Method for managing the energy of a hybrid vehicle equipped with an on-board navigation system and an electric drivetrain comprising at least one electric machine

Abstract

The invention relates to a method for managing the energy of a vehicle having a navigation system and an electric machine (MEL) powered by a system for converting fuel into electricity and a system for storing electrical energy: a) entering a destination in a navigation system; b) the navigation system computing the route; c) applying a predictive model of the vehicle to the route in order to deduce the predicted required power profile (Preq); d) managing the power supply of the electric machine (MEL), with d0) measuring the state of charge (SoC) of the electrical energy storage system (BAT); d1) control computation of the electrical energy sources (BAT, FC) on the basis of the predicted power profile (Preq) predicted by Pontryagin's maximum principle (PMP) in order to determine the optimal profile of the state of charge (SoC); and d2) determining the electricity control in real time in order to power the electric machine (MEL).

Description

[0001]METHOD FOR MANAGING THE ENERGY OF A HYBRID VEHICLE EQUIPPED WITH AN ON-BOARD NAVIGATION SYSTEM AND AN ELECTRIC POWERTRAIN COMPRISING AT LEAST ONE ELECTRIC MACHINE Technical field The invention relates to the management of the energy supply, in particular electrical energy, of vehicles, in particular land vehicles, equipped with an electric powertrain comprising at least one electric machine. It concerns hybrid vehicles, comprising several energy sources, and in particular all-electric hybrid vehicles, which may comprise one or more electricity storage systems (such as batteries) and one or more gas conversion systems, in particular hydrogen, into electricity (such as fuel cells). The energy control of this type of hybrid vehicle is generally controlled in the vehicle by a so-called power management system (also known as a Power Management System or by its acronymPMS). This system is the control body responsible for distributing the power required by the electric machine between the different available energy sources. Generally speaking, this management system seeks to minimize the energy consumption of the electric machine, and / or to minimize the aging of the energy sources, by taking into account a certain number of constraints. Prior art The so-called optimal control theory is known, which makes it possible to determine the control of a system that minimizes or maximizes a given performance criterion, possibly under constraints that may relate to the control or the state of the system considered. Offline, that is to say when the driving cycle is known in advance, mathematical tools such as Dynamic Programming (DP) or the Pontryagin Maximum Principle (PMP) make it possible to find the optimal solution to this type of problem. Online, that is to say in real conditions, with a management systemembedded in the vehicle, the cycle is not known in advance, many methods have been studied to get closer to the optimal solution. Among them, we can cite the equivalent consumption minimization strategy (including the English expression "Equivalent Consumption Minimization Strategy", with its acronym ECMS), which is the most commonly used strategy. Its principle is to define, for a hybrid vehicle with an internal combustion engine and a battery, an equivalence factor between the use of the battery (electrical energy) and the fuel consumption of the internal combustion engine. The challenge, in this strategy, is then to find the equivalence factor which makes it possible to achieve a minimum consumption on a given cycle while respecting the constraints of the different power sources. Details on this strategy can be found, for example, in the article "Equivalent Consumption Minimization Strategy, Energymanagement controller for P0-P4 hybrid electric vehicles”, which can be found by following the following link: https: / / fr.mathworks.com / help / autoblks / ref / equivalentconsumptionminimizationstrategy.html citing the following publications: [1] Balazs, A., Morra, E., and Pischinger, S., Optimization of Electrified Powertrains for City Cars. SAE Technical Paper 2011-01-2451. Warrendale, PA: SAE International Journal of Alternative Powertrains, 2012. [2] Onori, S., Serrao, L., and Rizzoni, G., Hybrid Electric Vehicles Energy Management Systems. New York: Springer, 2016. This strategy, based on the heuristic principle of an equivalence between electrical energy and fuel consumed, is in fact derived from the Pontryagin Maximum Principle (PMP) mentioned above. In PMP theory, the factor that is called in ECMS "equivalence factor" corresponds to what is called an adjoint state associated with the system state variable (in this case the battery state of charge, SoC (for"State of Charge" in English), in the case of hybrid vehicle energy management). This adjoint state is a priori variable over time. In particular, when constraints on the battery state of charge are active (i.e. the state of charge at a given time approaches a minimum SoCmin or maximum SoCmax state of charge), its value is supposed to vary to avoid exceeding these constraints. Until now, online PMS algorithms based on the PMP / ECMS principle have assumed, to simplify the problem, a constant adjoint state / equivalence factor. In these cases, the optimization carried out is therefore blind to the constraints on the battery state of charge SoC: the optimal control is calculated without "knowing" that the battery state of charge SoC must not exceed certain values. However, on certain cycles (trips), particularly with large slope variations, this assumption of an adjoint state / constant equivalence factor can however prove to be limiting, and result in distributions between the different energy sources which are not optimal. The aim of the invention is to improve these energy supply management methods for hybrid vehicles, in particular for all-electric hybrid vehicles with an electricity storage system and a fuel conversion system, for example gas, into electricity. This involves in particular developing such a power management method which is more efficient, in particular which allows the electricity sources (batteries in particular) to be dimensioned as accurately as possible, and / or which is more robust to hazards, and / or which does not require - or less - adjustment upstream of the journey to be covered, and / or which takes greater account of the limitations of the overall system, whether these are constraints on the controls or on the states of charge. Summary of the invention The invention firstly aims tosubject matter a method for energy management of a hybrid vehicle equipped with a navigation system and an electric powertrain comprising at least one electric machine (MEL) which is supplied with electricity by electrical energy sources (BAT, FC) comprising at least one fuel-to-electricity conversion system, and at least one electrical energy storage system, such that said method comprises: a) a step of entering a destination into a navigation system which retrieves and uses satellite navigation data in real time, b) a step of calculation by the navigation system of the route to be traveled from navigation data, c) a step of applying a predictive model of the vehicle to said route, from at least some of said navigation data and from vehicle parameters, to predict the vehicle speed trace (Vvh) on the route and deduce therefrom the predicted required power trace (Preq) for theelectric machine (MEL) during said journey, d) a step of managing the electricity supply of the electric machine (MEL) by the electrical energy sources, which comprises d0) a sub-step of measuring the state of charge (SoC) of the electrical energy storage system(s) (BAT), d1) a sub-step of calculating optimal control of the electrical energy sources (BAT, FC) in a loop, in particular in a so-called slow loop, from the predicted power trace (Preq) in step c), said calculation being based on the Pontryagin Maximum Principle (PMP) and taking into account - the state of charge (SoC) of the electrical energy storage system(s) (BAT) measured in step d0) as the initial state of charge, - and the constraints on the