Method and system for predicting the degradation state of an electric battery

Abstract

The present invention relates to a method (100) for predicting the degradation state of at least one electric battery, said method (100) comprising at least: A first step of collecting (110) at least one piece of information relating to an initial cycling protocol; A step of selecting (120) at least one predictive cycling protocol; A second step of collecting (130) at least one data point relating to at least one set of parameters; A processing step (140) of said at least one piece of information, said at least one data point, and the predictive cycling protocol, comprising: A calculation step (141) of at least one descriptor; A classification step (142) of the degradation state; A calculation step (143) of the evolution of said degradation state; A step of obtaining (144) a calculated state of degradation of said electric battery. Fig. 1

Description

Title of the invention: Method and system for predicting the degradation state of an electric battery. Technical field

[0001] 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

[0002] 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 have a direct impact on range and overall vehicle performance. However, the aging of electric batteries, and in particular lithium-ion (LiB) batteries, is a complex process that can significantly impact battery performance and lifespan, making it an important consideration in the design and operation of LiBs.

[0003] The aging process of LiB is influenced by several factors, including cycling protocols and storage conditions. It is therefore difficult to accurately predict the aging process and to develop effective strategies to mitigate it. Experimental studies on LiB aging are generally very lengthy and very expensive.

[0004] An object of the present invention is therefore to propose a faster and more economical solution to the prediction of the evolution of the state of degradation of an electric battery.

[0005] 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.

[0006] SUMMARY

[0007] 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 at least: a. A first collection step, preferably performed by at least one first collection module of said electronic system, of at least one 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 one prediction cycling protocol taken from said plurality of 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 the 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 Cl relating to the first cycling protocol; G. A second charge rate C2 relating to the second cycling protocol; H. A third C3 charge rate relating to the third cycling protocol; I. A first discharge rate DI relating to the first cycling protocol; J. A second discharge rate D2 relating to the second cycling protocol; K. A third discharge rate D3 relating to the third cycling protocol; L. A load limit; Mr. A pause time with zero current; N. Another cycling protocol; iv. A step of obtaining a calculated state of the degradation of said electric battery, 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 as a function of at least said variable.

[0008] The present invention cleverly uses a mathematical model pre-trained on data from various electric batteries to generate a predictive mathematical model specific to a given electric battery. This results in considerable time and system resource savings because it is then unnecessary to rebuild a mathematical model from scratch.

[0009] The present invention makes it possible to improve the quality and reliability of electric batteries. One of the many applications of the present invention is to help electric battery manufacturers improve the quality and reliability of their batteries by reducing the number of premature failures.

[0010] The present invention makes it possible to optimize the production parameters of an electric battery. Indeed, the manufacturer can use the results of a prediction to adjust the production parameters in order to slow down battery degradation and thus improve battery quality without relying on cycling tests, which can be both time-consuming and expensive. The present invention is simple and easy to implement and allows for rapid results, leading to cost savings and improved efficiency in the production of electric batteries.

[0011] The present invention makes it possible to reduce the environmental impact of electric batteries. Indeed, by extending the lifespan of electric batteries, the number of electric batteries required can be reduced, thereby reducing the environmental impact of the production and disposal of electric batteries.

[0012] Furthermore, the present invention enables the optimization of the management of an electric battery. Indeed, by using the results of predicting the evolution of the degradation state of an electric battery, a user can then optimize the management of the electric battery by adjusting the cycling and / or storage conditions, for example, non-limitingly, to extend the life of the electric battery and avoid premature degradation.

[0013] Furthermore, the present invention makes it possible to plan the maintenance and repair of electric batteries. Indeed, by anticipating the degradation of the electric battery, a user can plan maintenance and repairs accordingly, which can reduce costs and improve the availability of electric batteries.

[0014] Finally, the present invention makes it possible to improve the energy efficiency of the electric battery sector. Indeed, by extending the lifespan of an electric battery, a user can reduce the frequency of battery replacement, which can reduce energy consumption and improve the energy efficiency of the sector.

