Method for managing energy in an electrically assisted vehicle

Abstract

The present invention relates to a method for managing energy in an electrically assisted vehicle (1) comprising an electric motor (5) and a battery (6) powering said electric motor (5), the method being characterized in that it comprises implementation, by data-processing means (11, 21) controlling said electric motor (5), of steps of: (a) predicting an assistance energy that must be provided by the vehicle (1) in order to travel a predefined route; (b) controlling implementation, by the electric motor (5), during the route, of a selected electric assistance mode depending on at least one power delivered by the user and on at least one power threshold; (c) comparing, during the route, an observed electrical consumption with an expected consumption dependent on said predicted assistance energy, and updating said predicted assistance energy depending on the result of said comparison; (d) modifying, during the route, the one or more power thresholds depending on the result of a comparison of said updated predicted assistance energy and a remaining charge of the battery (6).

Description

[0001] Description

[0002] Title of the invention: Method for managing energy in an electrically assisted vehicle.

[0003] GENERAL TECHNICAL FIELD

[0004] The present invention relates to the field of electric mobility. More specifically, it relates to a method for managing energy in an electrically assisted vehicle.

[0005] STATE OF THE ART

[0006] Electrically assisted bicycles (EABs) are electric bicycles whose motor only provides pedal assistance: the user must pedal for the motor to start working.

[0007] With reference to Figure 1a or Figure 1b, an EAB 1 conventionally comprises a front wheel 3a, a rear wheel 3b, a crankset 4, driving the rear wheel 3b in rotation via a chain or a belt, an electric assistance motor 5, powered with electricity by a battery 6, also driving the rear wheel 3b in rotation (when activated) (for example by being arranged in the hub, or via a friction roller) so as to relieve the user's effort. Note that the motor can alternatively drive the crankset 4 or even the front wheel 3a directly.

[0008] The VAE 1 further comprises control means 11 for the motor 5. There are generally several assistance modes defining power levels provided by the electric motor 5, and the control means 11 control the motor 5 according to the assistance mode selected.

[0009] The control means 11 are further connected to the battery 6, and can know the remaining charge. The VAE 1 generally comprises interface means 13 such as a screen, making it possible to display the remaining charge of the battery 6 in particular in the form of a digital value (in mAh) or a percentage, and various information such as the current assistance mode, the instantaneous speed, etc.

[0010] The problem is the lack of a precise indication of the bike's autonomy: if the route is too long, the user risks having exhausted the battery and having to finish without any assistance, which is uncomfortable because e-bikes are generally heavy.

[0011] Some e-bikes 1 display on the means 13 an estimate of a distance that the user can still travel with the current remaining charge of the battery 6. However, this is only a rough estimate based on average values, which may be completely wrong depending on the route.

[0012] More recently, application WO2012172227 has proposed a method for managing energy in the VAE, in which a route envisaged by the user is divided into a sequence of segments, and for each segment, the means 11 determine the total quantity of energy necessary to travel this segment (in particular on the basis of topographical data) and deduce the quantity of electrical energy necessary to travel the route.

[0013] This much more precise solution allows us to very reliably predict whether the e-bike will have enough battery (generally keeping a safety margin, for example the equivalent of 10 km). We can also adjust the electric assistance mode to make the best use of the battery capacity.

[0014] This solution is satisfactory, but it does not take into account unforeseen events (under-inflated tire, headwind), or user behavior which can cause fatigue and provide less power than expected.

[0015] The invention improves the situation.

[0016] PRESENTATION OF THE INVENTION

[0017] The present invention therefore relates, according to a first aspect, to a method for managing energy in an electrically assisted vehicle comprising an electric motor and a battery supplying said electric motor, the method being characterized in that it comprises the implementation by data processing means controlling said electric motor, of steps of:

[0018] (a) Prediction of an assistance energy to be provided by the vehicle to travel a predefined route;

[0019] (b) Control of implementation by the electric motor (5), during the journey, of an electric assistance mode selected as a function of at least one power supplied by the user and at least one power threshold;

[0020] (c) Comparison, during the route, of an observed electrical consumption with an expected consumption based on said predicted assistance energy, and updating of said predicted assistance energy based on the result of said comparison;

[0021] (d) Modification during the route, of the power threshold(s) according to the result of a comparison of said updated predicted assistance energy and a remaining charge of the battery (6).

