FR3140334A1 - METHOD FOR OPTIMIZING A CRITERIA OR A COMBINATION OF CRITERIA RELATING TO A MOTOR VEHICLE - Google Patents

Abstract

The invention relates to a method for optimizing a criterion or a combination of criteria relating to a motor vehicle (2) comprising a fuel or hydrogen tank, a battery and/or super-capacitors (30), a heat engine (M) or a fuel cell (P), an electric machine (ME), a plurality of devices, each characterized by at least one state variable, and a calculator (4) configured to manage the traction chain of the motor vehicle (2) on a predetermined path and being able to control the thermal engine (M) or the fuel cell (P), the electric machine (ME) and/or the devices by emission of a set of instructions. The invention also relates to a computer (4) and a computer program product for implementing such a method, as well as a motor vehicle (2) comprising such a computer (4). Abstract Figure: Fig. 1

Description

METHOD FOR OPTIMIZING A CRITERIA OR A COMBINATION OF CRITERIA RELATING TO A MOTOR VEHICLE

The present invention relates to the optimization of a criterion or a combination of criteria relating to a motor vehicle, and more particularly a method for optimizing a criterion or a combination of criteria of a vehicle automobile on a predefined route. The invention aims in particular to generate optimized instructions for controlling the vehicle on said route to be traveled by minimizing the criterion or the combination of criteria. The criterion to be optimized is typically a level of pollution, energy consumption, a state of health of the vehicle or a duration of a predetermined journey of the vehicle. The combination of criteria to be optimized consists of a combination of at least two criteria chosen from the group consisting of: a level of pollution, energy consumption, a state of health and a duration of a predetermined journey. By “energy consumption of the vehicle” is meant within the meaning of the present invention a consumption of fuel, of hydrogen, of electrical energy, of electrical current delivered by the branches of an electronic charger of an electric battery of the vehicle, in number restarts of a generator in a series hybrid vehicle (also called a “range extender” in English), or even a combined consumption of several of these elements. By “state of health of the vehicle” is meant within the meaning of the present invention a percentage which changes from 100% to 0% and which makes it possible to characterize the level of deterioration of an electric battery and/or one or more super- capacities and/or fuel cell of the vehicle, taking into account replacement and maintenance costs.

In a motor vehicle, it is known to optimize the energy consumption of the traction chain on a given or planned route. Such optimization can be carried out on fuel, on electrical energy, on hydrogen consumption (in the case of a vehicle equipped with a fuel cell) or on two or three of these criteria at the same time.

In a known manner, optimization can be carried out using the principle known as Pontryagin's Maximum Principle (PMP). This method consists of minimizing the Hamiltonian function (or Hamiltonian) based on the criterion to be optimized, for example the quantity of fuel or hydrogen or the electrical energy consumed, and the description of the dynamics of the system. The dynamics of the system are defined from the state of different vehicle variables (vehicle speed, state of charge of the battery, and/or super-capacities, temperatures, etc.) and different inputs or instructions (references of torque to be applied to the wheels of the vehicle for the thermal engine or for the electrical machine(s), torque instructions to be applied for the motor(s) of the generator, and/or power or current instructions for the battery. fuel, and/or even catalyst heating instructions, and/or even cooling circuit control instructions, etc.). The Hamiltonian function is minimized in order to determine the instructions allowing the minimum consumption of fuel or hydrogen, and/or electrical energy to be obtained.

Each input or instruction is dependent on the state of certain variables. For example, the torque setpoint for the electric machine to be applied to the wheels of the vehicle is dependent on the speed of the vehicle and the state of charge of the battery, the control setpoint for the cooling circuit is dependent on the temperature in real time in the cooling circuit, the catalyst heating setpoint is dependent on the real time temperature in the catalyst.

The Hamiltonian thus determined is then minimized. In other words, the setpoint values for which the Hamiltonian value is the lowest are selected and applied to the vehicle. The setpoint values are thus determined in real time based on the current state of the system.

The application of such a PMP model thus uses an internal model describing the dynamics of the system to be controlled (here the motor vehicle and its traction chain), via differential equations which give a representation of the system. Such a PMP model is therefore primarily based on an open-loop optimal control strategy, which calculates the optimal control solution based on the predictions of the internal model, with very little feedback from current measurements on the system. When the internal model differs very little from the real physical system, the use of such a PMP model provides an acceptable solution from an accuracy point of view, such that the model leads to a correct optimal solution. However, the internal model may differ from the real physical system, due in particular to model simplifications, external disturbances, aging effects, etc. The result provided by such a model then suffers from a lack of precision and/or stability from the point of view of the optimization control strategy employed.

There is therefore a need to be able to have an optimal control method allowing the optimization of a criterion or a combination of criteria relating to a motor vehicle by implementing a PMP model, while being self-adaptive in order to take into account, in an iterative manner, deviations from the real physical system that constitutes the vehicle.

The document “Adaptive Control of a Hybrid Powertrain with Map-based ECMS – IFAC Proceedings – Vol 44, Issue 1, 2011, p2949-2954” describes an optimal self-adaptive control strategy applied to hybrid vehicles (electric battery type and fuel cell). The optimal control strategy implements a principle known as the equivalent consumption minimization principle (ECMS principle in English, for “Equivalent Consumption Minimization Strategy”). The optimal control strategy implemented by this ECMS principle consists of considering the vehicle's electric battery as an auxiliary fuel tank, which allows it to choose at each moment the control which minimizes the total energy taken from the two tanks ( the hydrogen tank constituting the main source, the electric battery constituting the auxiliary tank) by the introduction of equivalence factors making it possible to bring the different consumptions into the same energy space. However, such a self-adaptive control strategy does not have the so-called “optimality conditions” equations using the necessary “co-states” or “dual variables”, and cannot be transposed as is into an optimal control process. implementing a PMP model.

There is therefore a need for a solution to at least partially remedy these drawbacks.

• a) détermination d’un domaine de consignes applicables comprenant un ensemble de valeurs pour chaque consigne,
• b) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de tous les gradients d’état possibles pour ladite consigne, en utilisant au moins les équations d’état,
• c) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles pour le niveau de pollution à optimiser, en utilisant au moins l’équation de critère,
• d) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles d’une fonction Hamiltonienne de ladite consigne, en utilisant les valeurs de gradients d’état et les valeurs possibles pour le niveau de pollution à optimiser déterminées pour ladite consigne,
• e) détermination de la valeur de chaque consigne, dans le domaine de consignes applicables déterminé, pour laquelle ladite fonction Hamiltonienne est la plus faible,
• f) calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur du gradient d’état pour ladite consigne, et/ou calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur du niveau de pollution pour ladite consigne,
• g) intégration temporelle de la valeur du gradient d’état calculée, fournissant une valeur d’état estimée, et/ou intégration temporelle de la valeur du niveau de pollution calculée, fournissant une valeur de niveau de pollution globale estimée,
• h) comparaison de la valeur d’état estimée à une valeur d’état mesurée, l’écart entre les deux valeurs fournissant un premier terme de correction, le premier terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul b) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul b), et/ou comparaison de la valeur de niveau de pollution globale estimée à une valeur de niveau de pollution mesurée, l’écart entre les deux valeurs fournissant un second terme de correction, le second terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul c) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul c).

