METHOD FOR PREDICTING THE SPEED OF A VEHICLE

Abstract

The invention relates to a method (21), implemented in a computer (4) embedded in a vehicle (2), for predicting the speed of the vehicle (2) on a journey between a starting point and an arrival point, the computer (4) being connected to a system (8) for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, the computer (4) being configured to calculate an optimal speed profile of the vehicle (2) according to a predefined calculation method, said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle (2) and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle (2) as well as the duration of the journey, the computer (4) storing one or more recordings of past or recent driving phases of the vehicle (2). Abstract figure: Fig. 1.

Description

Title of the invention: METHOD FOR PREDICTING THE SPEED OF A VEHICLE

[0001] The invention relates to a method for predicting the speed of a vehicle. The method is implemented in a computer embedded within the vehicle. The invention also relates to a method, implemented in a computer embedded within the vehicle, for optimizing the energy consumption of the vehicle, the method comprising such a sub-method for predicting the speed of the vehicle. The invention also relates to a method, implemented in a computer embedded within the vehicle, for controlling the speed of the vehicle, the method comprising such a sub-method for optimizing the energy consumption of the vehicle. The invention also relates to a computer intended to be embedded within a vehicle, the computer comprising means for implementing the steps of such a method for predicting the speed of the vehicle; as well as to a vehicle, in particular a motor vehicle, incorporating such a computer.The vehicle is typically but not limited to an autonomous or semi-autonomous vehicle. The invention finally relates to a computer program product comprising program instructions configured to implement the steps of such a method for predicting the speed of the vehicle.

[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 from the description of the dynamics of the system. The dynamics of the system is defined from the state of different variables of the vehicle (vehicle speed, state of charge of the battery, and / or super-capacitors, temperatures, etc.) and different inputs or instructions (torque instructions to be applied to the vehicle wheels for the thermal engine or for the electrical machine(s), torque instructions to be applied for the generator set engine(s), and / or power or current instructions for the fuel cell, and / or catalyst heating instructions, and / or cooling circuit control instructions. development, 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] 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.

[0005] 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.In this context, the prediction of the vehicle speed by a vehicle computer can be useful and is performed on a given or predicted driving scenario; this scenario can generally be provided by a navigation system (when the destination is known, given by the driver or guessed by a vehicle assistance system) or by an "electronic information horizon" type system (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 on any other type of device), giving information on the predicted journey characteristics "in front of the vehicle", valid for a short, medium or long term time forecast, depending on the available data, the location of the vehicle and the driving conditions.

[0006] When the criterion to be optimized takes into account the energy consumption of the vehicle as well as the duration of the journey, there is a need to be able to have a method for predicting the speed of the vehicle on a journey between a starting point and an arrival point, allowing a temporal forecast of the speed of the vehicle in the short, medium or long term.

[0007] For this purpose, several solutions are known, implementing for example statistical analysis (from speed recordings on observed road segments, moderated by traffic conditions, timetables, etc.); analytical modeling (from certain behavioral models of drivers, vehicle performance, journey characteristics, traffic conditions, etc.); fuzzy logic (modeled from measurements and recordings on recent past driving phases of the driver's activity on the pedals and current speeds, accelerations or decelerations, etc.); artificial intelligence and neural networks (modeled from machine learning phases during repetitive driving tests, simulated or on real roads).

[0008] However, none of these known methods allows a sufficiently correct, precise or stable prediction of the vehicle speed in the medium or long term. In addition, several disadvantages are associated with such methods of the prior art: - loss of precision in unexpected situations or during insufficiently learned phases; - need for a lot of data or complex behavioral models, which can also be specific to each type of driver, each vehicle, each traffic condition, etc.; - requires a long learning process and adaptation time before acquiring the required precision; - may not provide the future power demand, which is necessary for efficient energy saving control; - cost of the solution if it comes from external providers.

