the speed of travel on the section of road considered and / or the level of recharging when stopping at a potential recharging station, - an energy consumption model is constructed, the energy model defining the energy consumption of the electric vehicle according to predetermined parameters, taking into account possible recharges of the electric vehicle battery, - for each possible route, the energy consumption model is applied to the different arcs of the extended routing graph and the final route of the possible route is defined as the set of arcs enabling the travel time, or the energy consumption, or a compromise defined as a relative weight between these two parameters to be minimized using an optimization algorithm such as a Bellman-Ford shortest path algorithm, - the positions of the potential charging stations used and the level of charging achieved at each potential charging station used for the possible journey considered are determined from the final route defined.

[0018] According to a variant of the invention, the road sections take into account the topography of the road network, in particular the slope of the road section, the infrastructure and the signage by means of a geographic information system, the traffic data by means of a geolocation system or a geographic information system and preferably the meteorological data by means of an application programming interface system.

[0019] Advantageously, a measurement is acquired, on each portion of road in the road network, of the speed profile of several vehicles, by means of a geolocation system. lisation or an on-board telephone to determine, on the one hand, the average speed of the vehicles on the section of road, and on the other hand, the probability of stopping the vehicles at the end of the section of road considered, the probability of stopping being determined by the percentage of vehicles for which a stop is considered at the end of the section of road, the stopping of the vehicle being considered if, on at least one measurement point of the second half of the section of road, a speed of the vehicle is lower than a predefined value, the predefined value preferably being less than or equal to 10 km / h.

[0020] According to an implementation of the invention, once the position of the predefined number of effective charging stations has been defined in step e), step b) is repeated for each possible journey, considering only the defined position of the predefined number of effective charging stations instead of the potential charging stations, and the percentage of feasible journeys is determined, among all the possible journeys, for the electric vehicle, the journey being feasible if the electric vehicle can make the journey without completely discharging its battery, the journey being unfeasible in the opposite case.

[0021] Preferably, the steps of the method are repeated for different predefined numbers of effective charging stations, and different percentages of feasible journeys are determined to choose a final predefined number of effective charging stations.

[0022] The invention also relates to a computer program product downloadable from a communications network and / or recorded on a computer-readable medium and / or executable by a processor or a server, comprising program code instructions for implementing the method as described above, when said program is executed on a computer, a mobile telephone or a computing device.

[0023] The invention also relates to a method for constructing charging stations in a predetermined space, in which the steps of the method for positioning charging stations are carried out as described and in which, once all the steps of the positioning method have been carried out, the charging stations are constructed at the positions defined in the predetermined space. List of figures

[0024] Other characteristics and advantages of the method and / or the product according to the invention will appear on reading the following description of non-limiting examples of embodiments, with reference to the figures appended and described below. [Fig 1]

[0025] [Fig. 1] represents a first variant of the method of positioning the charging stations for electric vehicles according to the invention. [Fig 2]

[0026] [Fig.2] represents a second variant of the method of positioning the charging stations for electric vehicles according to the invention. [Fig 3]

[0027] [Fig. 3] shows locations and paths defined for the optimal search for the positions of the charging stations according to the invention. [Fig 4]

[0028] [Fig.4] represents an example of a route planner adapted to the method of positioning charging stations for electric vehicles according to the invention. [Fig 5]

[0029] [Fig.5] represents an example of a speed profile of the electric vehicle on a section of road for determining the probability of stopping at the end of the section of road, according to the invention. [Fig 6]

[0030] [Fig.6] represents a determination of the locations for an example of application of the method of positioning the charging stations for electric vehicles according to the invention. [Fig 7]

[0031] [Fig.7] represents the positioning of all potential charging stations for the application example of [Fig.6], of the method for positioning charging stations for electric vehicles according to the invention. [Fig 8]

[0032] [Fig.8] represents a comparison of the existing real charging stations and the charging stations resulting from the positioning method according to the invention for the locations of [Fig.6] and according to a first variant. [Fig 9]

[0033] [Fig.9] represents a comparison of the existing real charging stations and the charging stations resulting from the positioning method according to the invention for the locations of [Fig.6] and according to a second variant. [Fig 10]

[0034] [Fig. 10] represents a comparison of the existing actual charging stations and the charging stations resulting from the positioning method according to the invention for the locations of [Fig. 6] and according to a third variant. Description of the embodiments

[0035] The invention relates to a method for positioning charging stations for electric vehicles in a given predetermined space comprising a road network for constructing charging stations in the predetermined space (a territory physical data corresponding to the predetermined space, it can be for example, a country, a region, a department, etc.). By positioning and building charging stations in relevant locations, we can improve the charging service for electric vehicles and thus improve journey times in electric vehicles on the one hand and allow more possible journeys in electric vehicles on the other hand.

[0036] The aim of the invention is thus to determine positions of the infrastructure of charging stations for electric vehicles in a territory using, for example, statistical data on usage and attractiveness of geographical areas. This method uses in particular a route planner for electric vehicles which accurately estimates the energy consumption of the electric vehicle, the location of the charging needs and the level of charging required.

[0037] This is therefore a solution for developing the supply of charging stations in a given area. Indeed, certain current journeys are currently difficult, or even impossible, in electric vehicles given the positions of current charging stations, for example.

[0038] For the purposes of the invention, “charging stations” (also called “charging terminals”) relate to charging stations for electric vehicles, more precisely for recharging the batteries of electric vehicles.

[0039] An electric vehicle means a vehicle comprising only an electric motor powered by a battery. In other words, this vehicle does not have an internal combustion engine, such as a gasoline engine or a diesel engine or a hydrogen engine.

