Method for selecting an algorithm for controlling an electric vehicle charging infrastructure

Abstract

The invention relates to a method for selecting a control algorithm for an electric vehicle charging infrastructure, from among a plurality of predefined control algorithms, comprising steps of: a - simulating control of the charging infrastructure according to each of the control algorithms by following the same predefined activity scenario of the charging infrastructure over a given period of time, the predefined activity scenario comprising the charging of one or more electric vehicles, and for each electric vehicle a time slot for connecting the electric vehicle to one of the terminals of the charging infrastructure and a value of an electrical energy requirement of the electric vehicle, b - for each control algorithm, calculating a performance indicator of the control algorithm, c - selecting one of the control algorithms from among the plurality of predefined control algorithms,based on the performance indicators calculated for the different steering algorithms. Figure for the abstract: Figure 3,

Description

Title of the invention: Method for selecting an algorithm for controlling an electric vehicle charging infrastructure FIELD OF THE INVENTION

[0001] The invention relates to electric vehicle charging infrastructures. The invention relates more particularly to a method for selecting an algorithm for controlling an electric vehicle charging infrastructure, and a method for controlling an electric vehicle charging infrastructure, using the selected algorithm. STATE OF THE ART

[0002] Some electric vehicle charging infrastructures (IRVE) include a plurality of charging stations, each charging station being able to comprise one or more charging points, each charging point being intended to be connected to an electric vehicle. The charging stations of certain charging infrastructures can be controlled by a control algorithm which makes it possible to assign to each charging point an electrical power setpoint, corresponding to the electrical power to be delivered by the charging point to the electric vehicle to which it is connected.

[0003] There are control algorithms implementing different strategies, in order to provide the best possible quality of service to users of the charging infrastructure.

[0004] However, quality of service requirements may vary depending on the needs of the users of the charging infrastructure.

[0005] Furthermore, it is not always possible to determine which steering algorithm is best suited to a particular technical context. Indeed, many constraints can have an impact on the choice of steering strategy to adopt.

[0006] Certain constraints may for example be linked to the technical characteristics of the charging infrastructure itself, such as for example the maximum electrical power that can be delivered by each charging point, the maximum electrical power that can be delivered by each charging terminal and / or the maximum electrical power that can be delivered by the charging infrastructure, and / or, in the case where the charging points include three-phase electrical outlets, the distribution of the electrical power delivered between the different phases, and / or the types of charging terminals (direct current or alternating current).

[0007] Other constraints may, for example, be linked to other electrical equipment present on the site where the charging infrastructure is located. Indeed, this additional electrical equipment may include electrical equipment that consumes electrical energy (such as lighting equipment for example) or electrical equipment that produces electrical energy (such as solar panels for example). This electrical equipment may modify the electrical power available for recharging electric vehicles via the recharging infrastructure.

[0008] Finally, other constraints to be taken into account may, for example, be linked to the particular behavior of users of the electric vehicle charging infrastructure, in particular when the electric vehicles are part of a fleet of electric vehicles operated by the same operator. In particular, the arrival times, departure times, electrical energy requirements of the electric vehicles and the types of connection sockets to which users choose to connect the electric vehicles (power levels, phases used) may differ depending on the activity of this operator. For example, this may be a fleet of vehicles used for the delivery of goods or parcels, or a fleet of vehicles used for the transport of passengers, or a fleet of electric vehicles used for another business activity. Summary of the invention

[0009] One aim of the invention is to be able to control an electric vehicle charging infrastructure (IRVE), providing the best possible quality of service, while taking into account the technical constraints exerted on the charging infrastructure.

[0010] This aim is achieved in the context of the present invention by means of a method for selecting a control algorithm for a charging infrastructure for charging electric vehicles, from among a plurality of predefined control algorithms, the charging infrastructure comprising a plurality of charging terminals, each charging terminal being suitable for being connected to an electric vehicle to deliver to the electric vehicle an electric power according to an electric power setpoint assigned to the charging terminal, each control algorithm being suitable for assigning to each charging terminal, an electric power setpoint to be delivered to each electric vehicle connected to the charging terminal of the charging infrastructure, the method comprising steps of: a - simulate control of the charging infrastructure according to each of the control algorithms by following the same predefined scenario of activity of the charging infrastructure over a given period of time, the predefined scenario of activity of the charging infrastructure including the charging of one or more vehicles electric, and for each electric vehicle a time slot for connecting the electric vehicle to one of the charging stations of the charging infrastructure and a value of the electric energy requirement of the electric vehicle, b - for each control algorithm, calculate a value of a performance indicator of the control algorithm based on a result of the simulation over the given time period, c - selecting one of the driving algorithms from among the plurality of predefined driving algorithms, based on the values ​​of the performance indicator calculated for the different driving algorithms.

[0011] Thanks to the proposed method, it is possible to select and implement the charging infrastructure control algorithm that provides the best quality of service, among the predefined control algorithms, for the activity concerned. For each control algorithm, the quality of service is evaluated using the calculated performance indicator, the choice of this indicator being able to depend on the activity for which the electric vehicles are used.

[0012] It is also possible to vary the parameters of certain control algorithms and to compare the performance of the same control algorithm when this control algorithm is configured with different parameters, for the activity concerned.

[0013] It is also possible to test new steering algorithms, and to compare their performance with existing steering algorithms, for the activity concerned.

[0014] In one embodiment of the proposed method, the performance indicator can be chosen from: - a number of times during the period of time that the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, - an average amplitude of the overruns, over the period of time, - a rate of fully charged electric vehicles at the end of the time period, - a rate of charged electric vehicles beyond a predefined state of charge threshold at the end of the time period, - a total quantity of electrical energy delivered to all electric vehicles by the charging stations over the period of time, - an average quantity of electrical energy delivered per electric vehicle, over the period of time, - a rate of dispersion of the quantities of electrical energy delivered to the different vehicles over the period of time, - a rate of dispersion of the charge states of the different electric vehicles at the end of the time period, - an average of the full recharge times of electric vehicles, - a weighted average of the full charging times of electric vehicles, with weighting coefficients, each weighting coefficient being proportional to the power of a connection socket of the charging station to which the electric vehicle is connected, - a rate of energy delivered to electric vehicles, from a given local electrical energy source, - a ratio between a quantity of electrical energy delivered to electric vehicles, from a given local electrical energy source, and a total quantity of electrical energy produced by the given local electrical energy source, over the period of time - a number of vehicle charging stops during the time period.

