Estimation of electric vehicle battery degradation based on federated learning of a model predicting battery charge level decrease per vehicle trip

Abstract

The present invention relates to a method and system for estimating the battery degradation of an electric vehicle in a first set of electric vehicles, based on an actual decrease in battery charge level following journeys made by said vehicles and on an estimated decrease in battery charge level per journey obtained (32) from a global model predicting the decrease in battery charge level per journey obtained (31) from local models predicting the decrease in battery charge level per journey trained (310, 311) by electric vehicle devices based on data collected by said electric vehicles following journeys made by said electric vehicles. Figure for the abstract: Figure 4

Description

Title of the invention: Estimation of electric vehicle battery degradation based on federated learning of a model predicting the decrease in battery charge level per vehicle trip technical field

[0001] The present invention relates to estimating the battery degradation of an electric vehicle. In particular, the present invention relates to a method and a system for estimating the battery degradation of an electric vehicle based on model learning to predict the decrease in the battery charge level of an electric vehicle per trip. Technological background

[0002] Electric vehicles comprising a battery are equipped with a battery management system that monitors the battery's state of charge and its health status using signals from the battery. The battery health status provided by these management systems is a first method used in the prior art to estimate the degradation of an electric vehicle battery.

[0003] Another means is to transmit data relating to electric vehicles, data relating to their battery and data relating to the driving of these electric vehicles to a central device, such as a server, so that this central device trains a neural network dedicated to estimating battery degradation of these electric vehicles.

[0004] One of the problems of centralizing data collected by electric vehicles for the purpose of training a neural network in a centralized manner is transmitting this data to the central device because this data is private and the users of these electric vehicles do not want it to be transmitted over a communication network.

[0005] Communication network congestion problems may also appear when the number of electric vehicles is significant.

[0006] Summary of the present invention

[0007] One object of the present invention is to solve at least one of the problems of the technological background described above.

[0008] Another object of the present invention is to provide an estimate of electric vehicle battery degradation.

[0009] Another object of the present invention is to prevent electric vehicle battery failures.

[0010] According to a first aspect, the present invention relates to a method for degrading the battery of an electric vehicle from a first set of electric vehicles in communication with a central device, said central device also being in communication with electric vehicles from a second set of electric vehicles, said method comprising the following steps: - emission, by the central device and to each electric vehicle of the first and second sets of electric vehicles, of a global model for predicting the decrease in battery charge level per trip obtained from local models for predicting the decrease in battery charge level per trip driven by devices of electric vehicles of the second set of electric vehicles, each local model for predicting the decrease in battery charge level per trip being obtained from data collected by an electric vehicle of the second set of electric vehicles following trips made by said electric vehicle of the second set of electric vehicles and from actual decreases in battery charge level of said electric vehicle of the second set of electric vehicles after said trips; - following a journey made by an electric vehicle from the first set of electric vehicles, obtaining, by the device of said electric vehicle from the first set of electric vehicles, an estimate of a decrease in battery charge level per journey from the global model for predicting a decrease in battery charge level per journey; - calculation, by the device of said electric vehicle of the first set of electric vehicles, of an actual decrease in battery charge level following said journey made by said electric vehicle of the first set of electric vehicles; - obtaining, by the device of said electric vehicle of the first set of electric vehicles, an estimate of battery degradation of said electric vehicle of the first set of electric vehicles by calculating a difference between the estimated decrease in battery charge level per journey and the actual decrease in battery charge level.

[0011] The process makes it possible to preserve the anonymity of the data collected by an electric vehicle because it avoids the transmission of data relating to an electric vehicle and data relating to a journey made by the electric vehicle via a communication network.

[0012] The method makes it possible to conserve bandwidth of the communication network used because only parameters of models predicting level decrease of Battery charge per trip is transmitted between electric vehicles and a central device.

[0013] The method is advantageous because it uses the resources of each electric vehicle to train local models for predicting battery charge level decrease. The central device simply determines a global model for predicting battery charge level decrease per trip from said local models for predicting battery charge level decrease.

[0014] The method allows for an update of local models for predicting the decrease in battery charge level by receiving parameters from the global model for predicting the decrease in battery charge level received, but also following a journey made by an electric vehicle which can then launch a local learning phase in order to determine a new local model for predicting the decrease in battery charge level.

[0015] The method is advantageous because it allows for the determination of an update of the global model for predicting the decrease in battery charge level based on the local model(s) for predicting the decrease in battery charge level driven by a new electric vehicle from the second set of electric vehicles.