[0009] There is therefore a need for a solution that can 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 predicting the speed of the vehicle on a journey between a starting point and an arrival point, the vehicle being further provided with a vehicle navigation and positioning system connected to the computer, the vehicle computer being further connected to a system for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, said static and / or dynamic data relating to the road infrastructure and / or road traffic being provided as input to the computer, the route of the vehicle over a predetermined distance being predefined or predicted within the computer, the computer being configured to calculate an optimal speed profile of the vehicle according to a predefined calculation method,said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle as well as the duration of the journey, the calculator storing one or more recordings of past or recent driving phases of the vehicle, the method comprising the following steps: , - determination, based on one or more recordings of past or recent driving phases of the vehicle, of a driving style of the driver of the vehicle, - definition of a series of notable points on the journey characterized by a stop of the vehicle or a reduction in the speed of the vehicle, said series of points re- markable, dividing the route into a series of sections, - for each portion of the journey, calculating an optimal speed profile of the vehicle according to said predefined calculation method, the duration of the journey being weighted using a weighting coefficient in the Hamiltonian of said predefined calculation method, - adapting said weighting coefficient according to the driving style determined for the driver of the vehicle, said adaptation step providing, for each portion of the journey, an adapted speed profile of the vehicle, - for each portion of the journey, providing said adapted speed profile as a prediction of the speed of the vehicle on said portion of the journey.

[0011] The method 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 driving 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.), and dynamic data (average speed of the vehicles located on the road, traffic density, dynamic data relating to the 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).

[0012] By providing an optimal vehicle speed profile as close as possible to the predictable speed, the method according to the invention makes it possible to obtain a robust prediction of the vehicle speed, requiring no preliminary learning phases, complex behavioral models, or additional measurements of "external" or "specific" data related to the vehicle or the driver. The method according to the invention further makes it possible to efficiently use an existing optimal speed profile calculation solution to obtain an adequate predictive speed profile, directly including the static and dynamic data of an "electronic information horizon" type system, such as speed constraints, road types, traffic, conditions, etc.The method according to the invention also makes it possible to obtain, at the same time, a prediction of the power demand (with torque or current depending on the technology of the powertrain), necessary for a correct energy analysis, optimization and energy savings. Finally, in the method according to the invention, the step of adapting the weighting coefficient according to the driving style determined for the driver of the vehicle is quite fast, stable and precise, and is carried out by means of typical "closed loop" feedback corrections (such as PID - Proportional Integral Derivative correction, dichotomy, trend compensation, etc.), which are able to follow the slow and reasonable dynamic response time of usual variations in driving behavior or traffic conditions.

[0013] Preferably, said predefined calculation method is a calculation method implementing the Pontryagin maximum principle. Such a calculation method is advantageously based on the Pontryagin model, which is self-adaptive and allows real-time implementation for embedded computer systems.

[0014] According to a particular technical characteristic of the invention, the step of determining a driving style of the driver of the vehicle is carried out by comparing, in said one or more recording(s) of past or recent driving phases of the vehicle, an actual speed profile of the vehicle with an optimal speed profile of the vehicle calculated using said predefined calculation method. Such a comparison provides an indirect estimation means for determining the driving style of the driver of the vehicle.

[0015] According to another particular technical characteristic of the invention, the step of adapting the weighting coefficient according to the driving style determined for the driver of the vehicle is carried out according to a scale of continuous or discretized values.

[0016] Preferably, the step of adapting the weighting coefficient according to the driving style determined for the driver of the vehicle is carried out via a closed-loop feedback correction. Such a closed-loop feedback correction is able to follow the slow and reasonable dynamic response time of usual variations in driving behavior or traffic conditions.

[0017] According to a second aspect, the invention also relates to a method, implemented in a computer embedded in a vehicle, for optimizing the energy consumption of the vehicle on a journey between a starting point and an arrival point, the vehicle being further provided with a vehicle navigation and positioning system connected to the computer, the vehicle computer being further connected to a system for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, said static and / or dynamic data relating to the road infrastructure and / or road traffic being provided as input to the computer, the route of the vehicle over a predetermined distance being predefined or predicted within the computer, the computer being configured to calculate an optimal speed profile of the vehicle according to a predefined calculation method, said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle as well as the duration of the journey, the calculator storing one or more recordings of past or recent driving phases of the vehicle, the method further comprising a sub-method for predicting the speed of the vehicle as described above, and a step of taking into account the speed of the vehicle predicted on each portion of the journey, to apply or recommend to the vehicle said predicted speed on said portion of the journey.