To this end and according to a first aspect, the invention relates to a method, implemented in an on-board computer within a motor vehicle, for optimizing a pollution level of the vehicle, the vehicle comprising a fuel tank or hydrogen, an electric battery and/or supercapacitors capable of supplying electrical energy, a thermal engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at at least one electric machine powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the heat engine or the fuel cell, at least one device relating to the electric machine, and the less a device relating to the electric battery or the super-capacitors, the computer being configured to manage the traction chain of the motor vehicle on a predetermined route and being able to control the thermal engine or the fuel cell, the electric machine and/or or the devices by issuing a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine or fuel cell, the electric machine, and the devices being represented by a system of state equations modeling the dynamics of the vehicle, said state equations being functions at least of the instantaneous setpoint values and state variables, the pollution level to be optimized being represented by a criterion equation depending at least on the instantaneous setpoint values and the state variables, the vehicle further comprising means for measuring the state(s) described by the or each state variable and means for measuring the level of pollution of the vehicle, said method being implemented for a duration divided into constant sampling times, the method comprising, at each sampling time, the steps of:

• a) determination of a domain of applicable instructions comprising a set of values for each instruction,
• b) calculation, for each setpoint and in the determined applicable setpoint domain, of all possible state gradients for said setpoint, using at least the state equations,
• c) calculation, for each setpoint and in the determined applicable setpoint range, of all possible values for the pollution level to be optimized, using at least the criterion equation,
• d) calculation, for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the pollution level to be optimized determined for said instruction,
• e) determination of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• f) calculation, for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation, for each of the setpoints, as a function of said determined value of the setpoint , the value of the pollution level for said setpoint,
• g) temporal integration of the calculated state gradient value, providing an estimated state value, and/or temporal integration of the calculated pollution level value, providing an estimated global pollution level value,
• h) comparison of the estimated state value to a measured state value, the difference between the two values providing a first correction term, the first correction term thus determined being provided as input to the calculation step b ) at the next sampling instant and being used in said calculation step b), and/or comparison of the estimated global pollution level value to a measured pollution level value, the difference between the two values providing a second correction term, the second correction term thus determined being provided as input to the calculation step c) at the next sampling instant and being used in said calculation step c).

• i) détermination d’un domaine de consignes applicables comprenant un ensemble de valeurs pour chaque consigne,
• j) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de tous les gradients d’état possibles pour ladite consigne, en utilisant au moins les équations d’état,
• k) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles pour la consommation en énergie à optimiser, en utilisant au moins l’équation de critère,
• l) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles d’une fonction Hamiltonienne de ladite consigne, en utilisant les valeurs de gradients d’état et les valeurs possibles pour la consommation en énergie à optimiser déterminées pour ladite consigne,
• m) détermination de la valeur de chaque consigne, dans le domaine de consignes applicables déterminé, pour laquelle ladite fonction Hamiltonienne est la plus faible,
• n) calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur du gradient d’état pour ladite consigne, et/ou calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur de la consommation en énergie pour ladite consigne,
• o) intégration temporelle de la valeur du gradient d’état calculée, fournissant une valeur d’état estimée, et/ou intégration temporelle de la valeur de la consommation en énergie calculée, fournissant une valeur de consommation en énergie globale estimée,
• p) comparaison de la valeur d’état estimée à une valeur d’état mesurée, l’écart entre les deux valeurs fournissant un premier terme de correction, le premier terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul j) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul j), et/ou comparaison de la valeur de consommation en énergie globale estimée à une valeur de consommation en énergie mesurée, l’écart entre les deux valeurs fournissant un second terme de correction, le second terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul k) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul k).

According to a second aspect, the invention also relates to a method, implemented in an on-board computer within a motor vehicle, for optimizing the energy consumption of the vehicle, the vehicle comprising a fuel or hydrogen tank , an electric battery and/or supercapacitors capable of supplying electrical energy, a heat engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at least one electric machine powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the heat engine or the fuel cell, at least one device relating to the electric machine, and at least one device relating to the electric battery or to the super-capacitors, the computer being configured to manage the traction chain of the motor vehicle on a predetermined route and being able to control the thermal engine or the fuel cell, the electric machine and/or the devices by the emission of a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the state of operation of the device that it characterizes, the entire heat engine or fuel cell, the electric machine, and the devices being represented by a system of state equations modeling the dynamics of the vehicle, said equations of state state being functions at least of instantaneous setpoint values and state variables, the energy consumption to be optimized being represented by a criterion equation function at least of instantaneous setpoint values and state variables, the vehicle further comprising means for measuring the state(s) described by the or each state variable and means for measuring the energy consumption of the vehicle, said method being implemented for a duration divided into instants of constant sampling, the method comprising, at each sampling instant, the steps of:

• i) determination of a domain of applicable instructions comprising a set of values for each instruction,
• j) calculation, for each setpoint and in the determined applicable setpoint domain, of all possible state gradients for said setpoint, using at least the state equations,
• k) calculation, for each setpoint and in the determined applicable setpoint range, of all possible values for the energy consumption to be optimized, using at least the criterion equation,
• l) calculation, for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the energy consumption to be optimized determined for said instruction,
• m) determination of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• n) calculation, for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation, for each of the setpoints, as a function of said determined value of the setpoint , the value of the energy consumption for said setpoint,
• o) temporal integration of the calculated state gradient value, providing an estimated state value, and/or temporal integration of the calculated energy consumption value, providing an estimated overall energy consumption value,
• p) comparison of the estimated state value to a measured state value, the difference between the two values providing a first correction term, the first correction term thus determined being provided as input to the calculation step j ) at the next sampling instant and being used in said calculation step j), and/or comparison of the estimated overall energy consumption value to a measured energy consumption value, the difference between the two values providing a second correction term, the second correction term thus determined being provided as input to the calculation step k) at the next sampling instant and being used in said calculation step k).

