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

Abstract

The invention relates to a method for optimizing a criterion or combination of criteria relating to a motor vehicle (2) comprising a fuel or hydrogen tank, a battery and / or supercapacitors (30), an internal combustion 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 computer (4) configured to manage the powertrain of the motor vehicle (2) over a predetermined route and capable of controlling the internal combustion engine (M) or the fuel cell (P), the electric machine (ME) and / or the devices by issuing 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

Title of the invention: METHOD FOR OPTIMIZING A CRITERION OR A COMBINATION OF CRITERIA RELATING TO A MOTOR VEHICLE

[0001] The present invention relates to the optimization of a criterion or a combination of criteria relating to a motor vehicle, and more particularly to a method for optimizing a criterion or a combination of criteria of a motor vehicle 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 pollution level, an 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 pollution level, an energy consumption, a state of health and a duration of a predetermined journey.By "vehicle energy consumption" is meant, for the purposes of the present invention, a consumption of fuel, hydrogen, electrical energy, electrical current delivered by the branches of an electronic charger of an electric battery of the vehicle, in number of restarts of a generator set in a series hybrid vehicle (also called "range extender" in English), or even a combined consumption of several of these elements. By "health of the vehicle" is meant, for the purposes of the present invention, a percentage which varies 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-capacitors and / or a fuel cell of the vehicle, taking into account the replacement and maintenance costs.

[0002] In a motor vehicle, it is known to optimize the energy consumption of the powertrain over a given or planned journey. 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.

[0003] In a known manner, the optimization can be carried out using the principle known as the Pontryagin Maximum Principle (PMP). This method consists of minimizing the Hamiltonian function (or Hamiltonian) from 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 is defined from the state of different vehicle variables (vehicle speed, state of charge of the battery, and / or super-capacitors, temperatures, etc.) and from dif various inputs or instructions (torque instructions to be applied to the vehicle wheels for the thermal engine or for the electric machine(s), torque instructions to be applied for the generator set motor(s), and / or power or current instructions for the fuel cell, and / or catalyst heating instructions, and / or 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.

[0004] Each input or setpoint 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 of the cooling circuit is dependent on the real-time temperature in the cooling circuit, the heating setpoint of the catalyst is dependent on the real-time temperature in the catalyst.

[0005] 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.

[0006] 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 powertrain), via differential equations which give a representation of the system. Such a PMP model is therefore mainly based on an optimal open-loop 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 the point of view of accuracy, so that the model leads to a correct optimal solution. However, the internal model may differ from the real physical system, in particular due 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 used.

[0007] 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, the deviations from the real physical system that constitutes the vehicle.

[0008] The document “Adaptive Control of a Hybrid Powertrain with Map-based ECMS - IF AC Proceedings - Vol 44, Issue 1, 2011, p2949-2954 » describes a self-adaptive optimal control strategy applied to hybrid vehicles (electric battery and fuel cell type). The optimal control strategy implements a principle known as the Equivalent Consumption Minimization Strategy (ECMS). 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 instant the control that minimizes the total energy taken from the two tanks (the hydrogen tank constituting the main source, the electric battery constituting the auxiliary tank) by introducing equivalence factors allowing the different consumptions to be brought 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 such into an optimal control process implementing a PMP model.

[0009] There is therefore a need for a solution to at least partially remedy these drawbacks.

[0010] To this end and according to a first aspect, the invention relates to a method, implemented in a computer embedded in a motor vehicle, for optimizing a pollution level 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 the supercapacitors,the computer being configured to manage the powertrain of the motor vehicle over a predetermined route 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 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 the state variables, the pollution level to be optimized being represented by a criterion equation, function at least of 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 pollution level 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 range of applicable sets, 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 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 setpoint, - 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 instructions, as a function of said determined value of the instruction, of the value of the state gradient for said instruction, and / or calculation, for each of the instructions, as a function of said determined value of the instruction, of the value of the pollution level for said instruction, - g) time integration of the calculated state gradient value, providing an estimated state value, and / or time integration of the calculated pollution level value, providing an estimated overall pollution level value, - h) comparison of the estimated state value with 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 calculation step b) at the following sampling time and being used in said calculation step b), and / or comparison of the estimated overall pollution level value with 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 calculation step c) at the next sampling time and being used in said calculation step c).

