System and method for simulating the use of electric vehicles in metropolitan areas

Abstract

The invention relates to a system and a method for simulating travel within a metropolitan area in order to assess the suitability of electric vehicle infrastructure. The method comprises in particular: - a step (S01) of obtaining a set of routes for a given period within the metropolitan area, using a geographic database (DGeoA) and at least one simulation parameter (ParSSim), the step delivering a set of routes (EDS); - a step (S03) of obtaining, from the set of routes (EDS) of the at least one simulation parameter (ParSSim), at least one datum representative of the suitability (DrAIR) between electric vehicle charging infrastructure (DiBH, DiBA, SCP) and an electric vehicle adoption parameter (AdVE) for executing the routes of the set of routes (EDS).

Description

[0001] System and method for simulating the use of electric vehicles in metropolitan areas

[0002] Domain

[0003] The invention relates to the field of infrastructure optimization. More particularly, the invention relates to determining, within a given metropolitan area, the suitability of infrastructure for electric vehicle use, such as charging stations, for the electric vehicle fleet. A specific object of the invention is to determine geographical data for the deployment of infrastructure for electric vehicle use.

[0004] Previous technique

[0005] The advent of navigation software, designed to provide everyone with a route between a starting point and a destination, has freed all drivers from the constraints of manually planning journeys. This software is now largely integrated into vehicle operating systems, which, thanks to onboard connectivity features, can not only plan an initial route but also modify it based on traffic conditions. For electric vehicles, these functionalities are extended to include the ability to schedule charging stops for users.Depending on the vehicle's characteristics (particularly its initial range), the embedded software can therefore allow for planning more or less frequent stops of the vehicles at charging areas, or within the home or even within the company.

[0006] The growing number of electric vehicles on the road is, however, straining the capacity of existing electric vehicle charging infrastructure. Indeed, in a given location, such as a shopping center, the number of charging stations is relatively limited. The consequences of this limitation include increasingly longer waiting times to access a charging station. Furthermore, the charging time itself adds to the overall travel time. Currently, waiting and charging times are uncontrolled variables that significantly impact the adoption of electric vehicles in society. This situation also greatly affects the charging infrastructure at various rest areas and stations along road networks, for example, in metropolitan areas.Indeed, the number of people using charging points is uneven, making access to some of these facilities very difficult while others remain underutilized. Furthermore, the location of electric vehicle charging infrastructure is inconsistent. All of these problems hinder the widespread adoption of electric vehicles and also raise issues regarding the sizing of infrastructure, particularly electricity distribution networks.

[0007] Methods exist for simulating journeys within metropolitan areas. One such method involves simulating travel patterns based on household travel surveys. This approach uses collected household travel surveys and datasets to generate synthetic populations and simulate travel patterns on a larger scale. This approach notably involves using classification and regression trees (C&RT) to categorize households and individuals, applying Monte Carlo simulation techniques, and employing Bayesian updating to refine the results. The goal is to create simulated travel data that closely resembles observed data in various urban environments, without the need for costly new surveys.

[0008] This involves using agent-based modeling to simulate traffic behavior in metropolitan areas. This approach models individual vehicles or travelers as agents and simulates their interactions. In other words, agent-based models can use synthetic populations constructed from mobility surveys.

[0009] The choice of simulation method often depends on the size of the metropolitan area, available resources, and specific planning needs. Many regions are striving to improve their models by incorporating more detailed data, taking into account additional modes of transport, and better representing the interactions between land use and transport.

[0010] The problem with these methods is that any change to a simulation parameter (such as the percentage of trips made by electric vehicle) requires a complete rerun of the simulation to account for these new parameters, as this influences mobility demand. These simulations are computationally intensive and therefore require significant technical and IT resources. Even with these resources, rerunning such simulations often takes several hours or even several days, depending on the size of the metropolitan area considered and the duration of the simulation (a week, a month, etc.). Thus, using such simulations in operational conditions (i.e., to verify the impacts of parameter changes) is very difficult.

[0011] The invention aims to improve this situation.

[0012] Summary of the invention

[0013] Thus, a simulation method for travel within a metropolitan area is proposed to assess the suitability of the infrastructure for electric vehicles, a method implemented through an electronic processing system. According to the disclosure, such a method includes:

[0014] - a step of obtaining a set of journeys for a given period within the metropolitan area, using a geographic database and at least one simulation parameter, delivering a set of journeys;

[0015] - a step of obtaining, from the set of journeys of said at least one simulation parameter, at least one data representative of the suitability between an electric vehicle charging infrastructure and an electric vehicle adoption parameter for the execution of journeys from the set of journeys.