[0015] In one embodiment of the proposed method, steps a and b are repeated with a new predefined scenario of activity of the charging infrastructure, so as to obtain, for each control algorithm, several values ​​of the performance indicator.

[0016] In particular, steps a and b may be repeated with a new predefined scenario of activity of the charging infrastructure over a subsequent given time period of the same duration as the previous given time period.

[0017] In one embodiment of the proposed method, the method may further comprise a step of: d - for each control algorithm, determine a value of an aggregated performance indicator calculated based on the values ​​of the performance indicator obtained by repeating steps a and b, step c comprising a step of comparing the values ​​of the aggregated performance indicator of the steering algorithms with each other, and selecting the steering algorithm based on a result of the comparison.

[0018] The aggregated performance indicator may be chosen from: - an average of the values ​​of the performance indicators calculated over the different time periods, and / or - a standard deviation of the values ​​of the performance indicators calculated over the different time periods.

[0019] In one embodiment of the proposed method, the method may comprise a prior step of: e - generate a series of N activity scenarios of the charging infrastructure, steps a and b being executed N times, following the activity scenarios, so as to to obtain N values ​​of the performance indicator for each control algorithm.

[0020] The N activity scenarios of the charging infrastructure can be scenarios successive activity periods, each i+1-th scenario taking place over a period of time which immediately follows a period of time over which the i-th preceding scenario takes place, steps a and b being executed N times, following the successive activity scenarios, so as to obtain the N values ​​of the performance indicator for each control algorithm.

[0021] In one embodiment of the proposed method, during step e, each activity scenario of the infrastructure can be obtained by randomly drawing parameters of the activity scenario.

[0022] In one embodiment of the proposed method, the method may comprise a step prior to step e of: f - from historical data of the charging infrastructure, determine an average value and / or a standard deviation value of the arrival times of the electric vehicles, an average value and / or a standard deviation value of the departure times of the electric vehicles, and an average value and / or a standard deviation value of the electrical energy requirements of the electric vehicles, and in which during step e, each infrastructure activity scenario is obtained by randomly drawing parameters from the activity scenario, so as to respect the average and / or standard deviation values ​​calculated in step f over all the drawings.

[0023] In one embodiment of the proposed method, the method may comprise a step prior to step e of: g - based on historical data from the charging infrastructure, classify each electric vehicle into a group of electric vehicles, each group of electric vehicles grouping together a plurality of electric vehicles, and each group being associated with an average value and / or a standard deviation value of the arrival times of the electric vehicles in the group, an average value and / or a standard deviation value of the departure times of the electric vehicles in the group, and an average value and / or a standard deviation value of the electrical energy requirements of the electric vehicles in the group, and in which during step e, each activity scenario of the charging infrastructure is obtained by random drawing of parameters of the activity scenario, so as to respect a proportion of electric vehicles in each group as well as the average and / or standard deviation values ​​calculated in step g associated with each group of electric vehicles, over all of the drawings.

[0024] In one embodiment of the proposed method, each activity scenario of the charging infrastructure may further comprise, for each electric vehicle, one of the following parameters: - a moment of arrival of the electric vehicle in the charging infrastructure, - a time of departure of the electric vehicle from the charging infrastructure, - an initial state of charge of the electric vehicle, at the time of connection of the vehicle to the charging station, - a capacity of an electric vehicle battery, - a minimum charging power of the electric vehicle, - maximum charging power of the electric vehicle, - an identifier of a connection socket to which the electric vehicle is connected, - an initial state of the electric vehicle charging mode at the start of the time period.

[0025] In one embodiment of the proposed method, the proposed method may comprise a step of: h - control the electric vehicle charging infrastructure by executing the control algorithm selected in step c.

[0026] The invention further relates to a computer program product comprising program code instructions for executing the steps of the method as defined above, when this program is executed by a computer.

[0027] The invention also relates to a computer-readable memory storing computer-executable instructions for executing the steps of the method as defined above. PRESENTATION OF THE DRAWINGS

[0028] Other characteristics and advantages will emerge from the following description, which is purely illustrative and non-limiting, and must be read in conjunction with the appended figures, among which:

[0029] - [Fig.l] schematically represents a recharging infrastructure of electric vehicles,

[0030] - [Fig.2] schematically represents steps of a selection process of an algorithm for controlling an electric vehicle charging infrastructure,

[0031] - [Fig.3] schematically represents an architecture of a simulator which can be used for the implementation of a method for selecting an algorithm for controlling the electric vehicle charging infrastructure. detailed description of an embodiment

[0032] In [Fig.l], the electric vehicle charging infrastructure 1 shown comprises a plurality of charging stations 2.

[0033] Each charging station 2 is connected to a public electrical energy distribution network or to an internal electrical installation network 3. The public electrical distribution network or the internal electrical installation network 3 is specific to supply charging station 2 with electrical energy.

[0034] In addition, each charging terminal 2 is capable of being temporarily connected to one or more electric vehicles to deliver electrical power to the electric vehicle(s), over time, according to an electrical power setpoint assigned to the charging terminal.

[0035] More specifically, each charging station 2 may comprise one or more charging points, for example two charging points. Each charging point is capable of being connected to a respective electric vehicle. Thus, in the case where the charging station 2 comprises two charging points, the charging station 2 may be connected simultaneously to two electric vehicles.

[0036] Furthermore, each charging point may comprise one or more connection sockets, each connection socket being capable of delivering a predefined maximum electrical power. For example, the same charging point may comprise a low-power connection socket (for example capable of delivering an electrical power of 7 kilowatts) and a high-power connection socket (for example capable of delivering an electrical power of 22 kilowatts) which can be controlled. When an electric vehicle is connected to a charging point, it is selectively connected to one of the connection sockets of the charging point, depending on requirements.