• q) détermination d’un domaine de consignes applicables comprenant un ensemble de valeurs pour chaque consigne,
• r) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de tous les gradients d’état possibles pour ladite consigne, en utilisant au moins les équations,
• s) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles pour l’état de santé à optimiser, en utilisant au moins l’équation de critère,
• t) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles d’une fonction Hamiltonienne de ladite consigne, en utilisant les valeurs de gradients d’état et les valeurs possibles pour l’état de santé à optimiser déterminées pour ladite consigne,
• u) détermination de la valeur de chaque consigne, dans le domaine de consignes applicables déterminé, pour laquelle ladite fonction Hamiltonienne est la plus faible,
• v) calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur du gradient d’état pour ladite consigne, et/ou calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur de l’état de santé pour ladite consigne,
• w) intégration temporelle de la valeur du gradient d’état calculée, fournissant une valeur d’état estimée, et/ou intégration temporelle de la valeur de l’état de santé calculée, fournissant une valeur d’état de santé global estimée,
• x) comparaison de la valeur d’état estimée à une valeur d’état mesurée, l’écart entre les deux valeurs fournissant un premier terme de correction, le premier terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul r) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul r), et/ou comparaison de la valeur d’état de santé global estimée à une valeur d’état de santé mesurée, l’écart entre les deux valeurs fournissant un second terme de correction, le second terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul s) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul s).

According to a third aspect, the invention also relates to a method, implemented in an on-board computer within a motor vehicle, for optimizing a state of health of the vehicle, the vehicle comprising a fuel or fuel tank. hydrogen, an electric battery and/or supercapacitors capable of supplying electrical energy, a heat engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at least one machine electric powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the heat engine or the fuel cell, at least one device relating to the electric machine, and at least one device relating to the electric battery or super-capacitors, the computer being configured to manage the traction chain of the motor vehicle on a predetermined route and being able to control the thermal engine or the fuel cell, the electric machine and/or the devices by issuing a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the state of operation of the device that it characterizes, the entire heat engine or the fuel cell, the electric machine, and the devices being represented by a system of state equations modeling the dynamics of the vehicle, said equations of the state being functions at least of the instantaneous setpoint values and the state variables, the state of health to be optimized being represented by a criterion equation depending at least on the instantaneous setpoint values and the state variables, the vehicle comprising furthermore means for measuring the state(s) described by the or each state variable and means for measuring the state of health of the vehicle, said method being implemented for a duration divided into constant sampling times, the method comprising, at each sampling time, the steps of:

• q) determination of a domain of applicable instructions comprising a set of values for each instruction,
• r) calculation, for each setpoint and in the determined applicable setpoint domain, of all possible state gradients for said setpoint, using at least the equations,
• s) calculation, for each setpoint and in the determined applicable setpoint range, of all possible values for the state of health to be optimized, using at least the criterion equation,
• t) calculation, for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the state of health at optimize determined for said setpoint,
• u) determination of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• v) calculation, for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation, for each of the setpoints, as a function of said determined value of the setpoint , the value of the state of health for said instruction,
• w) temporal integration of the calculated state gradient value, providing an estimated state value, and/or temporal integration of the calculated health state value, providing an estimated global health state value,
• x) comparison of the estimated state value to a measured state value, the difference between the two values providing a first correction term, the first correction term thus determined being provided as input to the calculation step r ) at the next sampling instant and being used in said calculation step r), and/or comparison of the estimated global health status value to a measured health status value, the difference between the two values providing a second correction term, the second correction term thus determined being provided as input to the calculation step s) at the next sampling instant and being used in said calculation step s).

• y) détermination d’un domaine de consignes applicables comprenant un ensemble de valeurs pour chaque consigne,
• z) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de tous les gradients d’état possibles pour ladite consigne, en utilisant au moins les équations d’état,
• aa) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles pour la durée du trajet prédéterminé à optimiser, en utilisant au moins l’équation de critère,
• bb) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles d’une fonction Hamiltonienne de ladite consigne, en utilisant les valeurs de gradients d’état et les valeurs possibles pour la durée du trajet prédéterminé à optimiser déterminées pour ladite consigne,
• cc) détermination de la valeur de chaque consigne, dans le domaine de consignes applicables déterminé, pour laquelle ladite fonction Hamiltonienne est la plus faible,
• dd) calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur du gradient d’état pour ladite consigne, et/ou calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur de la durée du trajet pour ladite consigne,
• ee) intégration temporelle de la valeur du gradient d’état calculée, fournissant une valeur d’état estimée, et/ou intégration temporelle de la valeur de la durée du trajet calculée, fournissant une valeur de durée du trajet globale estimée,
• ff) comparaison de la valeur d’état estimée à une valeur d’état mesurée, l’écart entre les deux valeurs fournissant un premier terme de correction, le premier terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul z) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul z), et/ou comparaison de la valeur de durée du trajet globale estimée à une valeur de durée du trajet mesurée, l’écart entre les deux valeurs fournissant un second terme de correction, le second terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul aa) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul aa).

According to a fourth aspect, the invention also relates to a method, implemented in an on-board computer within a motor vehicle, for optimizing a duration of a predetermined journey of the vehicle, the vehicle comprising a fuel tank or hydrogen, an electric battery and/or supercapacitors capable of supplying electrical energy, a thermal engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at at least one electric machine powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the heat engine or the fuel cell, at least one device relating to the electric machine, and the less a device relating to the electric battery or the super-capacitors, the computer being configured to manage the traction chain of the motor vehicle on a predetermined route and being able to control the thermal engine or the fuel cell, the electric machine and/or or the devices by issuing a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine or fuel cell, the electric machine, and the devices being represented by a system of state equations modeling the dynamics of the vehicle, said state equations being functions of at least the instantaneous setpoint values and the state variables, the duration of the predetermined journey to be optimized being represented by a criterion equation that is a function of at least the instantaneous setpoint values and the state variables, the vehicle further comprising means for measuring the state(s) described by the or each state variable and means for measuring the duration of the predetermined journey of the vehicle, said method being implemented during a duration divided into constant sampling instants, the method comprising, at each sampling instant, the steps of:

• y) determination of a domain of applicable instructions comprising a set of values for each instruction,
• z) calculation, for each setpoint and in the determined applicable setpoint domain, of all possible state gradients for said setpoint, using at least the state equations,
• aa) calculation, for each setpoint and in the determined applicable setpoint range, of all possible values for the duration of the predetermined journey to be optimized, using at least the criterion equation,
• bb) calculation, for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the duration of the predetermined journey at optimize determined for said setpoint,
• cc) determination of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• dd) calculation, for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation, for each of the setpoints, as a function of said determined value of the setpoint , the value of the journey duration for said instruction,
• ee) temporal integration of the calculated state gradient value, providing an estimated state value, and/or temporal integration of the calculated journey duration value, providing an estimated overall journey duration value,
• ff) comparison of the estimated state value to a measured state value, the difference between the two values providing a first correction term, the first correction term thus determined being provided as input to the calculation step z ) at the next sampling instant and being used in said calculation step z), and/or comparison of the estimated global journey duration value to a measured journey duration value, the difference between the two values providing a second correction term, the second correction term thus determined being provided as input to the calculation step aa) at the next sampling instant and being used in said calculation step aa).