limitations of the controls of the energy sources (BAT, FC) and on the limitations of the state of charge (SoC) of the electrical energy storage system(s) (BAT), to determine at each position of the vehicle on the path predicted in step b) theoptimal trace of the state of charge (SoC) and the optimal adjoint states (pi), d2) a sub-step of determining in real time the control of the share of electricity coming from the electrical energy storage system(s) (BAT) and the share of electricity coming from the fuel conversion system(s) (FC) to power the electric machine (MEL) during said journey, by minimizing the Hamiltonian of the optimal control problem which is defined by the Pontryagin Maximum Principle (PMP) and which depends on the optimal adjoint states (pi) calculated in step d1). The calculation loop carried out in step d1) is preferably carried out according to a certain frequency, and not in real time, that is to say that the calculation is done “off line” or “off line” (that is to say with calculation means which are indeed embedded in the vehicle, but that the calculation is carried out on the basis of a predicted power trace and not on the basis of a power trace measured in real time)This is preferably a so-called slow calculation loop. A so-called "slow" loop corresponds to a calculation task called at a low frequency (for example, at least a few minutes) in which the optimal control calculation will be carried out "offline" (a term to be understood, as indicated above, in the sense that the algorithm uses a predicted power trace to calculate, here, the adjoint state. In other words, we act as if the cycle were known from start to finish even before having been carried out). In contrast, a so-called "fast" loop corresponds to a real-time calculation task (also called "online" or "on line" (in practice, it has the same frequency as any actuator control, so it will generally be called at least every 100 ms). This may be the case for step d2) of determining in real time the control of the share of electricity coming from the electrical energy storage system(s) and the shareof electricity from the fuel conversion system(s), this determination being based on the adjoint state calculated by the “slow” loop according to step d1), in order to calculate the optimal actuator controls to satisfy the actual power demand at each instant. According to the invention, the or at least one of the (or all) electrical energy storage systems is preferably of the battery (BAT) and / or supercapacitor type. According to the invention, the or at least one of the (or all) fuel-to-electricity conversion systems is preferably of the fuel cell (FC) type, or of the turbogenerator type (generally gas) or of the heat engine type associated with an alternator. The term “fuel” is therefore understood as a fluid, in particular a gas such as hydrogen (for a fuel cell in particular) or a liquid such as gasoline (for a heat engine coupled to an alternator in particular, which may also, in addition to or instead of gasolinebe supplied with hydrogen-type gas). The invention thus defined is based on the theory of optimal control using the Pontryagin Maximum Principle (PMP). It takes into account the constraints on the state of charge of the electricity storage system (battery type) in the calculation of the optimal control, and thus allows the vehicle architecture to be dimensioned as accurately as possible: it is thus possible to provide smaller batteries for equivalent applications. It has the same utility in applications with hybrid storage combining one or more supercapacitors and one or more batteries, for example. The invention takes into account the limitations of the overall energy management system, in particular constraints on the controls or on the states of charge. The invention can be implemented online, and it is robust to hazards (for example, a traffic hazard or a hazard in driving behavior). Another advantageous point of the invention isthat it only requires data from a navigation system with geolocation, and that it does not require any particular adjustment before implementation. Advantageously, steps c), d0) and d1) can be repeated to determine the control of sub-step d2) according to a given time step Δt, constant or variable, depending on the duration of the journey. Restarting the calculation in this way makes it possible to obtain better precision, insofar as it may turn out that the predicted power trace only has the desired precision over a given time horizon, which may be a fairly short horizon (for example from 1 minute to a few tens of minutes, in particular a few minutes or at most 10 minutes). It is thus possible to take into account an unforeseen power demand, a state of charge, for example due to a change of route, one or more stops of the vehicle during the journey, etc. by repeating the calculation of step d1) at a given frequency (frequencyconstant or evolving in a predetermined or non-predetermined manner throughout the journey, in particular according to its predicted duration and / or according to the type of journey or portions of the journey). Preferably, this frequency can be fixed, but it is also possible to divide the cycle (the journey) according to the predicted power trace: to give an example, if the cycle provides for a stabilized portion of the journey, in particular on a motorway, it is possible to avoid restarting the calculation during this stabilized portion. Alternatively, it is advantageous to repeat steps c), d0) and d1) to determine the control of sub-step d2) when a given difference ΔSoC is reached between the measured state of charge (SoC) and the optimal state of charge predicted in sub-step d1). Here again, an unforeseen power demand and / or state of charge are taken into account, but only when a difference is observed between the predicted optimal charge rate and the actual charge rate: it is thus possible to limitthe calculation iterations to only perform them when deemed necessary, only when there is a deviation deemed too significant compared to the predictions. Advantageously, the navigation data may include traffic information, in particular the traffic state, the speed limits and the gradients of the possible traffic lanes for completing the journey. Preferably, in step d), the fuel consumption of the fuel-to-electricity conversion system(s) (in particular hydrogen-type gas when it is a fuel cell) can be minimized. Cumulatively or alternatively to this minimization of fuel consumption, the aging of the storage system (battery(ies)) or the fuel-to-electricity conversion system (fuel cell) can also be minimized. This aging can be evaluated / quantified, in particular by measuring the voltage at the terminals of the systems in question. In step d), the constraintson the limitations of the controls of the energy sources (BAT, FC) and on the limitations of the state of charge can be chosen from at least one of the following constraints, in particular all of the following constraints: - Preferably, in step d), a state of charge (SoC) of the or at least one of the electrical energy storage system(s) (BAT) is imposed between a minimum state of charge (SoCmin) and a maximum state of charge (SoCmax). - Preferably, in step d), a state of charge (SoC) of the or at least one of the electrical energy storage system(s) (BAT) at the end of the journey is imposed equal to said state of charge at the start of the journey. - Preferably, in step d), a given state of charge (SoCfin) of the or at least one of the electrical energy storage system(s) (BAT) at the end of the journey is imposed. - Preferably, in step d), an IFC current generated by the gas to electricity conversion system (FC) is imposed between a minimum current IFCmin and amaximum current IFC max. - Preferably, in step d), a current IBAT generated by the electricity storage system (BAT) is imposed between a minimum current IBATmin and a maximum current IBATmax. - Preferably, in step d), a current gradient gamma FC generated by the fuel-to-electricity conversion system (FC) is imposed between a minimum gradient gamma FCmin and a maximum gradient gamma FCmax. The vehicle parameters used in step c) of applying a predictive model may include at least the architecture of the vehicle's