[0037] Over time, electric vehicles may enter and / or exit the charging infrastructure 1.

[0038] In the example illustrated in [Fig.l], an electric vehicle 4 (called an “entering electric vehicle”) enters the charging infrastructure 1 in order to be recharged.

[0039] In addition, several electric vehicles 5 are already present in the charging infrastructure 1. Each of the electric vehicles 5 already present is connected to a respective charging station 2. The electric vehicles 5 already present are being recharged.

[0040] An electric vehicle 6 (called an “outgoing electric vehicle”) leaves the charging infrastructure 1, after having been recharged via one of the charging stations 2.

[0041] The charging infrastructure 1 may further comprise additional electrical equipment 7, also connected to the public electrical energy distribution network or to the internal electrical installation network 3. This additional electrical equipment 7 may comprise electrical equipment consuming electrical energy (for example equipment for lighting the installation) and / or electrical equipment producing electrical energy (for example solar panels, wind turbines or other local sources of electrical energy) and / or electrical energy storage equipment, capable to receive electrical energy, store it (possibly in another form) and return it later (for example a battery).

[0042] Furthermore, in the example illustrated in [Fig.l], the electric vehicle charging infrastructure 1 comprises a management center 8. The management center 8 comprises a management module 10, configured to control the different charging terminals 2, and a database 11, configured to store data from the charging terminals 2.

[0043] In the example illustrated in [Fig.l], the management center 8, including the management module 10 as well as the database 11, is part of the charging infrastructure 1. However, in another example, the management module 10 and / or the database 11 might not be part of the charging infrastructure 1. In particular, the management center 8 and / or the management module 10 and / or the database 11 may be located remotely from the charging infrastructure 1. In addition, the management module 10 may be configured to control charging stations located in several electric vehicle charging infrastructures.

[0044] Each charging terminal 2 is capable of transmitting a charging request signal S1 to the management module 10.

[0045] The management module 10 is configured to, as a function of the data contained in the recharge request signal SI, generate a recharge control signal S2 intended for the recharge terminal 2 having emitted the recharge request signal SL.

[0046] The charging terminal 2 is configured to recharge the electric vehicle according to a set electrical power value transmitted via the charging control signal S2.

[0047] Furthermore, the management module 10 is configured to record in the database 11 data relating to the arrival times, the departure times, the electrical energy requirements of the electric vehicles, as well as the data representative of the set electrical power values ​​calculated by the management module 10 for the different electric vehicles.

[0048] In practice, the management module 10 comprises one or more processor(s). The management module 10 comprises or is coupled to a memory in which is recorded software for controlling the charging infrastructure 1 and, where appropriate, data manipulated by the control software (input data of the control software and / or output data generated by the control software).

[0049] The electric vehicles 4, 5, 6 may be part of a fleet of electric vehicles operated by the same service operator, for example in the context of a passenger transport activity, or a goods delivery activity, or a breakdown and maintenance activity, or even an electric vehicle rental activity, or any other “business” activity requiring the use of vehicles.

[0050] The software for controlling the electric vehicle charging infrastructure 1 was chosen to provide the best possible quality of service, taking into account the technical constraints exerted on the charging infrastructure and the context in which the business activity of the service operator is carried out.

[0051] [Fig.2] schematically represents steps of a method for selecting a control algorithm for an electric vehicle charging infrastructure, from among a plurality of predefined control algorithms.

[0052] The process comprises three phases: - a first phase 100 of collection and / or generation of input data for the simulation, - a second phase 200 of simulation of the execution of the control algorithms, based on the simulation input data, - a third phase 300 of implementation of the control algorithm selected at the end of the second phase.

[0053] These three phases include the following steps.

[0054] First phase: collection and / or generation of simulation input data

[0055] According to a first step 101, historical data relating to the charging infrastructure are collected.

[0056] As illustrated in [Fig.3], the historical data collected includes: - historical data 12 representative of the activity of the electric vehicle charging infrastructure, and - data 13 relating to the technical characteristics of the electric vehicle charging infrastructure.

[0057] The historical data 12 representative of the activity of the electric vehicle charging infrastructure 1 are grouped into a plurality of files. Each file contains historical data 12 representative of the activity of the electric vehicle charging infrastructure recorded during a respective given period of time. The time periods are of identical duration (for example a duration equal to one day, i.e. 24 hours). Thus the historical data 12 representative of the activity of the electric vehicle charging infrastructure 1 comprise a plurality of time periods, and for each time period: - an electric vehicle identifier, - a time of arrival of the electric vehicle in the charging infrastructure, - a time of exit of the electric vehicle from the charging infrastructure, - an initial state of charge of the battery of the electric vehicle (initial SoC) in kilowatt-hours (kWh), the initial state of charge being defined as the energy level battery electric at the beginning of the time period, - a capacity of the electric vehicle battery (maximum SoC) in kilowatt-hours (kWh), the battery capacity being defined as the maximum level of electrical energy that can be stored by the electric vehicle battery, - an electrical energy requirement of the electric vehicle (target SoC) in kilowatt hours (kWh), - a minimum charging power of the electric vehicle in kilowatts (kW), - a maximum charging power of the electric vehicle in kilowatts (kW), - an identifier of a connection socket to which the electric vehicle is connected, - an initial state of the charging mode of the electric vehicle at the start of the time period, the charging mode being able to selectively take one of the following values: “charging” (the vehicle is being charged), “charged” (the vehicle is fully charged and can no longer receive electrical energy), “paused” (the electric vehicle is not being charged, but the electric vehicle is not fully charged so that it can receive electrical energy).

[0058] The historical data 12 may have been collected over a period of several months, for example 3 months, or over a period of more than one year.

[0059] Data 13 relating to the technical characteristics of the electric vehicle charging infrastructure include: - a tree structure comprising a plurality of nodes, and for each node, connection data with other nodes of the tree structure, - for each node, the type of node, the type of node being able to selectively take one of the following values: “meter”, “charging station”, “charging point”, “connection socket”, - for each connection socket, the available phases, - for each available phase of the connection socket, the maximum authorized power, - for each available phase of the connection socket, the minimum authorized power.