[0016] The process is advantageous because it allows control of the battery degradation of an electric vehicle as soon as this vehicle is added to the first set of electric vehicles.

[0017] According to one variant, the transmission, by the central device and to each electric vehicle of the first and second sets of electric vehicles, of the overall model for predicting the decrease in battery charge level per trip comprises the following steps: a) transmission, by the central device, of parameters of an initial local model predicting the decrease in battery charge level per trip to the device of each electric vehicle in the first and second sets of electric vehicles; b) learning at least one local model for predicting battery charge level decrease per trip by the device of at least one electric vehicle from the second set of electric vehicles, each local model for predicting battery charge level decrease per trip being trained by an electric vehicle from the second set of electric vehicles using data collected by said electric vehicle from the second set of electric vehicles and using actual decreases in battery charge level of said electric vehicle from the second set of electric vehicles following a number predetermined journeys made by said electric vehicle of the second set of electric vehicles; (c) emission of the parameters of said at least one local model for predicting battery charge level decrease per driven trip to the central device, the parameters of each local model for predicting battery charge level decrease per driven trip are emitted by the device of the electric vehicle of the second set of electric vehicles which trained said local model for predicting battery charge level decrease per driven trip; d) determination, by the central device, of the parameters of the global model for predicting the decrease in battery charge level per trip from the parameters of said at least one local model for predicting the decrease in battery charge level per trip received; e) emission, by the central device and to the device of each electric vehicle of the first and second sets of electric vehicles, of the parameters of the global model for predicting the decrease in battery charge level per trip obtained and steps b), c) d), and e) are iterated.

[0018] According to one variant, steps b), c), d), and e) are iterated until a stopping condition is met.

[0019] According to one variant, the parameters of the global model for predicting the decrease in battery charge level per trip are determined from the parameters of said at least one local model for predicting the decrease in battery charge level per trip according to a method of averaging the parameters of said at least one local model for predicting the decrease in battery charge level per trip.

[0020] According to one variant, the device of an electric vehicle of the second set drives a local model of prediction of decrease in battery charge level per trip per initial battery charge level of said electric vehicle of the second set of electric vehicles and the central device obtains as many global models of prediction of decrease in battery charge level per trip as there are initial battery charge levels.

[0021] According to one variant, the parameters of a global model for predicting the decrease in battery charge level per trip for a given initial charge level are obtained from the parameters of at least one local model for predicting the decrease in battery charge level per trip corresponding to said initial charge level by means of a method of averaging the parameters of said at least one local model for predicting the decrease in battery charge level per trip corresponding to said initial charge level.

[0022] According to a second aspect, the present invention relates to a device comprising a memory associated with a processor configured for the implementation of at least one step of the process according to the first aspect of the present invention.

[0023] 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.

[0024] 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.

[0025] Such a computer program may use any programming language, and be in the form of source code, object code, or an intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

[0026] 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.

[0027] 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.

[0028] On the other hand, this recording medium can 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 radio frequency, by self-directing laser beam, or by other means. The computer program according to the present invention can, in particular, be downloaded from an Internet-type network.

[0029] 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.

[0030] According to a sixth aspect, the present invention relates to a battery degradation estimation system for an electric vehicle in a first set of electric vehicles, comprising a central device, said central device being in communication with electric vehicles in the first set of electric vehicles and electric vehicles in a second set of electric vehicles, characterized in that said central device implements at least one step of the method according to the first aspect of the present invention and each vehicle electric of the first and second sets of electric vehicles is an electric vehicle according to the third aspect of the present invention. Brief description of the figures

[0031] 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 4, in which:

[0032] [Fig-1] schematically illustrates a system (1) for estimating degradation of electric vehicle battery of a first set of electric vehicles, according to a particular and non-limiting embodiment of the present invention;

[0033] [Fig.2] schematically illustrates a device embedded in electric vehicles of the [Fig.1], according to a particular and non-limiting embodiment of the present invention;

[0034] [Fig.3] schematically illustrates a central device, according to a particular and non-limiting embodiment of the present invention;

[0035] [Fig.4] illustrates a flowchart of the different stages of a process for estimating the degradation of an electric vehicle battery in a first set of electric vehicles, according to a particular and non-limiting embodiment of the present invention.