[0011] 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 the supercapacitors,the computer being configured to manage the powertrain of the motor vehicle over a predetermined route 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 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 the state variables, the energy consumption to be optimized being represented by a criterion equation which is a function at least of 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 energy consumption 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: , - 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 range of applicable setpoints, of all possible values ​​for the energy consumption to be optimized, using at least the criterion equation, - 1) calculation, for each instruction and in the applicable instruction domain determined, of all 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 setpoint, - 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, of the value of the energy consumption for said setpoint, - o) time integration of the calculated value of the state gradient, providing an estimated state value, and / or time integration of the calculated value of the energy consumption, providing an estimated overall energy consumption value, - p) comparison of the estimated state value with 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 following sampling time and being used in said calculation step j), and / or comparison of the estimated overall energy consumption value with 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 following sampling time and being used in said calculation step k).

[0012] 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 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 the supercapacitors,the computer being configured to manage the powertrain of the motor vehicle over a predetermined route and being capable of controlling the thermal engine or the fuel cell, the electric machine and / or the devices by issuing a set of instructions, the thermal 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 thermal 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 the state variables, the state of health 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 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 range of applicable sets, 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 possible values ​​of a Hamiltonian function of said setpoint, using the state gradient values ​​and the possible values ​​for the health state to be optimized 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 instructions, as a function of said determined value of the instruction, of the value of the state gradient for said instruction, and / or calculation, for each of the instructions, as a function of said determined value of the instruction, of the value of the state of health for said instruction, - w) time integration of the calculated state gradient value, providing an estimated state value, and / or time integration of the calculated health state value, providing an estimated overall health state value, - x) comparison of the estimated state value with 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 following sampling time and being used in said calculation step r), and / or comparison of the estimated overall health value with 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 following sampling time and being used in said calculation step s).

[0013] 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 or hydrogen tank, an electric battery and / or super-capacitors 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 super-capacitors, 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 super-capacitors,the computer being configured to manage the powertrain of the motor vehicle over a predetermined route 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 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 the state variables,the duration of the predetermined journey to be optimized being represented by a criterion equation which 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 for a duration divided into constant sampling times, the method comprising, at each sampling time, 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 instruction and in the determined range of applicable instructions, 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 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 to be optimized 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) calculating, for each of the setpoints, as a function of said determined value of the setpoint, the value of the state gradient for said setpoint, and / or calculating, for each of the setpoints, as a function of said determined value of the setpoint, the value of the journey time for said setpoint, - ee) time integration of the calculated state gradient value, providing an estimated state value, and / or time integration of the calculated journey time value, providing an estimated overall journey time value, - ff) comparing the estimated state value with 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 following sampling instant and being used in said calculation step z), and / or comparing the estimated overall journey time value with a measured journey time 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 following sampling time and being used in said calculation step aa).,

[0014] According to a fifth aspect, the invention also relates to a method, implemented in a computer embedded in a motor vehicle, for optimizing a combination of at least two criteria relating to the vehicle and chosen from the group consisting of: a pollution level, an 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 super-capacitors 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 super-capacitors, 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 calculator being configured to manage the powertrain of the motor vehicle on said predetermined route and being capable of controlling 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 operating state of the device that it characterizes, the whole of the 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 state equations being functions at least of the instantaneous setpoint values ​​and the state variables, the combination of criteria to be optimized being represented by a criterion equation which is a function at least of 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 times, the method comprising, at each sampling time, 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 range of applicable sets, 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 domain of applicable setpoints, of all 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 setpoint, - kk) determination of the value of each setpoint, in the determined applicable setpoint domain, for which said Hamiltonian function is the lowest, - 11) calculation, for each of the instructions, based on said determined value of the setpoint, of the value of the state gradient for said setpoint, and / or calculation, for each of the setspoints, as a function of said determined value of the setpoint, of the value of each criterion of the combination of criteria for said setpoint, - mm) time integration of the calculated state gradient value, providing an estimated state value, and / or time integration, for each criterion of the combination of criteria, the calculated value of this criterion, providing an estimated overall criterion value, - nn) comparison of the estimated state value with 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 following sampling instant and being used in said calculation step hh), and / or comparison, for each criterion of the combination of criteria, of the estimated overall criterion value with 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 following sampling instant and being used in said calculation step ii).