[0018] According to a third aspect, the invention also relates to a method, implemented in a computer embedded in a vehicle, for controlling the speed of the vehicle, the vehicle being further provided with a vehicle navigation and positioning system and a powertrain both connected to the computer, the vehicle computer being further connected to a system for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, said static and / or dynamic data relating to the road infrastructure and / or road traffic being provided as input to the computer, the route of the vehicle over a predetermined distance being predefined or predicted within the computer, the computer being configured to calculate an optimal speed profile of the vehicle according to a predefined calculation method,said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle as well as the duration of the journey, the calculator storing one or more recordings of past or recent driving phases of the vehicle, the method further comprising a sub-method for optimizing the energy consumption of the vehicle as described above, and a step of transmitting, to the powertrain of the vehicle, a speed command established as a function of the predicted vehicle speed taken into account. Such a speed command thus established may then concern an autonomous or semi-autonomous vehicle, or an 'intelligent' cruise control, or a driving assistance system by displaying this set speed or by reactions of visual interfaces,auditory or haptic signals addressed to the driver of the vehicle.

[0019] The invention also relates to a computer intended to be embedded in a vehicle, the route of the vehicle over a predetermined distance being predefined or predicted within the computer, the computer being configured to calculate an optimal speed profile of the vehicle according to a predefined calculation method, said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle as well as the duration of a journey, the cal calculator storing one or more recordings of past or recent driving phases of the vehicle, the calculator comprising means for implementing the steps of the method for predicting the speed of the vehicle as described above and / or of the method for optimizing the energy consumption of the vehicle as described above and / or of the method for controlling the speed of the vehicle as described above.

[0020] The invention also relates to a vehicle, in particular a motor vehicle, in which the vehicle has a computer as described above and a vehicle navigation and positioning system connected to the computer, the computer being capable of being connected to a system for managing static and / or dynamic data relating to the road infrastructure and / or road traffic.

[0021] The invention also relates to a computer program product, downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor, the computer program product comprising program instructions, said program instructions being configured to implement the steps of the method for predicting the speed of the vehicle as described above and / or of the method for optimizing the energy consumption of the vehicle as described above and / or of the method for controlling the speed of the vehicle as described above when said instructions are executed on a computer as described above.

[0022] 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 method of controlling the speed of a vehicle, comprising a sub-method of predicting the speed of the vehicle on a path between a starting point and an arrival point, implemented by the computer of [Fig.l], according to the present invention.

[0023] Referring to [Fig. 2] the present invention relates to a method, implemented in a computer 4 embedded in a vehicle 2 (visible in [Fig. 1]), for controlling the speed of the vehicle 2. The vehicle 2 is typically a motor vehicle. The term "motor vehicle" means a road vehicle powered by at least one internal combustion engine, or at least one electric motor or at least one gas turbine such as, for example, a car, a van, a truck, etc., or even a hybrid motor vehicle.

[0024] 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 not shown in [Fig.l] for reasons of clarity). Alternatively, the application or computer program is stored directly in the computer 4. The computer 4 is connected to the powertrain of the vehicle (not shown) and to a system 8 for managing static and / or dynamic data relating to the road infrastructure and / or road traffic. More specifically, the computer 4 is for example connected to the system 8 for managing static and / or dynamic data via wireless communication means 10 embedded within the vehicle 2. The wireless communication means 10 are for example made up of a data transmitter / receiver coupled to an electronic communication card of the SIM card type (from the English “Subscriber Identity Module”). The system 8 for managing static and / or dynamic data is for example configured according to a “cloud” architecture.The management system 8 is for example 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). 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.), and dynamic data (average speed of vehicles located on the road, traffic density, dynamic data relating to the road infrastructure elements 6, 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 computer 4, and implements vehicle path prediction algorithms 2 using the notion of “most probable path or route”.

[0025] The computer 4 is configured to calculate an optimal speed profile of the vehicle 2 according to a predefined calculation method. As will be detailed later, this predefined calculation method (which is for example pre-recorded in the data processing unit comprising the computer 4) minimizes the Hamiltonian of a system of equations modeling the driving of the vehicle and is configured to allow optimization of a criterion taking into account the energy consumption of the vehicle 2 as well as the duration of the journey of the vehicle 2 between a starting point and an arrival point. This predefined calculation method is for example a calculation method implementing the Pontryagin maximum principle.Alternatively, the calculation method may be any other calculation method implementing optimization by road sections, considering different final speed constraints for each road section, free a priori or forced a posteriori to the speed limit values ​​provided by the road infrastructure, and allowing optimization. of a criterion taking into account the energy consumption of vehicle 2 as well as the duration of the journey of vehicle 2 between the starting point and the arrival point.

