Method and device for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) and associated computer program

Abstract

Method and device for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) and associated computer program The invention relates to a method (20) for predicting the electrical consumption of charging a set of electric vehicle(s) within their place of residence, comprising the following steps: - obtaining (22), for a plurality of predetermined daily moments, a prior estimate of the number of electric vehicle(s) present in said set at their place of residence;- prediction (24) of the electrical consumption associated with the charging of said assembly using a predetermined nomogram (32) indicating the average instantaneous charging power per vehicle present, as a function of said number of electric vehicle(s) present according to said estimate, said predicted electrical consumption corresponding to the multiplication of said average instantaneous charging power per vehicle present by said number of electric vehicle(s) present at their place of residence. Figure for the abbreviation: Figure 2;

Description

Title of the invention: Method and device for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) and associated computer program

[0001] The present invention relates to a method for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence, said method being implemented by an electronic device for predicting the electrical consumption associated with the charging of said set of electric vehicle(s).

[0002] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method of predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence.

[0003] The invention also relates to said electronic device for predicting the electrical consumption associated with the charging of said set of electric vehicle(s).

[0004] The present invention relates to the field of electricity network management and electricity markets. In particular, the present invention relates to the prediction of electricity consumption related to the charging of electric vehicle(s) (also referred to hereafter as EV(s)) at home for a fleet of electric vehicle(s) corresponding, for example, to a predetermined geographical area such as a region, a country, etc., or to a fleet of electric vehicle(s) managed by a given supplier, or to a fleet of electric vehicle(s) controlled by a consumption flexibility aggregator.

[0005] Currently, load aggregation methods exist. However, to predict the electrical consumption related to home charging of a predetermined fleet of electric vehicles, few effective solutions have been developed apart from methods based on machine learning which have the disadvantage of requiring a large amount of training data, complex processing and are unsuitable for predicting the impact of a control signal equivalent to a charging stop request.

[0006] The aim of the invention is therefore to propose a method for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence, making it possible to overcome the disadvantages of the state of the art by providing an efficient and less complex solution for predicting such electrical consumption.

[0007] To this end, the invention relates to a method for predicting the electricity consumption associated with the charging of a set of electric vehicle(s) at their place of residence, said method being implemented by an electronic device for predicting the electricity consumption associated with the charging of said set of electric vehicle(s) at their place of residence, said method being implemented by an electronic device for predicting the electricity consumption associated with the charging of said set of electric vehicle(s), and comprising the following steps:

[0008] - obtaining, for a plurality of predetermined daily moments, an estimate prior to the number of electric vehicle(s) present composing said set of electric vehicle(s) at their place of residence;

[0009] - prediction of the electrical consumption associated with the recharging of said set of electric vehicle(s) using a predetermined nomogram indicating the average instantaneous charging power per vehicle present at their place of residence at a time t, as a function of said number of electric vehicle(s) present at their place of residence at said time t according to said estimate, said predicted electricity consumption corresponding substantially to the multiplication of said average instantaneous charging power per vehicle present at said time t by said number of electric vehicle(s) present at their place of residence at said time t.

[0010] Thus, the present invention advantageously proposes to use a relevant nomogram indicating the average instantaneous charging power per vehicle present at its place of residence at a time t, as a function of said number of electric vehicle(s) present at their place of residence at said time t according to said estimate.

[0011] The use of such a nomogram is valuable because it is efficient, precise, and relevant while offering reduced complexity of use for stakeholders in electricity networks and electricity markets. It allows them to anticipate problems on the networks, to better utilize them, to optimize the management of a portfolio of production and consumption assets, and to address network issues related to demand response or load shedding.

[0012] According to other advantageous aspects of the invention, the method for predicting the electricity consumption associated with the charging of a set of electric vehicle(s) at their place of residence comprises one or more of the following features, taken individually or in all technically possible combinations:

[0013] - said predetermined nomogram corresponds to a defined closed power trajectory by a predetermined direction of temporal travel of said trajectory and by four points, predetermined by learning, each point being defined by: an abscissa, corresponding to a number of electric vehicle(s) present at their home location ciliation, and an ordinate corresponding to an average instantaneous charging power per vehicle,

[0014] said trajectory comprising substantially a portion of a semi-ellipse connecting the first three points of said four points, the first point of said four points having a minimum abscissa and corresponding to an endpoint of the horizontal semi-axis of said portion of the semi-ellipse, the second point of said four points corresponding to the vertex of said semi-ellipse,

[0015] said part of the semi-ellipse being connected, according to the predetermined direction of temporal traversal, to a broken line formed of two segments of which:

[0016] - the first vertical segment connects the third and fourth points of said four points, the said third and fourth points having the same abscissa corresponding to the number of electric vehicle(s) domiciled at their place of residence and being representative of the decreasing nocturnal evolution of the average instantaneous charging power per vehicle present;

[0017] - the second segment of said broken line connecting the fourth lower point of said first vertical segment at the first point corresponding to said end of the horizontal semi-axis of said part of semi-ellipse;

[0018] - each ordinate of said four points is respectively defined by means of a a predetermined parametric equation, for the number of electric vehicle(s) present at their place of residence corresponding to the abscissa of each of the four points, said predetermined parametric equation comprising at least one constant and having as parameter(s) at least one of the elements belonging to the group comprising at least:

[0019] - the average battery capacity of said electric vehicle(s) domiciled at their place of residence;

[0020] - the average autonomy factor of said electric vehicle(s) domiciled at their place of residence;

[0021] - the average energy consumption per kilometer of said electric vehicle(s) domiciled at their place of residence;

[0022] - the average behavior coefficient of the drivers of said vehicle(s) electric(s) domiciled at their place of residence;

[0023] - the average distance travelled per day by said home-based electric vehicle(s) to their place of residence;

[0024] each parameter being an unknown of said predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for N sets of parameter(s) of distinct values, N being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said N sets of parameter(s) of distinct values ​​and the result of said predetermined parametric equation associated with said N parameter set(s);

[0025] - the first point of said four points, corresponding to one end of the semi-axis horizontal, presents an abscissa corresponding to the minimum number of electric vehicle(s) present at their place of residence, said minimum number being proportional to the number of electric vehicle(s) domiciled at their place of residence according to a proportionality factor [3, such that 0< [3 <1, said proportionality factor [3 being obtained by statistical learning during said obtaining step;

