Method and device for controlling the acceleration of an electric vehicle

Abstract

The present invention relates to a method and device for controlling the acceleration of an electric vehicle. To this end, first data representing the speed of the electric vehicle and second data representing the environment in which the electric vehicle is traveling are received (51). A value representing a risk level for driving in the environment is predicted (52) by feeding a risk level prediction model with the first and second data. A set of acceleration control parameters is determined (53) as a function of a typical driving mode of the electric vehicle and the value. The acceleration of the electric vehicle is controlled (54) as a function of the set of acceleration control parameters. Figure for the abstract: Figure 5

Description

Title of the invention: Method and device for controlling the acceleration of an electric vehicle technical field

[0001] The present invention relates to methods and devices for controlling the acceleration of an electric vehicle, in particular but not exclusively a motor vehicle. The present invention also relates to a method and device for predicting the level of traffic risk in the electric vehicle's environment. The present invention further relates to a method and device for controlling a set of onboard vehicle systems to adapt the electric vehicle's driving mode to the level of risk. Technological background

[0002] Modern vehicles, particularly electric vehicles, are equipped with a system configured to select a driving mode from among several vehicle driving modes, such as economy, sport, or normal. The control or operating parameters of vehicle components, such as the engine or transmission, are modified according to the driving mode chosen by the driver; for example, acceleration is more rapid in sport mode than in economy mode.

[0003] When a driving mode is selected by the driver, that driving mode remains in effect until the driver selects another. However, this can be dangerous for the vehicle's occupants as well as for other road users.

[0004] In addition, driving modes are generally defined during the design of vehicles, these driving modes are therefore not adapted to all the life situations encountered by the vehicle. Summary of the present invention

[0005] One object of the present invention is to solve at least one of the problems of the technological background described above.

[0006] Another object of the present invention is to improve the control of the driving mode of an electric vehicle.

[0007] According to a first aspect, the present invention relates to a method for controlling the acceleration of an electric vehicle, the electric vehicle traveling in an environment according to a normal driving mode, the method being implemented by at least one processor embedded in the electric vehicle, the method comprising the following steps: - receiving initial data representing the speed of the electric vehicle and second data representing the environment from a set of devices on board the electric vehicle; - prediction of a representative value of a traffic risk level in the environment from a risk level prediction model fed by the first and second data; - determination of a set of acceleration control parameters for the electric vehicle based on the current driving mode and value; and - Electric vehicle acceleration control based on the set of acceleration control parameters.

[0008] Such a method makes it possible to determine a risk level associated with the environment in which an electric vehicle is operating. Using this risk level to determine a set of parameters for controlling the vehicle's acceleration according to the vehicle's current driving mode allows the acceleration control, as defined by the current driving mode, to be adapted to the risk associated with the environment. This makes it possible to adapt the behavior intended for each driving mode of the electric vehicle to the environment being traversed, for example, by modulating the acceleration according to the determined risk level.

[0009] This makes it possible to improve the safety of the occupants of the vehicle and of road users by avoiding, for example, excessively strong accelerations planned by a driving mode when the latter is not adapted to the current context of the environment in which the electric vehicle is operating (for example, a high density of users on the road).

[0010] According to one variant, the method further includes a step of comparing the value to a threshold value, determining the set of acceleration control parameters of the electric vehicle including an adjustment of default acceleration control parameters associated with the current driving mode when the value is greater than the threshold value.

[0011] According to yet another variant, the second data includes at least part of the following data: - representative data on the presence of objects detected in the environment; and / or - representative data on the density of objects detected in the environment; and / or - representative data of a type of environment; and / or - representative data on the curvature of road sections in the environment; and / or - representative data of events in the environment.

[0012] According to another variant, the acceleration control of the electric vehicle is further a function of third data representative of an acceleration command.

[0013] According to a further variant, the acceleration control of the electric vehicle includes control of an electric motor of the electric vehicle and / or control of a transmission of the electric vehicle.

[0014] According to yet another variant, the method further includes a step of rendering information representative of the adjustment intended for at least one occupant of the electric vehicle.

[0015] According to an additional variant, the prediction model implements a set of determined rules for predicting the value.