[0026] The computer 4 stores one or more recordings of past or recent driving phases of the vehicle 2.

[0027] In addition to the computer 4 and the wireless communication means 10, the vehicle 2 also has a system 12 for navigating and positioning the vehicle 2, connected to the computer 4. The navigation and positioning system 12 is for example a GPS system (from the English “Global Positioning System”). The navigation map implemented within the navigation and positioning system 12 is instrumented. Preferably, the vehicle 2 also has a display device 14 (such as a screen for example), connected to the computer 4. According to a particular exemplary embodiment of the invention, the vehicle 2 also includes a cruise control (not shown) provided with means for calculating a set speed and connected to the powertrain of the vehicle 2 on the one hand, and to the computer 4 on the other hand.

[0028] In order to collect the information necessary for managing the optimized speed profile calculation, the data processing unit which embeds the computer 4 is connected to different devices of the vehicle 2.

[0029] The data processing unit is for example connected to a speed measuring device, to a path determination device, and (optionally) to an event detection device (such devices not being shown in the figures for reasons of clarity). It goes without saying that, in another embodiment, the data processing unit can be connected to other devices or equipment (not shown), such as for example devices relating to a heat engine or a fuel cell, or even devices relating to an electric machine.

[0030] Each device is characterized by at least one state variable, making it possible to describe the operating state of the device.

[0031] The vehicle 2 further comprises means for measuring the state(s) described by the or each state variable and means for measuring the criterion to be optimized (such measuring means not being shown in [Fig.l] for reasons of clarity). Preferably, the 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).

[0032] The criterion to be optimized via the predefined calculation method implemented within the computer 4 takes into account the energy consumption of the vehicle 2 as well as the duration of the journey of the vehicle 2 between the starting point and the arrival point. The criterion 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 variables of state q. Preferably again, the criterion equation is also a function of the disturbances and / or the set points w applied to the vehicle 2, and is then written g (u,q,w).

[0033] 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 setpoints w applied to the vehicle 2, and are then written f (u,q,w).

[0034] The computer 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 an instruction, as a function of the value(s) of variables relating to this device.

[0035] 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 a domain of applicable setpoints. 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 state equations which represent the conditions of the dynamic behavior of the physical system, and will allow the complete resolution of the optimization problem.

[0036] The criterion to be optimized taking into account the energy consumption of vehicle 2 as well as the duration of the journey of vehicle 2 between the starting point and the arrival point, the Hamiltonian function H (u,q,X,w) is then expressed as: H ( u, q, X, w ) = [ g ( u, q , w ) ] + + ÂT. [ f ( u, q, w ) ] with XT the transpose of the adjoint vector X, and / 7 > 0; 7 being a weighting coefficient allowing the duration of the journey to be weighted.

[0037] The computer 4 comprises a processor capable of implementing a set of instructions making it possible to carry out these functions.

[0038] As illustrated in [Fig.2], the method for controlling the speed of the vehicle 2 comprises a sub-method 20, implemented in the computer 4 embedded within the vehicle 2, for optimizing the energy consumption of the vehicle 2. This sub-method 20 itself comprises a sub-method 21, implemented in the computer 4 embedded within the vehicle 2, for predicting the speed of the vehicle 2 on a journey between the starting point and the arrival point.

[0039] Initially, the static and / or dynamic data relating to the road infrastructure and / or road traffic and managed by the management system 8 are provided as input to the computer 4 via the management system 8. In addition, the route of the vehicle 2 over a predetermined distance is predefined (e.g. via user input) or predicted (e.g. via the management system 8 which transmits the information to the computer 4) within the computer 4. The computer 4 executes for example the application or the computer program for implementing the method.

[0040] The sub-method 21 comprises an initial step 22 during which the computer 4 determines a driving style of the driver of the vehicle 2, as a function of the one or more recordings of past or recent driving phases of the vehicle 2. Preferably, the step 22 of determining a driving style of the driver of the vehicle 2 is carried out by comparing, in said one or more recordings of past or recent driving phases of the vehicle 2, an actual speed profile of the vehicle 2 with an optimal speed profile of the vehicle 2 calculated using the predefined calculation method. The driving styles determined for the driver of the vehicle 2 can for example be divided between: an “economical” mode, a “sporty” mode and a “normal” mode.