[0015] In each of the methods described above, the steps are looped iteratively at each new sampling time. In addition, each of the methods is implemented for a predefined / given route for the vehicle. Such a route is for example predicted by an “electronic information horizon” type system (or “eHorizon” in English, which is conventionally based on the ADASIS data format standard – from the English “Advanced Driver-Assistance Systems Interface Specifications” – for predictive driver assistance systems, or on any other type of device), which is connected to the vehicle’s computer. In a manner known per se, 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, the regulatory speed limits applied, etc.), as well as dynamic data (average speed of vehicles 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), reconstructing it (via a data reconstructor), and transmitting it to the vehicle computer, and can implement vehicle route prediction algorithms using the concept of “most probable route or path” (“Most Probable Path” in English).

[0016] Each of the methods according to the invention makes it possible to define torque setpoints 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 pollution level, energy consumption and / or the state of health of the vehicle. In addition, 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 to compare it in real time with the behavior of the system physical system considered (the vehicle and its powertrain). 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 allows the model parameters to be adapted and made to correspond more closely to the real physical system (the vehicle and its powertrain). Such an optimal closed-loop (and feedback) control strategy is therefore self-adaptive, in the sense that the model parameters are adjusted at each sampling instant according to the behavior of the physical system, which makes each method 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.

[0017] Preferably, the steps of calculating, for each of the setpoints, the value of the state gradient and the value of the criterion, the time integration steps, and the comparison steps 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.

[0018] Preferably, the first correction term, respectively the second correction term, is used in the calculation step via a mathematical relationship of addition or multiplication with the state equations, respectively with the criterion equation.

[0019] Preferably, the or each comparison step is implemented via regulation, by a regulator embedded within the vehicle, of the proportional-integral-derivative regulation type, or via a re-estimation of the state variables.

[0020] The invention also relates to a computer for managing the powertrain of a motor vehicle over a predetermined route, the vehicle comprising, in addition to the computer, 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 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, of the machine, electrical, and 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 the 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, the computer being configured to implement the steps of one of the methods as described previously.

[0021] 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 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 battery or the supercapacitors, and a computer for managing the powertrain of a motor vehicle on a predetermined route, 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 thermal 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 the 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 computer for managing the powertrain as described previously.

[0022] The invention also relates to a computer program product which is remarkable in that it comprises a set of program code instructions which, when executed by one or more processors, configure the processor(s) to implement one of the methods as described above.

[0023] Embodiments of the present invention will be described below, by way of non-limiting examples, with reference to the appended figures in which: - [Fig.l] schematically illustrates a vehicle according to the invention, the vehicle being equipped with an on-board computer; and - [Fig.2] is a flowchart representing a process for optimizing a criterion or a combination of criteria relating to the vehicle, implemented by the computer of [Fig.l], according to the present invention.

[0024] Referring to [Fig. 2] the present invention relates to a method, implemented in a computer 4 embedded within a motor vehicle 2 (visible in [Fig. 1]), for optimizing 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 comprises 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 ME, often called “EMA machine” for “Electrical Machine” in English, a fuel tank or a hydrogen tank (not shown in [Fig.l]) and an electric power supply battery 30 (or super-capacitors in a variant not shown).