[0036] Description of examples of implementation

[0037] A method and device for estimating battery degradation of an electric vehicle of a first set of electric vehicles in communication with a central device will now be described in what follows with joint reference to Figures 1 to 4. The same elements are identified with the same reference signs throughout the description that follows.

[0038] [Fig.1] schematically illustrates a system 1 for estimating the degradation of an electric vehicle battery of a first set of electric vehicles of a first set 10 of electric vehicles 101, 102, ..., 101, according to a particular and non-limiting embodiment of the present invention.

[0039] System 1 comprises a first set 10 of electric vehicles and a second set 11 of electric vehicles 1 Ij.

[0040] The first set 10 of electric vehicles lOi may be a subset of a set of electric vehicles of the same brand, same model and using the same batteries.

[0041] For example, the first set 10 of electric vehicles lOi consists of the oldest electric vehicles (more than 6 months old) from the set of electric vehicles of the same brand, same model and using the same batteries.

[0042] The other electric vehicles in the set of electric vehicles of the same brand, same model and using the same batteries, i.e. the most recent electric vehicles (less than 6 months old) form the second set 11 of electric vehicles 1 Ij.

[0043] The first 10 and second 11 sets of electric vehicles are dynamic, i.e. new electric vehicles can be added to the second set 11 and electric vehicles 11j older than 6 months disappear from the second set 11 and are added to the first set 10 of electric vehicles 10i.

[0044] It is subsequently assumed that most of the batteries of the electric vehicles in the second set 11 function correctly and that if one of them is defective it is then considered as an aberration.

[0045] Each electric vehicle lOi and lOj includes a device 2 implementing part of the steps of the electric vehicle battery degradation estimation method 2 of the first assembly 10.

[0046] System 1 also includes a central device 14.

[0047] The central device 14 is located at a distance from the device 2 installed in each vehicle electrical lOi and 1 Ij. The central device 14 communicates with a device 2 by means of communication such as an interface element of a block 303 or a communication interface 304 of the central device 14 of [Fig.3] and an interface element of a block 203 or a communication interface 204 of the device 2 of [Fig.2],

[0048] Figure 1 illustrates the example of devices 2 communicating with the central device 14 via a relay antenna 12 of a communication network infrastructure 13. The various elements of the communication network infrastructure used to route a message between the antenna 12 and the central device 14 are illustrated here by a cloud. The communications between the central device 14 and a device 2 are bidirectional, meaning that devices 14 and 2, as well as the elements of the communication network infrastructure 13, are adapted so that the central device 14 can transmit information to a device 2 (downward communication) and receive information transmitted by a device 2 (upward communication).

[0049] [Fig.2] schematically illustrates a device 2 embedded in vehicles lOi and 1 Ij of [Fig. 1], according to a particular and non-limiting embodiment of the present invention. Examples of such a device 2 include, but are not limited to, various electronic devices such as a smartphone, a tablet, a laptop computer, and electronic equipment embedded in an electric vehicle l0i or l0j. The elements of the device Device 2, individually or in combination, may be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. Device 2 may be implemented as electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules. Device 2 comprises one (or more) processor(s) 201 configured for executing the instructions of the software embedded in Device 2. The processor 201 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art. Device 2 further comprises at least one memory 202, either volatile and / or non-volatile memory, and / or 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.

[0050] The computer code including the instructions to be loaded and executed by the processor is for example stored on the memory or the memory storage device 202.

[0051] According to a particular and non-limiting embodiment, the device 2 comprises a block 203 of interface elements for communicating with external devices, for example the remote device 14 or a relay antenna 12. The interface elements of the block 203 comprise one or more of the following interfaces:

[0052] RF radio frequency interface, for example of the Bluetooth® or Wi-Fi® type;

[0053] USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus") (in French);

[0054] HDMI interface (from the English "High Definition Multimedia Interface", or "High Definition Multimedia Interface" in French); - RFID interface (from the English Radio Frequency Identification).

[0055] According to another particular embodiment, the device 2 includes a communication interface 204 which enables communication with other devices. The communication interface 204 corresponds, for example, to a transmitter configured to transmit and receive information and / or data via a communication channel 205. The communication interface 204 includes, for example, a modem and / or a network card, and the communication channel can, for example, be implemented in a wired and / or wireless medium.