[0040] The road network comprises road sections (also called road segments) connected to each other by intersections (or crossroads). The intersections can thus correspond to points on the road network and the road sections to arcs connecting the points on the road network.

[0041] [Fig.l] illustrates, in a schematic and non-limiting manner, a first variant of a method of positioning charging stations according to the invention.

[0042] The positioning method comprises at least the following steps: a) defining (Traj) possible paths in the predetermined space, each possible path starting from an origin and arriving at a destination.

[0043] b) using (Plan) a route planner to determine at least one possible route for each possible journey, each possible route being defined by a succession of road sections, and assuming that each intersection includes a potential charging station, and determining the travel time to cover the possible route by an electric vehicle and / or the energy consumption by the electric vehicle of each possible route as well as the position of the potential charging stations used by the electric vehicle on each possible route, thanks to the route planner;

[0044] c) the predetermined space is discretized (Disc) by discretization meshes, of predetermined width and / or length, and the number of uses of potential charging stations used for at least one possible journey, located in each discretization mesh, is identified; d) we determine (Nb) a predefined number of discretization meshes corresponding to the predefined number of discretization meshes having the most potential charging stations used. e) we define (Pos) the position of the predefined number of effective charging stations as a point of each discretization mesh among the predefined number of discretization meshes determined in step d).

[0045] Furthermore, [Fig.l] comprises another optional step which consists of constructing (Cons) the actual recharging stations at the positions defined in step e) in the predetermined space (on the given physical territory corresponding to the predetermined space). This optional step is materialized by the dotted lines.

[0046] The predefined number can be chosen according to different criteria: coverage rate of journeys achievable by electric vehicles, investment cost, type of electric vehicle considered (size of the battery, energy consumption for example).

[0047] Thus, for specifically chosen journeys and a typical electric vehicle (known size and type of battery, known energy consumption of the vehicle as a function of various parameters), the method makes it possible, in a simple and rapid manner, to identify interesting (relevant) positions for the construction of charging stations in the territory.

[0048] Furthermore, at least steps b) to e) are implemented by computer means (computer or server for example) and preferably all steps are implemented by computer means. Thus, the method can be easily implemented in a computer or a server.

[0049] Step a) of defining the possible paths in the predetermined space

[0050] The possible routes may correspond, for example, to the most used routes (generally speaking, taking into account all vehicles and not only electric vehicles), the most probable or those connecting the largest cities. The “origin” and “destination” correspond to points on the road network. Each possible route thus corresponds to an Origin-Destination couplet and each of these possible routes is oriented. For example, the Origin-Destination route, which concerns the route leaving from Origin and arriving at Destination, is different from the Destination-Origin route which leaves from Destination and arrives at Origin.

[0051] According to a variant of the invention, in step a), it is possible to determine in the pre-space determined a predetermined number of locations and one can define the possible routes from these locations.

[0052] [Fig. 3] illustrates, in a schematic and non-limiting manner, an example of possible locations and routes for the method of positioning the charging stations according to the invention.

[0053] In the predetermined space shown in [Fig. 3], four locations E1, E2, E3 and E4 have been selected. Of course, a different number of locations could be used. The pairs of possible paths are represented by T1, T2, T3, T4 and T5. The pair of possible paths T1 connects E1 and E2 (one possible path from E1 to E2 and one possible path from E2 to E1).

[0054] The pair of possible paths T2 connects El and E3 (one possible path from El to E3 and one possible path from E3 to El).

[0055] The pair of possible paths T3 connects E3 and E2 (one possible path from E3 to E2 and one possible path from E2 to E3).

[0056] The pair of possible paths T4 connects El and E4 (one possible path from El to E4 and one possible path from E4 to El).

[0057] The pair of possible paths T5 connects E3 and E4 (one possible path from E3 to E4 and one possible path from E4 to E3).

[0058] The pair of possible paths T6 connects E2 and E4 (one possible path from E2 to E4 and one possible path from E4 to E2).

[0059] Thus, the possible routes include all routes connecting two distinct locations two by two.

[0060] Locations are understood to mean, for example, a point or position, such as a geolocation coordinate (for example, GPS, from the English "Global Positioning System", meaning global navigation system, or Galileo). A location may correspond to a city or a particular geographical reference point (a tourist location, for example).

[0061] Each possible route can thus be defined by a route joining two distinct determined locations (for example, a route can join two distinct cities), one of the two distinct determined locations being the origin of the possible route considered and the other being the destination of the possible route considered. Thus, it is possible to determine routes joining particular locations, where the trips to the destination leaving from or arriving at these particular locations are numerous. This allows the method to be more reliable and more relevant for the positioning of charging stations and their possible construction.

[0062] Preferably, data on the cities (or tourist places) in the predetermined space can be acquired (or measured or recorded) to choose the locations. This data can in particular include the total population of the cities (thus, it is possible choose the largest cities in terms of population and therefore users of electric vehicles) and / or their hotel capacity (in fact, hotel capacity directly reflects the tourist, commercial or industrial interest of the city and is linked to travel to or from other cities), using an online registration system or a database (preferably open access), such as the INSEE database (of the National Institute of Statistics and Economic Studies), to determine said locations. Thus, the possible routes correspond to the routes likely to be the most usable and therefore the most relevant for the positioning and construction of charging stations.Furthermore, this method makes it possible to avoid using current data on journeys in electric vehicles, which could give erroneous results, because this current data on journeys in electric vehicles does not take into account journeys that are not feasible or difficult to achieve today in an electric vehicle given the current lack of charging stations.