[0026] - the other end of the horizontal semi-axis of said semi-ellipse has an abscissa the maximum fictitious number of electric vehicle(s), said maximum number being proportional to the number of electric vehicle(s) registered at their place of residence according to a proportionality factor a, such that 1 < a, said proportionality factor a being the result of another predetermined parametric equation having as parameter(s) at least one of the elements belonging to the group comprising at least:

[0027] - the average battery capacity of said electric vehicle(s) domiciled at their place of residence;

[0028] - the average autonomy factor of said electric vehicle(s) domiciled at their place of residence;

[0029] - the average energy consumption per kilometer of said electric vehicle(s) domiciled at their place of residence;

[0030] - the average behavior coefficient of the drivers of said vehicle(s) electric(s) domiciled at their place of residence;

[0031] - the average distance travelled per day by said home electric vehicle(s) to their place of residence;

[0032] each parameter being an unknown of said other predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for M set(s) of distinct values, M being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said M set(s) of distinct values ​​and the result of said predetermined parametric equation associated with said M set(s);

[0033] - the second point of said four points corresponding to the vertex of said half- ellipse presents an abscissa corresponding to the average number of electric vehicle(s) present at their place of residence, said average number corresponding to the arithmetic mean between said minimum number and said maximum number of electric vehicle(s) that can be simultaneously present at their place of residence;

[0034] - the average instantaneous power P(t) of charge per vehicle present for all The point on the semi-ellipse connecting the first, second, and third points can be expressed in the following form at a given time t:

[0035] pz x _ [TppCZZZZIZï^^ W y!hl (NmkrNminyNd)

[0036] with:

[0037] N(t) the number of electric vehicle(s) present at said instant t; Nmid said average number, Nmin said minimum number, and E the average instantaneous charging power per vehicle present corresponding to the ordinate of the second point corresponding to the vertex of said semi-ellipse and whose abscissa is equal to said average number of electric vehicle(s) present at their place of residence,

[0038] said power E being defined by means of a predetermined parametric equation comprising at least one constant and having as parameter(s) at least one of the elements belonging to the group comprising at least:

[0039] - the average battery capacity of said electric vehicle(s) domiciled at their place of residence;

[0040] - the average autonomy factor of said electric vehicle(s) domiciled at their place of residence;

[0041] - the average energy consumption per kilometer of said electric vehicle(s) domiciled at their place of residence;

[0042] - the average behavior coefficient of the drivers of said vehicle(s) electric(s) domiciled at their place of residence;

[0043] - the average distance travelled per day by said home electric vehicle(s) to their place of residence;

[0044] each parameter being an unknown of said predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for N sets of parameter(s) of distinct values, N being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said N sets of parameter(s) of distinct values ​​and the result of said predetermined parametric equation associated with said N sets of parameter(s);

[0045] - the process further comprises:

[0046] - the reception of at least one signal constituting a request to stop said charging, said signal including the time interval during which said stop is required, said interval being defined by two terminals Ti and T2, with T2 > Th and

[0047] - a first adaptation of said prediction of electricity consumption, the The average instantaneous charging power per vehicle present at its place of residence is adapted to each reception of said signal constituting a request to stop said recharge to be zero over said time interval;

[0048] - the method further comprises, for t > T2, a second adaptation of said prediction of electricity consumption compared to the prediction of electricity consumption that would have been obtained in the absence of a signal indicating a request to stop said charging, the difference between:

[0049] - the average instantaneous power Ps(t) of charge per vehicle present associated with the prediction of electricity consumption after T2, and

[0050] - the average instantaneous power P(t) of charge per vehicle present associated with the prediction of the electrical consumption that would have been obtained in the absence of a signal indicating a request to stop said charging;

[0051] being a decreasing positive function dependent on TrT2, the energy W that would have been injected into the electric vehicle(s) in the absence of a signal constituting a request to stop said charging such that _ fr2 z , , . , , and the number W — JyjA'fZ )J (t jjdt means of electric vehicle(s) present between Ti and T2;

[0052] - said decreasing positive function is linear such that: Ps(t) - P(t) = - T2) +d2 with

[0053] dj < 0.

[0054] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method of predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence as defined above.

[0055] The invention also relates to a computer program comprising software instructions which, when executed by a computer, implement such a method of predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence as defined above.

[0056] The invention also relates to an electronic device for predicting the electricity consumption associated with the charging of a set of electric vehicle(s) at their place of residence, said set of electric vehicle(s) characterized in that it comprises:

[0057] - a obtaining module, configured to obtain, for a plurality of predetermined instants daily completions, a prior estimate of the number of electric vehicle(s) present comprising said set of electric vehicle(s) at their place of residence;

[0058] - a prediction module, configured to predict electricity consumption associated with the charging of said set of electric vehicle(s) using a predetermined abacus indicating the average instantaneous charging power per vehicle present at their place of residence at a time t, as a function of said number of electric vehicle(s) present at their place of residence at said time t according to said estimate, said predicted electricity consumption corresponding substantially to the multiplication of said average instantaneous charging power per vehicle present at said time t by said number of electric vehicle(s) present at their place of residence at said time t.

[0059] The invention will become clearer upon reading the following description, given solely by way of non-limiting example, and made with reference to the drawings in which:

[0060] - [Fig. 1] [Fig. 1] is a diagram illustrating an electronic device for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence according to an embodiment of the invention;

[0061] - [Fig.2] [Fig.2] is a flowchart of a consumption prediction system electric associated with the charging of a set of electric vehicle(s) within their place of residence according to an embodiment of the invention;

[0062] - [Fig.3] [Fig.3] illustrates an example of an abacus used advantageously according to the present invention;

[0063] - [Fig.4] [Fig.5] Figures 4 and 5 respectively illustrate an example of representation representation of the prior estimate of the number of electric vehicle(s) throughout a day and two examples of average charging power of vehicles present at home in the absence and in the presence of a signal equivalent to a request to stop charging.

[0064] In the following description, the expression "approximately equal to" is understood as a relationship of equality plus or minus 10%, that is to say with a variation of at most 10%, preferably also as a relationship of equality plus or minus 5%, that is to say with a variation of at most 5%.

[0065] Fig. 1 is a schematic representation of an embodiment of an electronic device 10 for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence.

[0066] More specifically, such an electronic device 10 includes first of all a obtaining module 12, configured to obtain, for a plurality of predetermined daily moments, a prior estimate of the number of electric vehicle(s) present composing said set of electric vehicle(s) at their place of residence.

[0067] For example, consider a fleet of 10,000 electric vehicles, each electric vehicle being domiciled at an identified location where it is suitable for recharging.