• gg) détermination d’un domaine de consignes applicables comprenant un ensemble de valeurs pour chaque consigne,
• hh) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de tous les gradients d’état possibles pour ladite consigne, en utilisant au moins les équations d’état,
• ii) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles pour la combinaison de critères à optimiser, en utilisant au moins l’équation de critère,
• jj) calcul, pour chaque consigne et dans le domaine de consignes applicables déterminé, de toutes les valeurs possibles d’une fonction Hamiltonienne de ladite consigne, en utilisant les valeurs de gradients d’état et les valeurs possibles pour la combinaison de critères à optimiser déterminées pour ladite consigne,
• kk) détermination de la valeur de chaque consigne, dans le domaine de consignes applicables déterminé, pour laquelle ladite fonction Hamiltonienne est la plus faible,
• ll) calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur du gradient d’état pour ladite consigne, et/ou calcul, pour chacune des consignes, en fonction de ladite valeur déterminée de la consigne, de la valeur de chaque critère de la combinaison de critères pour ladite consigne,
• mm) intégration temporelle de la valeur du gradient d’état calculée, fournissant une valeur d’état estimée, et/ou intégration temporelle, pour chaque critère de la combinaison de critères, de la valeur calculée de ce critère, fournissant une valeur de critère globale estimée,
• nn) comparaison de la valeur d’état estimée à une valeur d’état mesurée, l’écart entre les deux valeurs fournissant un premier terme de correction, le premier terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul hh) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul hh), et/ou comparaison, pour chaque critère de la combinaison de critères, de la valeur de critère globale estimée à une valeur de critère mesurée, l’écart entre les deux valeurs fournissant un second terme de correction, le second terme de correction ainsi déterminé étant fourni en entrée de l’étape de calcul ii) à l’instant d’échantillonnage suivant et étant utilisé dans ladite étape de calcul ii).

According to a fifth aspect, the invention also relates to a method, implemented in an on-board computer within a motor vehicle, for optimizing a combination of at least two criteria relating to the vehicle and chosen from the group consisting of in: a level of pollution, energy consumption, a state of health and a duration of a predetermined journey, the vehicle comprising a fuel or hydrogen tank, an electric battery and/or suitable supercapacitors ) to provide electrical energy, a heat engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at least one electric machine powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the thermal engine or the fuel cell, at least one device relating to the electric machine, and at least one device relating to the electric battery or the supercapacitors, the calculator being configured to manage the traction chain of the motor vehicle on said predetermined path and being capable of controlling the heat engine or the fuel cell, the electric machine and/or the devices by issuing a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine or the fuel cell, the electric machine, and devices being represented by a system of state equations modeling the dynamics of the vehicle, said state equations being functions of at least the instantaneous setpoint values and the variables d state, the combination of criteria to be optimized being represented by a criterion equation depending on at least the instantaneous setpoint values and the state variables, the vehicle further comprising means for measuring the state(s) described ) by the or each state variable and means for measuring each of the criteria of the combination of criteria to be optimized, said method being implemented for a duration divided into constant sampling instants, the method comprising, at each instant sampling, the steps of:

• gg) determination of a domain of applicable instructions comprising a set of values for each instruction,
• hh) calculation, for each setpoint and in the determined applicable setpoint domain, of all possible state gradients for said setpoint, using at least the state equations,
• ii) calculation, for each setpoint and in the determined applicable setpoint domain, of all possible values for the combination of criteria to be optimized, using at least the criterion equation,
• jj) calculation, for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the combination of criteria to be optimized determined for said instruction,
• kk) determination of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• ll) calculation, for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation, for each of the setpoints, as a function of said determined value of the setpoint , the value of each criterion of the combination of criteria for said instruction,
• mm) temporal integration of the calculated state gradient value, providing an estimated state value, and/or temporal integration, for each criterion of the combination of criteria, of the calculated value of this criterion, providing a criterion value estimated overall,
• nn) comparison of the estimated state value to a measured state value, the difference between the two values providing a first correction term, the first correction term thus determined being provided as input to the calculation step hh ) at the next sampling instant and being used in said calculation step hh), and/or comparison, for each criterion of the combination of criteria, of the estimated global criterion value to a measured criterion value, the difference between the two values providing a second correction term, the second correction term thus determined being provided as input to the calculation step ii) at the next sampling instant and being used in said calculation step ii).

In each of the processes described above, the steps are looped iteratively at each new sampling instant. Furthermore, each of the methods is implemented for a predefined/given route for the vehicle. Such a journey is for example predicted by a system of the “electronic information horizon” type (or “eHorizon” in English, which is classically based on the ADASIS data format standard – from the English “Advanced Driver-Assistance Systems Interface Specifications” - for predictive driver assistance systems, or any other type of device), which is connected to the vehicle's computer. In a manner known in itself, such an “eHorizon” type system makes it possible to manage both static data relating to the road infrastructure (such as for example the nature of the roads, intersections, regulatory speed limits applied, etc.) , as dynamic data (average speed of vehicles located on the road, traffic density, dynamic data relating to road infrastructure elements, etc.). Such an “eHorizon” type system is capable of receiving this data, decoding it (via a decoder), reconstituting it (via a data reconstructor), and transmitting it to the vehicle’s computer, and can implement Vehicle path prediction algorithms using the notion of “Most Probable Path” in English.

Each of the methods according to the invention makes it possible to define torque settings in order to provide the power and acceleration requested by the driver during operation of the vehicle on the given or planned route, while minimizing the level of pollution, consumption energy and/or the state of health of the vehicle. Furthermore, thanks to the calculation and determination of the first and second correction terms, which are reinjected as input to the first calculation steps via a feedback loop, each of the methods makes it possible to simulate the behavior of the model at each sampling instant. and compare it in real time to the behavior of the physical system considered (the vehicle and its traction chain). Each method uses the direct equations of state of the PMP model in steps to determine the first and second correction terms, in an integrated “observer” strategy. This makes it possible to adapt the model parameters and make them correspond more closely to the real physical system (the vehicle and its powertrain). Such an optimal control strategy by closed loop (and feedback) is therefore self-adaptive, in the sense that the parameters of the model are adjusted at each sampling instant according to the behavior of the physical system, which makes each process according to the invention more precise and/or more stable from the point of view of the optimization control strategy, compared to the methods of the prior art.