[0015] The present invention also relates to a computer program product, preferably stored on a non-transient memory medium, comprising instructions which, when executed by at least one of a processor, a computer, executes the process according to the present invention.

[0016] The present invention also relates to a non-transient memory support comprising a computer program product according to the present invention.

[0017] The present invention also relates to an electronic system configured to perform a process according to the present invention, said electronic system comprising at least: a. A communication module configured to communicate with at least one database and / or at least one set of sensors; b. A first collection module configured to collect, preferably via the communication module, information relating to the initial cycling protocol of the electric battery; c. A selection module configured to select at least one prediction cycling protocol from a plurality of cycling protocols; d. A second collection module configured to collect, preferably via the communication module, data relating to a set of parameters; e. A calculation module configured for: i. To process by machine learning from at least one pre-trained mathematical model, said at least one piece of information, said at least one data point and a prediction cycling protocol; ii. Calculate at least one descriptor by applying said pre-trained mathematical model in a multidimensional computational space; iii. Classify the state of degradation of said electric battery from said descriptor relative to a set of predetermined classes; iv. Calculate 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 Cl relating to the first cycling protocol; G. A second charge rate C2 relating to the second cycling protocol; H. A third C3 charge rate relating to the third cycling protocol; I. A first discharge rate DI relating to the first cycling protocol; J. A second discharge rate D2 relating to the second cycling protocol; K. A third discharge rate D3 relating to the third cycling protocol; L. A load limit; Mr. A pause time with zero current; N. Another cycling protocol; v. Generate at least one predictive mathematical model, said predictive mathematical model being configured to mathematically represent the evolution of the state of degradation of said electric battery as a function of at least said variable.

[0018] In particular, during the development of the present invention, a machine learning mathematical model capable of classifying aging conditions based on the degradation rate of a LiB was developed. This model was designed using a real-world dataset from a manufacturer of electric vehicle batteries. This development work made it possible to identify patterns and relationships between the aging process and various factors in a broad multi-parameter space. A pre-trained mathematical model was thus designed to predict the degradation rate of LiBs under different cycling conditions. The present invention has generated very promising results for effectively monitoring and mitigating battery aging in electric vehicles.

[0019] It is worth recalling here that machine learning methods make it possible to leverage large amounts of data to identify patterns and relationships between various factors, such as the aging process of an electric battery. This type of pre-trained model thus makes it possible, based on data specific to a given electric battery, to predict the evolution of its degradation state as a function of various variables through the generation of a new predictive mathematical model specific to that electric battery.

[0020] Thus, prior art is known, in particular from the article "Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory" (Energy, Volume 277, (2023), 127633, ISSN 0360-5442, by Zhang Chen, Liqun Chen, Zhengwei Ma, Kangkang Xu, Yu Zhou, Wenjing Shen), the general principle of using machine learning methods to make estimates and predictions related to the life cycle and degradation evolution of a Li-ion battery. However, prior art methods still lack accuracy and reliability. BRIEF DESCRIPTION OF THE FIGURES

[0021] The aims, objects, features and advantages of the invention will become clearer from the detailed description of an embodiment thereof, which is illustrated by the following accompanying drawings in which:

[0022] [Fig.1] Fig.1 represents a prediction method according to an embodiment of the present invention.

[0023] [Fig.2] Fig.2 represents an electronic prediction system according to a mode of realization of the present invention.

[0024] [Fig.3] Fig.3 represents a feedback loop for controlling the management system of an electric battery according to an embodiment of the present invention.

[0025] [Fig.4] Fig.4 represents obtaining a decision support graph in the within the framework of an embodiment of the present invention.

[0026] The drawings are given as examples and are not limiting of the invention. These are schematic representations of principle intended to facilitate understanding of the invention and are not necessarily to scale with practical applications. In particular, the dimensions are not representative of reality. DETAILED DESCRIPTION

[0027] Before proceeding to a detailed review of embodiments of the invention, optional features that may be used in combination or alternatively are listed below:

[0028] According to one example, the present invention includes at least one display step, by at least one display module of said electronic system, of at least one graphical representation of said predictive mathematical model.