[0016] The suitability of the infrastructure for electric vehicles is understood to mean the suitability between the charging infrastructure on the one hand and the electric vehicles on the other.

[0017] This suitability may depend, in particular, on the mobility demand of the electric vehicles in question and possibly on various parameters (battery capacity, charging behavior, availability of charging at home and at work, etc.).

[0018] For the purposes of this invention, a simulation parameter is understood to be a parameter related to the charging of electric vehicles, in particular chosen from at least one of the following parameters: battery capacity, charging behavior, availability of charging at home and at work, etc.

[0019] The adoption parameter according to the invention can correspond to the penetration rate of electric vehicles.

[0020] More specifically, the invention makes it possible to assess the suitability of infrastructure for electric vehicle mobility, which can be evaluated for different levels of the adoption parameter (i.e., different electric vehicle penetration rates). According to a particular feature, the method includes a step of determining the electric vehicle charging infrastructure and / or the electric vehicle adoption parameter from a geographic database of said at least one simulation parameter.

[0021] Depending on a particular characteristic, the step of determining the electric vehicle charging infrastructure and / or the electric vehicle adoption parameter includes:

[0022] - a step of assigning, within a data structure, home charging points, according to a first model;

[0023] - an assignment step within a data structure, of load points of professional activity locations, according to a second model;

[0024] - a step of determining, based on a third model, a population with an electric vehicle.

[0025] According to a particular characteristic, the step of determining, based on a third model, a population with an electric vehicle is carried out based on data representative of the population living in the metropolitan area.

[0026] According to a particular characteristic, the step of obtaining the data representative of the suitability is obtained at least in part by aggregating simulation results of journeys of at least some of the journeys of the set of journeys using an electric vehicle.

[0027] Depending on a particular characteristic, the step of obtaining the data representing the adequacy includes:

[0028] - a step of selecting a current route from among all the routes, the current route being carried out by an agent using a vehicle;

[0029] - a step to update the vehicle's battery charge status based on the current journey;

[0030] - a step to determine, based on the state of charge of the battery, whether it is necessary to recharge the vehicle's battery at the end of the current journey; and if it is determined that the vehicle's battery needs to be recharged: a step to determine the possibility of accessing a charging station at the location where the current journey ended; when access to a charging station at the location where the current journey ended is impossible, a step to mark the agent's incompatibility with the use of an electric vehicle.

[0031] According to a particular feature, when access to a charging station at the location where the current journey ended is impossible, the process includes a step of recording, within a data structure, the geographical position of the end of the current journey.

[0032] Depending on a specific characteristic, the step of updating the battery charge status of the vehicle used for the current journey includes:

[0033] - a step to estimate energy consumption by the agent's electric vehicle to complete the current journey;

[0034] - a step to update the vehicle's battery charge status based on estimated energy consumption.

[0035] According to a particular characteristic, the step of estimating energy consumption by the agent's electric vehicle to complete the current journey is carried out using a model that takes into account a speed profile of the vehicle and / or a topology of the road taken to complete the current journey.

[0036] According to a particular characteristic, the step of determining the need to recharge the vehicle's battery at the end of the current journey implements a charging behavior model in which the location of the vehicle at the end of the current journey is used.

[0037] In another aspect, the disclosure also relates to an electronic processing system for simulating travel patterns in a metropolitan area to assess the suitability of electric vehicle infrastructure. Such a system includes:

[0038] - means of obtaining a set of journeys for a given period within the metropolitan area, using a geographic database and at least one simulation parameter, delivering a set of journeys;

[0039] - means of obtaining, from the set of journeys of said system, at least one simulation parameter, at least one data point representative of the suitability between an electric vehicle charging infrastructure and an electric vehicle adoption parameter for the execution of journeys within the set of journeys. For the purposes of this invention, an electronic system is understood to mean any system encompassing electronic components / devices, including any computer device such as a computer, a calculator, a controller, or a server, or a network comprising at least one of these devices.

[0040] According to a preferred implementation, the various steps of the processes according to the proposed technique are implemented by one or more software programs or computer programs, comprising software instructions intended to be executed by a data processor of a relay module according to the proposed technique and designed to control the execution of the various steps of the processes.

[0041] Consequently, the proposed technique also aims at a program, capable of being executed by a computer or a data processor, this program comprising instructions to control the execution of the steps of a process as described above. This program can use any programming language and be in the form of source code, object code, or code intermediate between source and object code, such as in a partially compiled form, or in any other desirable form. The proposed technique also aims at an information carrier readable by a data processor, comprising instructions for a program as described above. The information carrier can be any entity or device capable of storing the program.For example, the medium may include a storage device, such as a ROM (e.g., a CD-ROM or a microelectronic circuit ROM), or a magnetic recording device, such as a floppy disk or a hard drive. Furthermore, the information medium may be a transmissible medium, such as an electrical or optical signal, which can be transmitted via an electrical or optical cable, by radio, or by other means. The program, according to the proposed technique, may, in particular, be downloaded from a network such as the Internet.