[0041] The sub-method 21 comprises a following step 24 during which the computer 4 defines a series of notable points of the predefined route of the vehicle 2, such notable points characterized by a stop of the vehicle 2 or a reduction in the speed of the vehicle 2. The series of notable points divides the route of the vehicle 2 into a series of portions. Such a series of notable points associated with a route makes it possible to divide the route into a series of portions. The notable points of the route preferably characterize known locations of the route where the vehicle 2 is likely to stop or slow down significantly (for example below 30 km / h or more than 50% of the speed). As non-limiting examples, these notable points may for example be a roundabout, a traffic circle, a speed bump, a change in the speed limit, etc. for predictable data of the static type.

[0042] The sub-method 21 comprises a following step 26 during which the computer 4 calculates an optimal speed profile of the vehicle 2 according to the predefined calculation method. The duration of the journey between the starting point and the arrival point of the vehicle 2 is weighted using the weighting coefficient in the Hamiltonian H (u,q,X,w) of the predefined calculation method.

[0043] The sub-method 21 comprises a following step 28 during which the computer 4 adapts the weighting coefficient according to the driving style determined for the driver of the vehicle 2. For each portion of the journey between the starting point and the arrival point of the vehicle 2, the adaptation step 28 provides an adapted speed profile of the vehicle 2. The impact of the coefficient is explained as follows: - when the value of the weighting coefficient is low, this means that the duration of the journey has little importance, and therefore the minimization of the Hamiltonian is more focused on the energy consumption of vehicle 2, with a lower average speed for vehicle 2 - this may typically correspond to an “economic” mode determined for vehicle 2; - when the value of the weighting coefficient is high, this means that the duration of the journey is of great importance, and therefore the minimization of the Hamiltonian will tend to minimize this duration of the journey, with a higher average speed for vehicle 2 - this can typically correspond to a “sporty” mode determined for vehicle 2; - when the value of the weighting coefficient V is intermediate between “low” and “high”, this leads to a compromise between the energy consumption of vehicle 2 and the duration of the journey - this can typically correspond to a “normal” mode determined for vehicle 2.

[0044] According to one embodiment of the invention, step 28 of adapting the weighting coefficient as a function of the driving style determined for the driver of the vehicle 2 is carried out according to a scale of continuous values. According to another embodiment of the invention, step 28 of adapting the weighting coefficient is carried out according to a scale of discretized values ​​(such a scale being for example the scale described above: value of the weighting coefficient 7 “low”, “intermediate” or “high”).Preferably, step 28 of adapting the weighting coefficient as a function of the driving style determined for the driver of the vehicle 2 is performed via a closed-loop feedback correction (which is typically implemented by a Proportional Integral Derivative regulator, by a dichotomy regulator, or by a trend compensation regulator, or by any other feedback-type computational 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). Step 28 of adapting the weighting coefficient thus allows the actual speed profile of the vehicle 2 to “stick” a posteriori to the optimal speed profile considered a priori as a prediction of this speed of the vehicle 2 calculated using the predefined calculation method.

[0045] The sub-method 21 comprises a following step 30 during which the computer 4 provides, for each portion of the journey between the starting point and the arrival point, the speed profile adapted during step 28. This adapted speed profile is provided as a prediction of the speed of the vehicle 2 on this portion of the journey.

[0046] Once the vehicle 2 has passed the end of the current portion of the journey, the sub-method 21 loops back to step 22 of determining a driving style of the driver of the vehicle 2, and so on until the vehicle 2 reaches the end point of the journey.

[0047] Sub-process 20 for optimizing the energy consumption of the vehicle 2 comprises a following step 32 during which the calculator 4 takes into account the speed of the vehicle 2 predicted on each portion of the journey during step 30, to apply or recommend to the vehicle 2 this predicted speed on the portion of the journey in question.