[0022] According to advantageous and non-limiting characteristics:

[0023] Step (a) includes verifying that the battery has sufficient remaining charge for the vehicle to provide said predicted assist energy, with a safety margin.

[0024] Step (c) comprises verifying that the battery has sufficient remaining charge for the vehicle to provide said updated predicted assist energy, with a safety margin; at least one of said power thresholds being increased in step (c) if the battery does not have sufficient remaining charge for the vehicle to provide said updated predicted assist energy, with the safety margin.

[0025] At least one of said power thresholds is decreased in step (c) if it has a value greater than an initial value and if the battery has sufficient remaining charge for the vehicle to provide said updated predicted assistance energy, with the safety margin. There is a first power threshold and a second power threshold greater than the first power threshold; step (b) comprising changing the electric assistance mode so as to decrease the assistance if the power provided by the user is less than the first power threshold, and changing the electric assistance mode so as to increase the assistance if the power provided by the user is greater than the second power threshold.

[0026] Step (a) includes the following sub-steps:

[0027] (a1) Cutting said predefined route into a sequence of segments;

[0028] (a2) Estimation, for each segment of said predefined route, of the energy required to travel said segment, and of the energy theoretically supplied by the user on the segment;

[0029] (a3) Estimation of the assistance energy to be provided by the vehicle to travel said predefined route based on the energies required and the energies provided estimated to travel each segment of said predefined route.

[0030] The energy required to travel a segment is expressed in step (a2) as the sum of a term representing the energy consumed by air friction, a term representing the energy consumed by rolling friction, a term representing the variation in potential energy, and a term representing the work of the forces applied to the vehicle.

[0031] The energy required to travel a segment is expressed in step (a2) by the formula, ENseg=( 1 / pSCxVseg 2 +Crmg+Sseqmg+asegm)Lseg, where p is the air density, S is the frontal area of ​​the vehicle, Cx is an air friction coefficient, Vseg is the expected average speed of the vehicle on the segment, Cr is a rolling friction coefficient, m is the mass of the vehicle, g=9.81 m / s 2 , Sseg the slope of the segment, a seg the acceleration between this segment and the next.

[0032] The assistance energy to be provided by the vehicle to travel said predefined route is expressed as the sum of the energies required to travel the segments of said route less the sum of the energies theoretically provided by the user on the segments of said route. Step (c) comprises calculating a correction factor expressed as the ratio between said observed electrical consumption and said expected consumption, the updated predicted assistance energy being expressed as the sum of the energies required to travel the remaining segments of said route, weighted with said correction factor, less the sum of the energies theoretically provided by the user on the remaining segments of said route.

[0033] Step (a) comprises a prior sub-step (aO) of definition by the user of said route, and where appropriate indication by the user of a mass of the vehicle, using interface means connected to said data processing means.

[0034] Step (b) is implemented by data processing means of the vehicle controlling said electric motor, steps (a), (c) and (d) being implemented by data means of a terminal connected to said data processing means of the vehicle.

[0035] Step (b) is repeated periodically, and steps (c) and (d) are implemented continuously from a given time during the journey of said route, over a sliding window.

[0036] Step (c) comprises estimating said electrical consumption of the current supplied by the battery observed over said sliding window, expressed as the ratio between an average intensity observed over said sliding window and an average speed of the vehicle observed over said sliding window.

[0037] According to a second aspect, the invention relates to a terminal comprising data processing means and connected to an electrically assisted vehicle comprising an electric motor, a battery powering said electric motor and data processing means controlling the implementation by the electric motor of an electrical assistance mode selected as a function of at least one power supplied by the user and at least one power threshold; the terminal being characterized in that the data processing means are configured to:

[0038] - Predict an assistance energy to be provided by the vehicle to travel a predefined route;

[0039] - Compare, during the route, an observed electrical consumption with an expected consumption based on said predicted assistance energy, and update said predicted assistance energy based on the result of said comparison;

[0040] - Modify during the route, the power threshold(s) according to the result of a comparison of said updated predicted assistance energy and a remaining battery charge.

[0041] According to a third aspect, the invention relates to a system comprising the terminal according to the second aspect and said connected electrically assisted vehicle.