powertrain, and the characteristics of the electric machine (MEL). This may include, in particular, taking into account the weight and sizing of the vehicle, the auxiliary equipment consuming electricity, or the sizing and design of the electric machine. This may also include the sizing / number of power converters that are generally provided foradapt the electrical voltage of the various electrical components to the network voltage. Preferably, in sub-step d1) and / or in sub-step d2), a so-called interior point method (MPI) can be used, in particular to manage the constraints of maximum voltage state of the electrical network Umax and the state of charge (SoC) of the electricity storage system(s) (BAT). It is indeed a very effective method for solving functional optimization problems under constraints. This interior point method, preferably used in both sub-steps d1) and d2), corresponds to the method for solving the problem defined by the Maximum Pontryaguin Principle mentioned above. Any primal-dual method can also be used for sub-steps d1) and / or d2): For real-time balancing and disturbance rejection (during sub-step d2), the proposed algorithm requires calculating the dual variables of the problem constraints. Then,to calculate the optimal trajectory, it is also possible to use so-called primal-dual methods, either in a discretization then optimization context, or in an optimization then discretization context. The choice of primal-dual methods in the optimization then discretization context is favored for reasons of speed of calculation time. Preferably, in step d1) of loop calculation, called slow, the calculation is carried out over the entire remaining path. The invention also relates to a computer, or calculator configured to implement the method as described above. Any computer means, on-board or not, can also be considered. The invention also relates to a hybrid type vehicle equipped with an on-board navigation system and an electric powertrain comprising at least one electric machine (MEL) supplied with electricity by electrical energy sources (BAT, FC) comprising at least one systemfor converting fuel into electricity, in particular of the fuel cell (FC) type and at least one electrical energy storage system, in particular of the battery (BAT) and / or supercapacitor type. The vehicle further comprises means for controlling the electrical systems in electrical supply of the electric machine (MEL) controlled according to the method described above or with the computer, or the calculator mentioned above and implementing said method. The vehicle according to the invention can be land-based (light vehicle, heavy goods vehicle, bus), but also maritime or air-based. Preferably, the navigation system is integrated into the vehicle, and in particular has a screen integrated into the dashboard. Alternatively, the navigation system is autonomous, and connected to the computer or the calculator implementing the method according to the invention mentioned above. The invention also relates to any computer program product downloadable from a network ofcommunication and / or recorded on a medium readable by a computer or a calculator and / or executable by a processor, comprising program code instructions for implementing the method described above, when said program is executed on a computer or a calculator. The invention also relates to any storage medium readable by a computer or a calculator and storing instructions, which, when executed by a computer, or a calculator, imply that the computer or the calculator implements the method described above. List of figures Figure 1 represents the general energy architecture of a vehicle equipped with an electric machine powered by electricity from a battery and two fuel cells. Figure 2 represents an example of an energy control algorithm for a vehicle according to the invention. Figure 3 represents the energy architecture of a vehicle using an algorithm according to the invention.Figure 4 is a zoom on the fuel cell part of the vehicle. Figure 5 is a zoom on the battery part of the vehicle. Figure 6a is a plot of two identical journeys (made at different times) by a GPS type navigation system, with the longitude on the abscissa and the latitude on the ordinate. Figure 6b is a corresponding power trace generated from the plot of the two journeys according to Figure 6a. Figure 7a is a plot of the state of charge SoC calculated "off line" of the vehicle battery during a journey as a function of the distance traveled (in km). Figure 7b is a plot of the hydrogen consumption (in kg) calculated "off line" of the vehicle fuel cell as a function of the distance traveled (in km). Figure 8a represents as a function of time the traces of the state of charge SoC of the optimal battery predicted at some of the iterations of the slow loop (1st and 37th iterations represented respectively by the curve insolid line and dotted curve). Figure 8b represents as a function of time the sum of the predicted SoC state of charge trace segments (bottom graph) between each update of the slow loop as well as the state of charge trace actually carried out (top graph). Figure 9a represents the optimal SoC state of charge trace calculated offline as a function of distance (in km). It serves as a reference (it is mathematically the best solution that can be found to minimize the criterion set by the invention (here the H2 fuel consumption)). Figure 9b represents the SoC state of charge trace actually made during the cycle (trip) as a function of distance (km). Figure 9c gives an example of a SoC state of charge trace result (actually made during the cycle) as a function of distance (in km) if we had an algorithm that does not take into account the limitations of the SoC state of charge. In other words, thiscurve shows what we would have as a trace with a prior art algorithm. Figure 10 is a fuel consumption graph for batteries of different sizes on the same journey, with the % reduction in battery capacity on the abscissa, and the % excess fuel consumption on the ordinate, the solid line curve corresponding to taking into account the state of charge constraints (according to the invention therefore) and the dotted line curve corresponding to the state of the art. The figures, in particular those which are not graphs, are very schematic, not to scale and concern the illustration of embodiments as non-limiting examples of the implementation of the invention. Each reference or acronym designates the same component, the same calculation from one figure to another and throughout this text. Description of embodiments The invention will be described below using embodiments and examples ofnon-limiting manner and for illustration purposes, with the help of the aforementioned figures. By definition, a hybrid vehicle has several energy sources. In this case, on a vehicle equipped with one or at least one fuel cell and one or at least one battery, a power request from the vehicle's engine can be achieved either by the or at least one of the battery(ies), denoted BAT in this text, or by the at least one of the fuel cell(s), denoted FC in this text, or by both the battery and the fuel cell. For the sake of brevity, unless otherwise indicated, when the term battery, or, respectively, fuel cell, is mentioned, it is the only battery, or respectively the only fuel cell equipping the vehicle, or at least one of them (or all of them) when the vehicle has several (connected in parallel). The vehicle's energy management system ("Power Management System" in Englishwith its acronym PMS), is the control body allowing to arbitrate between these different sources, with the aim of maximizing the energy efficiency of the vehicle. (Note that throughout this text and in a known manner, this system can also be designated, in an equivalent manner, by the English term Energy Power Management, or by its acronym EMS). Figure 1 thus schematically represents the general energy architecture of a vehicle equipped with an electric machine supplied with electricity by a battery and by two fuel cells: the energy management system EMS controls the operation of the battery BAT and the fuel cell system FC comprising here n stacks S1 to Sn. All the auxiliary equipment Aux allowing to