[0056] Data is exchanged, for example, with the central device 14 using a Wi-Fi® network as defined by IEEE 802.11 via an access point. Communication between a device 2 installed in an electric vehicle and this access point can then be established using a wireless communication protocol such as Wi-Fi® (as defined by IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth (as defined by IEEE 802.15.1), in the 2.4 GHz frequency band. The access point without The wire can also communicate with another access point via a wired link (or "backbone"), for example of the MoCA type (from the English "Multimedia over Coax Alliance" or in French "Alliance multimédia sur coax"), Ethernet, PLC (from the English "Powerline Communication" or in French CPL "Courants Porteurs en Ligne"), POF (from the English "Plastic Optical Fiber" or in French "Fête optique plastique") or ITU G.hn (corresponding to the standard for next generation home network technologies of ITU, from the English "International Telecommunication Union" or in French "Union internationale des télécommunications").

[0057] Data can also be exchanged with the central device 14 using a cellular network such as a 4G (or LTE Advanced according to 3GPP release 10 - version 10) or 5G network via the relay antennas 107 and / or 110.

[0058] Data can also be exchanged with the central device 14 by reading a "radio tag" (from the English RFID tag) then affixed to an element of the electric vehicles lOi or lOj or to equipment on board these electric vehicles lOi or lOj.

[0059] The communication interface 204 can also behave as an interface of a wireless ad hoc network (also called a WANET or MANET), corresponding to a decentralized wireless network. Unlike a centralized network that relies on an existing infrastructure including, for example, routers or access points connected to each other by a wired or wireless infrastructure, the wireless ad hoc network consists of nodes that each participate in data routing by retransmitting data from one node to another, from sender to receiver, according to network connectivity and a routing algorithm. The wireless ad hoc network advantageously corresponds to a vehicular ad hoc network (or VANET) or an intelligent vehicular ad hoc network (or InVANET).In such a network, two or more electric vehicles, each equipped with a node (i.e., a 204 communication interface behaving like a WANET or MANET interface), can communicate with each other in the context of vehicle-to-vehicle (V2V) communication or vehicle-to-infrastructure (V2I) communication. Each electric vehicle can also communicate with one or more pedestrians equipped with mobile devices (e.g., a smartphone) in the context of vehicle-to-pedestrian (V2P) communication.

[0060] According to one embodiment, a node of the ad hoc wireless network is connected to the communication network infrastructure 13 via a wired and / or wireless connection. The node can thus act as a relay between this communication network infrastructure and an electric vehicle 10i or 11j. A relay antenna 12 can also implement an interface of an ad hoc wireless network. Such an antenna therefore provides the link between the ad hoc network and the communication network infrastructure 13 of which it is a part.

[0061] The devices 2 embedded in electric vehicles lOi and 11 j can thus communicate with the central device 14 via at least one node of a wireless ad hoc network using communication technologies such as ITS G5 (from the English "Intelligent Transportation System G5" or in French "Système de transport intelligent G5") in Europe or DSRC (from the English "Dedicated Short Range Communications" or in French "Communications dédiés à courte portée") in the United States of America, both of which are based on the IEEE 802.1 Ip standard, or the cellular network-based technology called C-V2X (from the English "Cellular - Vehicle to Everything" or in French "Cellulaire - Véhicule électrique vers tout") which is based on 4G based on LTE (from the English "Long Term Evolution" or in French "Evolution à long terme") and soon 5G.

[0062] According to a further particular embodiment, the device 2 can provide output signals to one or more external devices, such as a display screen 206, one or more speakers 207 and / or other peripherals 208 via output interfaces 209, 210 and 211 respectively. According to a variant, one or more of the external devices is integrated into the device 2. The display screen 206 corresponds for example to a screen, touch or not.

[0063] [Fig.3] schematically illustrates a central device 14, according to an example of A particular and non-limiting embodiment of the present invention. Examples of such a central device 14 include, but are not limited to, various electronic devices such as one or more computers or servers, one or more calculators. The elements of the central device 14, individually or in combination, may be integrated into a single integrated circuit, into several integrated circuits, and / or into discrete components. The central device 14 may be implemented in the form of electronic circuits or software (or computer) modules, or a combination of electronic circuits and software modules. The central device 14 includes one (or more) processor(s) 301 configured for executing the instructions of the software embedded in the central device 14. The processor 301 may include integrated memory, an input / output interface, and various circuits known to those skilled in the art.The central device 14 further includes at least one memory 302, by volatile and / or non-volatile memory and / or includes a memory storage device which may include volatile and / or non-volatile memory, such as . such as EEPROM, ROM, PROM, RAM, DRAM, SRAM, flash, magnetic or optical disk.