[0042] According to a fourth and a fifth aspect, the invention relates to a computer program product comprising code instructions for executing a method according to the first aspect of energy management in an electrically assisted vehicle comprising an electric motor and a battery powering said electric motor; and a storage means readable by computer equipment on which is recorded a computer program product comprising code instructions for executing a method according to the first aspect of energy management in an electrically assisted vehicle comprising an electric motor and a battery powering said electric motor.

[0043] PRESENTATION OF FIGURES

[0044] Other characteristics and advantages of the present invention will appear on reading the following description of a preferred embodiment. This description will be given with reference to the appended drawings in which: [Fig. 1 a]Figure 1 a is a diagram of a first embodiment of a system for implementing the method according to the invention;

[0045] [Fig. 1 b]Figure 1 b is a diagram of a second embodiment of a system for implementing the method according to the invention;

[0046] [Fig. 2]Figure 2 is a flowchart illustrating the steps of an embodiment of the method according to the invention.

[0047] [Fig. 3]Figure 3 schematically represents a fragment of an embodiment of the method according to the invention;

[0048] [Fig. 4]Figure 4 schematically represents another fragment of an embodiment of the method according to the invention.

[0049] DETAILED DESCRIPTION

[0050] Architecture

[0051] The present invention relates to a method for managing energy in an electrically assisted vehicle 1 comprising an electric motor 5 and a battery 6 powering said electric motor 5.

[0052] Said electrically assisted vehicle 1 is preferably an electrically assisted bicycle (EAB) as previously described and represented in FIG. 1 a, but those skilled in the art will be able to adapt the present proposed method to other types of electrically assisted vehicles, for example an electrically assisted scooter, an electrically assisted boat, etc. More generally, an electrically assisted vehicle is understood to mean any vehicle intended to be moved by human power, and equipped with an electric motor 5 capable of providing electrical assistance capable of supplementing human energy or occasionally replacing it.

[0053] The present method is implemented by data processing means 11, 21, such as a processor or an electronic card, controlling said electric motor 5. These processing means can be integrated into the vehicle 1 (reference 11) and / or those of a terminal 2 (reference 21) connected to the vehicle 1, for example wirelessly (via Bluetooth) or via a cable (for example USB). Said terminal 2 is typically a mobile terminal of the user, of the smartphone type, itself advantageously connected to a network 20 such as the internet network. Note that alternatively or in addition, the vehicle 1 can be directly connected to the network 20.

[0054] According to a preferred embodiment, corresponding to figure 1a, there are both the means 11 of the vehicle 1 and the means 21 of the terminal 10, the former ensuring the control of said electric motor 5 and the latter implementing most of the steps of the present energy management method (steps (a), (c) and (d) as will be seen), by obtaining from the means 11 of the vehicle 1 operating data of the vehicle 1 (state of the battery 6, power supplied by the user, etc.), and sending commands to it in response.

[0055] The person skilled in the art will be able to implement all or part of the method steps on the means 11 of the vehicle 1, and where appropriate not have a terminal 2 involved (case of figure 1 b), or even control the engine 5 of the vehicle 1 with the means 21 of the terminal 10.

[0056] The present method also advantageously uses data processing means 12, 22 (a memory) and / or interface means 13, 23 (a screen, in particular a touch screen) which again can be integrated into the vehicle 1 (references 12, 13) and / or those of the terminal 2 (reference 22, 23).

[0057] Process

[0058] Referring to Figure 2, the present method begins with a step (a) of predicting an assistance energy to be provided by the vehicle 1 to travel a predefined route.

[0059] This step may be carried out in any known manner and in particular in accordance with document WO201217222. Preferably, step (a) comprises all or part of the following sub-steps:

[0060] (a1) Cutting said predefined route into a sequence of segments;

[0061] (a2) Estimation, for each segment of said predefined route, of the energy required to travel said segment, and of the energy theoretically supplied by the user on the segment;

[0062] (a3) Estimation of the assistance energy to be provided by vehicle 1 to travel said predefined route based on the energies required and the energies provided estimated to travel each segment of said predefined route.

[0063] The method may further comprise a preliminary step (aO) of definition by the user of said route, in particular using the interface means 13, 23. The route is typically represented by a GPX track (i.e. in the form of a collection of GPS coordinates). The user may in particular select a route from a list of proposed routes, take a route shared by another user (potentially routes retrieved from the network 20), or even define his own route, and generate the track via a GPS navigation application.

