method for managing travel routes, device, system and corresponding program

Abstract

The invention relates to a method for managing the movements of a set (AE) of vehicles (AE1, …, AEn) equipped with respective energy reserves and capable of making journeys within a network of traffic lanes equipped with a set of energy recharging infrastructures (IR1, …, IRm) capable of recharging vehicles with energy, the infrastructures being geographically distributed over the network of traffic lanes. Such a method comprises the steps of: calculating (S01), for vehicles (AE1, …, AEn) the set (AE) of at least one journey (TAE11, …, TAEnp) to be carried out, said calculation (S01) being a function of: data representing an available energy level of the vehicles (EDAE1, …, EDAEn) at a given time; data representing waiting times for access to the energy recharging infrastructures (IR1, …, IRm) of the traffic lane network; transmission (S02), to the vehicles (AE1, …, AEn) of said at least one journey (TAE11, …, TAEnp). Figure 2.

Description

Title of the invention: Travel route management method, device, system and corresponding program Field of the invention

[0001] The invention relates to the planning of travel routes for road vehicles. The invention relates more particularly to the overall planning of travel routes for a set of road vehicles. The invention finds particular application in the field of managing the use of energy charging infrastructures, such as electric vehicle charging stations, within one or more given geographical areas. Previous Art

[0002] The advent of navigation software designed to allow everyone to have a route between a starting point and an arrival point has allowed all road vehicle drivers to free themselves from the constraints of manual journey planning. This software is now mostly integrated into vehicle operating systems which are able, using on-board connectivity functions, to plan not only an initial journey, but also to make changes to this journey depending on traffic conditions. In particular, this software is able to determine alternative journeys, as far as possible, when slowdowns and traffic jams are observed on the initially planned journey. For electric vehicles, these functionalities are extended by also offering to determine recharging stages for these vehicles for users.Depending on the vehicle's characteristics (in particular its initial autonomy), the on-board software can therefore make it possible to plan more or less frequent stops for the vehicles at charging areas.

[0003] The growth in the number of electric vehicles on the roads, however, is putting a strain on the capacity of charging areas. Indeed, at a given location, for example a motorway service area, the number of electric vehicle charging stations is relatively limited. The consequences of this limit include, in particular, an increasing lengthening of the access time to a charging station. The time required to recharge the vehicle itself is also added to the overall time required for the entire journey. To date, waiting and recharging times are uncontrolled variables that greatly affect the ability of an electric vehicle, on the one hand, to follow the route initially planned during its planning and, on the other hand, to reach its destination with a certain predictability. This situation also affects greatly the electric charging infrastructure of the various areas and stations on the road network. Indeed, the number of people using the charging points is uneven, making access to some of these infrastructures very difficult while others remain little used. In addition, vehicle-by-vehicle journey planning does not a priori solve the problem of excessively large numbers of people using the charging infrastructure. All of these problems, on the one hand, hinder the mass adoption of electric vehicles and, on the other hand, also pose problems in terms of sizing the infrastructure, particularly that of electricity distribution.

[0004] The invention aims to improve this situation. Summary of the invention

[0005] Thus, the invention more particularly relates to a method for managing the movements of a set of vehicles equipped with respective energy reserves and capable of making trips within a road network equipped with a set of energy charging infrastructures capable of charging vehicles, the infrastructures being geographically distributed over the road network, the method being implemented within an electronic device connected to at least one communication network,

[0006] Such a method comprises the steps of: - calculating, for at least one vehicle of the set, a trip to be made, said calculation being a function: - of a data representative of the available energy level of the vehicles at a given instant; - of data representative of waiting times for accessing the energy charging infrastructures of the road network; - transmission, to vehicles, of said at least one journey.

[0007] Thus, this method not only makes it possible to have overall routes for a set of vehicles, but also to take into account, for this set of vehicles, waiting times and recharging times at energy recharging infrastructures. This consideration thus makes it possible to manage the journeys of hundreds of thousands of vehicles, simultaneously, while limiting the times required for energy recharging.

[0008] According to a particular characteristic, the calculation step is also a function of data representative of the capacities of the energy recharging infrastructures (vehicle recharging speed permitted by the recharging infrastructures) and of the vehicle recharging capacities (recharging speed possible / accepted by the vehicles).

[0009] Thus, it is possible to adapt the allocation of vehicles on the journeys to infrastructures which are adapted to their needs.

[0010] According to a particular characteristic, the calculation step is also a function of data representative of the duration of the journey as a function of the positions of the vehicles and the positions of the energy recharging infrastructures.

[0011] Thus, it is possible to adapt the allocation of vehicles to infrastructures according to their respective distances.

[0012] In other words, the calculation step is also a function of data representative of the expected loading time for the vehicles based on the loading capacities of the vehicles and the energy recharging capacities of the infrastructures.

[0013] According to a particular characteristic, the calculation is also a function of data representative of the time required, for the vehicles, to obtain a predetermined energy level.

[0014] Thus, it is possible to take into account a time to be spent at the terminals of the energy recharging infrastructures, for the vehicles of the set of vehicles, to allow them to obtain a sufficient charge level to continue their journeys until the next recharging stage or until their respective destinations and thus facilitate the use of the recharging infrastructures.

[0015] According to a particular characteristic, which can be implemented alternatively or cumulatively with the previous one, the calculation step is implemented so as to minimize the sum of the journey times, the sum of the waiting times for access to the energy recharging infrastructures which must occur during the energy recharging of the vehicles during the journeys and the sum of the durations of the recharges.