[0042] Alternatively, the information carrier may be an integrated circuit in which the program is embedded, the circuit being adapted to execute or to be used in the execution of the process in question. According to one embodiment, the proposed technique is implemented by means of software and / or hardware components. In this context, the term "module" may refer in this document to a software component, a hardware component, or a set of hardware and software components. A software component corresponds to one or more computer programs, one or more subroutines of a program, or more generally to any element of a program or software capable of implementing a function or set of functions, as described below for the module concerned. Such a software component is executed by a data processor of a physical entity (terminal, server, gateway, router, etc.).and is capable of accessing the hardware resources of this physical entity (memory, storage media, communication buses, input / output electronic cards, user interfaces, etc.). Similarly, a hardware component corresponds to any element of a hardware assembly capable of implementing a function or set of functions, as described below for the module in question. This may be a programmable hardware component or one with an integrated processor for software execution, for example, an integrated circuit, a smart card, a memory card, an electronic card for executing firmware, etc. Each component of the system described above naturally implements its own software modules. The various embodiments mentioned above can be combined to implement the proposed technique.

[0043] 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 server, comprising program code instructions for implementing the process described above, when said program is executed on a computer, mobile phone or computing device.

[0044] Brief description of the figures

[0045] Other features and advantages of the invention will become more apparent from the following description of a preferred embodiment, given by way of simple illustrative and non-limiting example, and the accompanying drawings, among which: illustrates the process of the present disclosure; illustrates the synthesis step of the electromobility architecture; illustrates the electromobility simulation step; schematically illustrates a system for implementing the process of the invention.

[0046] Description of a method of implementation

[0047] In the context of this disclosure, the inventors determined that the initial simulations were problematic because even minor changes to the simulation parameters necessitated restarting the entire simulation, making it impractical for operational use (i.e., when rapid or real-time results are required). To address this issue, as previously summarized, the inventors undertook to use the simulation output data and feed it into a mobility processing block. The advantage is that the parameters of this mobility processing block can be easily modified without needing to restart the simulation.

[0048] The main steps of the disclosure process are described in relation to Figure 1.

[0049] Step S01, implemented by a processing block BC#1, generates, for a given metropolitan area, a set of EDS simulations of travel within that metropolitan area, for a given time period (one day, one week, one month, etc.). This EDS simulation dataset includes, in particular, a set of agents (i.e., simulated individuals and / or households) and a set of journeys made by these agents. The method for obtaining this simulation data is described below. A DBS database is used to store this EDS simulation dataset. This database includes (a data structure) agents, each containing an agent identifier, a place of residence, and, if applicable, a place of work.This database comprises a data structure of journeys, each including: starting point, destination, route taken, and mode of transport (car, walking, cycling, public transport). It can be a relational database, or one or more flat files (XML, JSON, CSV, etc.). The DGeoA geographic database of the urban area can be linked to or included within this DBS database.

[0050] Step S02 (synthesis of the electromobility architecture), implemented by a processing block BC#2, consists of preprocessing some of the data from this EDS simulation dataset from step S01, as well as geographical data, according to ParSSim simulation parameters. Three types of preprocessing are performed (in whole or in part) on the simulation data: preprocessing S021 relating to the availability of charging stations in the homes of DiBH employees; preprocessing S023 relating to the adoption of electric vehicles in AdVE households; and preprocessing S022 relating to the availability of charging stations in DiBA professional activity locations (businesses, administrations).Following this S02 step, new data can be entered into the DBS database, particularly for employees (presence of a charging point at home / use of an electric vehicle). The DGeoA geographic database representing the urban area can also be updated with charging points at workplaces.

[0051] The S03 step, electromobility simulation, implemented by a BC#3 processing block, consists of performing the electromobility simulation as such, from the DBS database of the EDS simulation dataset.This step is implemented using several data sources: the list of journeys made (directly from the generation and / or obtaining of generic simulation data); the pre-processed data from step S02 (if this step S02 is implemented); as well as geographic data of locations of SCP public charging stations (within the metropolitan area), including the power and number of terminals of each station, and two numerical models: an electrical consumption model (which is responsible for evaluating the electrical energy consumption of a vehicle for a given journey) and a charging behavior model (which models the behavior of an agent after the end of a journey, and in particular determines whether an agent recharges their electric vehicle after the journey has been executed).This step delivers at least one data point representative of the suitability of the electric vehicle charging infrastructure (DiBH, DiBA, SCP) with the adoption of electric vehicles (AdVE).