[0025] The motor vehicle 2 also comprises at least one device relating to the heat engine M and in particular a cooling device 10 for the heat engine M and a catalyst 20, or at least one device relating to the fuel cell P (such a device not being shown in [Fig.l]). The cooling device 10 makes it possible to reduce the temperature of the heat engine M during its use. The cooling device 10 comprises in particular a coolant. The catalyst 20, connected to the heat engine M by the exhaust system, is capable of reducing the quantity of polluting products in the exhaust gases emitted by the heat engine M before discharging them outside the vehicle. The catalyst 20 also comprises a heating device capable of increasing the temperature in the catalyst 20, in order to implement 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 electric machine ME, in particular a set of voltage converters (not shown in [Fig.l]) making it possible to convert the voltage between the battery 30 and the electric machine ME. The motor vehicle 2 also comprises at least one device relating to the electric battery 30 (or to the super-capacitors), such a device not being shown in [Fig.l] for reasons of clarity.

[0026] The heat 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 comprises a system for exhausting the exhaust gases emitted during the combustion of the mixture of air and of fuel in the heat engine M. The electric machine ME is capable of being powered by the electrical energy supplied by the battery 30.

[0027] The vehicle 2 may comprise other devices relating to the thermal engine M or to the fuel cell P, and other devices relating to the electric machine ME.

[0028] 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 comprises all the devices described previously: the heat engine M or the fuel cell P, the electric machine ME, the cooling device 10, the catalyst 20 and the battery 30.

[0029] 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 still, 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 hydrogen and oxygen supply circuits, etc.

[0030] 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 combination of criteria to be optimized (such measuring means not being shown in [Fig. 1] for reasons of clarity). Preferably, the motor vehicle 2 also comprises means for measuring disturbances and / or setpoints applied to the vehicle 2 (such as for example torque and / or speed setpoints of the vehicle 2).

[0031] The criterion or combination of criteria to be optimized via the method according to the present invention is for example an energy consumption of the vehicle 2, a pollution level of the vehicle 2, a state of health of the vehicle 2, or a combination of at least two criteria relating to the vehicle 2 and chosen from the group consisting of: a pollution level, an energy consumption, a state of health and a duration of a predetermined journey. The criterion or combination of criteria to be optimized is represented by a criterion equation g (u,q). The criterion equation g (u,q) is a function at least of the instantaneous setpoint values ​​u and the state variables q. More preferably, the criterion equation is also a function of the disturbances and / or the setpoints w applied to the vehicle 2, and is then written g (u,q,w).

[0032] The system is represented by a system of state equations f (u,q) modeling the dynamics of the vehicle 2. The state equations f (u,q) are functions at least of the instantaneous setpoint values ​​u and the state variables q. More preferably, the state equations are also functions of the disturbances and / or the points of instructions w applied to vehicle 2, and are then written f (u,q,w).

[0033] The computer 4 is for example part of a data processing unit storing a computer application or program capable of cooperating with the computer 4 (the data processing unit and the computer application or program not being shown in [Fig.l] for reasons of clarity). Alternatively, the computer application or program is stored directly in the computer 4. The computer 4 is connected to the heat engine or to the fuel cell P, to the electric machine ME, to the electric battery 30, as well as to all of the devices described previously including in particular the cooling device 10 and the catalyst 20.

[0034] The computer 4 is capable of receiving the measurement of each state variable value relating to each device. In addition, the computer 4 is capable of controlling each device to which it is connected, by issuing an instruction, as a function of the value(s) of variables relating to this device.

[0035] Thus, for example, the instruction sent to the heat engine M designates the value of the torque to be applied to the heat engine M and is in particular a function of the speed of the vehicle and the power demand 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 supply and is a function of the state of charge of the battery 30 and / or the super-capacitors, as well as the power demand of the driver. For example again, the instruction sent to the electric machine ME designates the torque to be applied to the electric machine ME and is a function of the state of charge of the battery 30 and the power demand of the driver.