[0016] Thus, the calculation determines the journeys of the vehicles by limiting the periods of non-use of the charging infrastructures, by efficiently distributing the vehicles to the charging infrastructures, by limiting the waiting times of the vehicles to the charging infrastructures, optionally by reducing the cost of successive recharging for the vehicles.

[0017] An optimization is thus carried out for the entire community of vehicles by minimizing the overall durations which include the journey times, the waiting times at the energy recharging infrastructures and the actual charging times.

[0018] According to a particular characteristic, which can be implemented alternatively or cumulatively with the previous one, the calculation step is implemented so as to minimize the sum of the waiting times for access to the energy recharging infrastructures which must occur during the energy recharging of the vehicles during the journeys.

[0019] Thus, the calculation seeks to minimize a waiting time which is outside the control of the drivers. Indeed, the journey time is under the control of the drivers. Here, the method therefore seeks above all to minimize the waiting times of the drivers in energy charging infrastructure which is the element most negatively felt by drivers.

[0020] According to a particular characteristic, which can be implemented alternatively or cumulatively with the previous ones, the calculation step, for vehicles of said at least one journey to be made, comprises: - at least one step of grouping vehicles into groups of vehicles sharing at least one portion of the journey; and - a step of distributing the use of the plurality of energy recharging infrastructures located on a portion of traffic lanes of the traffic lane network, shared by said groups of vehicles.

[0021] Thus, the method makes it possible to pool not only the journey calculations of several vehicles but also the use of the energy recharging infrastructures of these different vehicles during the journey. Therefore, the method makes possible the rapid processing and optimization of the movement of several thousand vehicles, but mainly the optimization of the distribution of these vehicles within the recharging infrastructures. The calculation load of the management device is therefore reduced and the use of resources is optimized.

[0022] According to a particular characteristic, which can be implemented alternatively or cumulatively with the previous ones, the calculation step comprises an allocation to vehicles of the set, called priority vehicles, of a priority level higher than other vehicles of the set and the calculation step is implemented so as to minimize the duration of the journeys transmitted to said priority vehicles.

[0023] Thus, the method makes it possible to take into account the management of a fleet of vehicles comprising priority vehicles such as fire engines, police vehicles, ambulances or others. The method makes it possible to optimize the movements of an entire fleet of vehicles, mainly composed of private vehicles, but can, within this fleet, take into account distinct priority levels and favor the journeys of priority vehicles.

[0024] According to a particular characteristic, which can be implemented alternatively or cumulatively with the previous ones, the step of calculating at least one journey comprises a step of distributing the use of the plurality of energy recharging infrastructures located on the portion of traffic lanes of the traffic lane network and at least one step of transmitting, to at least one energy recharging infrastructure among the plurality of energy recharging infrastructures, a request to reserve a recharging time slot for said at least one vehicle.

[0025] According to a particular characteristic, which can be implemented alternatively or cumulatively with the previous ones, the method comprises at least one iteration of the calculation steps, transmission followed by a selection step and characterized in that the calculation step, for vehicles of said at least one journey to be made to their destinations, is also a function of a journey selected during a previous iteration of said method.

[0026] Thus, to facilitate the calculations and reduce the use of computing resources, it is possible to take into account the calculations previously carried out, and therefore the journeys individually selected by the drivers of the vehicles, to optimize the journey updates over time.

[0027] According to another aspect, the invention also relates to an electronic device for managing the movements of a set of vehicles circulating within a network of traffic lanes, connected to at least one communication network, device characterized in that it comprises a processor configured to carry out the following steps: - calculation, for vehicles of the set of at least one journey to be made to their respective destinations, a journey optionally including a vehicle reloading step, said calculation being a function of: - data representative of the energy level available to vehicles at a given time; - data representative of waiting times for access to a plurality of energy charging infrastructures located on the traffic lane network; - transmission, to the vehicles of said at least one journey.

[0028] According to another aspect, the invention also relates to a computer program capable of implementing the method described as well as to a data medium for recording this computer program.

[0029] The management device has the architecture of a computer. It is equipped with one or more processors capable of executing all types of computer programs, from operating systems to application software, written in compiled or interpreted languages. The different components of the device are connected to each other by a communication bus. The device may optionally be equipped with a communication system to communicate via protocols such as Bluetooth, Ethernet or WiFi with other systems and to connect to mobile or non-mobile telecommunications networks. The device also includes memory components that will record the data and programs necessary for the operation of the device.The device is further modified so that it can perform management operations involving a large number of vehicles and manage several thousand simultaneous operations per second, in particular through parallel implementation of route calculations.

[0030] The data carriers may be any entity or device capable of storing the programs. For example, the carriers may comprise a storage means, such as a ROM, for example a CD-ROM or a microelectronic circuit ROM, or a magnetic recording means such as a hard disk, or more often a Flash memory. On the other hand, the carriers may be transmissible media such as an electrical or optical signal, which may be conveyed via an electrical or optical cable, by radio or by other means. The programs according to the invention may in particular be downloaded from a network such as the Internet. Alternatively, the information carrier may be an integrated circuit in which the program is incorporated, the circuit being adapted to execute or to be used in the execution of the method in question. Brief description of the figures

[0031] Other characteristics and advantages of the invention will appear more clearly on reading the following description of a particular embodiment, given as a simple illustrative and non-limiting example, and the appended drawings, among which: - [Fig.l] illustrates a system for implementing the management method; - [Fig.2] represents the main steps of the invention management process.