[0068] Furthermore, such an electronic device 10 also includes a prediction module 14, configured to predict the electrical consumption associated with the charging of said set of electric vehicle(s) using a predetermined nomogram indicating the average instantaneous charging power per vehicle present at its home location at a time t, as a function of said number of electric vehicle(s) present at their home location at said time t according to said estimate, said predicted electrical consumption corresponding substantially to the multiplication of said average instantaneous charging power per vehicle present at said time t by said number of electric vehicle(s) present at their home location at said time t.

[0069] In an optional variant not shown in [Fig. 1], the electronic device 10 also includes a receiving module configured to receive at least one signal constituting a request to stop said charging, said signal including the time interval during which said stop is required, said interval being defined by two terminals Ti and T2, with T2 > Tb

[0070] Said signal, which serves as a request to stop said charging, is not an instantaneous signal for the forecasting module, but it is for the charging station. More precisely, such a signal, which serves as a request to stop said charging, is a time series (similar to the time series indicating an estimate of the number of vehicles present at home; it is a signal fed into the forecasting module) that indicates known or potential requests (for example, when the user of this forecasting module wants to determine the optimum stop signal for their activity) to stop charging for the coming minutes / hours.

[0071] According to this optional variant not shown, the electronic device 10 also includes an adaptation module configured to implement a first adaptation of said prediction of electrical consumption, the average instantaneous charging power per vehicle present at its home location being adapted at each reception of said signal constituting a request to stop said charging to be zero over said time interval.

[0072] According to this optional variant (not shown), the adaptation module is also configured, as an optional complement, to implement, for t > T2, a second adaptation of the prediction of electrical consumption relative to the prediction of electrical consumption that would have been obtained in the absence of a signal indicating a request to stop the charging. This adaptation is based on the difference between, on the one hand, the average instantaneous charging power Ps(t) per vehicle present associated with the prediction of electrical consumption after T2, and on the other hand, the average instantaneous charging power P(t) per vehicle present associated with the prediction of electrical consumption that would have been obtained in the absence of a signal indicating a request.

[0073]

[0074]

[0075] of stopping said charging, being a decreasing positive function dependent on TrT2, of the energy W that would have been injected into the electric vehicle(s) in the absence of a signal equivalent to a request to stop said charging such that [^2 z \ A ' ct of the average number of electric vehicle(s) present IT -- J y. | ly ( t J .1 ( t J .Cl f between Ti and T2. Optionally, said decreasing positive function is linear such that: Ps(t) - P(t) = d / f- T2) +d2 with d^ 0. In the example of [Fig.1], the electronic device 10 for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence includes an information processing unit 16 formed for example of a memory 18 and a processor 20 associated with the memory 18.

[0076] In the example of [Fig. 1], the acquisition module 12, the prediction module 14, and, optionally (not shown), the reception module and the adaptation module, are each implemented as software, or a software component, executable by the processor 19. The memory 18 of the electronic device for predicting the electricity consumption associated with the charging of a set of electric vehicle(s) at their home location is thus capable of storing acquisition software, prediction software, and, optionally, reception software and adaptation software. The processor 19 is then capable of executing each of the following software programs: acquisition software, prediction software, and, optionally, reception software and adaptation software.

[0077] In an alternative not shown, the acquisition module, the prediction module, and optionally the reception module and the adaptation module, are each implemented as a programmable logic component, such as an FPGA (Field Programmable Gate Array), or as an integrated circuit, such as an ASIC (Application-Specific Integrated Circuit).

[0078] When the electronic device for predicting the electricity consumption associated with the charging of a set of electric vehicle(s) at their home location is implemented in the form of one or more software programs, i.e., in the form of a computer program, also called a computer program product, it is also capable of being stored on a computer-readable medium, not shown. A computer-readable medium is, for example, a medium capable of storing electronic instructions and being connected to a bus of a computer system. For example, a readable medium is an optical disc, a magneto-optical disc, ROM, RAM, any type of non-volatile memory (e.g., FLASH or NVRAM), or a magnetic card. The data is then stored on the readable medium. memorized a computer program containing software instructions.

[0079] An example of the operation of the electronic device 10 for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence, as illustrated previously in relation to [Fig. 1], is described below in relation to [Fig. 2].

[0080] More specifically, the method 20 for predicting the electrical consumption associated with the charging of a set of electric vehicle(s) within their place of residence of said set of electric vehicle(s), generally includes, a first step 22 of obtaining OBT, for a plurality of predetermined daily moments, a prior estimate of the number of electric vehicle(s) present composing said set of electric vehicle(s) at their place of residence.

[0081] In other words, according to said method 20, it is necessary to have access as "input" to an estimate / forecast of the number of electric vehicles (EVs) present at home at any given time.

[0082] An example of an estimate obtained during this step 22 is illustrated in particular by [Fig.4] described below.

[0083] For example, to obtain 22 such an estimate of the number of electric vehicle(s) N(t), for the fleet considered, present at home at time t, a simulation is used beforehand, for example by using a means such as a software tool for simulating the movement of vehicles or the presence of vehicles on a predetermined typology of sites (home, workplace, public places, ...).

[0084] More specifically, the approach, for example, adopted consists of individually simulating a large number of electric vehicles / users, considering the possible journeys, for example: from home to work, then from work to school to carry out a "school pick-up"), then from school to home, etc. which are characterized by distances, durations or speeds, start / end times.

[0085] Sociological studies of use make it possible to feed these simulations with databases or descriptions in statistical form, an example of which is the document entitled "The mobility of the French: Overview from the national survey on transport and travel 2008", accessible in the journal of the General Commission for Sustainable Development CGDD of December 2010.

[0086] According to one example among a plurality of possible examples, such a simulation consists of distinguishing working days from non-working days by associating four possible routes with working days against only two with non-working days.

[0087] The four journeys associated with working days include, in particular, a first journey from home to work and then back home from work, a second journey from home to work and then to another destination and returning from this other destination to home, a third journey leaving home going to another destination and returning from this other destination to home, a fourth journey leaving home going to a first other destination then a second other destination and returning from this second other destination to home.

[0088] For example, "other destination" means a place, separate from home and work, for shopping, errands, leisure, private travel, such as school pick-up or family visits, to accompany, pick up or for business trips.

[0089] The two journeys associated with non-working days (i.e., when it is not necessary to go to work) then include only the third and fourth journeys mentioned above.