[0060] The collected data may also include data 14 relating to additional electrical equipment present on the site of the electric vehicle charging infrastructure, such as, for each period of time: - a quantity of electrical energy consumed by additional electrical equipment (e.g. lighting equipment), and / or - a quantity of electrical energy produced by additional electrical equipment (for example photovoltaic panels), as well as a maximum electrical power that can be supplied by the public electrical energy distribution network or the internal electrical installation network 3.

[0061] The historical data 12 collected in the first step is then used to generate a plurality of activity scenarios 15 of the electric vehicle charging infrastructure.

[0062] For this purpose, according to a second step 102, the following values ​​are determined: - an average value of the arrival times of the electric vehicles, over all the time periods (each time period having for example a duration equal to one day), - a standard deviation value of the arrival times of electric vehicles, over all time periods, - an average value of the departure times of electric vehicles, over all time periods, - a standard deviation value of the departure times of electric vehicles, over all time periods, - an average value of the electrical energy requirements of electric vehicles, over all time periods, - a standard deviation value of the electrical energy requirements of electric vehicles, over all time periods.

[0063] The average values ​​and standard deviations indicated above can be calculated for all electric vehicles in the fleet.

[0064] Alternatively, during this second step 102, each electric vehicle can be classified into a group of electric vehicles (or “cluster”), each group of electric vehicles grouping together a plurality of electric vehicles, and each group of electric vehicles being associated with an average value and / or a standard deviation value of the arrival times of the electric vehicles in the group, an average value and / or a standard deviation value of the departure times of the electric vehicles in the group, and an average value and / or a standard deviation value of the electrical energy requirements of the electric vehicles in the group.

[0065] Furthermore, during this second step 102, for each group of electric vehicles, a proportion of electric vehicles in the group is calculated. The proportion of vehicles in a group is defined as a ratio between the number of electric vehicles forming part of the group and the total number of electric vehicles. The total number of electric vehicles is the sum of the numbers of electric vehicles in all the groups. In other words, in the case where the electric vehicles are part of the same fleet, the total number of electric vehicles is the number of electric vehicles forming part of the fleet.

[0066] According to a third step 103, a plurality of infrastructure activity scenarios are generated from the average values ​​and standard deviations calculated during the second step 102.

[0067] Each infrastructure activity scenario can be obtained by random drawing of parameters of the activity scenario, so as to respect the proportion of electric vehicles in each group as well as the average and / or standard deviation values ​​calculated during the second step 102 on all of the drawings.

[0068] The random drawing can be carried out by means of a random generator 17 using a probability law, for example a Gaussian probability law on each group of electric vehicles.

[0069] More precisely, the third step 103 leads to generating a series of N scenarios.

[0070] According to a first example, it is possible to generate N successive scenarios of activity of the charging infrastructure, each i+1-th scenario taking place over a period of time which immediately follows a period of time over which the i-th preceding scenario takes place.

[0071] The number N of scenarios in the series may for example be equal to 365, i.e. the number of days in a year, so as to simulate the activity of the motor vehicle charging infrastructure over a full year, each scenario taking place over one day.

[0072] According to a second example, it is also possible to generate several activity scenarios of the charging infrastructure taking place over the same period of time.

[0073] It is thus possible to generate for each time period of a series of n time periods, m activity scenarios of the charging infrastructure. In this case, the number N of scenarios generated is equal to nx m.

[0074] Each generated activity scenario i includes, over the time period associated with the activity scenario, the recharging of one or more identified electric vehicles of the fleet, and for each electric vehicle, the following parameters: - a moment of arrival of the electric vehicle in the charging infrastructure, - a moment of departure of the electric vehicle from the charging infrastructure, - a value of an electric vehicle's electrical energy requirement, - an initial state of charge of the electric vehicle, at the time of connection of the vehicle to the charging station, - a capacity of an electric vehicle battery, - a minimum charging power of the electric vehicle, - maximum charging power of the electric vehicle, - an identifier of the connection socket to which the electric vehicle is connected, - an initial state of the charging mode of the electric vehicle at the start of the time period.

[0075] All of the N scenarios generated respect the proportion of electric vehicles in each group as well as the average and / or standard deviation values ​​calculated during the second step 102.

[0076] Each generated scenario i is saved in a corresponding file, and will be used as input data 16 of the simulation.

[0077] In the case where the historical data 12 are complete and have been collected over a long period (for example over one or more years), the input data 16 of the simulation may comprise only real scenarios representative of real activity of the electric vehicle charging infrastructure, obtained using the historical data previously collected.

[0078] On the other hand, in the case where the historical data 12 are incomplete and / or have been collected over a short period (for example over a few months), it is possible to include in the input data 16 of the simulation, both real scenarios representative of the real activity of the electric vehicle charging infrastructure, obtained using the historical data previously collected, and additional scenarios generated according to the preceding steps.

[0079] In the case where very little historical data is available or there is no historical data, the input data for the simulation is data that has been generated by random selection, from values ​​previously defined by the operator of the charging infrastructure, namely: - an average value of the arrival times of electric vehicles, and - an average value of the electrical energy requirements of electric vehicles. In addition, the standard deviations are defined arbitrarily and the random draws are made from a Gaussian distribution.

[0080] Second phase: execution of the control algorithms, based on the simulation input data

[0081] This second phase 200 is implemented by a computer. The computer comprises or is coupled to a memory in which a charging simulator 17 is recorded. In addition, the computer comprises or is coupled to a memory in which one or more control software programs 18 for the charging infrastructure 1 and, where appropriate, data manipulated by the control software programs (namely the input data collected and / or generated previously, and / or the output data generated by the simulation) are recorded.

[0082] The computer is programmed to perform the following steps: for i ranging from 1 to N:

[0083] According to a first step 201, the computer simulates a control of the charging infrastructure 1 according to each of the control algorithms by following the activity scenario i of the charging infrastructure over the period of time associated with the scenario i, via the charging simulator 17.