[0016] According to a second aspect, the present invention relates to an acceleration control device for an electric vehicle, the device comprising a memory associated with a processor configured for the implementation of the steps of the process according to the first aspect of the present invention.

[0017] According to a third aspect, the present invention relates to an electric vehicle, for example of the automobile type, comprising a device as described above according to the second aspect of the present invention.

[0018] According to a fourth aspect, the present invention relates to a computer program which includes instructions adapted for carrying out the steps of the process according to the first aspect of the present invention, in particular when the computer program is executed by at least one processor.

[0019] Such a computer program may use any programming language, and be in the form of source code, object code, or an intermediate form between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0020] According to a fifth aspect, the present invention relates to a computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the process according to the first aspect of the present invention.

[0021] On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium can include a storage means, such as a ROM, a CD-ROM or a microelectronic circuit-type ROM, or a magnetic recording means or a hard disk drive.

[0022] On the other hand, this recording medium may also be a transmissible medium such as an electrical or optical signal, such a signal being able to be transmitted via an electrical or optical cable, by conventional or Hertzian radio, by self-directing laser beam, or by other means. The computer program according to the present The invention can, in particular, be downloaded onto an Internet-type network.

[0023] Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the process in question. Brief description of the figures

[0024] Other features and advantages of the present invention will become apparent from the description of the particular and non-limiting embodiments of the present invention below, with reference to the attached Figures 1 to 5, in which:

[0025] [Fig.1] schematically illustrates an environment in which an electric vehicle is circulating, according to a particular and non-limiting embodiment of the present invention;

[0026] [Fig.2] illustrates a flowchart of the different operations of a process of acceleration control of the electric vehicle of [Fig.1], according to a first particular and non-limiting embodiment of the present invention;

[0027] [Fig.3] illustrates a set of acceleration profiles for a driving mode of the electric vehicle of the [Fig.1], according to a first particular and non-limiting embodiment of the present invention;

[0028] [Fig.4] schematically illustrates a device configured for control acceleration of the electric vehicle of the [Fig.1], according to a particular and non-limiting embodiment of the present invention;

[0029] [Fig. 5] illustrates a flowchart of the different stages of a control process acceleration of the electric vehicle of [Fig.1], according to a particular and non-limiting embodiment of the present invention. Description of examples of achievements

[0030] A method and device for controlling the acceleration of an electric vehicle will now be described in what follows with joint reference to Figures 1 to 5. The same elements are identified with the same reference signs throughout the description that follows.

[0031] The terms "first," "second" (or "firsts," "seconds"), etc., are used in this document by arbitrary convention to allow for the identification and distinction of different elements (such as operations, means, etc.) implemented in the embodiments described below. Such elements may be distinct or correspond to a single element, depending on the embodiment.

[0032] According to a particular and non-limiting embodiment of the present invention, the acceleration control of an electric vehicle is, for example, implemented by one or more vehicle computers, for example via one or more processors. The acceleration control advantageously includes the determination of a value or a score representing the level of risk associated with the environment in which the electric vehicle is operating. Such a value is determined or predicted by feeding a pre-generated predictive model with initial data representing the speed of the electric vehicle and with secondary data representing the environment in which the electric vehicle is operating (for example, the type of environment, the presence of objects such as other vehicles, pedestrians, bicycles, or the number or density of these objects). The initial and secondary data are received or obtained from devices onboard the electric vehicle (for example, one or more cameras, one or more radars or lidars, a navigation system, an odometer, a wireless communication interface connected to the cloud, etc.).The value representing the risk level is used to determine a set of vehicle acceleration control parameters (e.g., electric motor and / or vehicle transmission response parameters to an acceleration command) associated with a current driving mode, for example, by adjusting the default control parameters associated with the electric vehicle's current driving mode. Finally, the electric vehicle's acceleration is controlled according to the determined set of acceleration control parameters.

[0033] Fig. 1 schematically illustrates an environment 1 in which an electric vehicle 10 evolves, according to a particular and non-limiting embodiment of the present invention.