[0090] In a specific example, for the sake of simplicity, for a given electric vehicle, it is assumed that the same route is always taken on weekdays, and similarly on non-weekdays. The choice of routes, from among the two or four possible routes mentioned above, as well as their associated characteristics, are determined randomly according to predetermined probability distributions based on data from sociological surveys such as that in the aforementioned document.

[0091] This refers to: the type of journey, the distances travelled for each journey, the time of departure in the morning to go to the workplace or the time of departure from the workplace for the first and second journeys mentioned above, the time of departure from home for days off for the third and fourth journeys mentioned above whether the day is a working day or not, the duration for which the electric vehicle remains parked for locations of type "other destination".

[0092] Thus, the simulation makes it possible to define for each electric vehicle EV its location at each instant of the plurality of predetermined daily instants (i.e. at each simulated time step), and then makes it possible to deduce for each time step the number of electric vehicle(s) EV present at home (i.e. whose location is equal to the value "home" of said electric vehicle EV).

[0093] Then, as illustrated by Fig. 2, the method 20 for predicting electrical consumption according to the present invention comprises a step 24 for predicting the electrical consumption associated with the charging of said set of electric vehicle(s) using a predetermined nomogram indicating the average instantaneous charging power per vehicle present at its home location at time t, as a function of said number of electric vehicle(s) present at said home location at said time t according to said estimate, said predicted electrical consumption corresponding substantially to the multiplication of said average instantaneous charging power per vehicle present at said time t by said number of vehicle(s) electrical(s) present at their place of residence at said instant t.

[0094] Optionally, as illustrated by the dotted line in [Fig. 2], the method 20 for predicting the electrical consumption associated with charging a set of electric vehicle(s) at their home location further includes a step 26 for receiving at least one signal constituting a request to stop said charging, said signal comprising the time interval during which said stop is required, said interval being defined by two terminals Ti and T2, with T2 > Tb

[0095] According to this option, the reception step 26 is followed by a first adaptation step 28 Ai of said prediction of electrical consumption, the average instantaneous charging power per vehicle present at its place of residence being adapted at each reception of said signal constituting a request to stop said charging to be zero over said time interval.

[0096] In other words, during the time interval between Ti and T2, the vehicles are not recharged, which means that the average instantaneous charging power Ps(t) per vehicle is equal to zero during this period, whereas before Tb the average instantaneous charging power Ps(t) per vehicle is equal to the average instantaneous charging power P(t) per vehicle present associated with the prediction of the electrical consumption that would have been obtained in the absence of a signal equivalent to a request to stop said charging (i.e. without step 26).

[0097] As an optional complement to this option, the first adaptation step 28 is further followed, for t > T2, by a second adaptation step 30 A2 of said prediction of electrical consumption with respect to the prediction of electrical consumption that would have been obtained in the absence of a signal equivalent to a request to stop said charging, the difference between, on the one hand, the average instantaneous charging power Ps(t) per vehicle present associated with the prediction of electrical consumption after T2, and on the other hand, the average instantaneous charging power P(t) per vehicle present associated with the prediction of electrical consumption that would have been obtained in the absence of a signal equivalent to a request to stop said charging, being a decreasing positive function dependent on TrT2, of the energy W that would have been injected into the electric vehicle(s) in the absence of a signal equivalent to a request to stop said charging such that:

[0098] ) p and the average number of electric vehicle(s) present between Ti and T2

[0099] In other words, after T2, Ps(t) is calculated based on both P(t) and the energy that should have been injected into the electric vehicles EV during the period TrT2 but which was not due to the control signal serving as a request to stop charging.

[0100] According to an optional complementary variant, said decreasing positive function is linear such that: Ps(t) - P(t) = -T2) +d2 with di < 0, with di and d2 depending of TrT2, of the energy that would have been injected into the electric vehicles (EVs) if there had been no control signal requesting a charging stop Jr2jV ( i ) p ( t ) h Ct of the average number of electric vehicles present between T i and T2.

[0101] In other words, we consider that the difference between Ps(t) and P(t) is a positive, decreasing and linear function of time, and that when it reaches zero, we then consider that again Ps(t) = P(t) until the next control signal is received, which is a request to stop the charging.

[0102] Note that in the case where there are two successive control signal periods, the same approach is applied, considering, for example, that the first period goes from Ti to T2 and the second from T3 to T4. For the first period from Ti to T2, the aforementioned approach is applied. For the second period from T3 to T4, the same approach is applied with one exception, namely that the temporality is considered by means of the difference between, on the one hand, the average electricity consumption for home charging of all home vehicles of the model during the first period from Ti to T2, and on the other hand, the average electricity consumption for home charging of all home vehicles of the model during the second period from T3 to T4, in order to find the average electricity consumption for home charging after time T4.

[0103] The impact of the optional reception steps 26, first adaptation steps 28 and second adaptation steps 30 will be described in particular later in relation to [Fig.5] which illustrates two examples of average charging power of vehicles present at home in the absence and in the presence of a signal equivalent to a request to stop charging.

[0104] The implementation of these optional steps makes it possible, in particular, to consider that at certain times of the day a signal constituting a request to stop charging is received and used by the charging system as a command to stop charging. Such a signal corresponds, for example, to a signal representing a period of high electricity prices (i.e., a peak-hour signal), a signal to reduce consumption (e.g., a reduction by a certain factor, for example, expressed as a percentage), or a signal representing a significant network load, which requires an overall reduction in consumption, depending on the case, at the distribution or transmission network level. Such a signal may be binary or not, for example, with a request for a progressive reduction in consumption.

[0105] [Fig.3] illustrates an example of an abacus used specifically according to the present invention during step 24 of prediction in [Fig.2].

[0106] More precisely, the predetermined abacus 32 corresponds, according to this example, to a closed power trajectory defined by a predetermined direction of temporal path of said trajectory, illustrated by the arrows of [Fig.3], and by four points E2, Ep L2 and Li, predetermined by learning.

[0107] Each point E2, Ep L2 and Li is defined by: an abscissa, corresponding to a number N of electric vehicle(s) present at their place of residence, and an ordinate corresponding to an average instantaneous power P(kW) of charge per vehicle.

[0108] Said trajectory 32 substantially comprises a semi-ellipse portion connecting the first three points E2, Ep L2 of said four points E2, Ep L2 and Lp

[0109] The first point E2 of said four points E2, Ep L2 and Li has a minimum abscissa Nmin and corresponds to an end of the horizontal semi-axis of said part of semi-ellipse.

[0110] The second point Ei of said four points E2, Ep L2 and Li corresponds to the vertex of said semi-ellipse.