[0032] Description of an embodiment

[0033] As previously disclosed, an object of the present disclosure is to provide a mechanism for managing the movements of a set of vehicles within a road network, so as to enable each vehicle in this set to complete a journey within a given time, taking into account not only the destinations of the vehicles, but also their characteristics, traffic conditions and access conditions to energy charging infrastructures on the road network. By energy recharge, the disclosure means not only battery recharge for electric vehicles, but also fuel filling, for example if the process which is the subject of the disclosure is implemented for this type of vehicle, the fuel in question being for example hydrogen, liquid or gaseous or more conventional gasoline.The disclosure method reverses the classical paradigm of route planning, since it is not oriented towards optimizing the route variables for each vehicle in the vehicle set. On the contrary, the disclosure method manages the routes of each vehicle to optimize access to energy charging infrastructures. To do this, vehicle-specific characteristics are used (in particular their autonomy), as well as the different waiting times at the infrastructures and additionally the recharging times required by each vehicle.

[0034] [Fig.l] illustrates a system in which the management method can be implemented artwork.

[0035] Such a system comprises a DO management device, which may take the form of one or more servers or a cloud-type infrastructure, connected to at least one NTWK communication network. This or these NTWK communication networks are connected, on the one hand, to a set of AE vehicles, for example via a 5G infrastructure, and on the other hand, to a set of EIR charging infrastructures, which are disseminated over the territory covered by the traffic lanes within which the management is carried out. This territory may be a country, a region, a department or an inter-municipality, for example. The infrastructures of the set of EIR charging infrastructures are configured to allow the transmission, to the DO management device, of data relating to the expected waiting times for access to a charging station.The DO management device also has a connection with one or more traffic data supply services SFDC, such as national or regional vehicle flow counting services (real time or deferred time) for example. The DO device also has at least one StrD data structure, for example in the form of a relational database, to record, in real time, the data for implementing the management method that is the subject of the disclosure. Finally, the DO management device has access to a geographic information system SiG and a meteorological information system SiM. As explained below, this data is used to weight in particular the autonomy of the vehicles in the set of AE vehicles.

[0036] The management method that is the subject of the present disclosure is described in relation to [Fig. 2]. More particularly, a method is proposed for managing the movements of a set (AE) of vehicles (AEb ..., AEn) equipped with respective energy reserves and capable of making journeys within a network of traffic lanes equipped with a set of energy recharging infrastructures (IR1, ..., IRm), belonging to a set EIR, capable of recharging vehicles with energy, the infrastructures being geographically distributed over the network of traffic lanes, the method being implemented within an electronic device connected to at least one communication network,

[0037] Such a method comprises (a plurality of iterations comprising) the steps of: - calculation (SOI), for vehicles (AEb ..., AEn) of the set (AE) of at least one journey (T^'i, ..., T^p) to be carried out, said calculation (SOI) being a function of: - data representing an available energy level of the vehicles (ED^i, ..., ED^n) at a given time; - data representative of the duration of the journey depending on the position of the vehicle and the position of the recharging infrastructure (eg, terminals); - data representative of waiting times for access to infrastructure energy recharge (IRb ..IRm) of the traffic lane network; - representative data of expected charging times for vehicles based on the charging capacities of the vehicles and the energy charging capacities of the infrastructures (eg, for each terminal of a charging infrastructure, the capacity of the terminal, a Tesla™ super charger allowing for example a charge to 80% in 30 minutes, while the individual charging terminal made available by an individual allows the same charge in 6 hours and / or for each vehicle, its nominal charging speed, depending on the capacities offered by the terminals); - transmission (S02), to the vehicles (AEb ..., AEn) of said at least one journey (T ...,1^%).

[0038] This method offers the possibility of staggering access to and use of the charging infrastructure while optimizing the stages of the journeys of the vehicles in the set of vehicles. This method thus allows the determination of particular routes in order to reduce overall travel times at the level of a set of vehicles. Travel costs are also reduced. This method thus makes it possible to resolve at least some of the drawbacks of the state of the art, by increasing the number of potential charging points for each vehicle, by smoothing in an optimized manner the successive charges per charging point and by optimizing for each vehicle its cost and time constraints for the completion of a given journey.This is made possible by two important factors: on the one hand, the feature that waiting times at charging infrastructure are taken into account in their own right when calculating the route, and on the other hand, the feature that routes are calculated for a set of vehicles and not just for one, in order to optimize the use of charging infrastructure overall and over time. Data on the capacity of the terminals (permitted charging speeds) and the charging capacities of the vehicles are also taken into account.The set of vehicles EA receives the routes calculated by the DO management device, each vehicle executing its route and each vehicle regularly transmitting updates of its position and its autonomy (and possibly its destination if this changes) to the DO management device so that the latter continuously adapts the routes of the set of vehicles in order to globally optimize the use of the charging infrastructure over time.

[0039] The method is implemented iteratively to allow route updates that take into account not only the evolution of waiting times at the recharging infrastructures but also the evolution of the autonomy of the vehicles, which can vary according to factors known or unknown to the DO management device, such as the final destinations, or even other journeys previously selected by vehicle drivers. The DO management device therefore periodically receives dynamic data from AE vehicles and dynamic data from EIR charging infrastructures.

[0040] Furthermore, the calculation also takes into account the times required for the vehicles to obtain a predetermined energy level, also called recharging time in the case of an electric vehicle for example, and the calculation is implemented so as to minimize the sum of the waiting times for access to the energy recharging infrastructure that must occur during the energy recharging of the vehicles (in other words the calculation minimizes the sum of the journey times, the sum of the waiting times for access to the energy recharging infrastructure that must occur during the energy recharging of the vehicles during journeys and the sum of the recharging durations).