operate the fuel cell, this includes in particular at least one compressor and at least one radiator, these auxiliaries Aux require an electrical power PAux to operate. The battery BAT can providean electrical power PBAT to the electric motor MEL of the vehicle, and the fuel cell system FC can provide it with an electrical power PFC. The power required by the electric motor to complete a journey Preq is therefore the sum of these two powers PBAT, PFC provided, less the power Paux spent on the auxiliary equipment. The sum of the two powers PBAT, PFC provided makes it possible to provide the power required by the auxiliaries Paux and the power Preq requested by the electric machine MEL. The role of the EMS management system is to determine the optimal values ​​PBAT, PFC making it possible to provide the required power Preq while minimizing the fuel consumption (hydrogen) of the fuel cell and / or the aging of the cell and / or the battery over a given cycle (i.e. over a given journey of the vehicle). Note that the term "fuel" within the meaning of the invention, as already seen, designates the fluid, in particular gas, for examplehydrogen, or gasoline to be supplied to the gas-to-electricity conversion system. In the examples described below, the conversion system is a fuel cell whose fuel is therefore gaseous hydrogen. The objective, for a given driving cycle, is to control the two energy sources (FC and Battery) so as to minimize a criterion: here, in a non-limiting embodiment of the invention, we choose to minimize the consumption of H2, while respecting the following constraints: - The power balance must be satisfied, that is to say that the power ^ ^^^^^^ requested by the electric motor must be satisfied at each moment of the cycle. - The limits of the actuators must be respected in this embodiment^^^^^^ < ^^^^^^ < ^^^^^^ (minimum / maximum limits of the battery state of charge SoC)^^^^^^ < ^^^^^^ < ^^^^^^ (minimum / maximum limits of current coming from the FC cell)^^^^^^ < ^^^^ ^^ < ^^^^^^ (minimum / maximum limits of the current gradient coming from the FC cell) - The expected final state of charge must be reached: for example, we can impose a state of charge SoCfin of the battery at the end of the journey / cycle which is equal to its initial state of charge: -^^^^^^ = ^^^^^^^in order to directly realize the gain in hydrogen consumption without having to convert the energy of the battery used into hydrogen equivalent. But in another embodiment, it is possible to impose a determined charge level at the end of the journey SoCfin (and therefore different from the initial charge state).This amounts to an optimal control problem whose mathematical formulation is (on a cycle of T seconds): Minimization of a criterion J ^^^^^, ^^^^ = ^ ^^^^!^" + $). * %^ (^ With ^ ^^^^!^" the constraint on the final state of charge of the battery, and $)* %^ &'^^^^^^^^(^ the With the following constraints: (Qbat being the battery capacity) ^^^^^*^ = ^^^^^^^> Initial condition and dynamics of the system: with 9 :^^ the battery current depending on the and the power required by the electric motor ^ ^^^ ^^^ ^^^^^^ < ^^^^^^ < ^^^^^^> State / command constraints: > Global constraints: ^^^^!^ = ^^^^^*^^^^^^, ^^^^ = $) %^ (^We therefore seek to minimize the criterion: * If we rely, as in the present invention, on the Pontryaguin Maximum Principle to solve this problem, then: This amounts to minimizing the Hamiltonian H associated with the problem: ^^^ ^ ^ ^ -. / 0^^23^^^,@567^^^^^^ ^ = %^ ^^^^^^ − B^^^ ⋅ With B^^^ the adjoint state associated with the state variable ^^^^^^. The prior art using PMP would consider the following assumption to solve the problem: Assuming that EFG^H = 0, we have B^^^ = B* = ^^^J and therefore:9:^^^^^ ^^^, ^ ^^^^^ ^^^^ − ⋅ ^ ^^^ After changing variables: ^ NO⋅@^-&C = %^ &C ⋅ ^^9&C, ^:^^ = L:^^ ⋅ 9:^^ and M* = −P. / 08. / 0We find that the optimal order ^ ^ ∗ ^ is obtained by minimizing at each instant a weighting function between thermal and electrical power: ^ ∗ ^^ = arg^ min ^&C^^^^^ + M* ⋅ ^:^^^^^^^ This weighting function depends on a parameter λ0 which is generally called the "equivalence factor" and which in fact corresponds to the adjoint state p0 associated with the state variable soc. The challenge is then to find the λ0 (or the p0) which allows, for a given cycle, to respect the different constraints which we have set, in particular the constraints at both ends (Soc(t=0) = Soc_init and Soc(t=T) = Soc_init). Let's take the "off line" case, when the cycle is known in advance: The simplest thing is then to determine M *iteratively so as to respect the final state of charge constraint (^^^^!^ = ^^^^^*^)Let's take the "on line" case, when the cycle is unknown in advance: The equivalence factor λ will be very dependent on the driving cycle as well as on random events (traffic, driving behavior, etc.). Consequently, finding an adequate λ is the main issue in on line algorithms if we want to have performances close to the global optimum. Many methods exist, we can cite in particular: - methods based on λ regulation: an initial λ is calculated upstream. During the cycle, the λ is then regulated by a PI controller to ensure that the SoC remains close to the final target SoC. - methods based on pattern recognition: upstream, a neural network is trained to recognize driving cycle patterns (highway, mountain, city, etc.) and associate the appropriate λ with them.- methods based on load prediction: using navigation data, we can estimate the power required over a more or less long time horizon. We then perform an optimization calculation on this time window, which will give a reference SoC state of charge trace to follow, and we adapt the λ accordingly. These methods, which are based on the principle of ECMS or PMP and aim to adapt the λ in real time to take into account the vagaries of the cycle, are often called A-ECMS or A-PMP (for "Adaptive" ECMS / PMP). The so-called "Model Predictive Control" (MPC) methods are in line with methods based on load prediction and optimization. The general principle is to use past information, real-time vehicle data and navigation data to deduce the control of energy sources. Among all the MPC methods, some are based on the PMP principle.Their principle is to predict the power over a given time horizon, and then perform an optimization calculation based on the PMP to output a corresponding adjoint state p. This adjoint state can be updated during the cycle if the road conditions, and therefore the power prediction, change. This approach is interesting because, being based on the optimal control theory, it guarantees to approach the global optimum provided that the hazards of the cycle are correctly predicted, and this, for a relatively low necessary computing power. The present invention is part of this type of approach (MPC – PMP), with - Prediction of the power demand from GPS data - Carrying out an optimal control calculation under constraint using the PMP. - Calculation of the adjoint state p, calculation of the Hamiltonian from p, minimization of the Hamiltonian. - Reupdating during the cycle of the predicted power trace and the adjoint state p.Plusieurs publications ont déjà proposé des méthodes « on line » de type MPC – PMP ou A- ECMS, on peut citer notamment : Musardo C., Staccia B., Bittanti S., Guezennec Y., Guzzella L.,Rizzoni G. (2004a) An adaptive algorithm for hybrid electric vehicles energy management, FISITA 2004 World - Automotive Congress, Barcelona, Spain R. Schmid, J. Bürger and N. Bajcinca, "Efficient Optimal Control of Plug-in-Hybrid Electric Vehicles including explicit Engine on / off Decisions," 2018 European Control Conference (ECC), Limassol, Cyprus, 2018, pp.596-601, doi: 10.23919 / ECC.2018.8550516 : J. Buerger and M. Cannon, "Nonlinear MPC for supervisory control of hybrid electric vehicles," 2016 European Control Conference (ECC), Aalborg, Denmark, 2016, pp.135-140, doi: 10.1109 / ECC.2016.7810276: Simona Onori, Laura Tribioli, Adaptive Pontryagin's Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt, Applied Energy, Volume 147, 2015, Pages 224-234, ISSN 0306-2619, https: / / doi.org / 10.1016 / j.apenergy.2015.01.021. These works all promise satisfactory results and not far from the global