[0111] Said part of semi-ellipse, connecting points E2, Ep L2, is itself connected, according to the predetermined direction of travel, to a broken line formed of two segments.

[0112] The first segment of said broken line, according to the predetermined direction of travel of said trajectory, illustrated by the arrows in [Fig. 3], is vertical and connects the third and fourth points L2 and Li of said four points E2, Ep L2 and Ll

[0113] The third and fourth points L2 and Li have the same abscissa Nev corresponding to the number of electric vehicle(s) domiciled at their place of residence and being representative of the decreasing nocturnal evolution of the average instantaneous charging power per vehicle present (i.e. as they are recharged and consequently no longer require charging compared to the moment of their return home).

[0114] The number Nev of electric vehicle(s) domiciled at their place of residence corresponds to the size of the electric vehicle fleet considered and is known as an input of said process.

[0115] The second segment of said broken line, according to the predetermined direction of travel of said trajectory, illustrated by the arrows of [Fig.3], connecting the fourth lower point Li of said first vertical segment [L2Li] to the first point E2 corresponding to said minimum abscissa end of the horizontal semi-axis of said part of semi-ellipse connecting points E2, Ep L2.

[0116] The so-called main part corresponds to the daytime trajectory connecting points Li to E2 to Ei to L2, and is used during the day temporally by following the direction-of-travel arrows shown in [Fig. 3]. The vertical part connecting points L2 to Li along the vertical line corresponds to nighttime use between returning home and leaving home, and by assumption this segment is traversed linearly by default with the time.

[0117] According to an optional complement, each ordinate Fb E, F3 and F2 of said four points E2, Ei, L2 and Li respectively is defined by means of a predetermined parametric equation, for the number of electric vehicle(s) present at their place of residence corresponding to the abscissa Nmin, Nmid, Nev of each of the four points, said predetermined parametric equation comprising at least one constant and having as parameter(s) at least one of the elements belonging to the group comprising at least:

[0118] - the average battery capacity Bavg (kWh) of said electric vehicle(s) domiciled at their place of residence, corresponding to the average battery capacity of the electric vehicle fleet considered, for example between 20 kWh and 70 kWh;

[0119] - the average range factor Ravg of said electric vehicle(s) domiciled at their place of residence, the value of which is for example between 1 and 2;

[0120] - the average energy consumption per kilometer Davg (kWh / km) of said electric vehicle(s) domiciled at their place of residence, corresponding to the average kilometer consumption of electric vehicles (EVs) in the electric vehicle fleet considered, and which may possibly be "weather dependent", for example between 0.12 and 0.20 kWh / km;

[0121] - the average behavior coefficient Uavg of the drivers of said vehicle(s) electrical(s) domiciled at their place of residence, for example between 0.8 and 1.5;

[0122] - the average distance travelled per day Kavg (km) of said electric vehicle(s) domiciled at their place of residence, corresponding to the average daily distances travelled by the electric vehicles (EVs) in the electric vehicle fleet considered, for example between 10 km and 50 km.

[0123] It should be noted that the average range factor Ravg and the average behavior coefficient Uavg are defined in particular in the document by F. Gonzalez et al. entitled “Plug-in behavior of electric vehicles users: Insights from a large-scale trial and impacts for grid integration studies”, eTransportation, vol. 10, p. 100131, Nov. 2021.

[0124] More specifically, according to these definitions, the average range factor Ravg corresponds to a number greater than one and indicates the average margin that users allow for the absolute necessity of recharging the electric vehicle (EV) given the planned trips for the following day. Indeed, drivers do not systematically plug their EV into their charging station upon returning home. If a driver considers that the battery is not sufficiently charged for the next day's trips, then in that case, they plug in the EV.

[0125] According to these definitions, the average behavior coefficient Uavg then characterizes the The fact that a driver plugs in their electric vehicle (EV) or not, because if the range is sufficient for the next day's travel, the driver is not obliged to plug their electric vehicle (EV) into their charging station.

[0126] Each parameter Bavg, Ravg, Davg, Uavg or Kavg is characteristic of both the fleet of electric vehicles and the behavior of their drivers.

[0127] Each parameter Bavg, Ravg, Davg, Uavg or Kavg is treated according to the present invention as an unknown of said predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for N sets of parameter(s) of distinct values, N being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said N sets of parameter(s) of distinct values ​​and the result of said predetermined parametric equation associated with said N sets of parameter(s).

[0128] More precisely, according to this optional complement, the ordinate Fi of point E2, corresponding to an endpoint of the horizontal semi-axis of said part of the semi-ellipse, is obtained for example by applying the following first-degree parametric equation and optimized:

[0129] F! = -0.0077 -0.00003 Bavg + 0.0585 Davg+ 0.0008 Uavg+ 0.0009 Ravg +0.0006 Kavg

[0130] after optimization using N sets of parameters Bavg, Ravg, Davg, Uavg or Kavg of distinct values ​​(i.e. at least one parameter value being distinct from one set to another) with, for example, Bavg values ​​between 20 kWh and 70 kWh, Ravg values ​​between 1 and 2, Davg values ​​between 0.12 and 0.20 kWh / km, Uavg values ​​between 0.8 and 1.5 and Kavg values ​​between 10 km and 50 km.

[0131] Similarly, according to this optional complement, the ordinate E of the point Ei corresponding to the vertex of said semi-ellipse is obtained, for example, by applying the following first-degree parametric equation and optimized:

[0132] E = -0.6375 -0.0053 Bavg +4.1829 Davg+0.0132 Uavg+0.1685 Ravg +0.0172 Kavg

[0133] after optimization also using the N sets of parameters Bavg, Ravg, Davg, U avg or Kavg of distinct values ​​(i.e. at least one parameter value being distinct from one set to another) with for example values ​​of Bavg between 20 kWh and 70 kWh, values ​​of Ravg between 1 and 2, values ​​of Davg between 0.12 and 0.20 kWh / km, values ​​of Uavg between 0.8 and 1.5 and values ​​of Kavg between 10 km and 50 km.

[0134] Note that certain parameter sets are such that they provide a negative resulting E value, which is relatively normal since such sets are not realistic. For example, if the value of Bavg is 70 kWh, which corresponds roughly to a fleet that would currently be composed of Tesla electric vehicles, the The value of Kavg is 10 km, which corresponds to a short daily distance, and the value of Davgest is 0.12, which corresponds to low consumption, the "average" battery then allows for an "average" range of almost two months of electric vehicle use, so that combined with Uavg values ​​of 0.8 and Ravg of 1, representative of the fact that drivers of the electric vehicle fleet considered have a strong tendency to wait until the last moment before recharging their electric vehicle, the value of E corresponding to this unrealistic combination of parameters would be such that E= -0.155 kW.