Preferably, the steps of calculating, for each of the instructions, the value of the state gradient and the value of the criterion, the steps of temporal integration, and the steps of comparison are all implemented in each of the methods according to the invention. In other words, in each of the methods according to the invention, the first and second correction terms are preferably determined and provided as input to the first calculation steps.

Preferably, the first correction term, respectively the second correction term, is used in the calculation step via a mathematical relation of addition or multiplication with the state equations, respectively with the criterion equation.

Preferably, the or each comparison step is implemented via regulation, by an on-board regulator within the vehicle, of the proportional-integral-derivative regulation type, or via a reestimation of the state variables.

The invention also relates to a computer for managing the traction chain of a motor vehicle on a predetermined route, the vehicle comprising, in addition to the computer, a fuel or hydrogen tank, an electric battery and/or superchargers. -capacities capable of supplying electrical energy, a heat engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at least one electric machine powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the heat engine or the fuel cell, at least one device relating to the electric machine, and at least one device relating to the electric battery or the supercapacitors , the computer being capable of controlling the heat engine or the fuel cell, the electric machine and/or the devices by issuing a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine or the fuel cell, the electric machine , and devices being represented by a system of state equations modeling the dynamics of the vehicle, said state equations being functions of at least instantaneous setpoint values and state variables, the vehicle further comprising means of measurement of the state(s) described by the or each state variable and means for measuring a criterion or a combination of criteria of the vehicle to be optimized, the computer being configured to implement the steps of one of the processes as described above.

The invention also relates to a motor vehicle comprising a fuel or hydrogen tank, an electric battery and/or supercapacitors capable of supplying electrical energy, a thermal engine powered by the fuel tank or a fuel cell powered by the hydrogen tank, at least one electric machine powered by electrical energy supplied by the battery and/or the supercapacitors, at least one device relating to the heat engine or the fuel cell , at least one device relating to the electric battery or super-capacitors, and a computer for managing the traction chain of a motor vehicle on a predetermined route, the computer being capable of controlling the thermal engine or the fuel cell. fuel, the electric machine and/or the devices by issuing a set of instructions, the heat engine or the fuel cell, the electric machine, and the devices each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine or fuel cell, the electric machine, and the devices being represented by a system of equations of state modeling the dynamics of the vehicle, said state equations being functions of at least instantaneous setpoint values and state variables, the vehicle further comprising means for measuring the state(s) described by the or each state variable and means for measuring a criterion or a combination of criteria of the vehicle to be optimized, and a calculator for managing the traction chain as described previously.

The invention also relates to a computer program product distinguished in that it includes a set of program code instructions which, when executed by one or more processors, configure the one or more processors to implement a processes as described above.

We will describe below, by way of non-limiting examples, embodiments of the present invention, with reference to the appended figures in which:
- schematically illustrates a vehicle according to the invention, the vehicle being equipped with an on-board computer; And
- is a flowchart representing a method of optimizing a criterion or a combination of criterion(s) relating to the vehicle, implemented by the computer of the , according to the present invention.

Referring to the the present invention relates to a method, implemented in a computer 4 on board a motor vehicle 2 (visible on the ), optimization of a criterion or a combination of criteria relating to the motor vehicle 2. The motor vehicle 2 is for example (but not limited to) a hybrid vehicle. In addition to the computer 4, such a hybrid vehicle 2 also conventionally includes a thermal engine M (in the case of a hybrid vehicle 2 of the “thermal – electric” type), often called an “ICE” engine for “Internal Combustion Engine”. in English, or a fuel cell P operating for example on hydrogen (in the case of a hybrid vehicle 2 of the “hydrogen – electric” type). Such a hybrid vehicle 2 further comprises at least one electric machine M _E , often called "EMA machine" for "Electrical Machine" in English, a fuel tank or a hydrogen tank (not shown on the ) and a power supply battery 30 (or supercapacitors in a variant not shown).

The motor vehicle 2 also comprises at least one device relating to the thermal engine M and in particular a cooling device 10 of the thermal engine M and a catalyst 20, or at least one device relating to the fuel cell P (such a device not being not shown on the ). The cooling device 10 makes it possible to reduce the temperature of the heat engine M during its use. The cooling device 10 notably comprises a cooling liquid. The catalyst 20, connected to the thermal engine M by the exhaust system, is capable of reducing the quantity of polluting products in the exhaust gases emitted by the thermal engine M before evacuating them outside the vehicle. The catalyst 20 also comprises a heating device capable of increasing the temperature in the catalyst 20, in order to carry out the depollution of the exhaust gases. The catalyst heating device 20 must be supplied with electrical energy to operate. The motor vehicle 2 also comprises at least one device relating to the electrical machine M _E , in particular a set of voltage converters (not shown in the figure). ) making it possible to convert the voltage between the battery 30 and the electric machine M _E. The motor vehicle 2 also comprises at least one device relating to the electric battery 30 (or the supercapacitors), such a device not being shown on the for reasons of clarity.

The thermal engine M is in particular capable of being powered from the fuel supplied by the fuel tank, and the fuel cell P is capable of being powered by the hydrogen tank. The heat engine M also includes a system for exhausting the exhaust gases emitted during the combustion of the mixture of air and fuel in the heat engine M. The electric machine M _E is capable of being powered by electrical energy supplied by battery 30.

The vehicle 2 may include other devices relating to the thermal engine M or the fuel cell P, and other devices relating to the electric machine M _E.

The term “system” refers to all the elements mounted in the vehicle 2, capable of consuming or producing electrical energy, fuel or hydrogen. For example, the system includes all of the devices described above: the thermal engine M or the fuel cell P, the electric machine M _E , the cooling device 10, the catalyst 20 and the battery 30.

Each device is characterized by at least one state variable, making it possible to describe the operating state of the device. For example, the cooling device 10 is characterized by a coolant temperature. For example again, the catalyst 20 is characterized by an internal temperature value. For example, the battery 30 is characterized by a state of charge variable, the heat engine M is characterized by a rotation speed, the fuel cell P is characterized by a temperature or pressures prevailing in the power circuits in hydrogen and oxygen, etc.

The motor vehicle 2 further comprises means for measuring the state(s) described by the or each state variable and means for measuring the criterion or the combination of criteria to be optimized (such means of measurement not being represented on the for reasons of clarity). Preferably, the motor vehicle 2 also includes means for measuring disturbances and/or set points applied to the vehicle 2 (such as for example torque and/or speed set points of the vehicle 2) .