[0041] Interestingly, these calculations can be performed by grouping vehicles that share identical portions of journeys, and the use of the infrastructure located on these portions of journey is distributed among the grouped vehicles. The system can optionally propose infrastructure requiring leaving a common portion of journey when this optimizes the time or cost to complete a journey (e.g., leaving a highway to go to a town or village offering a terminal from a company, a town hall, a community (etc.) or an individual who offers this public access for a fee).

[0042] The management device DO can transmit, via the NTWK communication network, reservation requests to the charging infrastructures for the vehicles under management. These requests allow the charging infrastructures to correctly manage the influx of vehicles and the vehicles to present themselves optimally to the charging infrastructures at the agreed time slot.

[0043] The method implemented by the DO management device uses several types of technical data to provide its results. These data can be divided into two categories: on the one hand, static data, such as the characteristics of the vehicles (weight, theoretical autonomy, battery power, etc.), the locations and characteristics of the charging infrastructures (number of terminals, powers delivered, accepted connection standards, etc.); and on the other hand, dynamic data, such as traffic data on the road network, data on waiting times at the various charging infrastructures and, of course, data on the destination and autonomy of the vehicles.

[0044] Thus, the method comprises a step of obtaining, for AEi vehicles (i.e. all or part) of the set of AE vehicles, in real time, so-called dynamic data, in particular charge levels and destination. This obtaining, by the device of DO management is carried out by receiving messages of a predetermined format from AEi vehicles.

[0045] According to the disclosure, the communication of dynamic data between the DO management device and the vehicles of the vehicle set generates a significant volume. Among the possible transmission formats, it could be envisaged to use standardized CAM messages (acronym for "Cooperative Awareness Message" - (ETSI EN 302 637-2)). Such messages, however, are transmitted with a significant frequency of between 1 and 10 Hz. Assuming that the management device manages the movement of a hundred thousand vehicles simultaneously, this would involve receiving between a hundred thousand and a million messages per second, which is unthinkable.Thus, in order to reduce the number of messages processed by the management device, to make the best use of IT resources and to limit risks to privacy, the data including instantaneous positions and potential route data (target destination, waypoints, etc.) can be transmitted separately (i.e. independently of the CAM messages) to the management device, for example using RCS messages (acronym for "Rich Communication Suite"). In this case, the vehicle is identified by its MSISDN transmission number, and a series of RCS messages makes it possible to communicate the desired information in a codified manner.

[0046] This transmission technique is also interesting because it is much faster to implement, and the management device does not require any particular needs in terms of latency time and because of a simpler implementation at the interface level (API) with the navigation means embedded in the vehicles. The periodicity (and volume) of the exchanges is thus limited.

[0047] Furthermore, still with the aim of limiting the computing resources to be implemented, in one embodiment, the method comprises a prior step of determining the (final) destination of the vehicles which comprises for a current vehicle of the set of vehicles: - the receipt, from the current vehicle, of an encrypted identifier of the current vehicle and at least one data item representing the destination coordinates of the current vehicle; - the search, within a data structure, for the presence of a previously determined journey for this current vehicle based on the encrypted identifier; and - when a previously determined route is present for this current vehicle, a step of providing this route, as a parameter of the calculation step.

[0048] Thus it is possible to take into account the route previously selected for the vehicle, this route having for example been recorded within the data structure, for vehicles, after receiving the route selected by the vehicles. Thus, the calculation time for successive routes is reduced.

[0049] An important piece of data, in the context of the implementation of the disclosure, is that relating to the charge level at the current time, which allows the step of determining the assignment to a terminal to determine its theoretical autonomy according to this type of vehicle at the time of this determination, and also its remaining autonomy in terms of kilometers that can be traveled according to the destination determined during this assignment, and according to the parameters measured or recovered during this trajectory,

[0050] Thus, in an exemplary implementation, a message, transmitted by an AEi vehicle of the set of vehicles, comprises at least one triplet of data consisting of the identifier of the AEi vehicle, its destination in the form of GPS coordinates for example, and data representing a battery charge level (or a tank filling level, for example in the case of a standard vehicle or a hydrogen vehicle).This data is transmitted using the message, for example a message of the type presented previously. This type of message then consists of a list of successive geolocation points, said succession being representative of the configured route: it incorporates for example at least the departure and arrival points (namely their respective geolocations), and intermediate geolocation points, said intermediate points being defined in such a way that in the event of a potential plurality of routes, said defined points make it possible to find the determined route. Any other suitable type of message can be used to transmit the data.Alternatively, such messages can be transmitted by a communication terminal of a passenger of the vehicle, or of its driver, as part of the implementation of a specific application (of the guidance application type), the communication terminal then having the capacity to recover the data representative of the charge level coming from the vehicle, for example as part of the implementation of a communication interface within the vehicle (Bluetooth, WiFi, wired).

[0051] It may be noted that although the transmission of these dynamic data can be carried out on demand (i.e. by a passenger or driver of a given vehicle, during the journey), the implementation of this transmission is more efficiently carried out in an automated manner, for example from the start of the vehicle's journey, regardless of the vehicle's remaining autonomy. As indicated later, such automated transmission makes it possible to multiply the number of potential alternative routes, compared to a conventional journey (of the straight line type or as direct as possible) while optimizing the times (travel time, waiting time and recharging time) at the level of both the AE set and at the level of each AEi vehicle. Alternatively, the transmission can be automated, for example, be triggered as soon as a predetermined autonomy threshold is reached, either from the vehicle's battery level (at the vehicle level therefore), or from a remaining autonomy in terms of kilometers that can be traveled per trip, determined as described below.