[0052] One advantage of this implementation, as already explained, is that the overall mobility simulation is performed once and for all, and a subsequent change to one of the parameters described previously does not impact this initial overall mobility simulation. Furthermore, these parameter changes are rapid, and the implementation of step S03 relating to electromobility is equally so, since, as explained later, it does not require convergence, unlike the overall mobility simulation of step S01.

[0053] In one implementation, the algorithm of step S03 is described in relation to figure 3 and it includes a plurality of iterations of the following steps (the plurality of iterations corresponding to the total number of paths to be processed):

[0054] - Selection S031 of a current TCi route from among all routes (i.e., those that are eligible, either at the end of step S01 or at the end of step S02); such a route includes: the identifier of agent A#k, the origin (geographic data), the destination (geographic data), the departure and arrival times (optionally: the trajectory); the vehicle VeCj; the current route corresponds to the route not yet processed in chronological order of the routes; - Update S032 of the state of the electric vehicle VeCj used by agent A#k to perform the current TCi route, including:

[0055] - a step to estimate energy consumption by the agent's electric vehicle VeCj to complete the current journey TCi;

[0056] - a step to update the state of charge of the ECBatj battery of the VeCj vehicle based on the estimated energy consumption.

[0057] - determination S033, based on the state of charge of the ECBatj battery, of the need to recharge the VeCj vehicle battery at the end of the current journey; and if it is determined that the VeCj vehicle battery needs to be recharged (if there is no need to recharge, we move on to the processing of the next journey, step S031):

[0058] - determination S034 of the possibility of having access to a charging station at the location where the current TCi journey ended;

[0059] - when access to a charging station at the location where the current TCi journey ended is possible, an update step S035 of the ECBatj battery charge status (then we move on to processing the next journey, step S031);

[0060] - when access to a charging station at the location where the current TCi journey ended is impossible, a marking step S036 of the incompatibility of agent A#k with the use of an electric vehicle and a recording step S037, within a data structure, of the geographical end position (destination) of the journey which led to the incompatibility.

[0061] In the above, we assume that there are N trips in the database (1 < i < N). We assume that there are M agents in the database (1 < k < M). We assume that there are L electric vehicles in the database (1 < j < L). Two agents can share a single vehicle.

[0062] As can be understood from the preceding process, implemented by block BC#3, the iterations continue until all the routes to be processed have been exhausted. During the iterations, some agents are declared ineligible and marked as such in the database. This means that if there are still routes to simulate for these agents, those subsequent routes taken after the agent's disqualification are no longer taken into account. Depending on the duration of the simulation (one day, one week, two weeks, one month, etc.), there can therefore be numerous iterations, making computer processing essential to obtain a reliable result. Furthermore, the algorithm in step S03 also includes an initialization phase. During this phase, a battery charge percentage is assigned to all the agents' VeC vehicles.The load percentage can be fixed (for example, 80%) or randomly distributed (within a range from 10% to 100%, for example). In the latter case, a random load distribution based on a Gaussian distribution is used, for example.

[0063] The following elements are also specified, although their implementation may vary depending on the specific project:

[0064] - the determination S033, based on the state of charge of the ECBatj battery, of the need to recharge the VeCj vehicle battery at the end of the current journey takes into account a level of "caution", which is fixed either as a parameter (for example 5%), or based on a random draw (for example draw between 1 and 10%) at each test to represent reality more accurately (namely the fact that agents are not always "attentive");

[0065] - When access to a charging station is possible at the location where the current TCi journey ended (the vehicle needs charging and a station is available), the S035 battery charge status update step ECBatj distinguishes between at least two cases. If the destination is the workplace or residence of agent A#k, the charge level is updated (randomly) to a value between 80% and 100%. If the destination is a location with a public charging station, the charge level is updated (randomly) to a value between 15% and 60% (this difference reflects the fact that agent A#k is unlikely to wait for a full charge in a "realistic" situation). Alternatively, the charge level at the end of charging at a public station depends on the simulated power and downtime of the agent.

[0066] At the end of the execution of step S03, several important structural data are obtained: general data (such as an electromobility simulation success index, electromobility compatibility index, estimation of hourly energy demand in the metropolitan area, impact on CO2 equivalent based on life cycle analysis model, charging station utilization rate, etc.), but also specific data (such as geographical data representing electromobility simulation failures, geographical data representing the most promising charging station locations, data relating to charging station saturation, data relating to the charging station utilization rate (and therefore the profitability rate of the stations).These specific data are, at least in some examples of implementations, the result of calculations performed on unit data which are aggregated.