[0048] The method for controlling the speed of the vehicle 2 comprises a final step 34 during which the computer 4 transmits, to the powertrain of the vehicle 2, a speed command established as a function of the speed of the vehicle 2 predicted during step 30. When the vehicle 2 comprises a cruise control, the speed command is transmitted to this regulator by the computer 4, the cruise control then calculating a set speed established as a function of this speed command. The speed of the vehicle 2 is then regulated according to this set speed (which can vary as a function of the calculated speed command). The vehicle 2 may also be, as a variant, an autonomous or semi-autonomous vehicle, in which case the speed command may be transmitted directly by the computer 4 to the powertrain of the vehicle 2.As a further variant, the method 20 for optimizing the energy consumption of the vehicle 2 can be used to recommend to the driver the optimal speed profile of the vehicle 2 taken into account by the computer 4 (in this case, there is no longer a final step 36). This recommendation is for example carried out via a display of information on the display device 14, such information taking for example the form of a colored (dynamic) graphic recommendation zone, within which the driver is encouraged to position a needle materializing the speed of the vehicle 2 (the position of the needle being controlled by the accelerator pedal), or a haptic pedal (reacting under the foot), or an auditory interface.

[0049] The method for controlling the speed of the vehicle 2 then loops back to step 22 of determining a driving style of the driver of the vehicle 2, in order to be able to follow the changes in the route predictions given over time by the navigation and positioning system 12 of the vehicle 2.

[0050] The method for predicting the vehicle speed according to the invention makes it possible to obtain a robust prediction of the vehicle speed, not requiring preliminary learning phases, complex behavioral models, or additional measurements of "external" or "specific" data related to the vehicle or the driver. The method according to the invention further makes it possible to efficiently use an existing optimal speed profile calculation solution to obtain an adequate predictive speed profile, directly including the static and dynamic data of an "electronic information horizon" type system, such as speed constraints, road types, traffic, conditions, etc. The method according to the invention also makes it possible to concomitantly obtain a prediction of the power demand (with torque or current depending on the powertrain technology), necessary for correct energy analysis, optimization and energy savings. Finally, in the method according to the invention, the step of adapting the weighting coefficient according to the driving style determined for the vehicle driver is quite fast, stable and accurate, and is carried out by means of typical "closed loop" feedback corrections, which are able to follow the slow and reasonable dynamic response time of usual variations in driving behavior or traffic conditions.

Claims

Claims

1. Method (21), implemented in a computer (4) embedded in a vehicle (2), for predicting the speed of the vehicle (2) on a journey between a starting point and an arrival point, the vehicle (2) being further provided with a system (12) for navigation and positioning of the vehicle (2) connected to the computer (4), the computer (4) of the vehicle (2) being further connected to a system (8) for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, said static and / or dynamic data relating to the road infrastructure and / or road traffic being provided as input to the computer (4), the route of the vehicle (2) over a predetermined distance being predefined or predicted within the computer (4), the computer (4) being configured to calculate an optimal speed profile of the vehicle (2) according to a predefined calculation method,said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle (2) and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle (2) as well as the duration of the journey, the calculator (4) storing one or more recording(s) of past or recent driving phases of the vehicle (2), characterized in that the method (21) comprises the following steps:, - determination (22), based on the one or more recordings of past or recent driving phases of the vehicle (2), of a driving style of the driver of the vehicle (2), - definition (24) of a series of notable points of the journey characterized by a stop of the vehicle (2) or a reduction in the speed of the vehicle (2), said series of notable points dividing the journey into a series of portions, - for each portion of the journey, calculation (26) of an optimal speed profile of the vehicle (2) according to said predefined calculation method, the duration of the journey being weighted using a weighting coefficient in the Hamiltonian of said predefined calculation method, - adaptation (28) of said weighting coefficient as a function of the driving style determined for the driver of the vehicle (2), said adaptation step (28) providing, for each portion of the journey, an adapted speed profile of the vehicle (2), - for each portion of the journey, providing (30) said adapted speed profile as a prediction of the speed of the vehicle (2) on said portion of the journey.

2. Method (21) according to claim 1, characterized in that said predefined calculation method is a calculation method implementing the Pontryagin maximum principle.

3. Method (21) according to claim 1 or 2, characterized in that the step (22) of determining a driving style of the driver of the vehicle (2) is carried out by comparing, in said one or more recordings of past or recent driving phases of the vehicle (2), an actual speed profile of the vehicle (2) with an optimal speed profile of the vehicle (2) calculated using said predefined calculation method.