[0052] The messages transmitted from a given vehicle to the management device are encrypted by a session key sharing mechanism. An asymmetric encryption type architecture (public key / private key) is implemented between each vehicle and the management device. A session key (with a limited and parameterized lifetime) is derived from an initial exchange between the vehicle (or the user's terminal) and the management device. This session key is used to transmit the messages and the routes between the vehicle and the management device. In addition, according to the disclosure, the vehicle identifier can also be derived from this initial exchange (or be cryptographically constructed when registering the vehicle (or the user's terminal) with the service associated with the present method): thus the implementation of the method at the management device level does not make it possible to identify the AEi vehicles of all the vehicles.This architecture also prevents information transmitted to vehicles from being intercepted (and modified).

[0053] The basic dynamic data can be supplemented or replaced by obtaining all or part of the following data: - a group of so-called trajectory data: the instantaneous geolocation of the AEi vehicle, the geolocation of its destination, its current route (real time), and its speed. It is understood that if the request is forecast (the vehicle has not yet left), the current route will be defined between its starting point (its current geolocation) and its destination to be provided by the user, for example via a user interface, while the speed will be zero; - a priority level, in order to manage different types of vehicles or situations in a differentiated manner: electric fire engines, ambulances, etc. in intervention for example; - any preferences and acceptances in terms of costs and deadlines associated with the vehicle (provided by the passenger(s), in the event that the vehicle provides a default acceptance.

[0054] According to the present disclosure, the identifiers transmitted by the vehicles are associated, within the StrD data structure, with a set of data representative of characteristics relating to the vehicles themselves, such as: - the make, model and vintage of the vehicle; - the unladen weight of the vehicle; - the type of battery fitted to this vehicle, including in particular its capacity nominal; - the average autonomy in relation to this nominal capacity; - the type of sockets accepted by the vehicle and the type of terminals and charging points corresponding to it; - the type of recharging accepted (ultra fast, fast, normal, etc.); - the charging curve, with in particular for each type of recharging (fast, etc.) the time necessary to reach the different % of recharging (for example, non-exhaustive 70%, 80%, 90 and 100%): this parameter is important, because unlike thermal vehicles for which recharging is linear (the pump pours so many liters per minute into a tank), the charging curve is not for an electric vehicle: the initial charge is fast, then from a rate depending on the type of battery, slows down; the last 2% of charge thus often requires a duration greater than the 70% initial charge;in the event of overcrowding in the charging areas, the process thus limits the charging time, based on an optimum charging time, compatible with having sufficient autonomy to reach other terminals on the determined route (in an interesting variant, this principle aimed at limiting the charging time to the needs of the journey could apply to all journeys (with or without overcrowding at the charging infrastructure)); ; - the average consumption of this vehicle modulo the speed of the vehicle according to the types of journey: in town and similar (traffic jams), on open roads on flat surfaces, uphill and downhill depending on the gradient in the latter two cases: in cases where the latter, in particular the consumption data on the climb depending on the gradient, are not provided by the manufacturer, a calculation is carried out to determine the kinetic energy required for the climb rate, depending on the speed and weight of the vehicle; - changes in these consumptions depending on the temperature (in the case of temperatures close to 0 degrees Celsius, a lower autonomy of 15% is generally observed, or 5% for vehicles which can heat their battery using a heat pump type device); - for vehicle types with this capacity, the downhill recharging rate depending on the slope rate.

[0055] As previously indicated, an object of the disclosure is to manage vehicle movements while optimizing the use of energy recharging infrastructures. Whether or not these infrastructures are dedicated to the implementation of the method which is the subject of the present disclosure, it is necessary to obtain, periodically, dynamic data (availability or occupancy) of these charging infrastructures. This dynamic data is thus transmitted to the management device, either by the infrastructures themselves, or by a device managing all or part of these infrastructures. For a given time step, the management device receives in particular the waiting times for access to the different charging stations of the charging infrastructure. The data received from the infrastructures is stored within a data structure (which can be identical to the StrD data structure). The waiting data (and therefore availability) is used firstly to determine a live waiting time for each charging station and secondly to determine an expected waiting time at several future time steps. For example, this data is used to determine a probable waiting time at +20 minutes, +40 minutes, +60 minutes, etc.These probable waiting times at future time steps are used to determine the stopping times and schedules of each vehicle in the AE vehicle set.

[0056] For example (very simplified), assuming that a vehicle AEx and a vehicle AEy belong to the set of vehicles AE and that they are traveling together on a given section of highway. The vehicle AEx is approximately 150 km ahead of the vehicle AEy and is located in the immediate vicinity of the highway station with a charging point. The vehicle AEx has a battery charge level of 50% and the vehicle AEy has a charge level of 35%. The destination of the vehicle AEx is located 300 km away and that of AEy is 400 km away.