[0034] Vehicle 10 corresponds to an electric vehicle comprising one or more electric motors powered by a traction battery. Electric vehicle 10 thus corresponds, for example, to a land vehicle, such as a car, a truck, a bus, or a utility vehicle.

[0035] The vehicle 10 advantageously incorporates a set of devices or systems configured to obtain data relating to the environment 1 in which the electric vehicle 10 operates. The environment 1 corresponds to a road environment whose type or nature changes as the electric vehicle 10 moves.

[0036] The road environment corresponds for example to an urban type environment, a motorway type environment, a rural type environment (with national, departmental and / or municipal roads) or any other type of road environment.

[0037] The set of devices or systems for obtaining environmental data comprises one or more of the following devices or systems, in any possible combination: - one or more 110 cameras forming, for example, a 360° acquisition system with a 110 front camera, a rear camera, a right side camera and a left-side camera; and / or - one or more object detection sensors 102 (vehicle, motorcycle, bicycle, pedestrian, scooter, etc.) such as radars and / or lidars spatially arranged on the vehicle 10 so as to detect any object present around the vehicle 10 within the detection field of each sensor; and / or - a navigation system or a navigation and geolocation system, also called a GNSS system ("Geolocation and Navigation by a Satellite System"), configured to determine on which section of road the electric vehicle 10 is traveling, based on the geographical position of the electric vehicle 10 obtained from a geolocation system such as the GPS system (from the English "Global Positioning System" or in French "Système de géo-positionnement par satellites") or Galileo, and mapping data of the environment, the mapping data being for example stored in a memory of the electric vehicle 10 and / or obtained from a remote device such as a server 111 of the "cloud" 100, the mapping data including for example information on the environment 1 traversed, such as the type of environment, geometric information on the sections of road (for example the curvature),the presence of specific infrastructure (school, hospital, pedestrian crossing, etc.); and / or, - a wireless communication device or interface for communicating via a wireless connection with one or more remote server-type devices, such as the 111 server, for example via a wireless communication network infrastructure including 110 antennas or roadside units (RSUs): such a wireless communication interface is configured to obtain environmental data 1 transmitted via a wireless communication mode by one or more servers, this data including, for example, information on the presence of accidents, roadworks, meteorological information (rain, wind, ice, snow, fog, etc.), this information being associated with geolocation data allowing the geographical location of these events to be known.

[0038] The electric vehicle 10 advantageously also includes a device or system configured to determine at any time the current speed (for example in km / h or m / s) of the electric vehicle 10, such a system corresponding for example to an odometer.

[0039] The computers controlling the devices or systems described above form a multiplexed architecture for providing various services useful for the proper functioning of the electric vehicle 10. The computers communicate and exchange data with each other via one or more computer buses, for example a CAN data bus (of (from the English "Controller Area Network" or in French "Réseau de contrôles"), CAN FD (from the English "Controller Area Network Flexible Data-Rate" or in French "Réseau de contrôlers à débit de données flexible"), FlexRay (according to the ISO 17458 standard), LIN (from the English "Local Interconnect Network" or in French "Réseau interconnecté local") or Ethernet (according to the ISO / IEC 802-3 standard).

[0040] The electric vehicle 10 also includes, for example, a set of driver assistance systems, known as AD AS systems (from the English "Advanced Driver-Assistance System" or in French "Système d'aide à la conduite avancé").

[0041] The electric vehicle 10 is advantageously configured to operate according to a driving mode selected from a set comprising several driving modes, for example determined or defined in a development or design phase of the electric vehicle 10 or in a development or design phase of a driving mode control system.

[0042] The set of driving modes includes, for example, the following driving modes, or a part of the following modes: - sporty mode; - aggressive mode; - economic mode; - standard mode; - calm mode.

[0043] A set of control parameters for the components or systems of the electric vehicle is associated with each mode. This set of parameters includes, for example, acceleration control parameters in response to an acceleration command when the driver presses the accelerator pedal of the electric vehicle 10. The acceleration control parameters include, for example, parameters for controlling the electric motor or parameters for controlling a transmission of the electric vehicle to adapt the received acceleration command to the driving mode selected by the driver. Thus, for the same acceleration command received from the accelerator pedal, the acceleration will be faster or more aggressive in sport mode than in economy or standard mode. The response to the same acceleration command therefore varies depending on the driving mode selected.