[0084] According to the first example, in the case where the duration of the time period is one day, the activity scenario i is the scenario which takes place during the i-th day among the N days.

[0085] The activity scenario i of the charging infrastructure comprises the charging of one or more electric vehicles during the i-th time period, and for each electric vehicle a time of entry of the electric vehicle into the electric vehicle charging infrastructure (similar to a time of connection of the electric vehicle to one of the terminals of the charging infrastructure) and a value of an electrical energy requirement of the electric vehicle.

[0086] During this simulation step, each infrastructure control algorithm assigns to each charging station an electrical power setpoint value, based on the arrival times of the electric vehicles in the charging infrastructure and the electrical energy requirement values ​​of the electric vehicles.

[0087] According to the second example, in the case where the duration of the time period is one day, the activity scenario i is the j-th scenario among the m scenarios which take place during the k-th day among the n days, such that jxk = i.

[0088] According to a second step 202, for each control algorithm, the computer calculates one or more performance indicator(s) 19 of the control algorithm over the period of time.

[0089] The performance indicator(s) 19 may be chosen from: - a number of times a maximum electrical power that the charging infrastructure is capable of delivering is exceeded, during the period of time, - an average amplitude of the times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, during the period of time, - a rate of fully charged electric vehicles at the end of the period of time, - a rate of electric vehicles charged beyond a predefined state of charge threshold at the end of the period of time, - a total quantity of electrical energy delivered to all electric vehicles by the charging stations over the period of time, - an average quantity of electrical energy delivered per electric vehicle, over the period of time, - a rate of dispersion of the quantities of electrical energy delivered to the different vehicles over the period of time, - a rate of dispersion of the charge states of the different electric vehicles at the end of the time period, - an average of the full recharge times of electric vehicles, - a weighted average of the full charging times of electric vehicles, with weighting coefficients, each weighting coefficient being proportional to the power of a socket at the charging station to which it is connected the electric vehicle, - a rate of energy delivered to electric vehicles, from a given local electrical energy source, - a ratio between a quantity of electrical energy delivered to electric vehicles, from a given local electrical energy source, and a total quantity of electrical energy produced by the given local electrical energy source, over the period of time.

[0090] Other parameters can be calculated, such as the instant of end of charging (which makes it possible to deduce the recharging duration), the power of the sockets of the recharging station, the energy consumed from a given local source of electrical energy or from the public electricity distribution network or internal electrical installation network 3, the number of fully charged electric vehicles during the period, the total number of electric vehicles over the period. These parameters can be used subsequently to calculate an aggregated performance indicator during a third subsequent step.

[0091] Then, the computer repeats steps 201 and 202 with a new predefined scenario of activity of the charging infrastructure.

[0092] In other words, steps 201 and 202 are repeated by replacing activity scenario i with activity scenario i+1.

[0093] According to the first example, each i+1-th scenario takes place over a period of time which immediately follows the period of time over which the i-th preceding scenario takes place.

[0094] According to the second example: - if k < m, then the i+l-th scenario takes place over a time period identical to the time period during which the i-th scenario takes place, namely the time period j, and - if k = m, then the i+l-th scenario takes place over a time period that immediately follows the time period over which the i-th scenario takes place, namely the time period j + 1.

[0095] The execution of steps 201 and 202 with the N scenarios leads to obtaining, for each control algorithm, N values ​​of the performance indicator, each value of the performance indicator being associated with a period of time.

[0096] According to a third step 203, the computer determines, for each control algorithm, one or more aggregated performance indicator(s). Each aggregated performance indicator is calculated as a function of the N values ​​of the performance indicator associated with the different time periods.

[0097] The aggregated performance indicator(s) may be chosen from: - an average of the N values ​​of the performance indicator, calculated over the whole periods of time, and / or - a standard deviation of the N values ​​of the performance indicator, calculated over all time periods, and / or - an average of the best values ​​of the performance indicator (for example, an average of the 5% of the N highest or lowest values ​​of the indicator), and / or - an average of the worst values ​​of the performance indicator (for example, an average of the 5% of the N highest or lowest values ​​of the indicator).

[0098] The aggregated performance indicator can be defined as a combination of an average and a standard deviation in order to obtain an aggregated performance indicator that is as relevant as possible according to the desired quality of service criteria.

[0099] For example, the aggregated performance indicator(s) may be chosen from: - an average number of times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded over a period of time, - an average amplitude of the exceedances of the maximum electrical power that the charging infrastructure is capable of delivering, over a period of time, - a number of time periods during which a given number of exceedances occurred (e.g. number of time periods during which at least 3 exceedances occurred), - an average rate of use of the charging infrastructure in relation to the maximum capacity of the charging infrastructure, - an average rate of fully charged electric vehicles at the end of a period of time, - an average rate of electric vehicles charged beyond a predefined state of charge threshold at the end of a period of time, - an average quantity of total electrical energy delivered to all electric vehicles by the charging stations over a period of time, - an average quantity of electrical energy delivered per electric vehicle, over a period of time, - a rate of dispersion of the quantities of electrical energy delivered to the different vehicles over a period of time, - a rate of dispersion of the charge states of the different electric vehicles at the end of a period of time, - an average of the full recharge times of electric vehicles, - a weighted average of the full charging times of electric vehicles, with weighting coefficients, each weighting coefficient being proportional to the power of a connection socket of the charging station at to which the electric vehicle is connected, - an average rate of energy delivered to electric vehicles, from a given local electrical energy source, - a ratio between a quantity of electrical energy delivered to electric vehicles, from a given local electrical energy source, and a total quantity of electrical energy produced by the given local electrical energy source, over all time periods.

[0100] The aggregated performance indicator(s) chosen depend on the quality of service (QoS) criteria targeted by the operator of the electric vehicle charging infrastructure 1.