[0067] To do this, the geographic data retained when an agent is declared incompatible (because they were unable to recharge the vehicle's battery after one of the trips, as explained in step S037) is used. This geographic data is then processed using an appropriate method to aggregate and average the data, for example, by street, zone, or neighborhood, to estimate the feasibility of installing one or more public charging stations in that street, zone, or neighborhood. For example, after this processing, dummy (non-existent) charging stations are added to the public charging station database, and the entire S03 electromobility simulation is re-executed by block BC#3 to reassess the incompatibility of the agents.We can clearly see here the full advantage brought by the process of the invention: it is not necessary, as with prior art simulations, to relaunch the entire S01 simulation to obtain simulation results, which is significantly more efficient, both in terms of consumption of computing resources and in terms of industrialization.

[0068] Examples of implementing the steps of the process described above are then described. In at least one situation, this process is also implemented within an electronic system, including at least one computerized data processing device, comprising one or more processors, RAM, and mass storage. The computerized data processing device receives as input the data necessary for implementing the mobility simulation, using component BC#1 (which can be a distributed programming component, e.g., cloud-based). Thus, for example, a global mobility simulation is performed.The input data for this pre-simulation are as follows: transport network data (from sources such as OpenStreetMap™) for the metropolitan area under consideration; population data (synthetic or real) for this metropolitan area; places of activity (facilities); initial travel plans for agents (e.g. statistically, home-work journeys).

[0069] Based on this input data, the global mobility simulation BC#1 is implemented using an iterative approach to simulate agent behavior. Each agent in the simulation has a daily plan of activities and movements. The simulation runs in several iterations, for example, over a 24-hour period, and can be repeated for a month. The iteration steps are, for example, as follows: execution: agents execute their plans on the simulated (transport) network; this involves simulating how agents carry out their activities and move between different locations. rating: the plans are evaluated based on a utility function; the utility function evaluates each agent's daily plan based on their performance during the execution step. This function takes into account both participation in activities and movements.Replanning: Some agents modify their plans (e.g., by changing route, mode or time); agents adjust elements of their plans (e.g., departure times, modes of transport) based on the results; this allows plans to be adapted to the observed traffic flow.

[0070] The iterative process is repeated until it reaches an equilibrium state or a predetermined number of iterations. More specifically, these steps are repeated for a configurable number of iterations. The process continues until the average score of the best plans for all agents stabilizes. This stabilization indicates that an equilibrium state has been reached. The final state represents a stochastic equilibrium, where agents have optimized their plans in competition with other agents. The iterative process thus allows for finding an equilibrium where agents have optimized their daily plans based on simulated conditions and interactions with other agents.

[0071] When this equilibrium state is reached, an optimized travel dataset for all agents is produced. This data includes detailed information on each journey, such as start and end times, origin and destination (in the form of geographic data, for example), the route taken, the mode of transport used, and the duration of the journey.

[0072] This approach allows the BC#1 simulation component to generate a complete set of simulated journeys based on realistic agent behavior, taking into account the complex interactions between individuals, transport infrastructure, and urban dynamics. The data provided by the mobility simulation includes the journeys taken, the agents considered, their residences, and their places of work.

[0073] Depending on the operational implementation conditions, the simulation stage (i.e., the generation of mobility data – generic simulation data) itself may be followed by one or more stages of filtering the produced mobility data. Indeed, depending on the metropolitan area concerned, mobility simulations can generate several hundred thousand, millions, or even tens of millions of daily trips. Such quantities of data obviously cannot be processed manually; such processing would be worthless, ineffective, and would not lead to the desired result. Furthermore, such data volumes can also present processing difficulties even for powerful computer systems. It is therefore preferable to retain only the data that is useful for the electromobility simulation.For example, journeys made using a mode of transport other than a car can be excluded. Similarly, only a selected subpopulation of agents, such as those residing in a specific sub-area of ​​the metropolitan area, can be retained. Typically, the electromobility simulation can be performed over a large territory surrounding a metropolitan area, but a filter can be applied to retain only agents residing within the metropolitan area. Thus, the analysis focuses on a fleet of agents residing in a metropolitan area but takes into account their travel across a much larger territory surrounding that area.

[0074] The raw data (or filtered data as appropriate) can then be used to perform the electromobility simulation.