[0044] The acceleration control parameters associated with each driving mode correspond to so-called default or factory parameters, i.e. parameters defined in a design phase of the driving mode control system.

[0045] According to one variant, control parameters of other organs or systems of the vehicle are associated with each mode, for example control parameters of the shock absorber system of the electric vehicle 10, control parameters of the steering control system, etc.

[0046] A process for controlling the acceleration of the electric vehicle 10 is advantageously implemented by one or more devices embedded in the vehicle 10, for example by one or more computers of the vehicle 10's embedded network. Examples of implementation of such a process are described with regard to [Fig.2] below.

[0047] Figure 2 illustrates a flowchart of the different operations of a process for controlling the acceleration of the electric vehicle 10, according to particular and non-limiting examples of embodiments of the present invention.

[0048] A current driving mode is selected and active while the electric vehicle is traveling in environment 1. The current driving mode corresponds, for example, to a driving mode selected automatically and by default when starting the electric vehicle 10 or to a driving mode selected from a list of driving modes available for the electric vehicle 10 by the driver via a control means of a human-machine interface (HMI).

[0049] In an operation 201, first data representing the speed of the electric vehicle 10 and second data representing the environment are received from a set of devices on board the electric vehicle 10.

[0050] The assembly of devices comprises all or part of the following devices or systems: - one or more cameras; and / or - one or more object detection sensors (vehicle, motorcycle, bicycle, pedestrian, bicycle, scooter, animals, etc.) such as radars and / or lidars spatially arranged on the electric vehicle 10; and / or - a navigation system or a navigation and geolocation system, also called a GNSS system ("Geolocation and Navigation by a Satellite System; and / or - a wireless communication device or interface to communicate via a wireless connection with one or more remote server-type devices, such as the 111 server.

[0051] The second set of data includes all or part of the following data: - representative data on the presence of objects detected in the environment, this data being obtained from the camera(s) and / or radar(s) and / or lidar(s) of the electric vehicle 10; and / or - representative data on the density of objects detected in the environment, for example the density or number of objects present around the vehicle, this data being determined, for example, from object detection data by the camera(s) and / or radar(s) and / or lidar(s) of the electric vehicle 10; and / or - representative data of a type of environment (for example, environment urban or non-urban environment, the urban environment being such as city center, residential area, parking area, area with school, area with hospital and the non-urban environment being such as motorway, expressway, or other (national, departmental, municipal roads), this data being obtained for example from mapping data controlled by the navigation system; and / or - representative data of the curvature of road sections in the environment, this data being obtained for example from mapping data controlled by the navigation system; and / or - representative data of events (accidents, works, meteorological event, etc.) in the environment, this data being obtained from the wireless communication interface (for example the telecommunication unit of the electric vehicle 10, called TCU (from the English “Telematic Control Unit” or in French “Unité de Contrôle Télématique”)) which connects the vehicle 10 to the “cloud” 100 according to a wireless communication mode.

[0052] In an operation 202, a value (or score) representing the traffic risk level in environment 1 is predicted by feeding one or more prediction models with the first and second data. The prediction model(s) 203 include, for example, a set of rules generated to determine the level (represented by a value between 0 and 1 or by a value corresponding to a non-zero positive integer, the risk increasing with the value), these rules or the parameters of the model(s) having been defined in a learning or design phase of the system for determining the risk level associated with traffic in a given environment at a given speed.

[0053] These rules or model parameters are for example stored in a memory of the computer implementing the process.