[0064] Step (a0) may further comprise the indication, by the user, in particular again using the interface means 13, 23, of a mass of the vehicle 1 (including the user, and any passengers or luggage), which is preferentially used in step (a2). This mass may be entered directly, pre-recorded, or even selected from a plurality of reference masses associated with scenarios such as:

[0065] - Bike + user = 105 kg

[0066] - Bicycle + user + luggage = 120 kg

[0067] - Bicycle + user + luggage + trailer = 148 kg

[0068] - bike + user + luggage + trailer + child = 163 kg

[0069] - etc. Then, in step (a1), said route is divided into a sequence of segments, corresponding to elementary fragments for example of a fixed length. For example, a 65 km track can be divided into 650 segments of 100 m. If a travel time is known, the segments can be temporal. For example, the same 65 km route can be planned in 1440 segments of 5 seconds.

[0070] For each segment, we can define an expected (or "theoretical") average speed Vseg of vehicle 1 on the segment, either mathematically if all the lengths / times of the segments are known, for example from experience if users have already traveled routes passing through these segments (length of the segment Lseg divided by the time needed to travel the segment tseq), or according to an average gradient of the segment Sseg (itself easily calculated as the difference in altitude of the ends of the segment divided by the length of the segment): for example, for each assistance mode and each gradient value (rounded to the nearest %), we can associate a speed. Again, we typically have n electric assistance modes, with for example n=3, corresponding to increasingly strong assistance levels, numbered from 1 to n.

[0071] Note that terminal 2 will be able to retrieve the data it needs from a server on network 20.

[0072] In step (a2), the energy required to travel each segment is estimated. By "energy required" is meant a total energy of the vehicle 1 , in particular expressed in Joules (J) or Watt-hours (Wh), noting that 1 J=1Ws=1 kg.m 2 .s' 2 , and that 3600 Ws=1Wh.

[0073] This can be expressed as the sum of terms having a mechanical meaning:

[0074] - a term representing the energy consumed by air friction, in particular ( 1 / pSCxVseg 2 )Lseg, with p the density of air (about 1.2kg / m 3), S the frontal surface of the vehicle 1 , Cx an air friction coefficient (SCx depends on the type of vehicle 1 and its configuration, and can be defined at the same time as the mass in step (aO), for example depending on whether the user selects a trailer or not - the value can be determined empirically and is typically between 0.5 and 1 m for a bicycle 2 ), and we recall that Lseg = Vseg*tseg

[0075] - a term representing the energy consumed by rolling friction, in particular (Crmg)Lseg, with m the mass of the vehicle 1, g=9.81 m / s 2 , and Cr a rolling friction coefficient (Cr again depends on the vehicle type 1 and its configuration, and can be defined together with the mass in step (aO), for example depending on whether it selects a trailer or not - the value can again be determined empirically and is typically between 0.001 and 0.01 for a bicycle);

[0076] - a term representing the variation in potential energy, in particular (Sseqmg)Lseg (we note that Sseq*Lseg is the variation in altitude);

[0077] - a term representing the work of the forces applied to the vehicle in particular (asegm)Lseg, with a seg the acceleration between two segments (calculated as the speed difference between two segments Vseg+i-Vseg divided by the time Vseg), noting that ase g m=Zforcesext (PFD), that the path of the segment can be considered rectilinear and that the forces applied to vehicle 1 make it move forward.

[0078] We thus have ENseg=( pSCxVseg 2 +Crmg+Sseqmg+aseglTl)Lseg.

[0079] In step (a2), the energy theoretically supplied by the user on the segment, noted Ellseg, is estimated, in particular by the formula EUseg= Pseg*tseg, with Pseg the average power expected of the user on the segment, which can be, like the speed Vseg, either known from experience, or defined as a function of a slope of the segment Sseg, for example for each assistance mode and each slope value (rounded to the nearest %) a power can be associated.

[0080] Finally, we note that over the entire route we have EN=EU+EA (the last term is the assistance energy), so that we can predict at step (a3) ​​EA as EsegENseg-EsegEUseg — Eseg[( 1 / 2pSCxVseg 2 +CrmÇ|+SseqmÇ|+asegm)Lseg] - Eseg[Psegtseg] .

[0081] Preferably, step (a) comprises the prior verification that the battery 6 has a sufficient remaining charge for the vehicle 1 to provide said predicted assistance energy with a safety margin, for example the charge necessary to travel 10 km, so that the user always has a reserve in case of emergency.