[0075] Depending on the implementation examples, the raw or filtered data undergoes preprocessing (S02) to configure the "population" that will be the subject of the electromobility simulation. This preprocessing aims to create a subset of the overall metropolitan area population that will be the focus of the electromobility processing in step S03. In some situations, this preprocessing step S02 can be considered optional: for example, if the entire population (traveling by car) is considered to be the subject of the electromobility simulation. In this case, it may not be necessary to configure a specific population. It can also be assumed that all employees' residences (i.e., their places of residence) are equipped with individual charging stations.

[0076] If this preprocessing step S02 is implemented, its purpose is to build a realistic dataset for the electromobility simulation of step S03.

[0077] It is described in relation to Figure 2 and may include the following steps: a step S021 of allocation of home charging points, a step S022 of allocation of charging points at places of professional activity (places of work of the population), a step S023 of determination of a population with an electric vehicle.

[0078] Step S021, which allocates charging points to homes, and step S022, which allocates charging points to workplaces, can be implemented based on the geographic data of the metropolitan area under consideration. These two steps can be independent of each other and are also independent of the population considered in the electromobility simulation. The independent processing of these characteristics ensures that the events encountered in the electromobility simulation are realistic. Step S023, which determines the population with electric vehicles, is performed based on the individuals from the mobility simulation who travel by car. The function of step S023 is to identify which individuals use electric vehicles.

[0079] These steps can be implemented in different ways depending on the examples of implementation.

[0080] For example, according to the disclosure, the S021 home charging point allocation step can be implemented using a Home Charging Station (HCS) model. This model allocates home charging points to all households in the simulation. A home charging point could correspond, for example, to detached houses that have the option of plugging in an electric vehicle in a garage or in front of the house, or even to apartment buildings equipped with charging stations. The number of charging points per zone to be distributed among households can, for example, be determined based on the percentage of detached houses in that zone. Finally, the selection of households within each zone can be based on various criteria, such as household income.At the end of this step, for each agent in the electromobility driving simulation, we have data representing the presence of an electric vehicle charging station at home for that agent.

[0081] For example, according to the disclosure, the S022 allocation step for charging points at workplaces can be implemented using a WCS (Work Charging Station) model. This model estimates the number of charging stations in each workplace in each zone i. To do this, the model performs an initial estimate by applying a coefficient to the average number of jobs per workplace charging station, denoted p. This is an average coefficient, for example, at the national level, determined from the total number of jobs and the total number of workplace charging stations. This coefficient can be calculated at a more local level if such data is available. This initial estimate can, for example, be written as:

[0082] N wcs = pN em P lois

[0083] Where N^ cs is the number of company charging stations in zone i and is the number of jobs in that same area. Then, this first estimate is corrected with a second coefficient Cj to reflect the characteristics of the different areas. The estimate of the number of public charging stations in area i is then written as:

[0084] N wcs = pN emplois c-

[0085] For example, an upward correction is applied to the initial estimate in areas with high employment density and low population density. This has the effect of correcting it downward in areas with low employment density and high population density in order to align with the average coefficient (national or local) calculated from the available data. In this case, the correction coefficient could, for example, be written as:

[0086] Ci = (c max - c min (d? - l) 2 + c min

[0087] Where is min etc maxare the limit values ​​of this coefficient, and dj is a normalized distance that can be written as:

[0088] , IPi - P min l, I - P max l 2 rnax |pj — p min | 3 max |pi — p max |

[0089] Where p represents population density and p represents employment density. When the mobility simulation in step S01 is configured to group employees by company, then the charging stations within companies can be allocated, in this step S022, between the companies in each zone. If this is not the case, or if the allocation in S022 is implemented independently of the mobility simulation S01, then the charging stations are simply allocated probabilistically at the zone level.

[0090] For example, according to the disclosure, the S023 electric vehicle adoption determination step, for a given electric vehicle penetration rate (which is a simulation parameter), selects the households in which agents adopt an electric vehicle. In one configuration, the selection of households (and agents) is random. In another configuration, selection criteria are implemented. For example, priority may be given to high-income households and / or households in areas with a high rate of single-family homes. Thus, for a household j J located in an area ie I, this sorting criterion y tj can be written: m iealx |Ti — T | m jeajx | J i — |

[0091] Where eq and a2 sont weighting coefficients. The variables -q and r maxare the rate of single-family homes in zone i and the maximum value of this rate. The variables Rj and R max correspond to the income of household j and the highest income.

[0092] Furthermore, in one example implementation, the allocation of electric vehicles also includes a step of assigning characteristics to the vehicle allocated to the household (or the individual), such as the maximum number of kilometers that can be traveled on a full charge, and / or the time required to charge from 5% to 80% or 100%. These vehicle characteristics can again be distributed randomly and / or according to household characteristics (as with the initial allocation, for example).