[0063] Advantageously, the different locations may be separated two by two by at least a certain criterion, preferably at least 20 km, and even more preferably, at least 40 km. Indeed, cities that are too close, such as cities in the same megalopolis, are not taken into account here. In addition, a distance between two locations less than the criterion is not very relevant, for example because the journey between these two locations can be made without a charging station. Thus, by removing some of the locations that are too close to each other, the method remains reliable and precise, while allowing a faster result and requiring less memory for the computing resources.

[0064] Preferably, a defined number of locations can be chosen, the defined number of locations being less than 200, and preferably less than 100. Thus, it is possible to limit the memory of the computing resources and the calculation time and to concentrate on the main routes and therefore the most interesting charging stations to position and build. Step b) route planning

[0065] A route planner is used to determine at least one possible route for each possible journey, each possible route being defined by a succession of road sections of the road network allowing the possible journey concerned from the origin to the destination. The route planner is used assuming that each intersection includes a potential charging station. The route planner may in particular include a GPS (for "Global Positioning System" meaning global navigation system) or a mobile phone to know the position of the vehicle concerned and it may also include software capable of determining a possible journey from an origin to a destination according to different parameters (travel time, energy consumption, vehicle type, road types, travel speed, etc.) In other words, we consider that a charging station can potentially be positioned at each intersection of the road network. Consequently, a journey can be made by planning a possible battery recharge at at least one intersection.

[0066] The route planner makes it possible to determine the travel time (or journey time) for covering the possible route (via the road sections of this route) by an electric vehicle and / or the energy consumption by the electric vehicle of each possible route (on the road sections of this route).

[0067] The route planner also makes it possible, for each possible route, to identify, among the potential charging stations (at each intersection of the road network), which are used by the electric vehicle to recharge the battery of the electric vehicle. It also makes it possible to determine the battery recharge level to be reached. In other words, the route planner takes into account the recharge time as a function of the recharge level: the battery is not systematically recharged to 100% at each recharge station. Indeed, the battery recharge time is not linear and the planner seeks the best compromise between recharge level and recharge time.

[0068] According to a configuration of the invention and as illustrated in [Fig.4], in a schematic and non-limiting manner, the route planner can carry out at least the following steps: - we can construct (Gr) a routing graph from the road network, the vertices of the routing graph representing the intersections of each road section and the arcs of the routing graph representing the road sections. In other words, the routing graph represents the road network of the territory considered (of the predetermined space). - we can position (Sp) the potential charging stations on the routing graph. In other words, in step b), we assume that the potential charging stations are located on all the vertices of the routing graph (at each intersection of each road section). - the routing graph can be extended (GrE) by multiplying at least some arcs according to predefined parameters, the predefined parameters including the speed of travel on the road section considered and / or the charging level when stopping at a potential charging station. Each arc of the extended graph thus corresponds to a movement along each road section with one or more distinct parameters: for example, several different travel speeds and / or several different charging levels for each arc connecting two same vertices. - we can construct (Conso) an energy consumption model, the model energy defining the energy consumption of the electric vehicle (a typical electric vehicle for which the type and size of the battery are known and the energy consumption is also known) according to predetermined parameters taking into account possible recharges of the battery of the electric vehicle. Thus, at each point of the route, the charge level of the battery can be known, thanks to the route planner. - for each possible route, we can apply the energy consumption model to the different arcs of the extended routing graph and we can define as the final route of the possible route, the set of arcs allowing to minimize (Opt), by an optimization algorithm such as a Bellman-Ford shortest path algorithm, the travel time, or the energy consumption or a compromise defined as a relative weight between these two parameters. The algorithm allows to minimize the travel time or the energy consumption or to find a mix between these two parameters. - from the defined final route, the positions of the potential charging stations used and the charging level achieved at each potential charging station used for the possible journey considered (on the defined final route) can be determined (Pos_niv). Thus, the user knows which charging stations are to be used on the route and what is the charging level to be achieved in order to optimize the route.

[0069] Advantageously, the first extension variable of the routing graph may be the travel speed to be adopted on certain portions of road along the route in order to find the right compromise between travel time and energy consumption. In other words, on certain portions of the road, particularly at high speed, it may be advantageous to travel at a speed lower than the average traffic speed or the maximum authorized speed in order to reduce energy consumption, and therefore reduce the amount of energy required to complete the journey, which may result in a reduction in the number of stops to recharge the battery. The portions of road affected by this speed reduction may be identified, for example, by an average travel speed greater than a certain threshold, to identify portions of motorway, for example.Thus, the graph of each section of road concerned is multiplied by the same average speed envisaged for this section of road. For example, several possible speed reductions can be envisaged compared to the traffic speed or the maximum authorized speed. Advantageously, the speed reductions envisaged in the route planner can be 0, 5, 10 and 15 km / h. Thus, each arc of the sections of road concerned is multiplied by four arcs in the extended routing graph corresponding to the choices of possible envisaged speed reductions.

[0070] Additionally or alternatively, the second extension variable of the graph of routing can be the charging level at each potential charging station. Therefore, the routing graph can be extended to contain as many copies of the outgoing edges of the vertices corresponding to a potential charging station as there are predefined levels of battery energy recovery during charging. For example, the allowed charging levels can correspond to {0,10,20, ..., 100}%, with a step size of 10%. Thus, each outgoing edge of the vertices of the potential charging stations in the extended routing graph is multiplied by eleven edges in the extended routing graph for all possible predefined values ​​of charging level.

[0071] Advantageously, the road sections can take into account the topography of the road network, in particular the slope of the road section, the infrastructure and the signage by means of a geographic information system (GIS), the traffic data by means of a geolocation system, such as a GPS, or a geographic information system (GIS) and preferably the meteorological data by means of an application programming interface system (an API which stands for "application programming interface" in English).