[0054] Some examples of rules for predicting a risk level and the associated value are provided as illustrative examples in the list below, this list not being exhaustive: - Rule 1: the density of moving objects detected by the vehicle's cameras 10 for each 120° sector within a distance between 30 m and 150 m is greater than or equal to 1 AND the type of environment corresponds to one of the following types: city center, residential area or parking area, the associated value being 1 when the conditions of rule 1 are met; - Rule 2: the density of moving objects detected by the vehicle's cameras 10 for each 90° sector within a distance between 30 m and 150 m is greater than or equal to 2 AND the type of environment corresponds to one of the following types: city center, residential area or parking area, the associated value being 2 when the conditions of rule 2 are met; - Rule 3: the density of moving objects detected by the vehicle's cameras 10 for each 60° sector within a distance of 25 m to 60 m is greater than or equal to 3 AND the density of moving objects detected by the vehicle's radars 10 for each 90° sector within a distance of 15 m to 30 m is greater than or equal to 2 AND the type of environment corresponds to one of the following types: city center, school, residential area, parking area or area with roads with low curvatures (i.e. less than a specified threshold), the associated value being 3 when the conditions of rule 3 are met; - Rule 4: the density of moving objects detected by the vehicle's cameras 10 for each 45° sector within a distance between 6 m and 30 m is greater than or equal to 5 AND the density of moving objects detected by the vehicle's radars 10 for each 45° sector within a distance between 3 m and 15 m is greater than or equal to 3 AND the type of environment corresponds to one of the following types: city center, school, residential area, parking area or area with roads with low curvatures (i.e. less than a specified threshold), the associated value being 4 when the conditions of rule 4 are met.

[0055] According to another example, the rules and / or prediction models are learned according to any machine learning method in a learning phase from training data, the learning method being supervised or unsupervised.

[0056] In an optional operation 204, the value is compared to a determined threshold value, which corresponds to a fixed or adjustable parameter of the electric vehicle acceleration control system 10.

[0057] The threshold value is the same for all driving modes available for the electric vehicle 10. According to another example, the threshold value depends on the current driving mode. According to this other example, the threshold value is, for example, equal to 1 for sport mode, 2 for standard mode, and 3 for economy or calm mode.

[0058] When the result of the comparison indicates that the value is greater than the threshold value, the process continues with operation 205.

[0059] When the result of the comparison indicates that the value is less than the threshold value, the process loops back with operation 201.

[0060] In an operation 205, a set of acceleration control parameters for the electric vehicle 10 is determined as a function of the current driving mode and the value predicted in operation 202.

[0061] The acceleration control parameter set is obtained, for example, from a register or memory 206 of the electric vehicle 10. The acceleration control parameter set is selected, for example, from a lookup table, called LUT (from the English "Look-Up Table"), which associates a determined set of acceleration control parameters for each input torque current driving mode / representative value of a risk level.

[0062] According to one embodiment, the acceleration control parameter set is determined by adjusting the default acceleration control parameters of the current driving mode according to the value predicted at operation 202.

[0063] Thus, the higher the level of risk, the more the response to the acceleration command received from the accelerator pedal is dampened over time to reduce the acceleration.

[0064] When the level of risk increases, reducing the intensity of the acceleration allows for a more gradual acceleration over time, even if the acceleration command obtained from the accelerator pedal is of high intensity.

[0065] Figure 3 illustrates a diagram representing different acceleration response profiles, each associated with a risk level value. The x-axis represents, for example, time (in seconds) and the y-axis represents velocity (in m / s).

[0066] Response profile 30 represents the acceleration obtained for a given acceleration command with the default acceleration control parameters associated with the current driving mode. Response profile 31 represents the acceleration obtained for the given acceleration command with the acceleration control parameters associated with the current driving mode and a risk level of 1. Response profile 32 represents the acceleration obtained for the given acceleration command with the acceleration control parameters associated with the current driving mode and a risk level of 2. Response profile 33 represents the acceleration obtained for the given acceleration command with the acceleration control parameters associated with the current driving mode and a risk level of 3.Response profile 34 represents the acceleration obtained for the given acceleration command with the set of acceleration control parameters associated with the current driving mode and a risk level of value 4.

[0067] As shown in [Fig. 3], the higher the risk level, the less rapid and significant the acceleration for a given driving mode. The maximum acceleration value is lower the higher the risk level, and the increase in acceleration is slower and smaller the higher the risk level.

[0068] The acceleration control parameter set includes, for example, control parameters for the electric motor of the electric vehicle 10 and, optionally, control parameters for the transmission of the electric vehicle 10.