[0036] The setpoint sent to the catalyst 20 relates to the temperature in the catalyst 20 and is a function of the temperature measured in the catalyst 20. The setpoint sent to the cooling device 10 relates to the temperature of the coolant and is dependent on the measured temperature of the coolant.

[0037] 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 controllable switching semiconductor member for supplying 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.

[0038] The calculator 4 is also configured to determine the setpoint domain applicable including a set of values ​​for each setpoint. The computer 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*, X) from the different setpoint values ​​of the applicable setpoint domain. The notation u* is introduced here, which represents the optimal control. Adjoint states X (also called “adjoint parameters”, “Lagrange parameters”, “adjoint vectors”, or even “co-state vectors”) are also introduced. These adjoint states are associated with the equations of state which represent the conditions of the dynamic behavior of the physical system, and will allow the complete resolution of the optimization problem.

[0039] The computer 4 comprises a processor capable of implementing a set of instructions making it possible to carry out these functions.

[0040] With reference to [Fig. 2], an embodiment of the method for 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 the vehicle 2. Of course, any other criterion or combination of criteria relating to the vehicle 2 can be optimized in the same way using the method illustrated in [Fig. 2], and in particular a pollution level of the vehicle 2, a state of health of the vehicle 2, a duration of a predetermined journey, or a combination of at least two criteria relating to the vehicle 2 and chosen from the group consisting of: a pollution level, an energy consumption, a state of health and a duration of a predetermined journey.

[0041] In order to simplify the description, the different variables considered are the following: the temperature of the coolant of the cooling device 10 and the state of charge of the battery 30 or of the super-capacitors. The different control setpoints considered are for example: the torque of the thermal engine M and the torque of the electric machine ME. Other parameters could be considered, concerning the at least one device relating to the electric machine ME or parameters concerning 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 to the super-capacitors.

[0042] The method is implemented for a duration divided into constant sampling instants, and comprises steps which are looped iteratively at each new sampling instant. 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.

[0043] The method comprises an initial step 22 during which the computer 4 determines a domain of applicable instructions u comprising a set of values ​​for each instruction u to be applied to the variables.

[0044] The method then comprises a following step 24 during which the computer 4 calculates, for each setpoint and in the determined applicable setpoint domain u, all the 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 domain u are calculated by further using a first correction term Kq, the calculation method of which will be detailed later by describing the comparison step 40. During the first iteration of the method, the first correction term Kq is initialized to a pre-parameterized value. The first correction term Kq is for example used in the calculation step 24 via a mathematical addition or multiplication relationship with the state equations f (u,q,w).

[0045] The method comprises a parallel or subsequent step 26 during which the computer 4 calculates, for each setpoint and in the determined applicable setpoint domain u, all the possible values ​​for the criterion or the 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 domain u are calculated by further using a second correction term Kg, the calculation method of which will be detailed later by describing the comparison step 42. During the first iteration of the method, the second correction term Kg is initialized to a pre-parameterized value. The second correction term Kg is for example used in the calculation step 26 via a mathematical addition or multiplication relationship with the criterion equation g (u, q, w).In a variant not shown, the calculation steps 24, 26 can be reversed.

[0046] The method then comprises a following step 28 during which the computer 4 calculates, for each setpoint and in the determined domain of applicable setpoints u, all the possible values ​​of a Hamiltonian function H (u,q,X,w) of said setpoint, using the state gradient values ​​and the possible values ​​for the criterion or combination of criteria to be optimized determined for this setpoint. To do this, the computer 4 uses the principle of the PMP method, in other words the Pontryagin Maximum Principle method.

[0047] The Hamiltonian function H (u,q,X,w) is then expressed as: H(u,q,To,w) = [g(u,q,w)+^]+[f(u,q.w)+

[0048] with XT the transpose of the adjoint vector X.

[0049] 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,X,w) is then expressed as: H. (u, q f A, w) = [g (u, q, w) + / Ç] + [f (u, q, w) +

[0050] with i] > 0; q then being a weighting coefficient allowing the duration of the journey to be weighted.