[0072] Taking the slope into account directly impacts energy consumption.

[0073] Taking into account road and signaling infrastructure makes it possible to take into account traffic lights, “give way” or “stop” signs where a stop may take place, speed limits or speed bumps for example. All of these elements can impact the average speed and the speed profile on a section of road.

[0074] By speed profile, we mean the variation of instantaneous speeds along the road section. The speed profile can be represented by a curve of the instantaneous speed as a function of the distance along the road section. The speed profile thus takes into account the accelerations and decelerations that may occur on the road section, which is not possible with the average speed information alone. This speed profile can in particular be established from measurements of vehicles, electric or not, moving on the road network. For example, this speed profile can be established from data from the Geco air® application (IFP Energies nouvelles, France) which records the speeds of users on the road sections. Geco air® is an application to reduce pollution linked to travel and to do this, it collects position and instantaneous speed data.

[0075] Traffic data allows the traffic density and its impact on average speed to be taken into account. The traffic data may in particular correspond to the average speed and / or the journey time of vehicles on the portion of the road network in time slots, for example every ten minutes. For this, it is possible, for example, to measure the average speed during the day, preferably over several days. Thus, we can retrieve a history of traffic and its daily variation recorded during the day.

[0076] By applying the traffic data, it is possible to more precisely evaluate the travel time and / or the energy consumption depending on the day and time considered and / or depending on the real-time traffic data.

[0077] Weather data, particularly temperature data, is also of interest for determining energy consumption if the vehicle's heating or air conditioning is activated. To do this, a REST weather API, such as the "Météo France™" API, can be used to retrieve real-time weather conditions at the start and update them regularly along the route, for example at fixed time intervals (particularly every hour) or space intervals (for example, every 60 km). Interpolation methods can then be used to obtain weather information for each section of road. The ambient temperature can be obtained for each section of road by interpolating the temperature forecasts obtained from the weather API.

[0078] The topography can be assumed to be time-invariant, or time-varying. Therefore, this data can be stored offline on local databases for faster online route calculation. To populate such an offline database, one can, for example, use REST (Representational State Transfer) web services from map and location data providers, for example, HERE Maps.

[0079] Each road section can also be classified according to its topology, in particular with an associated "functional" class, ranging for example from 1 for main motorways to 5 for secondary urban streets. In addition, each road section can be defined by a unique identifier and by its geometry with three-dimensional coordinates (at least the coordinates of the two ends of the road section). The coordinates can preferably be expressed in latitude, longitude and altitude above sea level. Finally, the type of intersection and / or signage at the downstream end of each road section can also be retrieved: in particular, the types of road infrastructure and signage such as traffic lights, stop signs, yield signs, roundabouts and toll booths can be taken into account.

[0080] According to a configuration of the invention, it is possible to acquire a measurement (or measure), on each portion of road of the road network, of the speed profile of several vehicles, by means of a geolocation system or an on-board telephone to determine, on the one hand, the average speed of the vehicles on the portion of road, and on the other hand the probability of stopping the vehicles at the end of the portion of road. considered, the probability of stopping being determined by the percentage of vehicles for which a stop is considered at the end of the road section, the stopping of the vehicle being considered if, on at least one measuring point of the second half of the road section, a vehicle speed is lower than a predefined value, the predefined value preferably being less than or equal to 10 km / h. Therefore, the statistics of stopping at the end of the road section are taken into account, for example due to a traffic light, a "stop" or "give way" sign, a motorway toll. When the stop occurs, it directly impacts the speed profile, the average speed on the road section and therefore it exerts an influence on the journey time and on the energy consumption of the vehicle.

[0081] [Fig.5] illustrates, in a schematic and non-limiting manner, an example of a vehicle speed profile used to determine the probability of stopping.

[0082] In this figure, the black curve illustrates the speed profile Pv of the vehicle on a section of road PR. This speed profile Pv defines the instantaneous speed v of the vehicle over time t going from the intersection Ptl to the intersection Pt2. The section of road PR comprises a first half PI and a second half P2, downstream of PI in the direction of travel of the vehicle on the section of road PR. The point M represents the middle of the section of road PR and therefore corresponds to the point separating the first half PI from the second half P2.

[0083] The speed profile Pv shows variable instantaneous speeds along the road portion PR. In particular, a part Vc of the speed profile Pv comprises instantaneous speeds lower than the criterion C. In this case, since at least one instantaneous speed of the part Vc located on the second part P2 of the road portion T1 is lower than the predefined criterion C, it is considered that this vehicle stops in the second portion P2 of the road portion PR.

[0084] Advantageously, the type of intersection and / or signaling can be used to determine the probability of stopping (i.e., the vehicle speed goes to zero) at the corresponding infrastructure element. This probability of stopping can then replace the average traffic speed and be used to refine the predicted driving behavior. Such a simple probabilistic model can be obtained by a classification method relating the stopping behavior to the type of intersection, the functional class of the road section considered and the time of day in order to capture the impact of peak and off-peak traffic.

[0085] According to an advantageous implementation of the invention, the route planner may comprise a travel time model, i.e. a model for estimating the time required to travel a portion of road.

[0086] The estimation of the travel time on an arc of the graph can come from the routing API, for example based on real-time traffic conditions, this time of route being noted Tj- On arcs where the speed is reduced compared to the average speed of circulation Vj (or compared to the maximum authorized speed), the journey time v increases compared to T (and can be calculated as follows:

[0087]

[0088] With Lia length of the road section and the reduction in speed considered on the section of road in relation to the average traffic speed.