[0082] The purpose of this check is simply to ensure that the route is feasible, i.e. that the vehicle will be able to provide assistance throughout.

[0083] We can simply do the following calculation: (EA / eff)+Margin S ec, where eff is the efficiency of vehicle 1, i.e. the conversion rate of electrical energy into mechanical energy, and Margesec is the safety margin, expressed in charge (for example in Ah). The result of the calculation is then compared to the remaining charge of battery 6 (which is not necessarily full).

[0084] If the verification is conclusive (battery 6 has sufficient remaining charge), the process continues normally. Otherwise (battery 6 does not have sufficient remaining charge), the user is advantageously warned (via interface means 13, 23), and is offered to shorten, or even change, the route. Step (a) is then repeated.

[0085] In a main step (b), implemented throughout the duration of the route (a repetition frequency of, for example, 0.1 Hz to 10 Hz can be provided, depending on the computer processing capabilities), the means 11, 21 control the implementation by the electric motor 5 (in particular the means 11 of the vehicle 1), of an electric assistance mode selected as a function of at least one power supplied by the user and at least one power threshold. The power supplied by the user, noted Puser, can be measured directly by the motor 5 or with a suitable sensor. Step (b) in practice comprises said selection of an electric assistance mode (to be implemented) as a function of at least one power supplied by the user and at least one power threshold. It is understood that by "throughout the duration of the route" is meant during the use of the vehicle 1 after having validated the route in step (a).Naturally, it remains possible that the user does not follow the route he himself chose, but for means 11, 21 there will be no difference and it will remain "for the entire duration of the route", just the energy management will not be optimal.

[0086] Such a step is also known to those skilled in the art: for example, n electric assistance modes and n-1 thresholds can be provided, the i-th mode being implemented if the power supplied by the user is between the i-th and i+1 thresholds.

[0087] Alternatively, and in particular with reference to Figure 3, we can provide a more intelligent operation, still with n modes (typically with n=3 but we can have more) but with only a first power threshold A and a second power threshold B>A, advantageously B of the order of 2*A, typically configurable manually or around a power of an average user (generally around 75W), we will take for example A=50W and B=100W, but for a more sporty user we can take A=60W and B=120W or even A=75W and B=150W:

[0088] - if possible the electric assistance mode is changed so as to reduce the assistance if the power supplied by the user is lower than the first power threshold. In other words, if User^A we move to the assistance mode below (i.e. mode-1, we reduce the electric assistance by one level, because the user is not participating enough), unless we are already in the minimal mode;

[0089] - if possible the electric assistance mode is changed so as to reduce the assistance if the power supplied by the user is higher than the second power threshold. In other words, if User>B we move to the assistance mode above (i.e. mode+1, we increase by one level of assistance, because the user participates a lot), unless we are in the maximum mode; This means that if A <P u tiiisateur^B we do not change the assistance mode. In addition we can provide shortcuts, for example if Putiiisateur>B and V <seuil tel que 10km / h, on passe directement au mode maximal (cette vitesse faible signifiant que l’utilisateur est sur un passage compliqué).

[0090] We can provide a default mode, in particular a median mode (mode 2 if we have 3 modes).

[0091] We can also provide a “degraded” version of the above strategy, called “turtle”, using only the second threshold B, when the remaining charge of battery 6 is below a threshold of concern:

[0092] - if User^B we move to the assistance mode below (i.e. mode- 1, we reduce the electric assistance by one level, because the user does not participate enough), unless we are already in the minimal mode;

[0093] - if User>B we move to the assistance mode above (i.e. mode-1, we increase by one assistance level, because the user participates a lot), unless we are in maximum mode;

[0094] Note that the person skilled in the art may use any strategy of his choice for changing assistance mode based on power thresholds. Everything is potentially customizable via the interface means 13, 23.

[0095] As will be seen, Figure 3 represents thresholds A' and B' which are in fact dynamic versions of thresholds A and B, the present method proposing to modify the first and second thresholds according to the actual electrical consumption observed (A and B then being fixed values ​​used to initialize A' and B' which can vary over time).