[0064] The computer code including the instructions to be loaded and executed by the processor is for example stored on memory or memory storage device 302.

[0065] According to a particular and non-limiting embodiment, the central device 14 includes a block 303 of interface elements for communicating with external devices, for example a remote device 2 or a relay antenna 12. The interface elements of the block 303 include one or more of the following interfaces:

[0066] RF radio frequency interface, for example of the Bluetooth® or Wi-Fi® type;

[0067] USB interface (from the English "Universal Serial Bus" or "Universal Serial Bus") (in French);

[0068] HDMI interface (from the English "High Definition Multimedia Interface", or "High Definition Multimedia Interface" in French); - RFID interface (from the English Radio Frequency Identification).

[0069] According to another particular embodiment, the central device 14 includes a communication interface 304 that enables communication with other devices. The communication interface 304 corresponds, for example, to a transmitter configured to transmit and receive information and / or data via a communication channel 305. The communication interface 304 includes, for example, a modem and / or a network card, and the communication channel can, for example, be implemented in a wired and / or wireless medium.

[0070] Data is exchanged, for example, with a device 2 embedded in an electric vehicle lOi or 1 Ij using a Wi-Fi® network such as that according to IEEE 802.11 via an access point. Communication between the central device 14 and an access point can then be established according to a wireless communication protocol such as Wi-Fi® (according to IEEE 802.11), for example in the 2.4 or 5 GHz frequency bands, or Bluetooth (according to IEEE 802.15.1), in the 2.4 GHz frequency band. A wireless access point can also communicate with another access point via a wired connection (or "backbone"), for example of the MoCA (Multimedia over Coax Alliance), Ethernet, PLC (Powerline Communication), POF (Plastic Optical Fiber) or ITU G type.hn (corresponding to the standard for next-generation home network technologies of the ITU, from the English "International Telecommunication Union" or in French "Union internationale des télécommunications"). .

[0071] Data is exchanged for example with a device 2 on board an electric vehicle lOi or 1 Ij using a cellular network such as a 4G network (or LTE Advanced according to 3GPP release 10 - version 10) or 5G via relay antennas 107 and / or 110.

[0072] Data can also be exchanged with a device 2 on board an electric vehicle lOi or 11 j by reading a "radio tag" (from the English RFID tag) then affixed to an element of the electric vehicles lOi or 11 j or to equipment on board these electric vehicles lOi or 1 Ij.

[0073] According to a further particular embodiment, the central device 14 can provide output signals to one or more external devices, such as a display screen 306, one or more speakers 307 and / or other peripherals 308 via output interfaces 309, 310 and 311 respectively. In a variant, one or more of the external devices is integrated into the central device 14. The display screen 306 corresponds, for example, to a screen, whether touch-sensitive or not.

[0074] [Fig.4] illustrates a flowchart of the different stages of an estimation process degradation of a battery of an electric vehicle of a first set of electric vehicles, according to a particular and non-limiting embodiment of the present invention.

[0075] The method is, for example, partially implemented by the central device 14 and by the device 2 installed in each electric vehicle 1Ij and in each electric vehicle lOi. The electric vehicles lOi and 11j are in communication with the central device 14 as discussed previously.

[0076] In a first step 31, the central device 14 obtains a global model M for predicting the decrease in battery charge level per trip from local models Mj for predicting the decrease in battery charge level per trip trained by the devices 2 of electric vehicles 1 Ij from data collected by said electric vehicles 1 Ij following trips made by said electric vehicles 1 Ij and from actual decreases in battery charge level of said electric vehicles 1 Ij after said trips.

[0077] In a second step 32, the central device 14 emits the parameters of the global model M for predicting the decrease in battery charge level per trip to a device 2 of each electric vehicle lOi of the first set of electric vehicles.

[0078] In a third step 33, following a journey made by an electric vehicle lOi, the device 2 of the electric vehicle lOi obtains an estimate of a decrease in battery charge level per journey from the global model M of prediction of decrease in battery charge level per journey.

[0079] In a fourth step 34, the device 2 of the electric vehicle lOi calculates an actual decrease in battery charge level following said journey made by the electric vehicle lOi.

[0080] According to one variant, said actual decrease in charge level of a battery of an electric vehicle lOi is calculated by calculating the difference between an initial charge level of the battery of the electric vehicle lOi before the start of said journey and a charge level of this battery at the arrival of said journey.