[0057] The steps of the method described above are implemented to determine the best overall strategy for managing the movements of the two vehicles. The initial journeys of the two vehicles are known, the method therefore updates these journeys. It notes that the charging infrastructure reports a waiting time of 0 minutes. It calculates, from the history of the waiting data of the charging infrastructure, that a waiting time of more than 60 minutes is foreseeable upon arrival of the vehicle AEy at this terminal in approximately Ihl0 (taking into account the distance separating AEy from this terminal). It also calculates that the vehicle AEx, although having sufficient charge to reach another charging point located 100 km further, can now charge up to a level of 65% (this calculation being carried out iteratively).It therefore directs the AEx vehicle to charge now and once the AEx vehicle confirms this choice, it directs the AEy vehicle to charge, at the same terminal, after the AEx vehicle has finished charging. Once the AEx vehicle is charged, it transmits its autonomy again, at the next time step, to the management device, which will calculate an update of the journey to be made. In this simplified example, the 65% charge level offered to the AEx vehicle allows it to reach its destination and no further update. no update is required.

[0058] Optionally, other data may be used such as expected periods of unavailability of charging infrastructure, for example when reservations of charging infrastructure are made in advance.

[0059] Thus, the determination of the route is therefore linked to the determination of the allocation to the most appropriate terminals according to the destination, the remaining autonomy of the vehicle and according to the expected duration of stay at these terminals (waiting time and recharging time). Symmetrically, the choice of a recharging terminal does not result, as in the state of the art, from the route, but from the expected availability of these terminals, availability which is first determined to optimize the waiting times and distances at the level of all the vehicles, and not individually at the level of a vehicle according to its route.Finally, as mentioned previously, the management device does not aim at a global recharging of the vehicle, but at a level of recharging allowing an optimization of the waiting times at the level of all the vehicles, by ensuring that the vehicles have enough energy to at least reach the next recharging stage. The management device can also be configured to take into account the evolution of the consumption of the vehicles according to the current route (selected or accepted by the vehicle), and more particularly according to the level of inclination portion of trajectory by portion of trajectory according to each potential route calculated. The management device uses altitude information for this purpose, in order to determine average inclination information on each portion of a succession of portions of the trajectory evaluated and therefore evaluate an electrical consumption function.

[0060] Electricity consumption, and therefore the remaining level of autonomy, varies greatly depending on the said inclination, a vehicle being able to easily double its consumption on a slope of 8% for example, and conversely reduce it significantly on a descent, which is also the case for thermal vehicles. On the other hand, certain electric vehicles are natively equipped to exploit the kinetic energy of the vehicle (descents, braking, etc.) to partially recharge their battery. Knowing that the management device retrieves this information and the associated parameters by querying the brand model reference of each AEi vehicle taken into account, it is therefore able to take this data into account and better calculate the remaining autonomy of the vehicle (and therefore the accessible terminals).

[0061] Finally, as mentioned previously, for each AEi vehicle depending on its characteristics, the management device corrects the pre-calculated autonomies by a factor depending on the temperature observed on each portion of potential route on the one hand, the speed and the direction of the wind relative to the direction of its de- potential placement on the other hand.

[0062] In one possible embodiment, the SOI calculation step, for vehicles on the journey to be made to their respective destinations, further comprises: - obtaining data on the availability of the charging stations and data on the position of the vehicles; - data representative of the duration of the journey depending on the position of the vehicle and that of the terminals; - obtaining vehicle characteristics data (AEb ..., AEn), including in particular the recharging speeds according to the types of recharging terminals (in particular the capacities of the terminals, in terms of amperages, power, etc.); - Obtaining characteristic data from the charging stations: i.e. charging capacities of these stations (presence of a super charger, conventional chargers, etc.) and / or effective capacity of the vehicles involving potential charging speeds depending on these stations; - the (iterative) determination of the routes including: - the determination for vehicles of at least one optimum route based on the characteristics of each vehicle and the characteristics of traffic lanes, giving an initial list of possible routes; - the deletion of the first list of journeys presenting an incompatibility with regard to the data on the energy level available to the vehicles, giving a second list; - the deletion of this second list of journeys presenting, at the energy recharging infrastructures, waiting times greater than a predetermined waiting threshold giving a third list which constitutes the journeys to be transmitted to the vehicles for their selection.

[0063] More particularly, in this embodiment, the predetermined waiting threshold is calculated so that the standard deviation of the total waiting times of all the vehicles is minimized, for example according to an approach of searching for a global minimum in an approach of optimizing access to the recharging infrastructures.

[0064] In other words, the taking into account of these different data and constraints by the management device is carried out for example in several elementary stages when calculating the different journeys: - A first step during which a calculation component, based on the respective final destinations of each AEi vehicle, determines a first list L1 of potential routes to be evaluated by AEi vehicle, including the geolocation of each potential terminal, and recovers for each of them for segments on the one hand, elementary portions on the other hand, of each potential route distance data, possibly current or forecast traffic by time step (for example 10 minutes in 10 minutes, to have a final forecast over n* 10 minutes, 2 hours if n = 12 therefore), a probability that can be assigned to each time step, in the direction of the potential route, and levels of inclination (positive for an ascent or negative for a descent), while calculating the overall cumulative distance of each of these routes from list Ll.