optimum (generally around +1% on the cycles considered). One limitation that they all have in common, however, concerns the consideration of the SoC battery state of charge constraints: ^^^^^^ ≤ ^^^^^^ ≤ ^^^^^^Indeed, in all these works, the assumption taken is that the Hamiltonian: ^^^ ^ -. / 0^^23^^^,@567^^^^^^ ^ = %^ − B^^^ ⋅. does not depend on the state of charge SoCEt so that: B^ = EFG^H = 0 and therefore B^^^ = B* = ^^^J Even in prior art algorithms that update the adjoint state p, it is considered constant between each update and these updates do not predict variations in p. Algorithms that regulate the adjoint state to follow a reference of the SoC state of charge are necessarily far from the optimal solution. Most of the time, this assumption is justified as noted in the aforementioned publication Simona Onori et al., because although the battery current 9 :^^depends on battery parameters which themselves depend on the SoC state of charge, in reality this evolution over time is small. However, when the minimum or maximum limits are reached, the hypothesis of the constant adjoint state is no longer valid: indeed, if the optimal control took into account the state constraints linked to the SoC state of charge, the adjoint state linked to said SoC state of charge would evolve during the cycle to avoid going beyond the limits. In the prior art, the optimization carried out is therefore blind to the constraints on the SoC state of charge: the optimal control is calculated without "knowing" that the SoC state of charge must not exceed certain values. To ensure that the SoC state of charge remains within the admissible limits, a penalty function is generally applied to the Hamiltonian, but this can only lead to suboptimal results when the SoC state of charge limits are reached.Some studies, such as the aforementioned publication J Buerger et al., consider that on hybrid vehicles with internal combustion engines (so-called "HEV"), the SoC limits are only rarely reached because the battery is sized accordingly, the assumption of constant p is then not a problem. This argument may be admissible most of the time, but can become false on certain mountain-type cycles with large variations in slopes or if one wants to size the battery as accurately as possible. More generally, taking into account the state constraints linked to the SoC state of charge in the optimal control can allow for more accurate sizing of the vehicle architecture, for example with smaller batteries for equivalent applications. This is also useful in applications with supercapacitor / battery hybrid storage.The invention therefore proposes an algorithm for an EMS energy management system of a hybrid vehicle in order to control the distribution of the power coming from the battery and that coming from the combustion cell to supply the electric motor of a vehicle, said system having the following properties: - based on the theory of optimal control with use of the Pontryagin Maximum Principle PMP - which can be implemented online, which is robust to hazards (traffic conditions, route changes, etc.) with the sole need of data from a GPS-type geolocation system. - taking into account all the limitations of the system whether on the commands (for example: ^^^^^^, ^^^^^, ^^^^^^, ^^^^^^, etc.) or on the states (here ^^^^^^, ^^^^^^) - not requiring upstream development (other than informing. of the vehicle necessary for the models used by the algorithm) An embodiment of the algorithm according to the invention is described below from a methodological point of view, with the description of the vehicle architecture model, the definition of the optimal control problem, and the precise description of the resolution algorithms: - Before departure, a destination is entered into the GPS which gives the route to be covered, the speed limits and the altitude - A model taking into account the vehicle parameters makes it possible to predict from the navigation data (traffic, speed limits, slopes, etc.) the speed trace and therefore the power trace requested by the electric motor at each instant - The PMS takes as input the measured SoC state of charge and the predicted power to perform two types of calculation: > a so-called "slow" loop calculation: in this loop, an "off line" calculation is performed from the predicted power trace, taking as the initial SoC state of charge the SoC state of charge measured at the instant when the calculation is launched. This calculation is based on the PMP and takes into account the constraints on the controls and on the states (here SoC state of charge). It makes it possible to determine the optimal SoC trace and the adjoint states B. ^corresponding to each position in the cycle. > a so-called "fast (real-time)" loop calculation: at each time step, the power actually requested may be different from the predicted power. This loop ensures that the power requirement is met, while minimizing the chosen criterion (here the H2 consumption). We therefore write the Hamiltonian at time t from the adjoint states B ^ calculated by the slow loop:A^^, ^^^ , ^\])^ = ^^^^^&C^^^^^ − ∗ ^^^^ ^\])^^^"We then solve a the commands^^^ / ^\]) which minimize A^^, ^^^ , ^\])^ at time t while respecting the constraints:^^^def < ^^^^^^ < ^^^^^^The solutions ^ ^^ / ^ \]) will be to the vehicle. The fast loop occurs at each time step. The so-called slow loop can be recalculated during the cycle when the actual SoC state of charge trace deviates from the calculated reference, or more simply at regular time intervals (e.g. every 10 minutes). If there was no slow loop to calculate a precise adjoint state value, it would be necessary to use a default value in the fast loop, and performance would be significantly degraded. The slow loop is used to find the optimal adjoint state value that allows the fast loop to calculate the optimal actuator control values. This is what Figure 2 represents, with some of the references in Figure 2 already described earlier in this text.The other references correspond respectively to: - 1: route = origin and destination - 2: traffic information - 3: calculation of the route to be taken - 4: speed limit data, altitude - 5: calculation of achievable speed - 6: optimal control (calculation of the co-states or adjoint states p mentioned above) - 7: calculation of the power distribution between the battery and the two fuel cells - Pm: measured power - Ppred: predicted power over the entire cycle (the route) - VH: vehicle - Dr: actual distance - SoCm: measured state of charge of the battery The fast loop is described in more detail below: This is a numerical optimization problem under constraints, that is to say that the solutions sought belong to a numerical space. One of the most effective methods for solving this type of problem is the so-called interior point method.This method consists of adding logarithmic penalties to the initial cost to relax the inequality constraints. In other words, the original problem, which is to minimize the Hamiltonian under inequality constraints (here the inequality constraints are the constraints on the ^^^ , ^^^ and ^^^ ), is rewritten as an unconstrained minimization of the Hamiltonian under inequality constraints to which penalties corresponding to each constraint have been added. We therefore transform a problem under constraints that is difficult to solve into an unconstrained problem that we know how to solve and whose solution is the same as the solution to the original problem. The slow loop is described in more detail below: This time, the problem we want to solve does not correspond to a numerical optimization problem (solution belonging to a numerical space) but to a functional optimization problem (solution belonging to a space of functions). In other words, we seek to minimize a function of a function.Here it is a question of H2 consumption which is among other things a function of the SoC (itself being a function of 9. \])). One of the reasons why state constraints were not taken into account in online algorithms until now is that this greatly complicates the equations to be solved and there were no resolution algorithms (and whose convergence to a solution was proven) efficient enough to allow a real-time implementation. Recently