[0051] The computer 4 then determines, during a following step 31, the set of so-called “optimal” setpoints u*, by minimizing, for each of the setpoints, the Hamiltonian function calculated during the previous step 28. In the embodiment considered, the computer 4 thus determines a first optimal “thermal” setpoint intended to control the thermal engine M, and a second optimal “electrical” setpoint intended to control the electrical machine ME.

[0052] Preferably, the method then comprises a following step 32 during which the computer 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.

[0053] 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. In a variant not shown, the calculation steps 32, 34 can be reversed.

[0054] When the method comprises the calculation step 32, the method comprises a following step 36 during which the calculator 4 carries out a time integration of the value of the state gradient calculated during the step 32. This time integration step 36 provides an estimated value of the state vector.

[0055] The method then comprises a following step 40 during which the computer 4 compares the value of the state vector estimated during the previous step 36 with 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 Kq. At the end of this comparison step 40, the first correction term Kq is provided as input to the calculation step 24 at the following sampling instant.

[0056] When the method comprises the calculation step 34, the method comprises a following step 41 during which the calculator 4 carries out a time integration of the value of the or each criterion to be optimized calculated during the step 34. This time integration step 41 provides an estimated global (or cumulative) criterion value.

[0057] The method then comprises a following step 42 during which the computer 4 compares, for the or each criterion to be optimized, the overall criterion value estimated during the previous step 41 with a criterion value measured by the measuring means of the motor vehicle 2. The difference between the two values ​​provides the second correction term Kg. At the end of this comparison step 42, the second correction term Kg is provided as input to the calculation step 26 at the following sampling instant.

[0058] Preferably, each of the comparison steps 40, 42 is implemented via regulation, by a regulator on board the vehicle 2, of the proportional-integral-derivative regulation type, or via a re-estimation 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.

[0059] The method can be repeated during use of the vehicle.

[0060] The method thus makes it possible to obtain setpoint values ​​for which the pollution level, and / or the energy consumption, and / or the state of health of the vehicle 2, and / or the journey time are minimal.

[0061] Furthermore, the method makes it possible, thanks to the calculation and determination of the first and second correction terms Kq, Kg, 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 with the behavior of the physical system (namely the motor vehicle 2). This makes it possible to adapt the parameters of the model and to make them correspond more closely to the real physical system. Such an optimal closed-loop (and feedback) control strategy is therefore self-adaptive, in the sense that the parameters of the model are adjusted at each sampling instant as a function of the behavior of the physical system, which makes the method 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.

[0062] Furthermore, the method according to the invention: • allows the internal PMP model known per se to be reused to perform the adaptation to the real physical system (calculation of gradients). Only digital time integrations (and means to perform them) are added within the calculator 4 and in the PMP method of the prior art to perform the self-adaptation; • is flexible and in particular has the 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 combination of criteria to be optimized; makes better use of existing measurement inputs (of current states, and current criteria), with consolidation of the feedback control of the PMP optimization method.