[0093] In another implementation, a multi-variable regression model (age of agents, household income, number of people in the household, type of housing, population density statistics in the area of ​​residence of the households, etc.) is implemented to determine the probability that an agent will adopt an electric vehicle.

[0094] Following the completion of these steps, to obtain a realistic starting configuration for the electromobility simulation, statistically, some households have a home charging point without adopting an electric vehicle, and vice versa. The coverage rate of the electrified population by home charging points varies according to the simulated penetration rate.

[0095] Depending on the achievements, as explained previously, the electromobility simulation of the S03 stage of the electromobility simulation can be based on raw data (from the mobility simulation), filtered data (from the mobility simulation), configured data (by the S02 stage, to provide a variable and configurable starting framework for the electromobility infrastructure and the adoption of electric vehicles).

[0096] In step S03, only agents who have adopted an electric vehicle (either the subpopulation determined in the previous step S02 or the entire filtered population from step S01) are "resimulated," meaning that the routes determined in step S01 are repeated. This re-execution is a key feature of the disclosure process, as it avoids running a new generic simulation (that of step S01). To achieve this, as explained previously, the electromobility algorithm is implemented.

[0097] In summary, the electromobility simulation stage processes all journeys made by the entire electrified population in chronological order over the simulated period. When a journey is completed, the electric vehicle's battery charge level is updated according to the consumption model described earlier. Depending on the implementation, this model can incorporate, among other things, the battery capacity of different electric vehicles. Electricity consumption can be determined by a consumption coefficient (expressed in Wh per km) or by a more refined model that takes into account the vehicle's speed profile and road topology when such data is available from the first stage of the sequential approach. As explained previously, the electromobility simulation also incorporates a charging behavior model.This model aims to determine whether each employee charges their electric vehicle between two consecutive stops. To do this, the model incorporates the stop location to determine if it is the employee's home or workplace. Public charging station locations (SCP data) are provided as input to this step to determine if the employee stops near a public charging station. In addition to analyzing stop locations, the charging behavior determination method considers that some employees have a charging point at home and / or a charging station available at their workplace, while others do not. The charging behavior determination method can also incorporate employee preferences for certain types of charging. This could, for example, translate into different minimum thresholds for triggering each type of charging.The method for determining charging behavior can also impose a minimum stop duration requirement to trigger charging. All of these elements allow us to determine whether or not charging occurs at each stop, and, if so, to specify the type of charging. For greater realism, it is also possible to integrate scenarios where employees rotate access to charging stations at work: for example, a rotation from one day to the next, or even within the same day. Finally, after charging, the electric vehicle's battery charge level is updated, as described previously.

[0098] In addition to the previously presented geographical data (location of public charging stations to be installed, key charging points, etc.), the electromobility algorithm can determine which agents are compatible with electromobility based on their mobility needs, and which are not because their battery was completely depleted without the possibility of recharging during the simulation. The approach also allows for the determination of a probability of power demand evolution for each type of charging, enabling, for example, the analysis of the magnitude of power demand peaks. The utilization rate of different types of charging stations can also be analyzed. Life cycle analysis of electric vehicles can also be integrated to determine the reduction in carbon dioxide emissions for a given market penetration rate.

[0099] A simplified architecture of an electronic processing system for simulating travel in a metropolitan area is presented in relation to Figure 4, in order to assess the suitability of the electric vehicle infrastructure. Such a system comprises a memory 41, a processing unit 42 equipped, for example, with a microprocessor, and controlled by a computer program 43 implementing the method as previously described. In at least one embodiment, the invention is implemented in the form of one or more applications installed on such a system.For example, the system implements a cloud computing infrastructure within which the electromobility simulation (which corresponds to steps S03 and S02 of the previously presented process) can be implemented multiple times on the basis of a "generic" mobility simulation (corresponding to step S01).

[0100] Such a system comprises: - means for obtaining BC#1 a set of journeys for a given period within the metropolitan area, using a geographic database and at least one simulation parameter, delivering a set of journeys; means for determining BC#2 the electric vehicle charging infrastructure and / or the electric vehicle adoption parameter from the geographic database of said at least one simulation parameter; means for obtaining BC#3, from the set of journeys of said at least one simulation parameter, at least one data point representing the suitability between an electric vehicle charging infrastructure and an electric vehicle adoption parameter for the execution of journeys from the set of journeys.