[0135] As an alternative, a parametric equation of a different degree may be used. For example, the ordinate E of the point Ei corresponding to the vertex of said semi-ellipse is obtained, for example, by applying the following optimized parametric equation of the second degree:

[0136] E = -0.5821 -0.0081 Bavg +2.7157 Davg -0.0351 Uavg +0.4888 Ravg +0.0117 Kavg +0.00003 Bavg² + 4.8245*Davg² +0.01988*Uavg² - 0.1067*Ravg² + 0.00007*Kavg², and this after optimization also using the N sets of parameters Bavg, Ravg, Davg, Uavg or Kavg of distinct values ​​(i.e. at least one parameter value being distinct from one set to another) with, for example, Bavg values ​​between 20 kWh and 70 kWh, Ravg values ​​between 1 and 2, Davg values ​​between 0.12 and 0.20 kWh / km, Uavg values ​​between 0.8 and 1.5 and Kavg values ​​between 10 km and 50 km.

[0137] In a manner similar to what has been indicated previously, according to this optional complement, the ordinate F2 of the point Li is obtained for example by applying the following first-degree parametric equation and optimized:

[0138] F2 = -0.1928 +0.0015 Bavg +0.697 Davg-0.015 Uavg+0.0012 Ravg +0.0025 Kavg

[0139] after optimization using the N parameter sets Bavg, Ravg, Davg, Uavg or Kavg values ​​are distinct (i.e., at least one parameter value is distinct from one set to another), for example, Bavg values ​​between 20 kWh and 70 kWh, Ravg values ​​between 1 and 2, Davg values ​​between 0.12 and 0.20 kWh / km, Uavg values ​​between 0.8 and 1.5, and K avg values ​​between 10 km and 50 km.

[0140] The ordinate F3 of the point L2 is likely to be obtained in a similar way or more simply by considering that the point L2 is the point of intersection between the semi-ellipse passing through the points E2 and Ei and the vertical of abscissa Nev corresponding to the number of electric vehicle(s) domiciled at their place of domicile, which amounts to the size of the electric vehicle fleet considered, known as an input of said process.

[0141] As an optional addition, the first point E2 of said four points E2, Eh, L2 and Lb, this first point E2 corresponding to an endpoint of the horizontal semi-axis, presents a abscissa Nmin corresponding to the minimum number of electric vehicle(s) present at their place of residence, said minimum number being proportional to the number of electric vehicle(s) domiciled at their place of residence according to a proportionality factor [3, such that 0< [3 <1, said proportionality factor [3 being obtained by statistical learning during said acquisition step 22. For example, Nmin = [3 Nev = 0.1638 Nev.

[0142] According to a particular variant, [3 is directly obtained from said estimate presented in the form of a time series from which such a minimum number Nmin can be directly extracted.

[0143] As an optional addition, in [Fig. 3], the other end E3 of the horizontal semi-axis of said semi-ellipse is also shown, although it does not belong to the nomogram used as such. This other end E3 has as its abscissa a fictitious maximum number Nmax of electric vehicle(s), said maximum number being proportional to the number Nev of electric vehicle(s) registered at their place of residence according to a proportionality factor a, such that 1 < a. More precisely, said proportionality factor a is the result of another predetermined parametric equation also having as parameter(s) at least one of the elements belonging to the group comprising at least:

[0144] - the average battery capacity Bavg (kWh) of said electric vehicle(s) domiciled at their place of residence, for example between 20 kWh and 70 kWh;

[0145] - the average range factor Ravg of said electric vehicle(s) domiciled at their place of residence, for example between 1 and 2;

[0146] - the average energy consumption per kilometer Davg (kWh / km) of said electric vehicle(s) domiciled at their place of residence, for example between 0.12 and 0.20 kWh / km;

[0147] - the average behavior coefficient Uavg of the drivers of said vehicle(s) electrical(s) domiciled at their place of residence, for example between 0.8 and 1.5;

[0148] - the average distance travelled per day Kavg (km) of said electric vehicle(s) domiciled at their place of residence, for example between 10 km and 50 km.

[0149] Each parameter Bavg, Ravg, Davg, Uavg or Kavg is characteristic of both the fleet of electric vehicles and the behavior of their drivers.

[0150] Each parameter Bavg, Ravg, Davg, Uavg, or Kavg is treated according to the present invention as an unknown of said other predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for M sets of parameter(s) with distinct values, M being an integer, by minimizing the error between the instantaneous average power of load per vehicle obtained by simulation for said M sets of distinct parameter(s) and the result of said predetermined parametric equation associated with said M set(s).

[0151] More specifically, according to this optional complement, the proportionality factor a defining the abscissa Nmax of point E3, namely a fictitious maximum number of electric vehicles, corresponding to an end of the horizontal semi-axis of said part of the semi-ellipse, is obtained for example by applying the following optimized first-degree parametric equation:

[0152] a =0.9533 +0.00065 Bavg +0.1714 Davg-0.009 Uavg+0.0009 Ravg +0.0007 Kavg.

[0153] As an optional addition, the second point Ei of said four points E2, Eh, L2, and said second point Ei corresponding to the vertex of said semi-ellipse, has an abscissa Nmid corresponding to the average number of electric vehicle(s) present at their place of residence, said average number Nmid corresponding to the arithmetic mean between said minimum number and said maximum number of electric vehicle(s) that can be simultaneously present at their place of residence. In other words:

[0154] a / — 1Nmid 2

[0155] As an optional complement, the average instantaneous power P(t) of charge per vehicle present (i.e., the ordinate) for any point on the part of the semi-ellipse connecting the first, second and third points E2, Ei and L2 can be expressed in the following form, at a time t:

[0156] pzx A p i2 [Ëp 77 ~ nu - - N ^N(t) f V -mid 'hniiv

[0157] with:

[0158] N(t) the number of electric vehicle(s) present at said time t; Nmid said average number, Nmin said minimum number, and E the average instantaneous charging power per vehicle present corresponding to the ordinate of the second point Ei corresponding to the vertex of said semi-ellipse and whose abscissa is equal to said average number Nmid of electric vehicle(s) present at their place of residence, said power E being defined as described above by means of a predetermined parametric equation comprising at least one constant and having as parameter(s) at least one of the elements belonging to the group comprising at least Bavg, Ravg, Davg, Uavg or Kavg as described above.

