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EP-4498104-B1 - METHOD AND SYSTEM FOR PREDICTING DEGRADATION STATE OF ELECTRIC BATTERY

EP4498104B1EP 4498104 B1EP4498104 B1EP 4498104B1EP-4498104-B1

Inventors

  • ASCH, Mark
  • ATI, MOHAMED
  • EL-MALKI, Amina
  • FRANCO, ALEJANDRO

Dates

Publication Date
20260513
Application Date
20240726

Claims (15)

  1. Method (100) for predicting the degradation state of at least one electric battery (10) in an electric or hybrid motor vehicle, said method (100) being configured to be executed by at least one electronic system (200), said electronic system being configured to communicate with at least one database and/or at least one set of sensors, said method (100) comprising: a. a first collection step (110) of collecting at least one piece of information relating to an initial cycling protocol, said initial cycling protocol being taken from at least the following plurality of cycling protocols: i. a first cycling protocol, said first cycling protocol being a constant current-constant voltage cycling protocol; ii. a second cycling protocol, said second cycling protocol being a pulsed current cycling protocol; iii. a third cycling protocol, said third cycling protocol being a multi-step current cycling protocol; b. a step (120) of selecting at least two prediction cycling protocols taken from said plurality of cycling protocols, including the first cycling protocol and the second cycling protocol; c. a second collection step (130) of collecting at least one datum relating to at least one set of parameters, said set of parameters comprising at least one of the following parameters: i. an initial state of charge of the electric battery; ii. a final state of charge of the electric battery; iii. a number N of charge-discharge cycles of said battery; iv. a temperature T of the electric battery; v. a charge rate C, i.e., a charge speed, associated with at least one cycling protocol taken from at least said plurality of cycling protocols; d. a step (140) of processing, by machine learning based on at least one pre-trained mathematical model, said at least one piece of information, said at least one datum, and each selected prediction cycling protocol, said processing step comprising at least: i. a step (141) of calculating at least one descriptor by applying said pre-trained mathematical model in a multidimensional calculation space, said multidimensional calculation space being configured such that each parameter of said set of parameters defines a dimension of said multidimensional calculation space; ii. a step (142) of classifying the degradation state on the basis of said descriptor relative to a set of predetermined classes; iii. a step (143) of calculating the temporal evolution of said degradation state of said electric battery (10), by calculating a plurality of degradation states of said electric battery, as a function of at least one variable, said variable being taken from at least: • an initial state of charge of the electric battery (10); • a final state of charge of the electric battery (10); • a pressure applied to at least one part of the electric battery (10); • a number N' of charge-discharge cycles of said electric battery (10); • a temperature T' of the electric battery (10); • a first charge rate C1 relating to the first cycling protocol; • a second charge rate C2 relating to the second cycling protocol; • a third charge rate C3 relating to the third cycling protocol; • a first discharge rate D1 relating to the first cycling protocol; • a second discharge rate D2 relating to the second cycling protocol; • a third discharge rate D3 relating to the third cycling protocol; • a charge limit; • a zero-current pause time; • another cycling protocol; iv. a step (144) of obtaining a calculated state of the degradation of said electric battery (10), said calculated state comprising at least one predictive mathematical model, said predictive mathematical model being configured to mathematically represent the evolution of the degradation state of said electric battery (10) as a function of at least said variable, the method comprising at least one step of at least one management system (40) for the electric battery (10) feedback controlling at least one variable as a function of said predictive mathematical model and/or at least one predetermined setpoint.
  2. Method (100) according to the preceding claim, comprising at least one step of at least one display module of said electronic system (200) displaying at least one graphical representation (60) of said predictive mathematical model.
  3. Method (100) according to either of the preceding claims, comprising at least one step of a user interacting with said predictive mathematical model via a user interface (260) of said electronic system (200).
  4. Method (100) according to any of the preceding claims, wherein in the feedback control step, the management system (40) for the electric battery (10) adjusts charge and discharge parameters of the battery.
  5. Method (100) according to claim 4, wherein the management system (40) for the electric battery (10) operationally selects a cycling protocol from the plurality of cycling protocols.
  6. Method (100) according to claim 4, wherein the management system (40) for the electric battery (10) plans the maintenance schedules of the electric battery.
  7. Method (100) according to any of the preceding claims, wherein the feedback control step is performed in real time.
  8. Method (100) according to any of the preceding claims, wherein at least one of the first collection step (110) and the second collection step (130) comprises respectively acquiring said piece of information and said datum from at least one database (20) and/or at least one set of sensors (30).
  9. Method (100) according to any of the preceding claims, wherein the predictive-trained mathematical model has been trained on data from various electric batteries using electric battery data from manufacturers, integrators, or users.
  10. Method (100) according to any of the preceding claims, wherein said set of parameters comprises at least the following parameters: - an initial state of charge of the electric battery; - a number N of charge-discharge cycles of said battery; - a temperature T of the electric battery; - a charge rate C, i.e., a charge speed, associated with at least one cycling protocol taken from at least said plurality of cycling protocols.
  11. Method (100) according to any of the preceding claims, wherein the step (120) of selecting at least two prediction cycling protocols taken from the plurality of cycling protocols comprises three cycling protocols, namely the first cycling protocol, the second cycling protocol, and the third cycling protocol.
  12. Computer program product, preferably stored on a non-transitory memory medium, comprising instructions, which, when the instructions are performed by at least one from a processor and a computer, executes the method (100) according to any of the preceding claims.
  13. Non-transitory memory medium comprising the computer program product according to claim 12.
  14. Electronic system (200) configured to execute the method (100) according to any of claims 1 to 6, said electronic system (200) comprising: a. a communication module (250) configured to communicate with at least one database (20) and/or at least one set (30) of sensors; b. a first collection module (210) configured to collect, preferably via the communication module (250), a piece of information relating to the initial cycling protocol of the electric battery (10); c. a selection module (220) configured to select at least two prediction cycling protocols from a plurality of cycling protocols, including the first cycling protocol and the second cycling protocol; d. a second collection module (230) configured to collect, preferably via the communication module (250), a datum relating to a set of parameters; e. a calculation module (240) configured to: i. process, by machine learning based on at least one pre-trained mathematical model, said at least one piece of information and said at least one datum for each selected prediction cycling protocol; ii. calculate at least one descriptor by applying said pre-trained mathematical model in a multidimensional calculation space; iii. classify the degradation state of said electric battery (10) on the basis of said descriptor relative to a set of predetermined classes; iv. calculate the temporal evolution of said degradation state of said electric battery (10), by calculating a plurality of degradation states of said electric battery, as a function of at least one variable, said variable being taken from at least: • an initial state of charge of the electric battery (10); • a final state of charge of the electric battery (10); • a pressure applied to at least one part of the electric battery (10); • a number N' of charge-discharge cycles of said electric battery (10); • a temperature T' of the electric battery (10); • a first charge rate C1 relating to the first cycling protocol; • a second charge rate C2 relating to the second cycling protocol; • a third charge rate C3 relating to the third cycling protocol; • a first discharge rate D1 relating to the first cycling protocol; • a second discharge rate D2 relating to the second cycling protocol; • a third discharge rate D3 relating to the third cycling protocol; • a charge limit; • a zero-current pause time; • another cycling protocol; f. generate at least one predictive mathematical model, said predictive mathematical model being configured to mathematically represent the evolution of the degradation state of said electric battery (10) as a function of at least said variable; and perform at least one step of at least one management system (40) for the electric battery (10) feedback controlling at least one variable as a function of said predictive mathematical model and/or at least one predetermined setpoint.
  15. System (200) according to the preceding claim, comprising: a. at least one display module configured to display at least one graphical representation of said mathematical model, and b. at least one user interface (260) configured to allow a user to interact with said predictive mathematical model at least once.