[0065] According to one embodiment, a potential route segment is delimited either by a potential terminal and the position or destination of an AEi vehicle, or by two potential charging terminals. According to the present invention, an elementary route portion may correspond to a route portion either for which a level of inclination would be homogeneous, or a level of traffic would be homogeneous, or a combination of the two.Note that the calculation on the segments and / or the portions of potential routes can be shared, if several AEi vehicles are likely to use them: in which case, the vehicles are grouped (and form groups of vehicles which share portions of the journey) and the calculations of the portions of the journey are shared for these groups, as was for example the case in the example presented previously, which makes it possible to greatly limit the consumption of computing resources and to be faster in obtaining suitable journeys or itineraries. - A second step during which a calculation component determines for each AEi vehicle and for each potential route segment, on the one hand its compatibility with the autonomy in kilometers of said AEi vehicle according to the level of its battery, its average consumption according to speed parameters and by applying the consumption corrections of said vehicle route portion by route portion (on consumption when climbing, and possibly recharging when descending, temperature, speed and wind direction);

[0066] At the end of this second step, segment by segment, the management device purges per vehicle AEi the list L1 of non-compatible potential routes, to ultimately obtain per vehicle AEi a list L2 of compatible potential routes. - A third iterative step, of calculation at the level of the set AE of the vehicles AEi by a calculation component of the management device, and from the different lists L2 of each vehicle, of the optimal combination of the routes of the different vehicles AEi, combination aiming to optimize on the one hand the overall distance to be traveled by all of these vehicles, and on the other hand to optimize the overall journey time (including the times spent at the terminals, including queues) of all of these vehicles, the result of this third elementary step giving for each vehicle AEi its (or its) determined routes, and its assignment to a respective terminal, or a list of successive terminals, according to a time slot for arrival and start of charging determined terminal by terminal.

[0067] In determining these lists of journeys, so-called priority vehicles are taken into account, for which optimization is made on the distance (and therefore the travel time). Such priority vehicles are, for example, vehicles of the public force, emergency services or maintenance service. These vehicles may have a particular identifier which allows them to be recognized as such within the databases. These vehicles are favored, both in terms of waiting times at the charging stations and in the overall development of a route allowing them to reach their destination. The prioritization of these vehicles is carried over to the other vehicles in the set of vehicles.

[0068] The overall durations for each journey evaluated differ from the state of the art, knowing that as described below the management device takes into account for each journey two duration determinations, namely: - A basic duration calculated from a determination of basic circulation duration, and a basic recharging duration. The basic circulation duration is determined from the cumulative distance of each portion of the evaluated route and the speed limits retrieved from a dedicated database for each successive portion of the evaluated route. This basic duration therefore corresponds to optimal traffic conditions, without any stops. The basic recharging duration is determined for a recharging duration required at one or more recharging stations on the evaluated route and according to the autonomy of the AEi vehicle and the location of the stations, considering that this recharging would be done optimally (no waiting time). - An updated duration, varying over time, estimated from an estimate of an updated travel duration, and an updated recharging duration. The updated travel duration is defined from the cumulative distance of each portion of the evaluated route and an evaluation of the speed limit restrictions, based on the traffic observed at a moment in time, and extrapolations of this traffic over the following periods, on each of its successive portions of the evaluated route, the periods to be evaluated being those during which the AEi vehicle is supposed to travel. This updated duration therefore takes into account the estimated traffic conditions on each portion of each evaluated route, at the times when the vehicle is supposed to travel there. The differentiating point compared to the state of the art at this level is that the evaluated durations are not an aggregation durations on the evaluated journeys, because consecutive subsets of journey sections may be separated by stops for recharging the vehicle. The actualized recharging duration is an estimate of the recharging times at the recharging stations available on the evaluated journey, based on the recharging time required for the AEi vehicle, and an estimate of the queue at these stations, over the period during which the AEi vehicle will arrive there, said period being deducted from the actual durations on the relevant sections, as defined above.

[0069] Compared to the state of the art, any slowdowns in traffic due to traffic are therefore additionally supplemented by loading times, and any slowdowns in these loads linked to the queues evaluated.

[0070] In our example, at each determination, the management device updates the corresponding data at the level of the availability of said terminal on the time slot concerned, so that this new level of availability can be taken into account for the other determinations (this is why this step is iterative).

[0071] The management device can also be configured to favor global determinations at the overall level, either the shortest overall distances (with a view to an energy efficiency objective), or the shortest durations. According to one characteristic, the energy efficiency objective is the one defined by default. However, it is deactivated to favor the objective of the shortest overall durations, either when the traffic exceeds a certain density threshold, predefined at the level of the management device settings, or when the evaluated updated durations exceed the determined basic overall durations by a predefined threshold (for example if this evaluated duration doubles compared to the basic duration for an evaluated journey).

[0072] In order not to significantly disadvantage a particular AEi vehicle during optimization at the level of the AE set of AEi vehicles, the calculations carried out during the third step also take into account for each AEi vehicle a threshold both in terms of duration rate and in terms of distance rate, said distance threshold being determined on the one hand in relation to the shortest distance to the final destination according to the state of the art, while the duration threshold is evaluated in relation to the duration determined if the AEi vehicle were to travel on the shortest route. The distance rate is positive, while the duration rate is negative.

[0073] In other words, it is not a question, in order to promote an overall optimum at the overall level, of determining for a current AEi vehicle a duration longer than that which it would have had to undergo while queuing at the various terminals of the shortest route. In our example, the management device is therefore configured to make a determination limiting this duration (hence the negative rate) for each AEi vehicle, for example according to a rate of 20% (duration at least 20% less long).

[0074] With the same logic, it is not a question, even respecting the reduction in duration above, that the distance determined for a current AEi vehicle, following optimization at the level of all the vehicles, is greater than a rate, for example 30% of the distance (distance at most 30% greater, with a positive rate) corresponding to the shortest route as determined by the state of the art.

[0075] According to a particular characteristic, the energy efficiency parameterization introduced above corresponds, during its implementation, to a reduction in the positive rate on the distances and a reduction in the negative rate applied to the durations for each AEi vehicle, while a parameterization aimed at optimizing the durations would increase these two rates.