The criterion or combination of criterion(s) to be optimized via the method according to the present invention is for example an energy consumption of the vehicle 2, a level of pollution of the vehicle 2, a state of health of the vehicle 2, or even a combination at least two criteria relating to the vehicle 2 and chosen from the group consisting of: a level of pollution, energy consumption, a state of health and a duration of a predetermined journey. The criterion or combination of criterion(s) to be optimized is represented by a criterion equation g (u,q). The criterion equation g (u,q) is a function of at least the instantaneous setpoint values u and the state variables q. More preferably, the criterion equation is also a function of the disturbances and/or set points w applied to vehicle 2, and is then written g (u,q,w).

The system is represented by a system of state equations f (u,q) modeling the dynamics of vehicle 2. The state equations f (u,q) are functions of at least the instantaneous setpoint values u and the variables of state q. More preferably, the equations of state are also functions of the disturbances and/or set points w applied to vehicle 2, and are then written f (u,q,w).

The computer 4 is for example part of a data processing unit storing an application or computer program capable of cooperating with the computer 4 (the data processing unit and the application or the computer program not being represented on there for reasons of clarity). Alternatively, the application or computer program is stored directly in the computer 4. The computer 4 is connected to the heat engine or the fuel cell P, to the electric machine M_E, to the electric battery 30, as well as to all of the devices described above including in particular the cooling device 10 and the catalyst 20.

The calculator 4 is able to receive the measurement of each state variable value relating to each device. In addition, the computer 4 is able to control each device to which it is connected, by issuing a setpoint, as a function of the value(s) of variables relating to this device.

Thus, for example, the instruction sent to the thermal engine M designates the value of the torque to be applied to the thermal engine M and is in particular a function of the speed of the vehicle and the power request of the driver of the vehicle. For example again, the instruction sent to the fuel cell P designates the power that the fuel cell P is asked to provide and is a function of the state of charge of the battery 30 and/or the supercapacitors, as well as the driver's power demand. For example again, the instruction sent to the electric machine M _E designates the torque to be applied to the electric machine M _E and is a function of the state of charge of the battery 30 and the power demand of the driver.

The instruction issued to the catalyst 20 concerns the temperature in the catalyst 20 and is a function of the temperature measured in the catalyst 20. The instruction issued to the cooling device 10 concerns the temperature of the cooling liquid and is dependent on the measured temperature of the cooling liquid. cooling.

In a particular case, when the criterion to be optimized is the energy consumption of the vehicle 2, the energy consumption to be optimized may be the consumption of an electric current delivered by the branches of an electronic charger supplying the electric battery 30 of the vehicle 2 (such branches being for example electronic branches of a bridge downstream of a rectifier bridge, and each comprising at least one semiconductor member with controllable switching to power the electric battery 30 during its charging phase). Each of the branches of the electronic charger supplies an electric current, the sum of the electric currents supplied by the branches then being represented by an instruction to be sent to the electronic charger.

The calculator 4 is also configured to determine the applicable setpoint range comprising a set of values for each setpoint. The calculator 4 is also configured to implement the principle of the PMP method, in other words the Pontryagin Maximum Principle method, by determining the Hamiltonian function H (x, u∗, λ) from the different setpoint values of the area of applicable instructions. The notation u* is introduced here, which represents the optimal order. Adjoint states λ are also introduced (also called “adjoint parameters”, “Lagrange parameters”, “adjoint vectors”, or even “co-state vectors”). These adjoint states are associated with the state equations which represent the conditions of the dynamic behavior of the physical system, and will allow the complete resolution of the optimization problem.

The computer 4 includes a processor capable of implementing a set of instructions allowing these functions to be carried out.

In reference to the , an embodiment of the method of optimizing a criterion or a combination of criteria of the vehicle 2 according to the invention will now be described, implemented by a computer 4 as described previously. In order to simplify the description, the criterion to be optimized is for example the energy consumption of vehicle 2. Of course any other criterion or combination of criterion(s) relating to vehicle 2 can be optimized in the same way using the process illustrated on the , and in particular a level of pollution of the vehicle 2, a state of health of the vehicle 2, a duration of a predetermined journey, or even a combination of at least two criteria relating to the vehicle 2 and chosen from the group consisting of: a pollution level, energy consumption, state of health and duration of a predetermined journey.

In order to simplify the description, the different variables considered are as follows: the temperature of the coolant of the cooling device 10 and the state of charge of the battery 30 or the supercapacitors. The different control instructions considered are for example: the torque of the thermal engine M and the torque of the electric machine M _E. Other parameters could be considered, concerning the at least one device relating to the electric machine M _E or parameters relating to the fuel cell P or the at least one device relating to the fuel cell P, or even parameters concerning the at least one device relating to the battery 30 or the supercapacitors.

The method is implemented for a duration divided into constant sampling times, and includes steps which are looped iteratively at each new sampling time. The sampling duration between two consecutive sampling instants depends on the dynamics of the system and can be chosen for example between 10 ms and 500 ms, typically equal to 100 ms.

The method comprises an initial step 22 during which the calculator 4 determines a domain of applicable instructions u comprising a set of values for each instruction u to be applied to the variables.

The method then comprises a following step 24 during which the calculator 4 calculates, for each setpoint and in the range of applicable setpointsudetermined, all possible state gradients for said setpoint, using at least the state equations f (u,q,w) describing the system. Preferably, during the calculation step 24, the possible state gradients for each setpoint in the applicable setpoint domainu are calculated using in addition a first correction term K_q, the method of calculation of which will be detailed subsequently by describing the comparison step 40. During the first iteration of the process, the first correction term K_q is initialized to a pre-configured value. The first correction term K_q is for example used in calculation step 24 via a mathematical relation of addition or multiplication with the state equations f (u,q,w).

The method comprises a parallel or following step 26 during which the calculator 4 calculates, for each setpoint and in the domain of applicable setpointsudetermined, all possible values for the criterion or combination of criteria to be optimized, using at least the criterion equation g (u,q,w). Preferably, during the calculation step 26, the possible values for the criterion or the combination of criteria to be optimized for each setpoint in the applicable setpoint domainu are calculated using in addition a second correction term K_g, the method of calculation of which will be detailed subsequently by describing the comparison step 42. During the first iteration of the process, the second correction term K_g is initialized to a pre-configured value. The second correction term K_g is for example used in calculation step 26 via a mathematical relation of addition or multiplication with the criterion equation g (u,q,w). In a variant not shown, the calculation steps 24, 26 can be interchanged.