[0089] Additionally or alternatively, on arcs where a recharge of the vehicle battery occurs at a charging station, the estimation of the travel time T ic can also take into account several contributions as follows:

[0090] . fP, siôc = 0 ' l Tt + Tj + Ts+T» siôc>0

[0091] Where the travel time from the routing API Tj could be replaced by T to take into account the desired speed reduction on the arc considered if, on the arc, there is a speed reduction option as discussed previously

[0092] And where the second term T d is the detour time required to reach the off-road charger location. This term depends on the distance from the nearest vertex of the routing graph and the assumed average speed of the detour.

[0093] The third term Ts is constant and represents the time spent after stopping the vehicle to interact with the charger and prepare the charge.

[0094] The fourth term Tc corresponds to the actual charging time, which depends on the initial state of charge of the battery at the start of charging, the final state of charge, which is not necessarily 100% as explained previously, the capacity of the battery and the power of the charger.

[0095] 5C is the recharge level on the section of road considered.

[0096] In order to correctly estimate the charging time Tc, a simple model inspired by the CC-CV (“constant-current constant-voltage”) charging method, with a pre-charge phase when the battery capacity is low, such as that described in the document “FAV Pinto, LHMK Costa, and MD de Amorim, “Modeling Spare Capacity Reuse in EV Charging Stations based on the Li-ion Battery Profile,” in International Conference on Connected Vehicles and Expo (ICCVE), 2014, pp. 92-98”, can be used.

[0097] Advantageously, the route planner may also comprise a steering model. In order to accurately estimate the energy consumption required for travel on an arc of the routing graph, the first step consists of predicting the driver's behavior on the section of road, in order to predict the vehicle's traction power demand. For example, a model such as

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] 1 'Intelligent Driver Model' (IDM) described in 'M. Treiber, A. Hennecke, and D. Helbing, “Congested Traffic States in Empirical Observations and Microscopy Simulations,” Physical Review E, vol. 62, pp. 1805-1824, 2000' to generate synthetic expected speed profiles on each arc of the routing graph based on dynamic traffic data. This model is a car-following model that describes the dynamics of the position and speed of vehicles in interaction with preceding vehicles. In a route planning framework according to the invention, since it is not possible to know precisely in advance the behavior of vehicles preceding the vehicle for which the route is optimized for the method according to the invention, this model can be adapted to be used for a vehicle without interaction with actual preceding vehicles.To do this, we determine the probability of stopping at the end of the road section, as described previously in this description, and we then assume that a virtual preceding vehicle appears to disrupt the speed of the vehicle for which the route is calculated. For example, we can use the data from the Geco air ® application for this. The presence of this virtual preceding vehicle forces a speed transition to zero speed, thus forcing a stop at the end of the road section considered. On the contrary, when the probability of stopping does not reflect a stop at the end of the road section considered, the vehicle for which the route is calculated reaches the average speed of the following road section. The speed dynamics depends on the boundary conditions of the road section of the road network considered, and in particular on the presence of a stopping event at the upstream and / or downstream end (intersection) of this road section. If no stopping event is planned at the downstream end (intersection) of the road section, only the free road term appears in the speed dynamics: Where a is the vehicle's maximum acceleration, v is the vehicle's speed, and x is its position, 1" can be considered a driver responsiveness parameter, and VT is the vehicle's target speed. vfree corresponds to the free road speed, i.e. without stopping at the downstream end (intersection) of the section of road. The target speed VT can be set according to the length L; the section of road and the position of the vehicle for which the route is calculated as follows: V, if x (t) < max j 0, - dh} 'Jf — VM, if x(t) ^max{0,4-4}

[0104] Where ÿ. is the average travel speed on arc i, V- j is the travel speed on the next arc i + 1, dh is the length of the driver's prediction horizon, i.e. a parameter that indicates the distance from which the driver starts to target the speed of the next road section.

[0105] In addition, the initial speed can be set to zero if there is a probability of stopping at the end of the upstream arc (of the road portion), and to y. otherwise.

[0106] On the other hand, if a stop is predicted at the downstream intersection of the road portion, the speed dynamics may also include an interaction term and a virtual preceding vehicle may be assumed to influence the behavior of the vehicle for which the route may be calculated, as follows:

[0108] where d0 is the minimum inter-distance from the virtual preceding vehicle, Th is the minimum distance from the virtual preceding vehicle, and b is a predetermined braking acceleration. The speed of the virtual preceding vehicle vi can be calculated as follows:

[0109] if x(t)< max{0, Lt -dh} 0, if x (t) > max {0, Lj - dh)

[0110] If the virtual preceding vehicle is stopped (yi = 0), the vehicle for which the route is calculated cannot stop exactly at the end of the road section, taking into account the fact that the virtual preceding vehicle is assumed to stop at the end of the section and that the inter-distance d0 and the length of the vehicle itself are taken into account.

[0111] Advantageously, the route planner may also include an energy consumption model.

[0112] The energy consumption of the electric vehicle is calculated by means of its dynamics and the modeling of the powertrain in order to accurately estimate the losses of the electric drive and the absorption of auxiliary power. The driving force Fw to drive the wheels of the vehicle in rotation, for a given speed v(t) (i.e. the speed provided by the control model described previously) can be defined as follows:

[0113] (t) = my (t) + c2v (t)2 + cp' (t) + c0 + mgsina

[0114] where m is the mass of the vehicle, * is the gravitational acceleration, a is the slope of the road which varies along the road section, and the coefficients co, ciet c2 are identified for a considered electric vehicle. The wheel force Fw can be converted into mechanical power Pm demanded from the propulsion system:

[0115] Pm (t) = Fw ( t ) V ( t )

[0116] where is the transmission efficiency.