Claims

Claims

1. Method, implemented in a computer (4) embedded in a motor vehicle (2), for optimizing a criterion relating to the vehicle from the group consisting of: a pollution level, an 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 super-capacitors (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 (ME) powered by electrical energy supplied by the battery and / or the super-capacitors (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 (ME), and at least one device relating to the electric battery or to the super-capacitors (30),the computer (4) being configured to manage the powertrain of the motor vehicle (2) over a predetermined route and being capable of controlling the heat engine (M) or the fuel cell (P), the electric machine (ME) 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 (ME), 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 (ME), 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 at least of the instantaneous setpoint values ​​and the state variables,the criterion to be optimized being represented by a criterion equation which is a function of at least the instantaneous setpoint values ​​and the state variables, the vehicle (2) further comprising means for measuring the state(s) described 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 times, the method being characterized in that it comprises, at each sampling time, the steps of:, - a) determination (22) of a domain of applicable instructions comprising a set of values ​​for each setpoint, b) 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, c) calculation (26), for each instruction and in the determined range of applicable instructions, 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 range, of all possible values ​​of a Hamiltonian function of said setpoint, using the state gradient values ​​and the possible values ​​for the criterion to be optimized determined for said setpoint, e) determination (31) of the value of each setpoint, in the determined applicable setpoint range, 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) time integration (36) of the calculated state gradient value, providing an estimated state value, and / or time 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 with a measured state value, the difference between the two values ​​providing a first correction term (Kq), the first correction term (Kq) thus determined being provided as input to the calculation step b) at the following sampling instant and being used in said calculation step b), and / or comparison (42) of the estimated global criterion value with a measured criterion value, the difference between the two values ​​providing a second correction term (Kg), the second correction term (Kg) thus determined being provided as input to the calculation step c) at the next sampling time and being used in said calculation step c).

2. 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 pollution level, energy consumption, a state of health and a duration of a predetermined journey, 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 instruction and in the determined range of applicable instructions, of all 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 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 setpoint, - 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 instructions, as a function of said determined value of the instruction, of the value of the state gradient for said instruction, and / or calculation (34), for each of the instructions, as a function of said determined value of the instruction, of the value of each criterion of the combination of criteria for said instruction, - gg) time integration (36) of the calculated state gradient value, providing an estimated state value, and / or time integration (41), for each criterion of the com- combination of criteria, of the calculated value of this criterion, providing an estimated global criterion value, - hh) comparison (40) of the estimated state value with a measured state value, the difference between the two values ​​providing a first correction term (Kq), the first correction term (Kq) thus determined being provided as input to the calculation step bb) at the following sampling instant and being used in said calculation step bb), and / or comparison (42), for each criterion of the combination of criteria, of the estimated global criterion value with a measured criterion value, the difference between the two values ​​providing a second correction term (Kg), the second correction term (Kg) thus determined being provided as input to the calculation step cc) at the following sampling instant and being used in said calculation step cc).

3. Method according to any one of the preceding claims, wherein the first correction term (Kq), respectively the second correction term (Kg), is used in the calculation step (24, 26) via a mathematical relationship of addition or multiplication with the state equations, respectively with the criterion equation.

4. Method according to any one of the preceding claims, in which the or each comparison step (40, 42) is implemented via regulation, by a regulator on board the vehicle (2), of the proportional-integral-derivative regulation type, or via a re-estimation of the state variables.

5. Computer (4) for managing the powertrain of a motor vehicle (2) over a predetermined route, the vehicle (2) comprising, in addition to the computer (4), a fuel or hydrogen tank, an electric battery 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 (ME) 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 (ME), and at least one device relating to the electric battery

6. or super-capacitors (30), the computer (4) being able to control the heat engine (M) or the fuel cell (P), the electric machine (ME) 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 (ME), 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 whole of the heat engine (M) or the fuel cell (P), the electric machine (ME), 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 at least of the instantaneous setpoint values ​​and the 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 computer (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 heat engine (M) powered by the fuel tank or a fuel cell (P) powered by the hydrogen tank, at least one electric machine (ME) 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 (ME), at least one device relating to the electric battery or to the supercapacitors (30), and a computer (4) for managing the powertrain of a motor vehicle (2) on a predetermined route, the computer (4) being capable of controlling the heat engine (M) or the fuel cell (P), the electric machine (ME) and / or the devices (10, 20,30) by the emission of a set of instructions, the heat engine (M) or the fuel cell (P), the electric machine (ME), 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 whole of the heat engine (M) or the fuel cell (P), the electric machine (ME), and the dis-, positive (10, 20, 30) being represented by a system of state equations modeling the dynamics of the vehicle (2), said state equations being functions at least of the instantaneous setpoint values ​​and the 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, and a computer (4) for managing the powertrain according to claim 5.

7. A 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 one of claims 1 to 4.