P. Malisani, in the publications P. Malisani, “Interior point methods in optimal control of affine systems: Convergence results and solving algorithms,” to appear in SIAM Journal on Control and Optimization, 2023, and P. Malisani. Interior point methods in optimal control. ESAIM COCV (in review), 2023, demonstrated that it was possible to apply interior point methods for functional optimization problems and that they provided an exact solution.Based on this work, we can therefore write the system of differential and algebraic equations defining the necessary conditions for optimality (given by the PMP) into a new unconstrained system using the interior point method. We can therefore quickly solve this system using an interior point algorithm. To make the calculation over the entire cycle even faster, we define 3 variables: - the "Start_time_undersampling": time over the predicted cycle from which we undersample - the "Under_sampling_steps": new sampling step - the "Actualization_horizon", which allows us to set the time after which we restart the calculation For the calculated values ​​of the adjoint states to be close to the optimal, we need to know the trend of the requested power ^. ^^^over the entire cycle. Indeed, to be optimal, the control needs to know if, later in the cycle, a very high demand will occur (e.g., mountain uphill) or, conversely, if there will be a high potential for energy recovery (mountain downhill). This is why the calculation of the slow loop is advantageously done over the entire remaining cycle. However, as the calculation is restarted after a certain time horizon (in order to take into account random events and correct prediction errors), we only need to have an accurate prediction (and therefore with a high sampling rate) over a relatively short time horizon. The rest of the cycle can be sampled more coarsely, the main thing being to keep enough information on the ^ ^^^average. We therefore preferably take a much lower sampling beyond the "actualization_horizon". This allows the problem to be solved in only a few tens of seconds. Example Example 1 Vehicle energy architecture As mentioned previously, we consider a hybrid architecture equipped with two identical FC fuel cells and a BAT battery supplying a DC bus from which an inverter draws power to operate the MEL electric motor. This example corresponds to Figure 3, with the following additional references compared to the previous figures: - PFC1: the power supplied by the first FC1 fuel cell - PFC2: the power supplied by the second FC1 fuel cell - Paux1: the power requested by the auxiliaries of the 1 ère fuel cell - Paux2: the power required by the auxiliaries of the 2 èrefuel cell - Ubus: the voltage at the terminals of the DC bus to which the MEL electric machine is connected. The most recent studies tend to show that if, on light vehicles, the non-hybridized complete electric remains more profitable, on heavier vehicles on the other hand this type of architecture would become much more interesting. We therefore consider here for example a heavy goods vehicle of 44 tonnes. This choice is all the more interesting for the implementation of the invention since this type of vehicle can require very high power and performs long but relatively predictable cycles (obligation to respect speed limits, GPS track necessarily known). Battery characteristics: Cell capacity: g:^^ = 113520 A.sMin current: 9\])^^^ = −378 k (charge rate up to 3 times)Max current: here assumed infinite for simplicity but a max constraint could be taken into account without problemNo DC / DC ^ bus voltage L:lF = L:^^ (we will subsequently note U the DC bus voltage)Nominal voltage: 780 V Characteristics of the FC modules: Number of S1-SN stacks: 4 Number of cells per stack = 200Min current: ^^^^^^ = 0 kMax current: ^^^^^^ = 600 kMin current gradient: ^^^^^^ = −120 k / ^Max current gradient: ^^^^^^ = 120 k / ^To be able to operate, the FC fuel cell consumes a power ^. ]l^. Fuel cell modules As shown in Figure 4, which is a part of the architecture shown in the previous figure, each FC module is composed of 4 stacks with an auxiliary equipment Aux including a compressor. The cooling system is common to both FC modules. Each FC module is connected to the bus by a DC / DC converter. - Calculation of the DC / DC current on the bus side ^ p^p^ : The stack is defined by its polarization curve which gives the cell voltage q H^rr depending on the current density s: qH^rr = t^s^From this polarization curve, and the DC / DC model (which takes into account conduction and switching losses), we define an analytical relationship between the current drawn on the FC and the current supplied on the DC bus: ^p^p^ = u_ ∗ ^' + u' ∗ ^ ∗ v + uw ∗ ^ + ux ∗ v' + uy ∗ v + uzwith (a1, a2, a3, a4, a5, a6 ) constants - Calculation of the auxiliary power ^ ]l^: The total auxiliary power (of the two FC fuel cells) is composed of the compressor power ^ HG^N)G^ for FC1 and FC2 and the cooling system power for both batteries ^ HGGr)G^ . Here again an analytical expression gives the relation ^]l^ = t^^^^^: With (^H{ , ^HC , ^H| , ^_, ^', ^}{ , ^}C , ^}| , ^J^~v%^J^^ constantsWe finally obtain:^]l^^^^^^^_, ^^^'^ = ^HG^N)G^^^^^^^_, ^^^'^ + ^HGGr)G^^^^^^^_, ^^^'^With P heat the power dissipated in the form of heat by the batteries Calculation of the consumption in H2: The total consumption (of the two batteries) in H2 is written: ^^^^_ + ^^^'^ ∗ ^^^^^HG^FGWith ^^^^^ HG^FG a constant depending on the stack parameters and the H2 stoichiometry. The battery As shown in Figure 5, which is part of the architecture shown in Figure 2, the system uses a BAT battery. - Calculation of the battery current: The battery current ^ \])is expressed as a function of ^ ^^_ , ^ ^^' , ^ ^^^ and L via the power balance:^^^^ = ^^^_ + ^^^' + ^\]) − ^]l^We also introduce a power ^ into this equation ^ which is called braking power. Indeed, it may happen that the power balance cannot be resolved only with^^^ and ^\]) to be controlled. If during a regenerative braking phase we have ^^^^ < ^:^^^^^ then the battery will only be able to absorb part of the energy. In reality, the rest of the energy would be dissipated during braking. So that the control can find a solution in these cases we add ^ ^ as a command with the constraint: ^t < 0. We therefore finally have:^^^^ = ^^^_ + ^^^' + ^\]) + ^^ − ^]l^or ^^^^ = ^^p^p^_ + ^p^p^' + ^\])^ ∗ L + ^^ − ^]l^ - Calculating the battery voltage: When current is drawn from the battery, its voltage and therefore the DC bus voltage will be impacted. This is governed by the following differential equation: 1 ^ ^ ^^^ − ^ ^ ^ ^ v ^ − ^^q " − With Cf (filtering capacitance), OCV (Open Circuit voltage) and Rohm (Internal resistance) as constants. Definition of the system of equations to be solved: slow loop The problem to be solved is as follows: We consider x the vector of state variables: ^ = [^^^ ^^^_ ^^^' v] and B = [B_ B' Bw Bx ] the vector of associated adjoint states. And c is the vector of control variables: ^ = [^^^_ ^^^' ^t].We note ^ ^^^ the vector of powers required at each time step along the entire predicted cycle. The state variables x are subject to the following differential equations: ^^ \]) ​​^ , ^ , v, ^ , ^ "^^^^ ^ ^ ^^' ^ ^^^ The state variables are subject to the constraints: ^^^^^^ − ^^^N ≤ 0 The mixed state / command constraints are:^^^_^^^ − ^_N ≤ 0 dual variables The Hamiltonian of the problem is written: A = ^^^^^^^^^_, ^^^'^ + B_ ∗ ^^^^ + B' ∗ ^^^_ + Bw ∗ ^^^' + Bx ∗ v^ The PMP and the interior point method give us the Necessary Conditions of Optimality through the following system of equations to be solved: - ODE (for “Ordinary Differential Equation” in English or ordinary differential equation in French) ^^ 1 l^^ ^^^'^ + ^^^^ − ^^ ^^^ ^^^^^ − ^v^ ^^\])^^^^_, ^^^', v, ^^^^ , ^^^-0 - Conditions at both ends0 = ^^^^^*^ − ^^^F^^^ ^^^^F^^^ = 0.5^0 = ^^^_^^*^ − ^^^_F^^^ ^^^^_F^^^ = 0 k^0 = ^^^'^^*^ − ^^^'F^^^ ^^^^'F^^^ = 0 k^0 = v^^*^ − vF^^^ ^vF^^^ = 770 q^0 With ^ ∈ [^*, ^^]The "solver" (English term that can be translated as solver) uses the systems of equations defined previously to find the solution vectors x* (state vector), p* (adjoint state vector) and z* (constrained command vector) by interior point algorithm