[0117] Finally, the energy consumption Eb of the battery can be defined as follows:

[0118] / / , . Eb=\0P„,(t)tr^.m+p„x dt

[0119] where represents the efficiency of the electric propulsion, tf is the travel time of the road section, and Paux is the auxiliary power absorption along the road section. For example, the auxiliary power demand can be assumed to come mainly from cabin air conditioning or driver comfort needs, and it can be a convex function of the ambient temperature, available on each road section.

[0120] According to a variant of the invention, the optimization algorithm can be defined by an optimization problem designed to find the best compromise between travel time and / or energy consumption.

[0121] The optimization problem can be formulated as follows:

[0122] minE^ 4 / ^ + ( 1 - 2)04)¾ r -L . 0, if i = ? if i-id otherwise Cmi„ IL£=p. <a’^c, v ie a {0,1}

[0123] With

[0124] t denoting the arcs of a linear graph L(G) obtained from the extended routing graph G, the linear graph having a vertex representing each arc of the extended routing graph and each arc of the linear graph representing a pair of adjacent arcs of the extended routing graph.

[0125] f the set of incoming arcs Ç,

[0126] i the set of outgoing arcs Ç,

[0127] 1 the original arc,

[0128] At the destination arc,

[0129] P( the route composed of all the arcs Ç connecting the origin 1 to

[0130] the time cost,

[0131] w <!--? le coût en énergie,<br-->

[0132] -V the decision variable which is 0 if the arc concerned does not belong to the route or 1 if it belongs to the route

[0133] 2 the weight of compromise,

[0134] A' represent the set of arcs of the extended routing graph,

[0135] Cmin: a minimum acceptable state of charge of the battery

[0136] C: battery charge status

[0137] Thus, the optimization problem is an objective function that is written as a weighted sum of the time costs and energy costs on each arc of the routing graph, as estimated by the travel time and energy consumption models described previously. The weight of the trade-off is denoted by Δ. The decision variable Xç takes binary values ​​depending on whether arc C belongs to the route or not. The second constraint is a classical flow conservation constraint. The third constraint requires each possible route part to verify the physical limits of the battery capacity, denoted by C. To reduce the concern about autonomy, the minimum state of charge of the battery Cmin can be strictly greater than zero, for example greater than a predefined minimum charge value.

[0138] Preferably, the optimization algorithm may be a Bellman-Ford shortest path algorithm to guarantee the optimality of the routing solution in order to avoid in particular negative values ​​due to the presence of the energy term.

[0139] Considering a potential charging station at each intersection makes it possible to spatially and temporally identify charging needs in order to minimize travel time.

[0140] Step c) of discretization of the predetermined space into meshes

[0141] The predetermined space is discretized by discretization meshes, of predetermined width and / or length. The meshes may be parallelograms, more particularly rectangles and preferably squares. For each discretization mesh, the number of uses of potential charging stations used for at least one possible journey (for each possible route of each possible journey) located in each discretization mesh is identified. In other words, for each discretization mesh, the number of times a potential charging station located in the discretization mesh considered is used on the different possible routes of the different possible journeys of step b) is counted. This step thus makes it possible to identify the areas where the greatest number of charging stations is used.Thus, each discretization mesh is associated with a number of uses of potential charging stations located in the space of the mesh.

[0142] According to a variant, the dimensions of the meshes can be adaptive, in particular to the area considered in the predetermined space. In other words, the mesh consisting of all the meshes can be adaptive. The mesh is then not constant but can adapt to road density or population density. For example, it is possible to use meshes of larger dimensions (length and / or width of the larger meshes) in rural or less dense areas than in areas urban areas where the meshes then have smaller dimensions (smaller mesh length and / or width). This solution saves memory space, by maintaining a more precise mesh in areas with more road sections or population.

[0143] Step d) of determining uses of charging stations by mesh

[0144] During this step, a predefined number N is chosen and the N meshes with the greatest number of uses of potential charging stations located in the mesh considered are determined, in step b). Indeed, these meshes correspond to those on which the most uses of charging appear. They are therefore the most relevant for constructing a charging station on this mesh.

[0145] Step e) of defining the positions of the effective charging stations

[0146] During this step, the position of the predefined number N of effective charging stations is defined as a point of each discretization mesh among the predefined number of discretization meshes determined in step d). Indeed, the charging stations can then be positioned in an area of ​​high charging need for electric vehicles.

[0147] The point of each mesh where the effective charging stations are positioned can advantageously be the center of the mesh in order to be positioned as much as possible in the center of the identified area of ​​interest (the mesh). Alternatively, it could also be a vertex of the mesh or any point in the mesh.

[0148] As we considered in step b) a potential charging station at each intersection in order to spatially and temporally identify the charging needs to minimize travel time, we classify here the places most frequently used for charging electric vehicles in order to optimize the location of the charging stations. Optional steps:

[0149] According to one embodiment of the invention, once the position of the predefined number of effective charging stations has been defined in step e), step b) can be repeated for each possible journey, considering only the defined position of the predefined number of effective charging stations instead of the potential charging stations. In other words, in this case, the potential charging stations (positioned at all intersections of road sections) are replaced by the defined effective charging stations. Thanks to this replacement, it is thus possible to determine the percentage (or rate) of feasible journeys, among all possible journeys, for the electric vehicle, taking into account only the defined effective charging stations.The journey is feasible if the electric vehicle can complete the journey without completely discharging its battery (or by respecting a minimum battery charge value, the minimum charge could be zero or a strictly positive value), the journey . being otherwise unfeasible. In this way, the impact of the predefined number of actual charging stations on the achievable travel rate can be assessed.