The method then comprises a following step 28 during which the calculator 4 calculates, for each setpoint and in the applicable setpoint domain u determined, all the possible values of a Hamiltonian function H ( u ,q,λ,w) of said setpoint, using the state gradient values and the possible values for the criterion or the combination of criteria to be optimized determined for this setpoint. To do this, calculator 4 uses the principle of the PMP method, in other words the Pontryagin Maximum Principle method.

The Hamiltonian function H ( u ,q,λ,w) is then expressed as:

with λ ^T the transpose of the adjoint vector λ.

It should be noted that, when the duration of the journey is part of the criterion(s) to be optimized, the Hamiltonian function H ( u ,q,λ,w) is then expressed as:

with ; η then being a weighting coefficient making it possible to weight the duration of the journey.

The calculator 4 then determines, during a following step 31, all of the so-called “optimal” instructions u∗, by minimizing, for each of the instructions, the Hamiltonian function calculated during the previous step 28. In the In the example of embodiment considered, the computer 4 thus determines a first optimal “thermal” setpoint intended to control the heat engine M, and a second optimal “electrical” setpoint intended to control the electric machine M _E.

Preferably, the method then comprises a following step 32 during which the calculator 4 calculates, for each optimal setpoint u*, the value of the state gradient for said setpoint u*, as a function of the value of the setpoint u* determined during step 31.

Preferably, the method comprises a parallel or following step 34 during which the calculator 4 calculates, for each optimal setpoint u*, the value of the or each criterion to be optimized for said setpoint u*, as a function of the value of the setpoint u* determined during step 31. As a variant not shown, the calculation steps 32, 34 can be inverted.

When the method comprises the calculation step 32, the method comprises a following step 36 during which the calculator 4 carries out a temporal integration of the value of the state gradient calculated during the step 32. This step d The temporal integration 36 provides an estimated value of the state vector.

The method then comprises a following step 40 during which the calculator 4 compares the value of the state vector estimated during the previous step 36 to a value of the state vector measured by the measuring means of the motor vehicle 2 . The difference between the two values provides the first correction term K _q . At the end of this comparison step 40, the first correction term K _q is provided as input to the calculation step 24 at the following sampling instant.

When the method comprises the calculation step 34, the method comprises a following step 41 during which the calculator 4 carries out a temporal integration of the value of the or each criterion to be optimized calculated during the step 34. This temporal integration step 41 provides an estimated global (or cumulative) criterion value.

The method then comprises a following step 42 during which the calculator 4 compares, for the or each criterion to be optimized, the overall criterion value estimated during the previous step 41 to a criterion value measured by the measuring means of the motor vehicle 2. The difference between the two values provides the second correction term K _g . At the end of this comparison step 42, the second correction term K _g is provided as input to the calculation step 26 at the following sampling instant.

Preferably, each of the comparison steps 40, 42 is implemented via regulation, by an on-board regulator within the vehicle 2, of the proportional-integral-derivative regulation type, or via a reestimation of the state variables, or by any other feedback type calculation means known to those skilled in the art providing a correction value to be applied at each instant to reduce the error between estimation and measurement.

The process can be repeated while the vehicle is in use.

The method thus makes it possible to obtain setpoint values for which the level of pollution, and/or the energy consumption, and/or the state of health of the vehicle 2, and/or the duration of the journey are minimal.

In addition, the method allows, thanks to the calculation and determination of the first and second correction terms K _q , K _g , which are reinjected as input to the first calculation steps 24, 26 via a feedback loop, to simulate the behavior of the model at each sampling instant and to compare it in real time to the behavior of the physical system (namely the motor vehicle 2). This makes it possible to adapt the model parameters and make them correspond more closely to the real physical system. Such an optimal control strategy by closed loop (and feedback) is therefore self-adaptive, in the sense that the parameters of the model are adjusted at each sampling instant according to the behavior of the physical system, which makes the process according to the invention more precise and/or more stable from the point of view of the optimization control strategy, compared to the methods of the prior art.

• permet de réutiliser le modèle interne PMP connu en soi pour effectuer l’adaptation au système physique réel (calcul des gradients). Seules des intégrations temporelles numériques (et des moyens pour les effectuer) sont ajoutés au sein du calculateur 4 et dans la méthode PMP de l’art antérieur pour réaliser l’auto-adaptation ;
• est flexible et présente en particulier une capacité à sélectionner et/ou ajuster différentes adaptations : sur un ou plusieurs états du modèle interne, et/ou sur un ou plusieurs termes du critère ou de la combinaison de critères à optimiser ;
• utilise mieux les entrées de mesures existantes (d'états courants, et de critères courants), avec une consolidation de la commande par rétroaction de la méthode d'optimisation PMP.

Furthermore, the method according to the invention:

• allows you to reuse the internal PMP model known in itself to carry out the adaptation to the real physical system (calculation of gradients). Only digital temporal integrations (and means for carrying them out) are added within calculator 4 and in the PMP method of the prior art to achieve self-adaptation;
• is flexible and presents in particular a capacity to select and/or adjust different adaptations: on one or more states of the internal model, and/or on one or more terms of the criterion or the combination of criteria to be optimized;
• better uses existing measurement inputs (of current states and current criteria), with a consolidation of the feedback control of the PMP optimization method.

Claims (7)

Method, implemented in a computer (4) on board a motor vehicle (2), for optimizing a criterion relating to the vehicle from the group consisting of: a level of pollution, energy consumption, a state of health and a duration of a predetermined journey, the vehicle (2) comprising a fuel or hydrogen tank, an electric battery and/or supercapacitors (30) capable of supplying energy electric, a thermal engine (M) powered by the fuel tank or a fuel cell (P) powered by the hydrogen tank, at least one electric machine (M_E) powered by electrical energy supplied by the battery and/or the supercapacitors (30), at least one device (10, 20) relating to the thermal engine (M) or the fuel cell (P), to least one device (30) relating to the electric machine (M_E), and at least one device relating to the electric battery or supercapacitors (30), the computer (4) being configured to manage the traction chain of the motor vehicle (2) on a predetermined route and being able to control the thermal engine (M) or the fuel cell (P), the electric machine (M_E) and/or the devices (10, 20, 30) by issuing a set of instructions, the heat engine (M) or the fuel cell (P), the electric machine (M_E), and the devices (10, 20, 30) each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine (M) or the fuel cell (P), the electric machine (M_E), and devices (10, 20, 30) being represented by a system of state equations modeling the dynamics of the vehicle (2), said state equations being functions of at least the instantaneous setpoint values and the variables d state, the criterion to be optimized being represented by a criterion equation depending on at least the instantaneous setpoint values and state variables, the vehicle (2) further comprising means for measuring the state(s) described ( s) by the or each state variable and means for measuring the criterion to be optimized, said method being implemented for a duration divided into constant sampling instants, the method being characterized in that it comprises, at each sampling instant, the steps of:

• a) determination (22) of a domain of applicable instructions comprising a set of values for each instruction,
• b) calculation (24), for each setpoint and in the determined applicable setpoint domain, of all the possible state gradients for said setpoint, using at least the state equations,
• c) calculation (26), for each setpoint and in the determined applicable setpoint domain, of all possible values for the criterion to be optimized, using at least the criterion equation,
• d) calculation (28), for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the criterion to be optimize determined for said setpoint,
• e) determination (31) of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• f) calculation (32), for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation (34), for each of the setpoints, as a function of said determined value of the setpoint, of the value of the criterion to be optimized for said setpoint,
• g) temporal integration (36) of the calculated state gradient value, providing an estimated state value, and/or temporal integration (41), for the criterion to be optimized, of the calculated value of this criterion, providing an estimated global criterion value,
• h) comparison (40) of the estimated state value to a measured state value, the difference between the two values providing a first correction term (K _q ), the first correction term (K _q ) thus determined being provided as input to calculation step b) at the next sampling instant and being used in said calculation step b), and/or comparison (42), of the value of the global criterion estimated at a value of measured criterion, the difference between the two values providing a second correction term (K _g ), the second correction term (K _g ) thus determined being provided as input to the calculation step c) at the instant of next sampling and being used in said calculation step c).

Method according to the preceding claim, making it possible to optimize a combination of at least two criteria relating to the vehicle (2) and chosen from the group consisting of: a level of pollution, energy consumption, a state of health and a duration of a predetermined path, the method being characterized in that it comprises, at each sampling instant, the steps of:

• aa) determination (22) of a domain of applicable instructions comprising a set of values for each instruction,
• bb) calculation (24), for each setpoint and in the determined applicable setpoint domain, of all possible state gradients for said setpoint, using at least the state equations,
• cc) calculation (26), for each setpoint and in the determined applicable setpoint domain, of all the possible values for the combination of criteria to be optimized, using at least the criterion equation,
• dd) calculation (28), for each setpoint and in the determined applicable setpoint domain, of all the possible values of a Hamiltonian function of said setpoint, using the state gradient values and the possible values for the combination of criteria to be optimized determined for said instruction,
• ee) determination (31) of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest,
• ff) calculation (32), for each of the setpoints, as a function of said determined value of the setpoint, of the value of the state gradient for said setpoint, and/or calculation (34), for each of the setpoints, as a function of said determined value of the setpoint, of the value of each criterion of the combination of criteria for said setpoint,
• gg) temporal integration (36) of the calculated state gradient value, providing an estimated state value, and/or temporal integration (41), for each criterion of the combination of criteria, of the calculated value of this criterion , providing an estimated global criterion value,
• hh) comparison (40) of the estimated state value to a measured state value, the difference between the two values providing a first correction term (K _q ), the first correction term (K _q ) thus determined being provided as input to the calculation step bb) at the next sampling instant and being used in said calculation step bb), and/or comparison (42), for each criterion of the combination of criteria, of the global criterion value estimated at a measured criterion value, the difference between the two values providing a second correction term (K _g ), the second correction term (K _g ) thus determined being supplied as input to the step of calculation cc) at the next sampling instant and being used in said calculation step cc).

Method according to any one of the preceding claims, in which the first correction term (K _q ), respectively the second correction term (K _g ), is used in the calculation step (24, 26) via a mathematical relationship addition or multiplication with the state equations, respectively with the criterion equation. Method according to any one of the preceding claims, in which the or each comparison step (40, 42) is implemented via regulation, by an on-board regulator within the vehicle (2), of the proportional-integral regulation type. derived, or via a reestimation of the state variables. Computer (4) for managing the traction chain of a motor vehicle (2) on a predetermined route, the vehicle (2) comprising, in addition to the computer (4), a fuel or hydrogen tank, a battery electric and/or supercapacitors (30) capable of supplying electrical energy, a heat engine (M) powered by the fuel tank or a fuel cell (P) powered by the hydrogen tank , at least one electric machine (M _E ) powered by electrical energy supplied by the battery and/or the supercapacitors (30), at least one device (10, 20) relating to the heat engine (M) or to the fuel cell (P), at least one device (30) relating to the electric machine (M _E ), and at least one device relating to the electric battery or the super-capacitors (30), the calculator (4) being capable of controlling the heat engine (M) or the fuel cell (P), the electric machine (M _E ) and/or the devices (10, 20, 30) by issuing a set of instructions, the engine thermal (M) or the fuel cell (P), the electric machine (M _E ), and the devices (10, 20, 30) each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine (M) or the fuel cell (P), the electric machine (M _E ), and the devices (10, 20, 30 ) being represented by a system of state equations modeling the dynamics of the vehicle (2), said state equations being functions of at least instantaneous setpoint values and state variables, the vehicle (2) further comprising means for measuring the state(s) described by the or each state variable and means for measuring a criterion or a combination of criteria of the vehicle (2) to be optimized, the calculator ( 4) being characterized in that it is configured to implement the steps of the method according to any one of claims 1 to 4. Motor vehicle (2) comprising a fuel or hydrogen tank, an electric battery and/or supercapacitors (30) capable of supplying electrical energy, a thermal engine (M) powered by the tank in fuel or a fuel cell (P) powered by the hydrogen tank, at least one electric machine (M _E ) powered by electrical energy supplied by the battery and/or the super-capacitors (30), at at least one device (10, 20) relating to the heat engine (M) or the fuel cell (P), at least one device (30) relating to the electric machine (M _E ), at least one device relating to the battery electric or super-capacity (30), and a computer (4) for managing the traction chain of a motor vehicle (2) on a predetermined route, the computer (4) being able to control the thermal engine ( M) or the fuel cell (P), the electric machine (M _E ) and/or the devices (10, 20, 30) by issuing a set of instructions, the heat engine (M) or the battery fuel (P), the electric machine (M _E ), and the devices (10, 20, 30) each being characterized by at least one state variable, each state variable making it possible to describe the operating state of the device that it characterizes, the entire heat engine (M) or the fuel cell (P), the electric machine (M _E ), and the devices (10, 20, 30) being represented by a system of state equations modeling the dynamics of the vehicle (2), said state equations being functions of at least instantaneous setpoint values and state variables, the vehicle (2) further comprising means for measuring the one or more state(s) described by the or each state variable and means for measuring a criterion or a combination of criteria of the vehicle (2) to be optimized, and a calculator (4) for managing the traction chain according to claim 5. Computer program product characterized in that it comprises a set of program code instructions which, when executed by one or more processors, configure the processor(s) to implement a method according to any of claims 1 to 4.