[0159] Such an equation also makes it possible to determine the ordinate F3 of the point L2 which is the last point of the part of the semi-ellipse taken into account within the abacus advantageously used according to the present invention.

[0160] Furthermore, in view of the foregoing, before returning to the moment which corresponds in The abscissa represents the minimum number Nrnin of electric vehicle(s) (EVs) present at their home (i.e., temporally according to the direction of travel of nomogram 32 on the segment going from point Li to point E2). The average home charging power P(t) is defined using two linear functions associated respectively with the two segments [L2Li] and [LiE2] forming the broken line comprising the points L2 with coordinates (Nev, F3), Li with coordinates (Nev, F2), and E2 with coordinates (Nmin, FJ).

[0161] The trajectory along the L2-LrE2 line corresponds substantially to the period extending from the end of the evening, when almost all electric vehicles have returned home, to the time in the morning when there are the fewest vehicles present.

[0162] On the first segment [L2Li] corresponding to the fact that N(t) = Nev, the average home charging power P(t) is then defined using the following linear function: [°163] _ / j with — TF2 - TF3 the period during which N(t) is equal to Nev, which corresponds in particular to night, when we consider that there is no more movement of vehicles.

[0164] On the second segment ]LiE2] corresponding to the fact that N(t) < Nev, the average home charging power P(t) is then defined using the following linear function:

[0165] P(t> b.+ b2N(t) with b _ (F2-Fl)NluiB and b0= t™--

[0166] Thus, for each part of the abacus 32, in particular the semi-ellipse part from E2 to Ei then L2 and the broken line part from L2 to Li then to E2, the total power for home charging can be predicted at each time step by multiplying the average home charging power P(t) by the number N(t) of electric vehicles EV present at home at each time step.

[0167] Figure 4 illustrates an example of an estimate 33 obtained during step 22 of Figure 2. More specifically, curve 33 represents on the ordinate 34 the number of electric vehicles present at their respective homes on a day whose hours are represented on the abscissa 36. According to this estimate 33, a minimum number Nmin of electric vehicles, less than one thousand electric vehicles, is estimated in particular around 1 p.m., while the number Nev of electric vehicles in the fleet considered is on the order of 5000 electric vehicles as estimated to be present at their homes between midnight and 5 a.m.

[0168] Figure 5 illustrates two modes of operation of the method according to the present invention, with the first view 38 representing the average charging power 40 of vehicles present at home, in the absence of a signal requesting the cessation of said charging (i.e., without implementation). optional steps 26, 28 and 30 of [Fig.2]).

[0169] Figure 42 illustrates the average charging power 44 of vehicles present at home, in the presence of a signal indicating a request to stop said charging (i.e., with implementation of optional steps 26, 28, and 30 of [Fig. 2]), said signal corresponding, for example, to a peak hour signal from 4 p.m. to 6 p.m. and then from 7 p.m. to 9 p.m. Such a figure 42 makes it possible to illustrate the impact of such a control signal indicating a request to stop said charging, in particular the "rebound" or "postponement" effect of consumption between 6 p.m. and 7 p.m., the prediction of which is made possible by the present invention.

[0170] A person skilled in the art will understand that the invention is not limited to the embodiments described, nor to the particular examples of the description, the embodiments and variants mentioned above being capable of being combined with each other to generate new embodiments of the invention.

[0171] The present invention thus makes it possible to efficiently predict the electrical consumption associated with the charging of a fleet of electric vehicles, which is useful for distribution system operators (DSOs) or transport system operators (TSOs) who can, from this consumption, predict the load on the network and balance production versus consumption.

[0172] Such a prediction is also useful for flexibility aggregators, which can then predict the impact of a control signal equivalent to a request to stop said charging on the consumption profile of a fleet of electric vehicles (EVs).

[0173] Moreover, such a prediction is useful for suppliers to anticipate their needs for electricity to be purchased on the markets, or for traders on the electricity market to anticipate changes in electricity prices.

[0174] In other words, the prediction of the load chart specifically used according to the present invention, and optionally of the impact on the latter of a control signal equivalent to a request to stop said charging, has value for the actors of the electrical networks and the electricity markets who can thus anticipate problems on the networks, better exploit the networks, or optimize the management of a portfolio of production or flexibility assets on the electrical networks.

Claims

Claims

1. Method (20) for predicting the electrical consumption associated with the recharging of a set of electric vehicle(s) within their place of residence, said method being implemented by an electronic device for predicting the electrical consumption associated with the recharging of said set of electric vehicle(s), and comprising the following steps: - obtaining (22), for a plurality of daily predetermined times, a prior estimate of the number of electric vehicle(s) present making up said set of electric vehicle(s) at their place of residence; - prediction (24) of the electrical consumption associated with the recharging of said set of electric vehicle(s) using a predetermined chart (32) indicating the average instantaneous charging power per vehicle present at its place of residence at a time t, as a function of said number of electric vehicle(s) present at their place of residence at said time t according to said estimate, said predicted electrical consumption corresponding substantially to the multiplication of said average instantaneous charging power per vehicle present at said time t by said number of electric vehicle(s) present at their place of residence at said time t.

2. Method (20) according to claim 1, wherein said predetermined abacus (32) corresponds to a closed power trajectory defined by a predetermined direction of time travel of said trajectory and by four points (E2, Eb L2, Li), predetermined by learning, each point being defined by: an abscissa, corresponding to a number of electric vehicle(s) present at their place of residence, and an ordinate corresponding to an average instantaneous charging power per vehicle, said trajectory (32) substantially comprising a part of a semi-ellipse connecting the first three points of said four points (E2, Eb L2, Li), the first point (E2) of said four points, having a minimum abscissa and corresponding to one end of the horizontal semi-axis of said part of the semi-ellipse, the second point (EJ) of said four points (E2, Ei, L2, LJ corresponding to the vertex of said semi-ellipse, said part of the semi-ellipse being connected,according to the predetermined direction of time travel, to a broken line formed of two segments including:, - the first vertical segment connects the third (L2) and fourth (LJ) points of said four points, said third and fourth points (L2, LJ) having the same abscissa corresponding to the number of electric vehicle(s) domiciled at their place of domicile and being representative of the decreasing nocturnal evolution of the average instantaneous charging power per vehicle present; - the second segment of said broken line connecting the fourth lower point of said first vertical segment to the first point corresponding to said end of the horizontal half-axis of said part of the half-ellipse.