[0150] Preferably, for different predefined numbers of effective charging stations, different percentages (or rates) of feasible trips can be determined (one for each predefined number of effective charging stations) to choose a final predefined number of effective charging stations. If the rate of feasible trips is lower than a certain criterion, then the predefined number of effective charging stations can be increased to improve the rate of feasible trips.

[0151] [Fig.2] illustrates, in a schematic and non-limiting manner, a second variant of the method of positioning the charging stations according to the invention. The references identical to those in [Fig.1] correspond to the same steps and will not be detailed again.

[0152] After step e) where the position of the predefined number of effective charging stations is defined (Pos) as a point (preferably the center) of each discretization mesh among the predefined number of discretization meshes determined in step d), step b) can be repeated (Plan B) for each possible route, considering only the defined position of the predefined number of effective charging stations instead of the potential charging stations. Then, the percentage (rate) of feasible routes, among all possible routes, for the electric vehicle can be determined by taking into account only the defined effective charging stations.The journey is feasible if the electric vehicle can complete the journey without completely discharging its battery (or by respecting a minimum battery charge value, the minimum charge could be zero or a strictly positive value), the journey being unfeasible otherwise. In this way, the impact of the predefined number of effective charging stations on the feasible journey rate can be assessed.

[0153] As for [Fig.l], the construction step (Cons) of the actual charging stations in the predetermined space is optional.

[0154] The invention also relates to a computer program product downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor or a server, comprising program code instructions for implementing the method as described above, when said program is executed on a computer, a mobile phone or a computing device. As a result, the use of the method is simple and rapid.

[0155] The invention also relates to a method for constructing charging stations in a predetermined space, in which the steps of the method for positioning charging stations are carried out as described above and in which, once all the steps of the positioning method have been carried out, the charging stations are constructed at positions defined in the predetermined space on the corresponding given physical territory. Thus, a network of charging stations for electric vehicles can be constructed on a given territory corresponding to the predetermined space, optimally for the main routes corresponding to the connections between specific locations on the territory, such as cities or major tourist sites. In other words, the invention may relate to the use of the positioning method as described, for the construction of the charging stations in the predetermined space, at the positions defined by the positioning method as described. Application example

[0156] The method according to the invention was implemented using data made available by the National Institute of Statistics and Economic Studies (INSEE) on French territory. The locations chosen to define the possible routes are considered, for this example, as the union between the hundred most populated French cities and the hundred French cities with the most hotel capacity (thus representing the hundred most touristic cities). Only cities at least 40 km apart were retained. Thus, 61 locations were chosen and allowed the definition of 3600 pairs of possible routes between these different locations (illustrated by the black points with white centers on [Fig.6] where the map of France is represented according to its Latitude Lat on the ordinate and its longitude Lon on the abscissa).

[0157] For this example, the traffic data on the road network comes from HERE Maps for March 15, 2022, at 8 a.m. The battery capacity of the electric vehicle considered here is 30 kWh.

[0158] For each possible route, a route calculation is performed by searching for the fastest route, using the Bellman-Ford algorithm, and considering possible speed reductions relative to the average traffic speed. Possible speed reductions are considered to be 0, 5 and 10 km / h relative to the average traffic speed for this example.

[0159] For the 3600 possible journeys between the different locations chosen on French territory, 29000 potential charging stations are considered, one at each intersection of road sections of the different journeys. These 29,000 potential charging stations are illustrated in [Fig.7] by the different dark gray points on the map of France represented according to its Latitude Lat on the ordinate and its longitude Lon on the abscissa. It can be noted that these potential charging stations are concentrated on the main roads connecting the different locations.

[0160] In Figures 8, 9 and 10, the points SF represented by light gray triangles represent the final effective charging stations obtained by the method according to the invention while the points SE represented by the black circles represent the existing actual charging stations. Figures 8, 9 and 10 are distinguished by the choice of the predefined number of actual charging stations which is 900 in [Fig.8], which is 600 in [Fig.9] and which is 300 in [Fig. 10], each predefined number thus corresponds to a different positioning strategy.

[0161] Step b) of the method according to the invention was then implemented again by only considering the actual charging stations determined (replacing the potential charging stations) by the invention, for all possible routes and for the three positioning strategies corresponding to figures 8, 9 and 10.

[0162] For the different possible journeys, from each positioning strategy (each corresponding to a predefined distinct number of effective charging stations) resulting from the method according to the invention, a rate (percentage) of journeys achievable by electric vehicle, an average journey time in hours and an average energy consumption per journey in kWh are deduced. These results are compared with the same results from the existing real charging stations illustrated in Figures 8 to 10. The following table illustrates these results: Existing charging stations (prior art) Strategy 1 (according to the invention) Strategy 2 (according to the invention) Strategy 3 (according to the invention) Predefined number of effective charging stations 750 900 600 300 Rate of feasible journeys (%) 96.5 98.4 97.5 80.2 Average journey time (h) 9.1 8.6 8.8 10.7 Average energy consumption (kWh) 122 118 120 149

[0163] The strategies corresponding to a predefined number of 900 and 600 (strategies 1 and 2) give better results, on the three criteria (rate of feasible journeys, average journey time and average energy consumption) compared to the current positioning of the existing real charging stations, which shows the interest of the method according to the invention. Strategy 3, which has only 300 actual charging stations, shows a lower result. However, these results are still interesting because the gain between the cost of building the stations and their strategic interest in recharging electric vehicles is nevertheless significant.