[0101] For example, the quality of service criteria may be the following: QoS Criteria Aggregated performance indicators QoS#l • Maximization of the charge of electric vehicles • Compliance with the physical constraints of the EVSE • Average quantity of total electrical energy delivered to all electric vehicles by the charging stations over a period of time • Average number of times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, during a period of time QoS#2 • Achievement of a target charge level (corresponding to the business needs of the operator or users), for all electric vehicles, for example a target charge level ranging from 80% to 90% of the battery capacity of the • Average rate of electric vehicles charged beyond a predefined state of charge threshold at the end of a period of time • Average number of times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, during a period of timeelectric vehicles • Compliance with the physical constraints of the IRVE during a period of time QoS#3 • Charging of electric vehicles as soon as possible (this criterion reflects the urgency of charging and favors solutions that will allow a target charge level to be reached as quickly as possible) • Compliance with the physical constraints of the IRVE • Average durations for full charging of electric vehicles • Average number of times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded,over a period of time QoS#4 • Maximizing the use of the power made available by the EVSE by providing a charging power proportional to the power of the connection socket for each electric vehicle (this criterion notably makes it possible to improve the user experience: a user connecting to a socket capable of delivering high power expects to receive the displayed power) • Compliance with the physical constraints of the EVSE • Weighted average of the full charging times of electric vehicles • Average number of times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, over a period of time, QoS#5 • Maximization of the • Ratio between a quantity of electrical energy consumption produced by a given local electrical energy source of electrical energy delivered to electric vehicles, from a given local electrical energy source • Achieving a given target charge level, and a total quantity of electrical energy produced by the given local electrical energy source • Compliance with the physical constraints of the EVSE • Average rate of electric vehicles charged beyond a predefined state of charge threshold at the end of a period of time • Average number of times the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, over a period of time

[0102] Furthermore, in addition to these aggregated performance indicators, the computer can generate curves for a more detailed analysis of the behavior of the control algorithms. These curves can make it possible, in the event of equal performance between two control algorithms, to select the best candidate by analyzing the dynamics of the behavior of the charging infrastructure over time. The curves generated by the computer can include: - for each control algorithm: for each electric vehicle, a curve showing the evolution of the state of charge of the electric vehicle during each period of time, for each electric vehicle, a curve showing the evolution of the electrical power consumed by the electric vehicle, during each period of time, - and for all the piloting algorithms: for each electric vehicle, a curve showing the evolution of the aggregate electrical power consumed by the electric vehicle, during each period of time.

[0103] According to a fourth step 204, the computer selects one of the piloting algorithms from among the plurality of predefined piloting algorithms, as a function of the aggregated performance indicators calculated for the different simulated piloting algorithms.

[0104] Third phase: implementation of the selected steering algorithm

[0105] According to a first step 301, the selected control algorithm is recorded in the memory of the management module 10 of the motor vehicle charging infrastructure, or where appropriate in the memory coupled to the management module 10.

[0106] According to a second step 302, the management module 10 of the charging infrastructure controls the electric vehicle charging infrastructure by executing the selected control algorithm. In other words, the control algorithm assigns to each charging terminal 2 of the electric vehicle charging infrastructure 1, an electrical power setpoint value. The management module 10 generates a charging control signal S2 intended for the charging terminal 2, representative of the electrical power setpoint value assigned by the control algorithm.

[0107] Example:

[0108] In this example, a company has a fleet of 10 electric vehicles and has deployed a charging infrastructure on its site to recharge the fleet's electric vehicles. The electric vehicles are used during the day by the company's employees to carry out their business activities.

[0109] The electric vehicle charging infrastructure has been sized with a distribution coefficient equal to 0.6.

[0110] The expansion coefficient is defined as the proportion of electric vehicles in the fleet that can be charged simultaneously by the electric vehicle charging infrastructure, at the maximum power of the connection sockets. This coefficient was chosen to be less than 1 in order to minimize the size of the electrical cables in the charging infrastructure and thus reduce the costs of the charging infrastructure.

[0111] The electric vehicle charging infrastructure comprises 10 charging stations, each equipped with a single connection socket. More specifically, the electric vehicle charging infrastructure comprises: - 2 sockets with a maximum power of 50 kW, - 2 sockets with a maximum power of 22kW, - 3 sockets with a maximum power of 7kW, - 3 sockets with a maximum power of 3kW.

[0112] It has been 3 months since the company deployed the electric vehicle charging infrastructure and the electric vehicles are using the charging infrastructure. The electric vehicles are charged at the maximum power available at the socket. There are From time to time, power overruns were observed on the electric vehicle charging infrastructure, which led to service outages and therefore an interruption in the charging of electric vehicles.

[0113] During these 3 months, and during each day, the company was able to collect the following historical data: - time of arrival of each electric vehicle in the charging infrastructure, this time of arrival having been obtained by timestamping the event of connection of the electric vehicle to the connection socket, - departure time of each electric vehicle from the charging infrastructure, obtained by timestamping the event of disconnection of the electric vehicle from the connection socket, - electrical energy requirement, assimilated to a quantity of electrical energy consumed (kWh) by each electric vehicle during charging, this quantity of electrical energy being measured directly by the charging station to which the electric vehicle is connected.

[0114] This historical data is stored in files, for example in the memory of the management module, to be used later by the charging simulator. One file per working day is generated, which makes it possible to constitute an initial base of 62 files, corresponding to 62 activity scenarios of the electric vehicle charging infrastructure.

[0115] At the end of these 3 months, the company draws the following two conclusions: - Power overruns (exceeding the maximum electrical power that the charging infrastructure is capable of delivering) are very penalizing because these overruns cause interruptions in the charging of electric vehicles for a significant period and impact business activity. - High power connection sockets (greater than or equal to 50kW) are intended for emergency charging and must allow rapid charging.

[0116] The company therefore identifies that the most relevant indicators for characterizing the performance of the recharging service are the following: - Maximizing the use of the power made available by the electric vehicle charging infrastructure by providing charging power proportional to the power of the connection socket for each electric vehicle (in other words, a user who connects his electric vehicle to a connection socket capable of delivering high power must receive the displayed power), - Respect for the physical constraints of the electric vehicle charging infrastructure (in other words, power overruns must be avoided).

[0117] Before being able to run a simulation with the different available control algorithms, the history of the charging sessions being shallow, it is necessary to complete it by performing clustering on the 3 months of historical data collected.