and damped Newton algorithm. Definition of the system of equations to be solved: the fast loop At each instant t, we want to find, for the real requested power ^ ^^^ ^ ^ ^ and the real voltage v^^ − 1^, the vector ^ = [^\])^^^ ^^^_^^^ ^^^'^^^ ^^^^^] which minimizes the consumption of H2, therefore according to the PMP, which minimizes of the problem. We recover the optimal p vector B∗ = [B∗ ∗ ∗ ∗_ B' Bw Bx] calculated by the slow loop. We can then write at each time step the Hamiltonian to minimize: ^^^^^^^ ^ ^ − ^\]) ^ ^ − 1^ v^^ − 1^ − ^^q^^' ' ^^' ^ to minimize: ^^^^^^^ ^ ^ 1 v^^ − 1^^^'^ − ∗ \]) − ∗ ^^ − ^^q This respecting the constraints: Equality: v^^^ ∗ ^^ + ^ ^ , v^^ − 1^" + ^ ^^^', v^^ − 1^"^ − ^^^^^^^ − ^ l^^^ , ^^^'^ + ^ Inequality: Using an interior point algorithm and a damped Newton, we find at each instant t the solution ^ = [^\])^^0 ^^^_^^0 ^^^'^^0 ^^^^0] and the commands ^^^_ / i ¡' to send to the two stacks. We consider two cycles for a Lyon-Bordeaux trip: - a "predicted" cycle: LB_TrajPred - a "real" cycle: LB_TrajReel The two cycles were generated by the power trace generation tool from the GPS trace. In order to simulate a real and predicted cycle, we simulated two cycles whose route is the same, but the traffic times differ: the goal is to simulate traffic hazards. We therefore consider: - the "trajPred" cycle as the "predicted" cycle used by the slow loop. - the "trajReel" cycle as the "real" cycle used by the real-time loop. Figure 6a is a plot of the route taken by “TrajPred” and “TrajReel” using a GPS-type navigation system (with longitude on the abscissa / latitude on the ordinate).Figure 6b (with the distance on the abscissa and the power required by the electric machine in kW on the ordinate) is a corresponding power trace generated from the plot of the two paths according to Figure 6a.Simulation assumptions: 2 fuel cells FC1 and FC2 with the characteristics indicated below (same characteristics for both cells: 4 stacks for each fuel cell i1m = i2m = 0 A (minimum cell current) i1p = i2p = 600 A (maximum cell current) gamma 1m = gamma 2m = - 120 A / S gamma 1p = gamma 2p = 120 A / S “Start_Time_Undersampling” = 500s (time from which the predicted cycle is undersampled) “µUnder_sampling_steps” = 70s (the undersampling step) “Actualization_horizon” = 500s Battery charge rate BAT = 3 (which means that here the battery can deliver a maximum power equal to three times the power it could deliver during 1 hour if fully charged). The battery is limited by the minimum and maximum current intensities.In order to calculate the global optimum and have a trace of the SoC state of charge and a reference H2 consumption, we perform an “off line” calculation on the “real” trace (trajReel): Figure 7a is a plot of the SoC state of charge calculated “off line” of the vehicle battery on the “real” travel trace (trajReel) (with the distance on the abscissa and the SoC state of charge on the ordinate). Figure 7b is a plot of the hydrogen consumption calculated “off line” of the vehicle fuel cell on the “real” travel trace (trajReel) (with the distance on the abscissa and the hydrogen consumption in kg on the ordinate). We obtain a reference consumption of 29.27 kg of H2.The calculation is carried out "on line", with the slow loop which predicts the cycle (trajPred) and the real-time loop which follows the cycle (trajReel): Figure 8a represents the traces of the optimal battery SoC state of charge predicted at several iterations of the slow loop (for the sake of readability, only two iterations are represented): each time the calculation starts from the current SoC value to recalculate a trace which satisfies the final constraint while minimizing the criterion. Figure 8b represents on the one hand the sum of the SoC state of charge trace segments predicted between each update of the slow loop and on the other hand the SoC state of charge trace actually carried out.The actual consumption is measured and compared to the reference as well as to an algorithm that does not take into account the constraints on the state of charge SoC and uses a penalty function instead: Figure 9a represents the trace of the optimal state of charge SoC of the vehicle battery during the journey (with the distance in km on the abscissa). This is mathematically the global optimum that was calculated offline. The consumption is 29.27 kg of hydrogen and serves as a reference to judge the efficiency of the online algorithms. Figure 9b represents the trace of the actual state of charge SoC of the BAT battery during the journey (i.e. online): it is 29.3 kg of hydrogen (i.e. + 0.1% compared to the reference consumption).Figure 9c represents the trace of the optimal state of charge SoC calculated over the cycle, with an algorithm that does not take into account the state of charge limits in its calculations and that uses a Hamiltonian penalty instead: it is 29.65 kg of hydrogen (+ 1.3% compared to the reference consumption). We can thus see that the solution according to the invention (figure 9b) is very close to the global optimum (figure 9a) and gives better results than the solution usually used with Hamiltonian penalization (figure 9c). Example 2 Taking into account the constraints linked to the SoC in the calculation of the optimal control allows better exploitation of the battery capacity and therefore potentially to optimize its sizing: We replay the Lyon-Bordeaux cycle in “off line” with or without taking into account the SoC limitations by taking increasingly smaller battery capacities. We start from the nominal case where the battery cell capacity is 113520 AsTwo offline calculations are then carried out on the cycle considered: one with the algorithm of the present invention which takes into account the state constraints linked to the battery SoC, the other with an algorithm representative of the prior art, which does not take into account these constraints on the SoC in the optimal control and which instead uses a Hamiltonian penalization to avoid the limits on the SoC being exceeded. The final hydrogen consumption is noted in both cases. This operation is then repeated on the same cycle but with an increasingly reduced battery capacity. Reducing the battery capacity necessarily leads to an increase in consumption on the same cycle and therefore overconsumption compared to the nominal case (battery cell capacity at 113520 As).In Figure 10, for each percentage reduction in battery capacity (on the abscissa), the percentage of overconsumption of hydrogen (on the ordinate) is plotted compared to the nominal case: - The solid curve is obtained with the algorithm of the present invention, which takes into account the state constraints on the SoC. - The dotted curve is obtained with a representative algorithm of the prior art which is also based on the PMP but without taking into account the state constraints linked to the SoC. As the battery capacity decreases, the curve without taking into account the SoC limits (dotted) deviates more and more from the curve produced with the algorithm according to the invention (solid line). This is due to the fact that the limits on the SoC states of charge are reached more and more frequently in the cycle. By taking them into account, the algorithm according to the invention is able to better manage the overconsumption due to the capacity reduction.In this specific case study, we see that the consumption of the representative algorithm of the prior art with nominal battery can be obtained with a battery capacity reduced by 25% if we use the algorithm of the present invention. We can conclude that the algorithm according to the invention can also allow the energy architecture of the vehicle to be dimensioned more efficiently: the invention offers greater room for maneuver to reduce in particular the size of the electricity storage system (battery). In addition, the invention makes it possible to avoid finding ourselves at points where the battery can no longer provide current and where the entire power must be provided by the fuel cells. It is therefore no longer necessary to have fuel cells sized to provide the entire power on their own: the power of the cells can also be adapted / adjusted downwards as needed thanks to the invention.