Claims

Claims

1. Method for positioning charging stations for electric vehicles in a given predetermined space comprising a road network for constructing charging stations in the predetermined space, the road network comprising road portions (PR) connected to each other by intersections, in which at least the following steps are carried out: a) we define (Traj) possible paths (Tl, T2, T3, T4, T5) in the predetermined space, each possible path (Tl, T2, T3, T4, T5) starting from an origin and arriving at a destination, b) using (Plan) a route planner to determine at least one possible route for each possible journey (T1, T2, T3, T4, T5), each possible route being defined by a succession of road sections (PR), and assuming that each intersection includes a potential charging station, and determining the travel time to cover the possible route by an electric vehicle and / or the energy consumption by the electric vehicle of each possible route as well as the position of the potential charging stations used by the electric vehicle on each possible route, using the route planner; c) the predetermined space is discretized (Disc) by discretization meshes, of predetermined width and / or length, and the number of uses of potential charging stations used for at least one possible journey (Tl, T2, T3, T4, T5), located in each discretization mesh, is identified; d) we choose a predefined number N and we determine the N discretization meshes having the greatest number of uses of potential charging stations and; e) we define (Pos) the position of the predefined number of effective charging stations (SF) as a point of each discretization mesh of the predefined number of discretization meshes; and wherein at least steps b) to e) are implemented by computer means and preferably wherein all steps are implemented by computer means.

2. Method for positioning charging stations according to claim 1, wherein, in step a), a predetermined number of locations (E1, E2, E3, E4) are determined in the predetermined space and we define the possible paths (Tl, T2, T3, T4, T5), each possible path being defined by a path joining two distinct determined locations (El, E2, E3, E4), one of the two distinct determined locations (El, E2, E3, E4) being the origin of the possible path considered and the other being the destination of the possible path (Tl, T2, T3, T4, T5) considered.

3. Method for positioning charging stations according to claim 2, wherein data on the cities of the predetermined space are acquired by means of an online registration system for choosing the locations (El, E2, E3, E4), the data preferably being the total population of the cities and / or their hotel capacity, to determine said locations.

4. Method for positioning charging stations according to one of claims 2 or 3, in which the different locations (E1, E2, E3, E4) are separated two by two by at least a certain criterion, preferably at least 20 km, and more preferably at least 40 km.

5. Method for positioning charging stations according to one of claims 2 to 4, in which a defined number of locations (El, E2, E3, E4) is chosen, the defined number of locations (El, E2, E3, E4) being less than 200, and preferably less than 100.

6. Method for positioning charging stations according to one of the preceding claims, in which the route planner carries out at least the following steps: - a routing graph is constructed (Gr) from the road network, the vertices of the routing graph representing the intersections of each road portion (PR) and the arcs of the routing graph representing the road portions (PR), - the potential charging stations are positioned (Sp) on the routing graph, - the routing graph is extended (GrE) by multiplying at least certain arcs according to predefined parameters, the predefined parameters comprising the speed of circulation on the road portion considered and / or the level of recharging when stopping at a potential charging station, - an energy consumption model is constructed (Conso),the energy model defining the energy consumption of the electric vehicle according to predetermined parameters taking into account, takes into account the possible recharges of the battery of the electric vehicle, - for each possible journey (Tl, T2, T3, T4, T5, T6), we apply the energy consumption model on the different arcs of the routing graph extended to each possible route for each possible journey and we define as the final route of the possible journey (Tl, T2, T3, T4, T5, T6), the set of arcs allowing to minimize (Opt), by an optimization algorithm such as a Bellman-Ford shortest path algorithm, the travel time, or the energy consumption or a compromise defined as a relative weight between these two parameters, - we determine (Pos_niv), from the final route defined, the positions of the potential charging stations used and the level of recharging achieved at each potential charging station used for the possible journey (Tl, T2, T3, T4, T5, T6) considered.

7. Method for positioning charging stations according to claim 6, wherein the road sections (PR) take into account the topography of the road network, in particular the slope of the road section, the infrastructure and the signage by means of a geographic information system, the traffic data by means of a geolocation system or a geographic information system and preferably the meteorological data by means of an application programming interface system.

8. Method for positioning charging stations according to one of claims 6 or 7, in which a measurement is acquired, on each road portion (PR) of the road network, of the speed profile (Pv) of several vehicles, by means of a geolocation system or an on-board telephone to determine, on the one hand the average speed of the vehicles on the road portion, and on the other hand the probability of stopping the vehicles at the end of the road portion considered, the probability of stopping being determined by the percentage of vehicles for which a stop is considered at the end of the road portion, the stopping of the vehicle being considered if, on at least one measurement point of the second half (P2) of the road portion (PR), a speed of the vehicle is less than a predefined value (C), the predefined value preferably being less than or equal to 10 km / h.

9. Method for positioning charging stations according to one of the preceding claims, in which, once the position of the predefined number of effective charging stations has been defined in step e), step b) is repeated (PlanB) for each possible route (T1, T2, T3, T4, T5, T6), considering only the defined position of the predefined number of actual charging stations instead of the potential charging stations, and determining (%) the percentage of feasible journeys, among all possible journeys (T1, T2, T3, T4, T5, T6), for the electric vehicle, the journey being feasible if the electric vehicle can complete the journey without completely discharging its battery, the journey being unfeasible otherwise.

10. A method of positioning charging stations according to claim 9, wherein the steps of the method are repeated for different predefined numbers of effective charging stations, and different percentages of feasible journeys are determined to choose a final predefined number of effective charging stations.

11. Computer program product downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor or a server, comprising program code instructions for implementing the method according to one of the preceding claims, when said program is executed on a computer, a mobile telephone or a computing device.

12. Method for constructing charging stations in a predetermined space, in which the steps of the method for positioning charging stations according to one of claims 1 to 10 are carried out and in which, once all the steps of the positioning method have been carried out, the charging stations are constructed (Cons) at the positions defined in the predetermined space.

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