Claims

Demands 1. Method for simulating travel in a metropolitan area to assess the suitability of the infrastructure for electric vehicles, implemented by means of an electronic processing system, comprising: a step of obtaining (S01) a set of journeys for a given period within the metropolitan area, using a geographic database (DGeoA) and at least one simulation parameter (ParSSim), delivering a set of journeys (EDS); a step of obtaining (S03), from the set of journeys (EDS) of said at least one simulation parameter (ParSSim), at least one data representative of the suitability (DrAIR) between an electric vehicle charging infrastructure (DiBH, DiBA, SCP) and an electric vehicle adoption parameter (AdVE) for the execution of journeys from the set of journeys (EDS).

2. Method according to claim 1, characterized in that it comprises a determination step (S02), of the electric vehicle charging infrastructure (DiBH, DiBA, SCP) and / or of the electric vehicle adoption parameter (AdVE) from the geographic database (DGeoA) of said at least one simulation parameter (ParSSim).

3. A method according to claim 2, characterized in that the determination step (S02) of the electric vehicle charging infrastructure (DiBH, DiBA, SCP) and / or the electric vehicle adoption parameter (AdVE) comprises: an assignment step (S021), within a data structure, of home charging points (DiBH), according to a first model (HCS); an assignment step (S022) within a data structure, of charging points at places of professional activity (DiBA), according to a second model (HCW); a determination step (S023), according to a third model, of a population with an electric vehicle (AdVE).

4. A method according to claim 3, characterized in that the determination step (S023), based on a third model, of a population having a electric vehicle (AdVE) is carried out based on data representative of the population living in the metropolitan area.

5. A method according to any one of claims 1 to 4, characterized in that the step of obtaining (S03) the representative data of the suitability (DrAIR) is obtained at least in part by aggregating simulation results of journeys of at least some of the journeys of the set of journeys (EDS) using an electric vehicle.

6. A method according to any one of claims 1 to 5, characterized in that the step of obtaining (S03) the representative data of the suitability (DrAIR) comprises: a step of selecting (S031) a current route (TCi) from among all the routes, the current route being carried out by an agent (A#k) using a vehicle (VeCj); a step of updating (S032) the state of charge of the battery (ECBatj) of the vehicle (VeCj) as a function of the current route (TCi); a step of determining (S033), as a function of the state of charge of the battery (ECBatj), whether it is necessary to recharge the battery of the vehicle (VeCj) at the end of the current route; and if it is determined that the vehicle battery (VeCj) needs to be recharged: a determination step (S034) of the possibility of having access to a charging station at the location where the current journey (TCi) ended;when access to a charging station at the location where the current journey (TCi) ended is impossible, a marking step (S036) of the agent's (A#k) incompatibility with the use of an electric vehicle.

7. Method according to claim 6, characterized in that when access to a charging station at the location where the current journey (TCi) ended is impossible, the method includes a step of recording (S037), within a data structure, the geographical position of the end of the current journey (TCi).

8. A method according to claims 6 and 7, characterized in that the step of updating the state of charge of the battery of the vehicle used to perform the current journey comprises: a step of estimating energy consumption by the agent's electric vehicle VeCj to complete the current journey TCi; a step of updating the state of charge of the battery ECBatj of the VeCj vehicle based on the estimated energy consumption.

9. Method according to claim 8, characterized in that the step of estimating energy consumption by the agent's electric vehicle VeCj to carry out the current journey TCi is carried out using a model taking into account a speed profile of the vehicle and / or a topology of the road taken to carry out the current journey TCi.

10. Method according to claim 6, characterized in that the step of determining (S033) the need to proceed with a recharging of the vehicle battery (VeCj) at the end of the current journey implements a recharging behavior model in which the location of the vehicle at the end of the current journey is used.

11. Electronic processing system for the simulation of travel in a metropolitan area to assess the suitability of the electric vehicle infrastructure comprising: means for obtaining (S01) a set of journeys for a given period within the metropolitan area, using a geographic database (DGeoA) and at least one simulation parameter (ParSSim), delivering a set of journeys (EDS); means for obtaining (S03), from the set of journeys (EDS) of said at least one simulation parameter (ParSSim), at least one data representative of the suitability (DrAIR) between an electric vehicle charging infrastructure (DiBH, DiBA, SCP) and an electric vehicle adoption parameter (AdVE) for the execution of journeys from the set of journeys (EDS).

12. Computer program comprising instructions for implementing the method according to any one of claims 1 to 10, when said instructions are executed by a processor of a computer processing circuit.

13. Product: computer program downloadable from a communication network and / or stored on a computer-readable medium and / or executable by a processor or server, comprising program code instructions for implementing the method according to any one of claims 1 to 10, when said program is executed on a computer, mobile phone, or computing device.

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