[0076] Note that the positive rate applied to the distances can, according to a variant, be incorporated into the parameters necessary for determining the list L1 of potential routes for each AEi vehicle in the first step described previously.

[0077] According to this, the term “duration” includes:

[0078] a) the values ​​relating to the road route on the route determined on each route segment, as a function of the forecast speed and the forecast traffic during the journey on this segment;

[0079] b) the duration of the queue at the determined terminal;

[0080] c) the duration of the recharging expected to reach the determined recharging level.

[0081] According to a particular characteristic, the calculation and the associated determination are definitive only for the first segment(s) of the (global) route determined. Once a reloading has been carried out, another optimization calculation including iterations on the previous steps is executed to verify that the successive segments initially determined are confirmed. These iterative calculations make it possible to validate the new time values, and thus take into account variations in traffic and / or queues, etc. that may occur for various reasons.

[0082] According to a particular characteristic, these iterative calculations are carried out periodically, for example at each new event (such as a release in a queue or on a terminal, or following a manual configuration for example,...).

Claims

Claims

1. Method for managing the movements of a set (AE) of vehicles (AEb AEn) equipped with respective energy reserves and capable of making journeys within a network of traffic lanes equipped with a set of energy recharging infrastructures (IR1, ..IRm) capable of recharging vehicles with energy, the infrastructures being geographically distributed over the network of traffic lanes, method implemented within an electronic device connected to at least one communication network, method characterized in that it comprises the steps of: - calculation (SOI), for vehicles (AEb ..., AEn) of the set (AE) of at least one journey (T^h, ..., T^p) to be made, said calculation (SOI) being a function of: - data representative of an available energy level of the vehicles (EDAHh ..., ED^J at a given time; - data representative of waiting times for access to the energy recharging infrastructures (IRb ..., IRm) of the traffic lane network; - transmission (S02), to the vehicles (AEb ..., AEn) of said at least one route (T^'i, ..., T^p).

2. Method according to claim 1, characterized in that the calculation step (SOI) is also a function of data representative of the recharging capacities of the energy recharging infrastructures (IR1, ..., IRm) and the recharging capacities of the vehicles (EDAHh ..., EDAH„).

3. Method according to one of claims 1 or 2, characterized in that the calculation step (SOI) is also a function of data representative of the duration of the journey as a function of the positions of the vehicles (ED^i, ..., EDAlj,) and the positions of the energy recharging infrastructures (IR1, ..., IRm).

4. Method according to one of claims 1 to 3, characterized in that the calculation step (SOI) is also a function of data representative of the time required, for the vehicles (AE1, ..., AEn), to obtain a predetermined energy level (NE1, ..., NEn).

5. Method according to one of claims 1 to 4, characterized in that the calculation step (SOI) is implemented so as to minimize the sum journey times, the sum of waiting times for access to energy charging infrastructure (IR1, ..IRm) which must occur when vehicles are recharging their energy (AE1, AEn) during journeys (TAE1, ..TAEn) and the sum of the charging times.

6. Method according to one of claims 1 to 5, characterized in that the calculation step (SOI) is implemented so as to minimize the sum of the waiting times for access to the energy recharging infrastructures (IR1, IRm) which must occur during the energy recharging of the vehicles (AE1, Aen) during the journeys (TAE1, TAEn).

7. Method according to one of claims 1 to 6, characterized in that the calculation step (SOI), for vehicles (AEb ..., AEn) of said at least one journey (T^h, ..., T^p) to be carried out comprises: - at least one step of grouping vehicles into groups of vehicles sharing at least one portion of the journey; and - a step of distributing the use of the plurality of energy recharging infrastructures (IRb ..., IRm) located on a portion of traffic lanes of the traffic lane network, shared by said groups of vehicles.

8. Method according to one of claims 1 to 7, characterized in that the step of calculating at least one route comprises a step of distributing the use of the plurality of energy recharging infrastructures (IR i, ..., IRm) located on the portion of traffic lanes of the traffic lane network and at least one step of transmitting, to at least one energy recharging infrastructure among the plurality of energy recharging infrastructures (IRb ..., IRm), a request to reserve a recharging time slot for said at least one vehicle (AEb ..., AEn).

9. Method according to one of claims 1 to 8, characterized in that the method comprises at least one iteration of the calculation steps (SOI), of transmission (S02) followed by a selection step (S03) and characterized in that the calculation step (SOI), for vehicles (AEb ..., AEn) of said at least one journey (T^h, ..., T^p) to be carried out to their destinations (DF^i, ..., DFAI „) is also a function of a selected journey (TpS^1, ..., TpSAH") during a previous iteration of said method.

10. Electronic device for managing the movements of an assembly (AE) of vehicles (AEb ..AEn) circulating within a network of traffic lanes, connected to at least one communication network, device characterized in that it comprises a component for implementing components of: - calculation, for vehicles (AEb ..., AEn) of the set (AE) of at least one journey (T^'i, ..., TAEnp) to be carried out to their respective destinations (DF^i, ..., DF^), a journey optionally comprising a vehicle recharging step, said calculation (SOI) being a function of: - data representative of an available energy level of the vehicles (EDAHh ..., ED^J at a given time; - data representative of waiting times for access to a plurality of energy recharging infrastructures (IRb ..., IRm) located on the network of traffic lanes; - transmission, to the vehicles (AEb ..., AEn) of said at least one journey (T^h, ..., T^p).

11. Computer program comprising instructions for implementing the method according to one of claims 1 to 9, when said instructions are executed by a processor of a computer processing circuit.

12. Data carrier on which a computer program according to claim 11 is recorded.

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