[0096] Finally, in original steps (c) and (d), also implemented throughout the duration of the route, advantageously from a given moment of the journey of said route, for example after a time T of a few minutes (1 to 15 minutes) after the start of the route (a repetition frequency can be provided, potentially much lower than that of step (b), in particular 10 or even 100 times lower, for example at a frequency 1 / T, and alternatively over a sliding window, in particular of said duration T), the means 11, 21 will, if necessary, modify said power thresholds, and thus interfere with the regulation of the assistance mode, in an agnostic manner,by simultaneously optimizing the discharge of battery 6 (by favoring, if necessary, changes to lesser assistance modes) and maximizing user comfort (by favoring, on the contrary, changes to stronger assistance modes if battery 6 allows it).,

[0097] To do this, step (c) implements the comparison of an observed electrical consumption with an expected consumption based on said predicted assistance energy, and the updating of said predicted assistance energy based on the result of said comparison.

[0098] Said observed and expected electrical consumption may be total consumption (in Ah) or kilometer consumption (in Ah / km), since the last occurrence of step (c), or over a predetermined sliding window (again for example of duration T).

[0099] The expected electrical consumption, or "theoretical" consumption, is easily calculated from the data from step (a), in practice by dividing the predicted assistance energy EA by the voltage of battery 6 (48V on an EAB), and where appropriate by the total length of the route if we want a kilometer consumption.

[0100] The observed electricity consumption, or “practical” consumption, can be calculated in many ways:

[0101] - simply by monitoring the remaining battery charge over time, and by taking the difference between the corresponding values ​​at the terminals of the window considered;

[0102] - noting that the mileage consumption is equal to the intensity of the current supplied by the battery 6 divided by the speed of the vehicle 1, we can use the values ​​lm and V m on the window recalculated iteratively, in particular in the following way: lm=lm+(T e / N)*(lk- lm) and Vm=Vm+(Te / N)*(Vk-Vm), where Te is the sampling time (inverse of the sampling frequency), for example of the order of a second, N the number of samples in the window, equal to T / T e , and Ik / Vk, the instantaneous intensity / speed values ​​directly measured at each sampling. Note that we can calculate the average values ​​lm and Vm

[0103] The comparison of observed and expected electricity consumption is preferably done by calculating their ratio (observed consumption divided by expected consumption), called the correction factor.

[0104] We can then simply update the predicted assistance energy by applying the correction factor, i.e. EA'=correction*EN-EU, to the required energy EN. In practice, we do not need to update the assistance energy over the entire route, but simply over the part not yet traveled, i.e. the remaining segments. We thus calculate EA rest = CORreCtion*Eseg=current^ n ENseg seg=current^ n EUseg —

[0105] CORreCtion*Zseg=current fin [( pSCxVseg 2 +Crmg+Sse q mg+asegm)Lseg] seg=current^' n [Psegtseg] .

[0106] It is then possible, in step (d), to modify the power threshold(s) according to the result of a comparison of said updated predicted assistance energy and a remaining charge of the battery 6.

[0107] In practice, the power thresholds are modified so as to regulate the power consumption and so that the updated assistance energy converges towards the value initially predicted in step (a).

[0108] There are many strategies that can be implemented here, the idea is that:

[0109] - if battery 6 discharges faster than expected, the thresholds are increased (the user must therefore provide more power to maintain the same level of electric assistance, which degrades comfort and ultimately leads to a reduction in electrical consumption, but allows the user to complete the route without running out of battery);

[0110] - if battery 6 discharges less quickly than expected, the thresholds are reduced or at least down to their initial values ​​(the user must therefore provide less power to maintain the same level of electric assistance, which restores comfort but ultimately results in an increase in electrical consumption that can be afforded);

[0111] For this, similarly to what could take place in step (a), step (c) can comprise the verification that the battery 6 has a sufficient remaining charge for the vehicle 1 to provide said updated predicted assistance energy with a safety margin.

[0112] Again we can simply do the following calculation: (EA'rest / eff)+Margin S ec. The result of the calculation is then compared to the remaining charge of battery 6 (naturally, the battery charge has decreased, but so has EA since only a fraction of the route remains to be covered).

[0113] If the check is successful (battery 6 has sufficient remaining charge), the thresholds gradually decrease (or are maintained if they are at their initial values). Otherwise (battery 6 does not have sufficient remaining charge), the thresholds gradually increase, possibly up to a ceiling.