[0081] In a fifth step 35, the device 2 of the electric vehicle lOi obtains an estimate of battery degradation of the electric vehicle lOi by calculating a difference between the estimated decrease in battery charge level per trip and the actual decrease in battery charge level.

[0082] According to one variant, if the calculated battery degradation is greater than a threshold value, in a sixth step 35, an actuator of the electric vehicle lOi is activated to indicate that a preventive maintenance phase of the electric vehicle lOi battery is to be expected.

[0083] According to one variant, an indicator light or an area of ​​an on-board human-machine interface of the electric vehicle lOi is activated.

[0084] The electric vehicle battery degradation prediction model M is federated training in the sense defined by the article by Brendan McMahan et al. entitled “Communication-Efficient Learning of Deep Networks from Decentralized Data (In Aarti Singh and Jerry Zhu, editors, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, volume 54 of Proceedings of Machine Learning Research, pages 1273-1282. PMLR, 20-22 Apr 2017. pages 1, 2, 3).

[0085] According to a particular and non-limiting embodiment, obtaining (step 31), by the central device 14, the global model M for predicting the decrease in battery charge level per trip comprises steps 310 to 314.

[0086] In step 310, the central device 14 transmits parameters of an initial local model MO predicting the decrease in battery charge level per trip to the device 2 of each electric vehicle 1 Ij of the second set 11 of electric vehicles and of each electric vehicle lOi of the first set 10 of electric vehicles.

[0087] In step 311, at least one local model Mj for predicting the decrease in battery charge level per trip is trained by the device 2 of at least one electric vehicle 11 j (learning said at least one local model Mj).

[0088] Each local model Mj for predicting battery charge level decrease per trip is trained by an electric vehicle 1 Ij using data collected by said electric vehicle 1 Ij and actual charge level decreases of battery of said electric vehicle 1 Ij following a predetermined number of journeys made by said electric vehicle 1 Ij.

[0089] According to one variant, a decrease in a battery charge level after a journey made by an electric vehicle 1 Ij is represented by a percentage of battery charge consumed for that journey.

[0090] A percentage of battery consumed for a journey of an electric vehicle 1 Ij is calculated by the difference between an initial battery charge level of the electric vehicle 1 Ij before the start of the journey and the charge level of this battery after the electric vehicle 1 Ij has completed this journey.

[0091] According to one variant, the data collected by an electric vehicle 1 Ij are at least one of the following data:

[0092] a distance of a journey made by the electric vehicle 1 Ij;

[0093] an average speed along a route taken by the electric vehicle 1 Ij;

[0094] a load (weight) of the electric vehicle 1 Ij along a journey;

[0095] a representative data of activation of an on-board air conditioning system of the electric vehicle 11 j during a journey;

[0096] a representative data on the activation of the headlights of the electric vehicle 1 Ij along a route;

[0097] an average outside temperature data along a route of the electric vehicle 1 Ij.

[0098] The data collected by an electric vehicle 1 Ij are normalized by maximum values.

[0099] In step 312, the parameters of said at least one local model Mj for predicting the decrease in battery charge level per driven trip (311) are sent to the central device 14. The parameters of each local model Mj for predicting the decrease in battery charge level per driven trip are sent by the device 2 of the electric vehicle 1 Ij which drove this local model Mj.

[0100] Thus, the parameters of each local model Mj driven by the device 2 of an electric vehicle 11 j are sent to the central device 14.

[0101] In step 313, the central device 14 determines parameters of the global model M for predicting the decrease in battery charge level per trip from the parameters of said at least one local model Mj received.

[0102] According to one variant, the parameters of the global model M are obtained from the parameters of said at least one local model Mj for predicting the decrease in battery charge level per trip according to a method of averaging the parameters of said at least one local model Mj.

[0103] According to one variant, the method of averaging the parameters of the Mj models is similar to that described in section 2 of the aforementioned article by Brendan McMahan et al.

[0104] In step 314, the central device 14 sends to device 2 of each electric vehicle 1 Ij, the parameters of the global model M obtained and steps 311, 312, 313 and 314 are iterated.

[0105] According to one variant, steps 311, 312, 313 and 314 are iterated until a stopping condition is met.

[0106] According to one variant, a stopping condition is checked when a maximum number of iterations is reached.