4. Method (21) according to any one of claims 1 to 3, characterized in that the step (28) of adapting the weighting coefficient according to the driving style determined for the driver of the vehicle (2) is carried out according to a scale of continuous or discretized values.

5. Method (21) according to any one of claims 1 to 4, characterized in that the step (28) of adapting the weighting coefficient according to the driving style determined for the driver of the vehicle (2) is carried out via a closed-loop feedback correction.

6. Method (20), implemented in a computer (4) embedded in a vehicle (2), for optimizing the energy consumption of the vehicle (2) on a journey between a starting point and an arrival point, the vehicle (2) being further provided with a system (12) for navigation and positioning of the vehicle (2) connected to the computer (4), the computer (4) of the vehicle (2) being further connected to a system (8) for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, said static and / or dynamic data relating to the road infrastructure and / or road traffic being provided as input to the computer (4), the route of the vehicle (2) over a predetermined distance being predefined or predicted within the computer (4), the computer (4) being configured to calculate an optimal speed profile of the vehicle (2) according to a predefined calculation method, said method of

7.

8. calculation minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle (2) and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle (2) as well as the duration of the journey, the calculator (4) storing one or more recordings of past or recent driving phases of the vehicle (2), characterized in that the method further comprises a sub-method (21) for predicting the speed of the vehicle (2) according to any one of claims 1 to 5, and a step (32) of taking into account the speed of the vehicle (2) predicted on each portion of the journey, to apply or recommend to the vehicle said predicted speed on said portion of the journey. Method, implemented in a computer (4) embedded in a vehicle (2), for controlling the speed of the vehicle (2), the vehicle (2) being further provided with a system (12) for navigation and positioning of the vehicle (2) and a powertrain both connected to the computer (4), the computer (4) of the vehicle (2) being further connected to a system (8) for managing static and / or dynamic data relating to the road infrastructure and / or road traffic, said static and / or dynamic data relating to the road infrastructure and / or road traffic being provided as input to the computer (4), the route of the vehicle (2) over a predetermined distance being predefined or predicted within the computer (4), the computer (4) being configured to calculate an optimal speed profile of the vehicle (2) according to a predefined calculation method,said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle (2) and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle (2) as well as the duration of the journey, the calculator (4) storing one or more recordings of past or recent driving phases of the vehicle (2), characterized in that the method further comprises a sub-method (21) for optimizing the energy consumption of the vehicle according to claim 6, and a step of transmitting (34), to the powertrain of the vehicle (2), a speed command established as a function of the predicted speed of the vehicle (2) taken into account., Calculator (4) intended to be embedded in a vehicle (2), the route of the vehicle (2) over a predetermined distance being predefined or predicted within the calculator (4), the calculator (4) being configured to calculate an optimal speed profile of the vehicle (2) according to a predefined calculation method, said calculation method minimizing the Hamiltonian of a system of equations modeling the driving of the vehicle (2) and being configured to allow optimization of a criterion taking into account the energy consumption of the vehicle (2) as well as the duration of a journey, the computer (4) storing one or more recordings of past or recent driving phases of the vehicle (2), characterized in that the computer (4) comprises means for implementing the steps of the method (21) for predicting the speed of the vehicle (2) according to any one of claims 1 to 5 and / or of the method (20) for optimizing the energy consumption of the vehicle (2) according to claim 6 and / or of the method for controlling the speed of the vehicle (2) according to claim 7.

9. Vehicle (2), in particular a motor vehicle, characterized in that the vehicle (2) has a computer (4) according to claim 8 and a system (12) for navigation and positioning of the vehicle (2) connected to the computer (4), the computer (4) being capable of being connected to a system (8) for managing static and / or dynamic data relating to the road infrastructure and / or road traffic.

10. Computer program product, downloadable from a communication network and / or recorded on a computer-readable medium and / or executable by a processor, characterized in that it comprises program instructions, said program instructions being configured to implement the steps of the method (21) for predicting the speed of the vehicle (2) according to any one of claims 1 to 5 and / or of the method (20) for optimizing the energy consumption of the vehicle (2) according to claim 6 and / or of the method for controlling the speed of the vehicle (2) according to claim 7 when said instructions are executed on a computer (4) according to claim 8.

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