[0114] Referring to Figure 4, denoting A' and B' the modified versions of the first and second power thresholds A and B, we can for example apply the following algorithm:

[0115] - We initialize A' and B' to the values ​​of A and B;

[0116] - if battery 6 has sufficient remaining charge, as long as A'>A we reduce A' and B' by X%, with X predetermined (for example 5%);

[0117] - if battery 6 does not have sufficient remaining charge, we increase A' and B' by X%. We can possibly provide a ceiling, for example if B' reaches F times B' (for example 1.5 times B), we cannot continue and we launch the "turtle" mode mentioned above in which there is no longer a first threshold A.

[0118] As explained, steps (b), (c) and (d) are repeated and / or implemented continuously over the duration of the route. We understand that EA'rest tends towards 0 as the route progresses and we therefore necessarily have a convergence of the prediction, and a stabilization of the electrical consumption, hence optimal management.

[0119] Terminal and vehicle

[0120] According to a second aspect, the invention relates to the terminal 2 for implementing the method according to the first aspect. It will be recalled that alternatively, the vehicle 1 can itself implement the method.

[0121] In all cases, the terminal 2 comprises data processing means 21, and the electrically assisted vehicle 1 comprises an electric motor 5, a battery 6 powering said electric motor 5 and data processing means 11 controlling the implementation by the electric motor 5 of an electrical assistance mode selected as a function of at least one power supplied by the user and at least one power threshold.

[0122] Advantageously, the vehicle 1 and / or the terminal 2 comprise data storage means 12, 22 and / or interface means 13, 23.

[0123] The data processing means 11, 21 are configured to implement steps consisting of:

[0124] - Predict an assistance energy to be provided by vehicle 1 to travel a predefined route;

[0125] - Compare, during the route, an observed electrical consumption with an expected consumption based on said predicted assistance energy, and update said predicted assistance energy based on the result of said comparison;

[0126] - Modify during the route, the power threshold(s) according to the result of a comparison of said updated predicted assistance energy and a remaining charge of the battery 6;

[0127] According to a third aspect, the invention proposes a system comprising said terminal 2, as well as the vehicle 1, connected (in particular via a wireless link, such as Bluetooth). Computer program product

[0128] According to a fourth and a fifth aspect, the invention relates to a computer program product comprising code instructions for the execution (on the data processing means 11, 21 of the vehicle 1 and / or the terminal 2) of a method according to the first aspect of energy management in an electrically assisted vehicle 1 comprising an electric motor 5 and a battery 6 powering said electric motor 5, as well as storage means readable by computer equipment (for example the data storage means 12, 22 of the vehicle 1 and / or the terminal 2) on which this computer program product is found.

Claims

CLAIMS 1. Method for managing energy in an electrically assisted vehicle (1) comprising an electric motor (5) and a battery (6) powering said electric motor (5), the method being characterized in that it comprises the implementation by data processing means (11, 21) controlling said electric motor (5), of steps of: (a) Prediction of an assistance energy to be provided by the vehicle (1) to travel a predefined route; (b) Control of implementation by the electric motor (5), during the journey, of an electric assistance mode selected as a function of at least one power supplied by the user and at least one power threshold; (c) Comparison, during the route, of an observed electrical consumption with an expected consumption based on said predicted assistance energy, and updating of said predicted assistance energy based on the result of said comparison; (d) Modification during the route, of the power threshold(s) according to the result of a comparison of said updated predicted assistance energy and a remaining charge of the battery (6); comprising verifying that the battery (6) has a remaining charge sufficient for the vehicle (1) to provide said updated predicted assistance energy, with a safety margin; at least one of said power thresholds being increased if the battery (6) does not have a remaining charge sufficient for the vehicle (1) to provide said updated predicted assistance energy, with the safety margin.

2. Method according to claim 1, wherein step (a) comprises verifying that the battery (6) has sufficient remaining charge for the vehicle (1) to provide said predicted assistance energy, with a safety margin.

3. Method according to one of claims 1 and 2, in which at least one of said power thresholds is reduced in step (c) if it has a value greater than an initial value and if the battery (6) has a remaining charge sufficient for the vehicle (1) to provide said updated predicted assistance energy, with the safety margin.

4. Method according to one of claims 1 to 3, in which there is a first power threshold and a second power threshold greater than the first power threshold; step (b) comprising changing the electric assistance mode so as to reduce the assistance if the power supplied by the user is less than the first power threshold, and changing the electric assistance mode so as to increase the assistance if the power supplied by the user is greater than the second power threshold.