[0069] In an optional additional operation 207, representative information adjustments to the current driving mode, i.e., adjustments to the response to the acceleration command, are made to at least one occupant of the electric vehicle.

[0070] The rendering includes, for example, controlling the display of graphic content on a screen of the electric vehicle to display a message and / or a pictogram on the screen warning the occupants, for example the driver, that the behavior of the electric vehicle 10 is automatically adapted to the environment 1, in particular in terms of acceleration.

[0071] According to one variant, the rendering includes the generation and broadcasting of sound content via the speakers of the electric vehicle 10.

[0072] In an operation 208, the acceleration of the electric vehicle 10 is controlled according to the set of acceleration control parameters determined in operation 205. The acceleration is controlled on the basis of an acceleration profile as represented in [Fig.3] upon receipt of third data representative of an acceleration command (this third data being received for example from a computer controlling the accelerator pedal of the electric vehicle 10).

[0073] The current driving mode is adjusted according to the level of risk represented by the value predicted at operation 202.

[0074] The process then loops back to operation 201 to obtain new first and second data to determine whether it is still necessary to adjust the current driving mode.

[0075] When the risk level falls below the threshold (when operation 204 is implemented), the acceleration control parameters then correspond to the default parameters associated with the current driving mode.

[0076] When operation 204 is not implemented, the acceleration control parameters correspond to the default parameters associated with the current driving mode when the predicted risk level is equal to a determined value, for example equal to 1.

[0077] Content is rendered, for example, to inform the driver that the current driving mode has been restored to its default operation.

[0078] According to a particular embodiment, the implementation of the current driving mode adjustment is inhibited at any time on command or request from the driver, for example by receiving data representative of a command to inhibit the current driving mode adjustment function (the command being required via a voice command or via a press of a physical or virtual button of an HMI).

[0079] Figure 4 schematically illustrates a device 4 configured for the acceleration control of a vehicle, for example an electric vehicle 10, according to an example of a particular and non-limiting embodiment of the present invention. Device 4 corresponds, for example, to a device embedded in the electric vehicle 10, for example a computer.

[0080] Device 4 is, for example, configured to carry out the operations described opposite Figures 1 to 3 and / or the steps of the process described opposite [Fig. 5]. Examples of such a device 4 include, but are not limited to, embedded electronic equipment such as a vehicle's on-board computer or an electronic control unit such as an ECU (Electronic Control Unit) or a TCU. The elements of device 4, individually or in combination, may be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. Device 4 may be implemented in the form of electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules.

[0081] The device 4 comprises one (or more) processor(s) 40 configured to execute instructions for carrying out the steps of the process and / or for executing instructions from the software embedded in the device 4. The processor 40 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. The device 4 further comprises at least one memory 41, for example, volatile and / or non-volatile memory, and / or includes a memory storage device that may include volatile and / or non-volatile memory, such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic disk, or optical disk.

[0082] The computer code of the embedded software(s), including the instructions to be loaded and executed by the processor, is for example stored on memory 4L

[0083] According to various particular and non-limiting embodiments, the device 4 is coupled in communication with other similar devices or systems (for example the computer controlling the electric motor and / or the computer controlling the transmission) and / or with communication devices, for example a TCU (from the English "Telematic Control Unit" or in French "Unité de Contrôle Télématique"), for example via a communication bus or through dedicated input / output ports.

[0084] According to a particular and non-limiting embodiment, the device 4 includes a block 42 of interface elements for communicating with external devices. The interface elements of block 42 include one or more of the following interfaces: - radio frequency (RF) interface, for example Wi-Fi® type (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth® type (according to IEEE 802.15.1), in the 2.4 GHz frequency band, or Sigfox type using a UBN radio technology (from the English "Ultra Narrow Band", in French "bande ultra étroite"), or LoRa in the 868 MHz frequency band, LTE (from the English "Long-Term Evolution" or in French "Evolution à long terme"), LTE-Advanced (or in French LTE-avancé); - USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus" in French); - HDMI interface (from the English "High Definition Multimedia Interface", or "High Definition Multimedia Interface" in French); - LIN interface (from the English "Local Interconnect Network", or in French "Réseau interconnecté local").