3. Method (20) according to claim 2, in which each ordinate of said four points is respectively defined by means of a predetermined parametric equation, for the number of electric vehicle(s) present at their place of domicile corresponding to the abscissa of each of the four points, said predetermined parametric equation comprising at least one constant and having as parameter(s) at least one of the elements belonging to the group comprising at least: - the average battery capacity of said electric vehicle(s) domiciled at their place of domicile; - the average autonomy factor of said electric vehicle(s) domiciled at their place of domicile; - the average energy consumption per kilometer of said electric vehicle(s) domiciled at their place of domicile; - the average behavior coefficient of the drivers of said electric vehicle(s) domiciled at their place of domicile;- the average distance traveled per day by said electric vehicle(s) domiciled at their place of domicile; each parameter being an unknown of said predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for N sets of parameter(s) of distinct values, N being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said N sets of parameter(s) of distinct values ​​and the result of said predetermined parametric equation associated with said N sets of parameter(s).;

4. Method (20) according to claim 2 or 3, in which the first point (E2) of said four points, corresponding to one end of the half-axis

5.

6. horizontal, presents an abscissa corresponding to the minimum number of electric vehicle(s) present at their place of domicile, said minimum number being proportional to the number of electric vehicle(s) domiciled at their place of domicile according to a proportionality factor [3, such that 0 < [3 <1, said proportionality factor [3 being obtained by statistical learning during said obtaining step. Method (20) according to any one of claims 2 to 4, in which the other end (E3) of the horizontal semi-axis of said semi-ellipse has as abscissa a maximum fictitious number of electric vehicle(s), said maximum number being proportional to the number of electric vehicle(s) domiciled at their place of domicile according to a proportionality factor a, such that 1 < a, said proportionality factor a being the result of another predetermined parametric equation having as parameter(s) at least one of the elements belonging to the group comprising at least: - the average battery capacity of said electric vehicle(s) domiciled at their place of domicile; - the average autonomy factor of said electric vehicle(s) domiciled at their place of domicile; - the average energy consumption per kilometer of said electric vehicle(s) domiciled at their place of domicile; - the average behavior coefficient of the drivers of said electric vehicle(s) domiciled at their place of domicile; - the average distance traveled per day by said electric vehicle(s) domiciled at their place of domicile; each parameter being an unknown of said other predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for M sets of parameter(s) of distinct values, M being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said M sets of parameter(s) of distinct values ​​and the result of said predetermined parametric equation associated with said M sets of parameter(s). Method (20) according to claims 4 and 5, in which the second point (Ei) of said four points corresponding to the vertex of said half-ellipse has an abscissa corresponding to the average number of electric vehicle(s) present at their place of residence, said average number corresponding to the arithmetic mean between said minimum number and said maximum number of electric vehicle(s) which may be simultaneously present at their place of residence.

7. Method (20) according to claim 6, in which the instantaneous average charging power P(t) per vehicle present for any point of the half-ellipse part connecting the first, second and third points is suitable for being expressed in the following form, at a time t: L ÎF|2 p®= J iv with: N(t) the number of electric vehicle(s) present at said time t;Nmid said average number, Nmin said minimum number, and E the average instantaneous charging power per vehicle present corresponding to the ordinate of the second point corresponding to the vertex of said half-ellipse and whose abscissa is equal to said average number of electric vehicle(s) present at their place of domicile, said power E being defined by means of a predetermined parametric equation comprising at least one constant and having as parameter(s) at least one of the elements belonging to the group comprising at least: - the average battery capacity of said electric vehicle(s) domiciled at their place of domicile; - the average autonomy factor of said electric vehicle(s) domiciled at their place of domicile; - the average energy consumption per kilometer of said electric vehicle(s) domiciled at their place of domicile;- the average behavior coefficient of the drivers of said electric vehicle(s) domiciled at their place of domicile; - the average distance traveled per day by said electric vehicle(s) domiciled at their place of domicile; each parameter being an unknown of said predetermined parametric equation, said unknown being associated with a weighting factor whose value and the value of said constant are optimized, for N sets of parameter(s) of distinct values, N being an integer, by minimizing the error between the average instantaneous charging power per vehicle obtained by simulation for said N sets of parameter(s) of distinct values ​​and the result of said predetermined parametric equation associated with said N sets of parameter(s).;

8. Method (20) according to any one of the preceding claims further comprising: - the reception (26) of at least one signal serving as a request to stop said charging, said signal comprising the time interval during which said stop is required, said interval being defined by two terminals Ti and T2, with T2> Ti, and - a first adaptation (28) of said prediction of the electrical consumption, the average instantaneous charging power per vehicle present at its place of residence being adapted at each reception of said signal serving as a request to stop said charging to be zero over said time interval.

9. Method (20) according to claim 8, further comprising, for t > T2, a second adaptation (30) of said prediction of the electrical consumption with respect to the prediction of the electrical consumption which would have been obtained in the absence of a signal equivalent to a request to stop said recharging, the difference between: - the average instantaneous charging power Ps(t) per vehicle present associated with the prediction of the electrical consumption after T2, and - the average instantaneous charging power P(t) per vehicle present associated with the prediction of the electrical consumption which would have been obtained in the absence of a signal equivalent to a request to stop said recharging; being a decreasing positive function dependent on TrT2, of the energy W which would have been injected into the electric vehicle(s) in the absence of a signal equivalent to a request to stop said recharging such that .f / x , , and the average number of electric vehicle(s) present between Ti and T2.

10. Method (20) according to claim 9, wherein said decreasing positive function is linear such that: Ps(t) - P(t) = d^t - T2) +d2 with di < 0.

11. Computer program comprising software instructions which, when executed by a computer, implement a method for predicting the electrical consumption associated with the recharging of a set of electric vehicle(s) within their place of domicile of said set of electric vehicle(s) according to any one of the preceding claims.

12. Device (10) for predicting the electrical consumption associated with the recharging of a set of electric vehicle(s) within their location of domiciliation of said set of electric vehicle(s) characterized in that it comprises: - an obtaining module (12), configured to obtain, for a plurality of daily predetermined times, a prior estimate of the number of electric vehicle(s) present making up said set of electric vehicle(s) at their place of residence; - a prediction module (14), configured to predict the electrical consumption associated with the recharging of said set of electric vehicle(s) using a predetermined chart indicating the average instantaneous charging power per vehicle present at their place of residence at a time t, as a function of said number of electric vehicle(s) present at their place of residence at said time t according to said estimate, said predicted electrical consumption corresponding substantially to the multiplication of said average instantaneous charging power per vehicle present at said time t by said number of electric vehicle(s) present at their place of residence at said time t.

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