5. Method according to one of claims 1 to 4, in which step (a) comprises the following sub-steps: (a1) Dividing said predefined route into a sequence of segments; (a2) Estimating, for each segment of said predefined route, the energy required to travel said segment, and the energy theoretically supplied by the user on the segment; (a3) Estimation of the assistance energy to be provided by the vehicle (1) to travel said predefined route based on the energies required and the energies provided estimated to travel each segment of said predefined route.

6. Method according to claim 5, in which the energy required to travel a segment is expressed in step (a2) as the sum of a term representing the energy consumed by air friction, a term representing the energy consumed by rolling friction, a term representing the variation in potential energy, and a term representing the work of the forces applied to the vehicle (1).

7. The method of claim 6, wherein the energy required to travel a segment is expressed in step (a2) by the formula, ENseg=( 1 / pSCxVseg 2+Crmg+Sseqmg+asegm)Lseg, where p is the air density, S the frontal surface of the vehicle (1 ), Cx an air friction coefficient, Vseg the expected average speed of the vehicle (1 ) on the segment, Cr a rolling friction coefficient, m the mass of the vehicle (1 ), g=9.81 m / s 2 , Sseg the slope of the segment, a seg the acceleration between this segment and the next.

8. Method according to one of claims 5 to 7, in which the assistance energy to be provided by the vehicle (1) to travel said predefined route is expressed as the sum of the energies necessary to travel the segments of said route minus the sum of the energies theoretically provided by the user on the segments of said route.

9. Method according to claim 8, wherein step (c) comprises calculating a correction factor expressed as the ratio between said observed electrical consumption and said expected consumption, the updated predicted assistance energy being expressed as the sum of the energies necessary to travel the remaining segments of said route, weighted with said correction factor, less the sum of the energies theoretically supplied by the user on the remaining segments of said route.

10. Method according to one of claims 1 to 9, in which step (a) comprises a prior sub-step (aO) of definition by the user of said route, and where appropriate of indication by the user of a mass of the vehicle (1), using interface means (13, 23) connected to said data processing means (11, 21).

11. Method according to one of claims 1 to 10, in which step (b) is implemented by data processing means (11) of the vehicle (1) controlling said electric motor (5), steps (a), (c) and (d) being implemented by data means (21) of a terminal (2) connected to said data processing means (11) of the vehicle (1).

12. Method according to one of claims 1 to 11, in which step (b) is repeated periodically, and steps (c) and (d) are implemented continuously from a given instant of the journey of said route, over a sliding window.

13. Method according to claim 12, in which step (c) comprises estimating said electrical consumption of the current supplied by the battery (6) observed over said sliding window, expressed as the ratio between an average intensity observed over said sliding window and an average speed of the vehicle (1) observed over said sliding window.

14. Terminal (2) comprising data processing means (21) and connected to an electrically assisted vehicle (1) comprising an electric motor (5), a battery (6) powering said electric motor (5) and data processing means (11) controlling the implementation by the electric motor (5) of an electrical assistance mode selected as a function of at least one power supplied by the user and at least one power threshold; the terminal (2) being characterized in that the data processing means (21) are configured to: - Predict an assistance energy to be provided by the vehicle (1) to travel a predefined route; - Compare, during the route, an observed electrical consumption with an expected consumption based on said predicted assistance energy, and update said predicted assistance energy based on the result of said comparison; - Modifying during the route, the power threshold(s) according to the result of a comparison of said updated predicted assistance energy and a remaining charge of the battery (6), comprising verifying that the battery (6) has a remaining charge sufficient for the vehicle (1) to provide said updated predicted assistance energy, with a safety margin; at least one of said power thresholds being increased if the battery (6) does not have sufficient remaining charge for the vehicle (1) to provide said updated predicted assistance energy, with the safety margin.

15. System comprising the terminal (2) according to claim 14 and said electrically assisted vehicle (1) connected.

16. Computer program product comprising code instructions for executing a method according to one of claims 1 to 13 for energy management in an electrically assisted vehicle (1) comprising an electric motor (5) and a battery (6) powering said electric motor (5), when said program is executed on a computer.

17. Storage means readable by computer equipment on which is recorded a computer program product comprising code instructions for the execution of a method according to one of claims 1 to 13 for energy management in an electrically assisted vehicle (1) comprising an electric motor (5) and a battery (6) powering said electric motor (5).

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