[0118] The company does not identify any change in electrical energy needs or arrival / departure patterns.

[0119] Clustering is carried out on the historical data, which leads to the identification of the following three groups (or “clusters”): • Group 1: o Contains 50% of electric vehicles, o Average arrival time: 6 p.m., associated standard deviation: 0.9 hours, o Average departure time: 9 a.m., associated standard deviation: 0.5 hours, o Average electrical energy requirement: 30 kWh, associated standard deviation: 2.5 kWh, • Group 2: o Contains 30% of electric vehicles, o Average arrival time: 12h, associated standard deviation: 1.1 hours, o Average departure time: 9h, associated standard deviation: 0.5 hours, o Average electrical energy requirement: 15 kWh, associated standard deviation: 1 kWh, • Group 3: o Contains 20% of electric vehicles, o Average arrival time: 5 p.m., associated standard deviation: 1.4 hours, o Average departure time: 2 p.m., associated standard deviation: 0.6 hours, o Average electrical energy requirement: 20 kWh, associated standard deviation: 1.6 kWh.

[0120] To obtain input data for a full year (only on working days) 190 additional files are generated, corresponding to 190 activity scenarios. The 190 additional files complement the 62 files already produced from the historical data. Each additional file is generated by random draws of the scenario parameters (Gaussian laws) while respecting the proportions of electric vehicles in each group as well as the average and standard deviation values ​​associated with each group of electric vehicles, over all the draws.

[0121] The simulator was configured as follows: • Technical characteristics of the electric vehicle charging infrastructure: o Creation of all the nodes of the electric vehicle charging infrastructure, with their backgrounds, o Settings of minimum and maximum powers (kW) of each node, o Assignment of phases to each node, • Simulator settings: o Selection of relevant performance indicators: 3 IND#1: Number of times the infrastructure exceeds its maximum power electric vehicle charging, 3 IND#2: Average amplitude of overshoots (kW), 3 IND#3: Total energy charged for all electric vehicles (kWh), 3 IND#4: Average duration of full charge proportional to the power of the connection socket (minutes), o Duration of the simulation time step: 5 minutes (the duration of the time step chosen results from a compromise between simulation precision and calculation time), • Selection of control algorithms: o All the driving algorithms currently available in the database are selected: 3 Algo#l: this control algorithm assigns to each electric vehicle the maximum electrical power of the connection socket, without taking into account the other constraints of the electric vehicle charging infrastructure; this control algorithm reproduces the control of the charging infrastructure, observed by the company during the 3 months, 3 Algo#2: this control algorithm distributes the available electrical power evenly between the electric vehicles being charged, taking into account the constraints of the electric vehicle charging infrastructure (the charging constraints of the charging infrastructure include for each node, a maximum electrical power that can be delivered by the node, and for each connection socket, a minimum electrical power that must be delivered by the connection socket), 3 Algo#3: this control algorithm distributes the total available electrical power proportionally to the power of the connection sockets, while respecting the constraints of the electric vehicle charging infrastructure; this algorithm integrates a function for re-distributing unused electrical power to seek to maximize the total charging power delivered to each electric vehicle, 3 Algo#4: this steering algorithm categorizes electric vehicles and prioritizes the charging of electric vehicles while respecting a priority level assigned to each electric vehicle, and while respecting the constraints of the electric vehicle infrastructure, 3 Algo#5: this algorithm prioritizes the charging of electric vehicles that arrived earliest in the electric vehicle charging infrastructure, respecting the constraints of the electric vehicle charging infrastructure; if a new electric vehicle cannot be charged (at the risk of violating a maximum power constraint) the steering algorithm looks for an electric vehicle that has been sufficiently charged to pause it and thus release electrical power for the new electric vehicle; this electric vehicle is permanently paused and will not be able to resume charging, 3 Algo#6: This driving algorithm is an improvement of Algo#5; it works in the same way but allows paused electric vehicles to resume charging later.

[0122] Once the configuration is complete, the simulation is launched on the 252 files, on a standard computer. The simulation takes approximately 1 minute per file and all the simulations complete after approximately 4.5 hours.

[0123] For each input file, an output file is generated. The output file contains values ​​of the performance indicators that have been selected. From these 252 output files, the statistical analysis phase is executed, this phase takes less than a minute.

[0124] The company prioritizes performance over robustness and gives zero weight to the latter. For each steering algorithm, each aggregated performance indicator is calculated as an average of the 252 calculated values ​​of the performance indicator.

[0125] The results obtained are as follows: Control algorithm IND#1 (number) IND#2 (kW) IND#3 (kWh) IND#4 (min) Algo#l 13 4.5 195 438 Algo#2 0 0 238 160 Algo#3 0 0 262 135 Algo#4 0 0 240 165 Algo#5 0 0 215 230 Algo#6 0 0 235 155

[0126] The Algo#3 steering algorithm stands out from the other steering algorithms on the 4 performance indicators calculated. The Algo#3 steering algorithm is selected as being the most relevant for the electric vehicle charging infrastructure and for the company's activity.

[0127] The Algo#3 control algorithm is recorded in the memory of the motor vehicle charging infrastructure management module.

[0128] The charging infrastructure management module controls the electric vehicle charging infrastructure by executing the selected control algorithm during an observation period. As a result of the observation period, the company identifies the following improvements, compared to the control algorithm Algo#l that was used previously: • Vehicles leave on average with a higher charge level, which increases the autonomy of electric vehicles to carry out business activities. • There are no more power cuts due to power overruns, which helps to increase the overall load and avoids having very lightly charged electric vehicles that do not have the necessary autonomy to carry out the business activity. • Electric vehicles that connect to 50kW sockets charge much faster than before, which meets the company's needs.