[0107] According to one variant, a stopping condition is verified when a variation of the parameters of the global model M between two successive iterations is less than a threshold value.

[0108] According to one embodiment, the device 2 of an electric vehicle 1 Ij drives a local model Mj,n predicting the decrease in battery charge level per trip for each initial battery charge level of said electric vehicle 1 Ij, and the central device 14 obtains as many global models Mn predicting the decrease in battery charge level per trip as there are initial battery charge levels. The index n belongs to a set of integer values ​​1 to N where N is a number of initial battery charge levels.

[0109] According to one variant, the parameters of a global model Mn for a given initial load level are obtained from the parameters of at least one local model Mj,n corresponding to said initial load level by means of an averaging process of the parameters of said at least one local model Mj,n.

[0110] Several local models Mj,n predicting the decrease in battery charge level per trip can thus be trained by the device 2 of an electric vehicle Ij, each corresponding to an initial battery charge level of the electric vehicle Ij (step 311). The parameters of each of the models Mj,n are sent to the central device 14 (step 312), and the central device 14 determines N global models Mn predicting the decrease in battery charge level per trip from the received local models Mj,n (step 313). The parameters of the global models Mn are sent to each electric vehicle Ij and to each electric vehicle Ij (step 314), and steps 311, 312, 313, and 314 are iterated.Following a journey undertaken by an electric vehicle lOi, the device (2) of the electric vehicle lOi selects one of the global models Mn for predicting the decrease in battery charge level per journey, based on the initial battery charge level at the start of the journey. The device (2) of the electric vehicle lOi obtains (step 32) an estimate of the decrease in battery charge level per journey from the selected global model Mn. The device (2) of the electric vehicle lOi then calculates (step 33) the actual decrease in battery charge level following said journey undertaken by the electric vehicle lOi. The device (2) of the electric vehicle lOi also obtains (34) an estimate of the degradation. of electric vehicle battery lOi by calculating a difference between the estimated decrease in battery charge level per trip and the actual decrease in battery charge level.

[0111] According to one variant, 10 models Mj,n (n=l to 10) are trained: A first model Ml,j is trained for an initial battery charge percentage between 1 and 10%, a second model M2,j is trained for an initial battery charge percentage between 11 and 20%, etc.

[0112] According to one variant, a local model Mj or Mj,n for predicting the decrease in battery charge level per trip and a global model M or Mn for predicting the decrease in battery charge level per trip are implemented by a neural network, also called a deep regression network. The parameters of a local model Mj or Mj,n and the parameters of a global model M or Mn are the internal parameters of the neural network.

[0113] According to one variant, the neural network can, for example, comprise three layers of neurons. The first layer has as many neurons as there are input data points. The second layer is a hidden, fully connected layer with 64 neurons, and the third layer has a single neuron corresponding to one output data point. The neural layers use a Rectified Linear Unit (ReLU) activation function, which basically allows negative results to be replaced by zero.

[0114] Training a local model Mj or Mj,n consists of determining internal parameters of the neural network so as to minimize a cost function defined as a function of values ​​of battery charge level decreases provided by the neural network when data collected by an electric vehicle 1 Ij following trips made by the electric vehicle 1 Ij, and as a function of values ​​of battery charge level decreases of the electric vehicle 11 j as they were actually recorded following these trips.

[0115] Of course, the present invention is not limited to the embodiments described above but extends to a method for estimating the battery degradation of an electric vehicle in a first set of electric vehicles communicating with a central device, which would include secondary steps without departing from the scope of the present invention. The same would apply to a device configured for implementing such a method.

[0116] The present invention also relates to an electric vehicle, for example a motor vehicle or more generally an autonomous electric vehicle with a land motor, comprising device 2 of [Fig.2].