[0085] According to another particular and non-limiting embodiment, the device 4 includes a communication interface 43 which allows communication to be established with other devices (such as other computers in the embedded system) via a communication channel 430. The communication interface 43 corresponds, for example, to a transmitter configured to transmit and receive information and / or data via the communication channel 430. The communication interface 43 corresponds, for example, to a wired network of the CAN (Controller Area Network), CAN FD (Controller Area Network Flexible Data-Rate), FlexRay (standardized by ISO 17458) or Ethernet (standardized by ISO / IEC 802-3) type.

[0086] According to a particular and non-limiting embodiment, the device 4 can provide output signals to one or more external devices, such as a display screen 440, touch or not, one or more speakers 450 and / or other peripherals 460 (projection system) via output interfaces 44, 45 and 46 respectively. According to a variant, one or more of the external devices is integrated into the device 4.

[0087] Figure 5 illustrates a flowchart of the different steps of a method for controlling the acceleration of an electric vehicle, for example, the electric vehicle 10, according to a particular and non-limiting embodiment of the present invention. The method is implemented, for example, by a device embedded in the electric vehicle 10 or by the device 4 of Figure 4.

[0088] In a first step 51, first representative speed data of the electric vehicle and second representative environmental data are received from a set of devices on board the electric vehicle.

[0089] In a second step 52, a representative value of a traffic risk level in the environment is predicted from a risk level prediction model fed by the first and second data.

[0090] In a third step 53, a set of acceleration control parameters for the electric vehicle is determined as a function of the current driving mode and value.

[0091] In a fourth step 54, the acceleration of the electric vehicle is controlled according to the set of acceleration control parameters.

[0092] According to one variant, the variants and examples of the operations described in relation to one of Figures 1 to 3 apply to the steps of the process in [Fig. 5].

Claims

Demands

1. A method for controlling the acceleration of an electric vehicle (10), said electric vehicle operating in an environment (1) according to a typical driving mode, said method being implemented by at least one processor embedded in said electric vehicle (10), said method comprising the following steps: - receiving (51) first data representing the speed of said electric vehicle (10) and second data representing said environment (1) from a set of devices (101, 102) embedded in said electric vehicle (10); - predicting (52) a value representing a risk level for driving in said environment (1) from a risk level prediction model fed by said first and second data; - determining (53) a set of acceleration control parameters for the electric vehicle (10) as a function of said typical driving mode and said value;and - acceleration control (54) of said electric vehicle (10) as a function of said acceleration control parameter set.;

2. A method according to claim 1, further comprising a step of comparing said value to a threshold value, the determination of said set of acceleration control parameters of the electric vehicle (10) comprising an adjustment of default acceleration control parameters associated with said current driving mode when said value is greater than said threshold value.

3. A method according to claim 1 or 2, wherein said second data comprise at least some of the following data: - data representing the presence of objects detected in said environment (1); and / or - data representing the density of objects detected in said environment (1); and / or - data representing a type of environment (1); and / or - data representing the curvature of road segments of said environment (1); and / or - data representing events in said environment (1).

4. A method according to any one of claims 1 to 3, wherein said control the acceleration of the electric vehicle (10) is also a function of third data representative of an acceleration command.

5. A method according to any one of claims 1 to 4, wherein said acceleration control of the electric vehicle (10) comprises control of an electric motor of said electric vehicle (10) and / or control of a transmission of said electric vehicle (10).

6. A method according to any one of claims 1 to 5, further comprising a step of rendering information representative of an adjustment of the current driving mode to at least one occupant of said electric vehicle (10).

7. A method according to any one of claims 1 to 6, wherein said prediction model implements a set of determined rules for predicting said value.

8. A computer program comprising instructions for carrying out the method according to any one of the preceding claims, when such instructions are executed by a processor.

9. Device (4) for controlling the acceleration of an electric vehicle, said device (4) comprising a memory (41) associated with at least one processor (40) configured for carrying out the steps of the method according to any one of claims 1 to 7.

10. Electric vehicle (10) comprising the device (4) according to claim 9.

Images