Claims

Claims

1. Method for selecting a control algorithm for a charging infrastructure (1) for charging electric vehicles, from a plurality of predefined control algorithms, the charging infrastructure (1) comprising a plurality of charging terminals (2), each charging terminal (2) being suitable for being connected to an electric vehicle (4, 5, 6) to deliver to the electric vehicle an electric power according to an electric power setpoint assigned to the charging terminal (2), each control algorithm being suitable for assigning to each charging terminal (2), an electric power setpoint to be delivered to each electric vehicle (4) connected to the charging terminal (2) of the charging infrastructure (1), the method comprising steps of: a - simulate a control of the charging infrastructure (1) according to each of the control algorithms by following the same predefined scenario (i) of activity of the charging infrastructure (1) over a given period of time, the predefined scenario (i) of activity of the charging infrastructure comprising the charging of one or more electric vehicles, and for each electric vehicle a time slot for connecting the electric vehicle to one of the charging terminals (2) of the charging infrastructure (1) and a value of an electrical energy requirement of the electric vehicle, b - for each control algorithm, calculate a value of a performance indicator (IND#) of the control algorithm according to a result of the simulation over the given period of time, c - select one of the control algorithms from the plurality of predefined control algorithms,based on the performance indicator values ​​calculated for the different control algorithms.,

2. Method according to claim 1, in which the performance indicator (IND#) is chosen from: - a number of times during the period of time that the maximum electrical power that the charging infrastructure is capable of delivering is exceeded, - an average amplitude of the overruns, over the period of time, - a rate of fully charged electric vehicles at the end of the time period, - a rate of electric vehicles charged beyond a predefined state of charge threshold at the end of the time period, - a total quantity of electrical energy delivered to all electric vehicles by the charging stations over the time period, - an average quantity of electrical energy delivered per electric vehicle, over the time period, - a dispersion rate of the quantities of electrical energy delivered to the different vehicles over the time period, - a dispersion rate of the states of charge of the different electric vehicles at the end of the time period, - an average of the full charging times of the electric vehicles, - a weighted average of the full charging times of the electric vehicles, with weighting coefficients,each weighting coefficient being proportional to a power of a connection socket of the charging station to which the electric vehicle is connected, - a rate of energy delivered to the electric vehicles, from a given local electrical energy source, - a ratio between a quantity of electrical energy delivered to the electric vehicles, from a given local electrical energy source, and a total quantity of electrical energy produced by the given local electrical energy source, over the period of time - a number of stops of the charging of the vehicles during the period of time.,

3. Method according to one of claims 1 and 2, in which steps a and b are repeated with a new predefined scenario (i+1) of activity of the charging infrastructure (1), so as to obtain, for each control algorithm, several values ​​of the performance indicator.

4. Method according to claim 3, wherein steps a and b are repeated with a new predefined scenario (i+1) of activity of the charging infrastructure (1) over a following given period of time of the same duration as the previous given period of time.

5. Method according to one of claims 3 and 4, further comprising a step of: d - for each control algorithm, determining a value of an in- aggregated performance indicator calculated based on the values ​​of the performance indicator obtained by repeating steps a and b, step c comprising a step of comparing the values ​​of the aggregated performance indicator of the steering algorithms with each other, and selecting the steering algorithm based on a result of the comparison.

6. Method according to claim 5, in which the aggregated performance indicator is chosen from: - an average of the values ​​of the performance indicators calculated over the different time periods, and / or - a standard deviation of the values ​​of the performance indicators calculated over the different time periods.

7. Method according to one of claims 1 to 6, comprising a prior step of: e - generating a series of N activity scenarios of the charging infrastructure, steps a and b being executed N times, following the activity scenarios, so as to obtain N values ​​of the performance indicator for each control algorithm.

8. Method according to claim 7, in which the N activity scenarios of the charging infrastructure are successive activity scenarios, each i+1-th scenario taking place over a period of time which immediately follows a period of time over which the i-th preceding scenario takes place, steps a and b being executed N times, following the successive activity scenarios, so as to obtain the N values ​​of the performance indicator for each control algorithm.

9. Method according to one of claims 7 and 8, in which during step e, each activity scenario of the infrastructure is obtained by randomly drawing parameters of the activity scenario.

10. Method according to one of claims 7 to 9, comprising a step prior to step e of: f - from historical data of the charging infrastructure, determining an average value and / or a standard deviation value of the arrival times of the electric vehicles, an average value and / or a standard deviation value of the departure times of the electric vehicles, and an average value and / or a standard deviation value of the electrical energy requirements of the electric vehicles, and in which during step e, each activity scenario of the infrastructure (i) is obtained by randomly drawing parameters of the activity scenario, so as to respect the average and / or standard deviation values ​​calculated in step f over all the drawings.

11. Method according to one of claims 7 to 10, comprising a step prior to step e of: g - from historical data of the charging infrastructure, classifying each electric vehicle into a group of electric vehicles, each group of electric vehicles grouping together a plurality of electric vehicles, and each group being associated with an average value and / or a standard deviation value of the arrival times of the electric vehicles of the group, an average value and / or a standard deviation value of the departure times of the electric vehicles of the group, and an average value and / or a standard deviation value of the electrical energy requirements of the electric vehicles of the group, and in which during step e, each activity scenario (i) of the charging infrastructure (1) is obtained by randomly drawing parameters of the activity scenario,so as to respect a proportion of electric vehicles in each group as well as the average and / or standard deviation values ​​calculated in step g associated with each group of electric vehicles, over all the draws.,

12. Method according to one of claims 1 to 11, in which each activity scenario (i) of the charging infrastructure (1) further comprises for each electric vehicle, one of the following parameters: - an arrival time of the electric vehicle in the charging infrastructure, - a departure time of the electric vehicle from the charging infrastructure, - an initial state of charge of the electric vehicle, at the time of connection of the vehicle to the charging terminal, - a capacity of a battery of the electric vehicle, - a minimum charging power of the electric vehicle, - a maximum charging power of the electric vehicle, - an identifier of a connection socket to which the electric vehicle is connected, - an initial state of charging mode of the electric vehicle at the start of the time period.

13. Method according to one of claims 1 to 12, comprising a step of: h - control the charging infrastructure (1) of electric vehicles by executing the control algorithm selected in step c.

14. Computer program product comprising program code instructions for executing the steps of the method according to one of claims 1 to 13, when this program is executed by a computer.

15. Computer readable memory storing computer executable instructions for carrying out the steps of the method according to one of claims 1 to 13.

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