Claims

1. Demands Method for estimating battery degradation of electric vehicles in a first set of electric vehicles communicating with a central device (14), said central device (14) further communicating with electric vehicles of a second set of electric vehicles, the first and second sets containing distinct vehicles, no vehicle of the first group belonging to the second group and vice versa, said method comprising the following steps: - emission (31), by the central device (14) and to each electric vehicle of the first and second set of electric vehicles, of a global model (M) of prediction of battery charge level decrease per trip obtained from local models (Mj, Mj,n) of prediction of battery charge level decrease per trip driven by devices (2) of electric vehicles of the second set of electric vehicles, each local model (Mj, Mj,n) of prediction of battery charge level decrease per trip being obtained from data collected by an electric vehicle of the second set of electric vehicles following trips made by said electric vehicle of the second set of electric vehicles and from actual decreases in battery charge level of said electric vehicle of the second set of electric vehicles after said trips; - following a journey made by an electric vehicle from the first set of electric vehicles, obtaining (32), by the device (2) of said electric vehicle from the first set of electric vehicles, an estimate of a decrease in battery charge level per journey from the global model (M) of prediction of decrease in battery charge level per journey; - calculation (33), by the device (2) of said electric vehicle of the first set of electric vehicles, of an actual decrease in battery charge level following said journey carried out by said electric vehicle of the first set of electric vehicles; - obtaining (34), by the device (2) of said electric vehicle of the first set of electric vehicles, an estimate of battery degradation of said electric vehicle of the first

2. A set of electric vehicles is calculated by determining the difference between the estimated decrease in battery charge level per trip and the actual decrease in battery charge level. The device (2) of an electric vehicle in the second set generates a local model (Mj,n) predicting the decrease in battery charge level per trip for each initial battery charge level of said electric vehicle in the second set of electric vehicles. The central device (14) obtains as many global models (Mn) predicting the decrease in battery charge level per trip as there are initial battery charge levels. A method according to claim 1, wherein the transmission (31), by the central device (14) to each electric vehicle in the first and second sets of electric vehicles, of the global model (M) predicting the decrease in battery charge level per trip comprises the following steps: a) transmission (310), by the central device (14), of parameters of an initial local model for predicting the decrease in battery charge level per trip to the device of each electric vehicle of the first and second sets of electric vehicles; b) learning (311) of at least one local model (Mj, Mj,n) of predicting battery charge level decrease per trip by the device (2) of at least one electric vehicle of the second set of electric vehicles, each local model (Mj, Mj,n) of predicting battery charge level decrease per trip is trained by an electric vehicle of the second set of electric vehicles from data collected by said electric vehicle of the second set of electric vehicles and from actual decreases in battery charge level of said electric vehicle of the second set of electric vehicles following a predetermined number of trips made by said electric vehicle of the second set of electric vehicles; (c) emission (312) of the parameters of said at least one local model (Mj, Mj,n) for predicting the decrease in battery charge level per driven trip to the central device (14), the parameters of each local model (Mj) for predicting the decrease in battery charge level per driven trip are emitted by the device (2) of the electric vehicle of the second set of electric vehicles that trained said local model (Mj) for predicting battery charge level decrease per trip; d) determination (313) by the central device (14), of the parameters of the global model (M) for predicting battery charge level decrease per trip from the parameters of said at least one local model (Mj, Mj,n) for predicting battery charge level decrease per trip received; e) transmission (314), by the central device (14) and to the device (2) of each electric vehicle of the first and second sets of electric vehicles, of the parameters of the global model (M) for predicting battery charge level decrease per trip obtained and steps b), c), d), and e) are iterated.

3. A method according to claim 2, wherein the parameters of the global model (M) for predicting the decrease in battery charge level per trip are determined (312) from the parameters of said at least one local model (Mj, Mj,n) for predicting the decrease in battery charge level per trip according to a method of averaging the parameters of said at least one local model (Mj, Mj,n) for predicting the decrease in battery charge level per trip.

4. A method according to claim 1, wherein the parameters of a global model (Mn) for predicting the decrease in battery charge level per trip for a given initial charge level are obtained from the parameters of at least one local model for predicting the decrease in battery charge level per trip corresponding to said initial charge level by a method of averaging the parameters of said at least one local model for predicting the decrease in battery charge level per trip corresponding to said initial charge level.

5. 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.

6. Computer-readable recording medium on which is recorded a computer program comprising instructions for carrying out the steps of the process according to any one of claims 1 to 4.

7. Device (2) comprising a memory (201) associated with at least one processor (200) configured to carry out at least one step of the process according to any one of claims 1 to 4.

8. Electric vehicle (lOi, 1 Ij) comprising device (2) according to claim 7.

9. System (1) for estimating the battery degradation of an electric vehicle of a first set of electric vehicles comprising a central device (14), said central device (14) being in communication with electric vehicles of the first set of electric vehicles and electric vehicles of a second set of electric vehicles, characterized in that said central device (14) implements at least one step of the method according to any one of claims 1 to 4 and each electric vehicle of the first and second sets of electric vehicles is an electric vehicle according to claim 8.

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