Claims

Claims 1. Method for energy management of a hybrid vehicle equipped with a navigation system and an electric powertrain comprising at least one electric machine (MEL) which is supplied with electricity by electrical energy sources (BAT, FC) comprising at least one fuel-to-electricity conversion system, in particular of the fuel cell (FC) or turbogenerator or heat engine type associated with an alternator, and at least one electrical energy storage system, in particular of the battery (BAT) and / or supercapacitor type, characterized in that said method comprises: a) a step of entering a destination in a navigation system which recovers and uses satellite navigation data in real time, b) a step of calculation by the navigation system of the route to be covered for the route from navigation data, c) a step of applying a predictive model of the vehicle to said route,from at least some of said navigation data and from vehicle parameters, to predict the vehicle speed trace (Vvh) on the journey and to deduce therefrom the predicted required power trace (Preq) for the electric machine (MEL) during said journey, d) a step of managing the electricity supply of the electric machine (MEL) by the electric energy sources, which comprises d0) a sub-step of measuring the state of charge (SoC) of the electric energy storage system(s) (BAT), d1) a sub-step of calculating optimal control of the electric energy sources (BAT,FC) in particular in a loop, from the predicted power trace (Preq) in step c), said calculation being based on the Pontryagin Maximum Principle (PMP) and taking into account - the state of charge (SoC) of the electric energy storage system(s) (BAT) measured in step d0) as the initial state of charge,- and the constraints on the limitations of the controls of the energy sources (BAT, FC) and on the limitations of the state of charge (SoC) of the electrical energy storage system(s) (BAT), to determine at each position of the vehicle on the path predicted in step b) the optimal trace of the state of charge (SoC) and the adjoint state (p, i ) associated optimal, d2) a sub-step of determining in real time the control of the share of electricity coming from the electrical energy storage system(s) (BAT) and the share of electricity coming from the fuel conversion system(s) (FC) to power the electric machine (MEL) during said journey, by minimizing the Hamiltonian of the optimal control problem which is defined by the Pontryagin Maximum Principle (PMP) and which depends on the adjoint states (p i ) optimal calculated in step d1).

2. Method according to the preceding claim, characterized in that steps c) d0) and d1) are repeated to determine the control of sub-step d2) according to a given time step Δt, constant or variable, depending on the duration of the journey.

3. Method according to claim 1, characterized in that steps c), d0) and d1) are repeated to determine the control of sub-step d2) when a given difference ΔSoC is reached between the measured state of charge (SoC) and the state of charge predicted in sub-step d1).

4. Method according to one of the preceding claims, characterized in that the navigation data comprise traffic information, in particular the traffic state, the speed limits and the gradients of the possible traffic lanes for carrying out the journey. 5.Method according to one of the preceding claims, characterized in that, in step d), the fuel consumption of the fuel-to-electricity conversion system(s) (FC) is minimized.

6. Method according to one of the preceding claims, characterized in that, in step d), a state of charge (SoC) of the or at least one of the electrical energy storage system(s) (BAT) is imposed between a minimum state of charge (SoCmin) and a maximum state of charge (SoCmax).

7. Method according to one of the preceding claims, characterized in that, in step d), a state of charge (SoC) of the or at least one of the electrical energy storage system(s) (BAT) is imposed at the end of the journey equal to said state of charge at the start of the journey.

8. Method according to one of claims 1 to 6, characterized in that, in step d), a given state of charge (SoCfin) is imposed on the or at least one of the electrical energy storage system(s) (BAT) at the end of the journey. 9.Method according to one of the preceding claims, characterized in that, in step d), an IFC current generated by the fuel-to-electricity conversion system (FC) is imposed between a minimum current IFCmin and a maximum current IFCmax.

10. Method according to one of the preceding claims, characterized in that, in step d), an IBAT current generated by the electricity storage system (BAT) is imposed between a minimum current IBATmin and a maximum current IBATmax.

11. Method according to one of the preceding claims, characterized in that, in step d), a gamma FC current gradient generated by the conversion system is imposed. fuel into electricity (FC) between a minimum gradient gamma FCmin and a maximum gradient gamma FCmax.

12. Method according to one of the preceding claims, characterized in that the vehicle parameters used in step c) of applying a predictive model comprise at least the architecture of the vehicle's powertrain, and the characteristics of the electric machine (MEL).

13. Method according to one of the preceding claims, characterized in that, in sub-step d1) and / or in sub-step d2), a so-called interior point method (MPI) is used, or any primal-dual method, in particular to manage the constraints of maximum voltage state of the electrical network Umax and the state of charge (SoC) of the electricity storage system(s) (BAT).

14. Method according to one of the preceding claims, characterized in that, in step d1) of slow loop calculation, the calculation is carried out over the entire remaining path. 15.Computer or calculator configured to implement the method according to one of the preceding claims.

16. Vehicle of hybrid type and equipped with an on-board navigation system and an electric powertrain comprising at least one electric machine (MEL) supplied with electricity by electrical energy sources (BAT, FC) comprising at least one fuel-to-electricity conversion system, in particular of the fuel cell (FC) type and at least one electrical energy storage system, in particular of the battery (BAT) and / or supercapacitor type, characterized in that it comprises means for controlling the electrical power supply of the electrical systems of the electric machine (MEL) controlled according to the method according to one of claims 1 to 14 or with the computer or calculator of claim 15. 17.Vehicle according to the preceding claim, characterized in that the navigation system is integrated into the vehicle, and in particular has a screen integrated into the dashboard, or in that said navigation system is autonomous, said navigation system being connected to the computer or to the calculator implementing the method according to one of claims 1 to 14.

18. Computer program product downloadable from a communication network and / or recorded on a medium readable by a computer or a calculator and / or executable by a processor, comprising program code instructions for the implementation. implementation of the method according to one of claims 1 to 14, when said program is executed on a computer or a calculator.

19. Computer-readable storage medium, or calculator and storing instructions, which, when executed by a computer or calculator, imply that the computer or calculator implements the method according to one of claims 1 to 14.

Images