Description

TECHNICAL FIELD The present invention relates to the field of electric batteries. Its application is particularly advantageous in the field of predicting the state of degradation of an electric battery. STATE OF THE ART The development of electric batteries, such as high-performance lithium-ion batteries, is a crucial research area, particularly in the context of electric vehicles (EVs) where battery longevity and efficiency directly impact vehicle range and overall performance. However, battery aging, especially in lithium-ion (LiB) batteries, is a complex process that can significantly affect battery performance and lifespan, making it an important consideration in LiB design and operation. The aging process of LiB is influenced by several factors, including cycling protocols and storage conditions. Therefore, accurately predicting the aging process and developing effective strategies to mitigate it is difficult. Experimental studies on LiB aging are generally very lengthy and expensive. An object of the present invention is therefore to offer a faster and more economical solution to predicting the evolution of the degradation state of an electric battery. The other objects, features, and advantages of the present invention will become apparent from an examination of the following description and accompanying drawings. It is understood that other advantages may be incorporated. SUMMARY The present invention relates to a method for predicting the state of degradation of at least one electric battery, preferably for an electric vehicle, said method being configured to be executed by at least one electronic system, preferably said electronic system being configured to communicate with at least one database and/or at least one set of sensors, said method comprising: a. A first collection step, preferably executed by at least one first collection module of said electronic system, of at least one piece of information relating to an initial cycling protocol, preferably said initial cycling protocol corresponding to the current cycling protocol of said electric battery, said initial cycling protocol being taken from at least the following plurality of cycling protocols: i. A first cycling protocol, said first cycling protocol being a constant current and constant voltage cycling protocol; ii. A second cycling protocol, said second cycling protocol being a pulsed current cycling protocol; iii. A third cycling protocol, said third cycling protocol being a multi-stage current cycling protocol; b. A selection step, preferably executed by at least one selection module of said electronic system, of at least two cycling protocols of prediction taken from said plurality of cycling protocols including the first and second cycling protocols; c. A second collection step, preferably executed by at least one second collection module of said electronic system, of at least one data point relating to at least one set of parameters, said set of parameters comprising at least one of the following parameters: i. An initial state of charge of the electric battery; ii. A final state of charge of the electric battery; iii. A number N of charge-discharge cycles of said battery; iv. A temperature T of the electric battery; v. A charge rate C, i.e. a charging speed, associated with at least one cycling protocol taken from at least said plurality of cycling protocols; d. A processing step, preferably executed by at least one computing module of said electronic system, by machine learning from at least one pre-trained mathematical model, of said at least one piece of information, of said at least one data point, and of each selected prediction cycling protocol, said processing step comprising at least: i. A calculation step of at least one descriptor by applying said pre-trained mathematical model in a multidimensional computational space, said multidimensional computational space being configured such that each parameter of said parameter set defines a dimension of said multidimensional computational space; ii. A step of classifying the state of degradation from said descriptor relative to a set of predetermined classes; iii. A step for calculating the temporal evolution of said degradation state of said electric battery, by calculating a plurality of degradation states of said electric battery, as a function of at least one variable, said variable being taken from at least: A) An initial state of charge of the electric battery; B) A final state of charge of the electric battery; C) Pressure applied to at least part of the electric battery; D) A number N' of charge-discharge cycles of said electric battery; E) A temperature T' of the electric battery; F) A first charge rate C1 relating to the first cycling protocol; G) A second charge rate C2 relating to the second cycling protocol; H) A third charge rate C3 relating to the third cycling protocol; I) A first discharge rate D1 relating to the first cycling protocol; J) A second discharge