[0029] According to one example, the present invention includes at least one step of interaction by a user with said predictive mathematical model via a user interface of said electronic system.

[0030] According to one example, the present invention includes at least one feedback control step, preferably by at least one electric battery management system, of at least one variable as a function of said predictive mathematical model and / or at least one predetermined setpoint.

[0031] According to one example, the feedback control step is carried out in real time.

[0032] According to an example, at least one of the respectively first collection step and the second stage of collection includes the respective acquisition of said information and said data from at least one database and / or at least one set of sensors.

[0033] By way of example, the present invention comprises: 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 configured to allow at least one interaction by a user with said predictive mathematical model.

[0034] Furthermore, the following description, listing the principles, aspects, and implementations of the present invention, along with their specific examples, aims to encompass both their structural and functional equivalents, whether currently known or developed in the future. Thus, for example, it will be understood by those skilled in the art that all the functional diagrams herein represent conceptual views of illustrative circuits incorporating the principles of the present invention. Similarly, it will be understood that all the flowcharts, and the like, represent various processes that can be substantially represented on computer-readable media and thus executed by a computer or processor, whether or not that computer or processor is explicitly shown.

[0035] The functions of the various elements shown in the figures, including any functional block referred to as a "processor" or "module," can be performed using dedicated hardware as well as hardware capable of executing software in conjunction with a computer program or appropriate instructions. When provided by a processor, the instructions can be provided by a single dedicated processor, a single shared processor, or by a plurality of individual processors, some of which may be shared. In certain embodiments of the present invention, the processor may be a general-purpose processor, such as a central processing unit (CPU), for example.Furthermore, the explicit use of the term "processor" should not be interpreted as referring exclusively to hardware capable of executing software and may implicitly include, but not be limited to, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), read-only memory (ROM) for storing software, random-access memory (RAM), and non-volatile storage. Other hardware, both conventional and / or custom, may also be included.

[0036] Software modules, or simply modules that are assumed to be software, can be represented here as any combination of flowchart elements or other elements indicating the execution of process steps and / or a textual description. Such modules can be executed by hardware that is expressly or implicitly represented. Furthermore, it must be understood that the module may include, for example, but not limited to, computer program logic, computer program instructions, software, firmware, hardware circuits, or a combination thereof that provides the required capabilities.

[0037] The present invention proposes using a machine learning model to classify aging cycling conditions based on the degradation rate of electric batteries, and in particular Li-ion (LiB) batteries, and to generate a predictive mathematical model configured to predict the evolution over time of the health status of a given electric battery as a function of various variables and / or parameters. It should be noted that in the following description, the terms "electric battery," "Li-ion battery," and "LiB" are interpreted identically.

[0038] Ingeniously, the present invention uses a dataset derived from real-world operating conditions of electric batteries under various conditions to identify the relationships between the aging process and different factors across a broad multiparameter range. In particular, the present invention can be applied to the automotive field, among other things.

[0039] As presented below, the present invention makes it possible to predict the rate of degradation of an electric battery, preferably of the Li-ion type, under different cycle / storage conditions.

[0040] Unexpectedly, the present invention makes it possible to identify the various factors that influence the aging process of an electric battery. The significant correlations observed during the development of the present invention make it possible to determine which variables have the most significant impact on the degradation rate of an electric battery.

[0041] These variables may correspond to specific aging mechanisms, and the results are essential for optimizing the designs of battery management systems capable of effectively monitoring and limiting the aging of an electric battery in electric vehicles, for example, but not limited to electric vehicles. The present invention enables improved performance, greater reliability, and the resolution of durability issues in the electric battery of a vehicle, for example, but not limited to electric vehicles.

[0042] The present invention provides a valuable tool for predicting, using at least one predictive mathematical model, the performance and lifespan of electric batteries, preferably Li-ion, advantageously in electric vehicles, under various cycling and storage conditions. The ability to identify the factors influencing battery aging and to predict its effects can lead to significant improvements in battery design and use, ultimately contributing to the development of more efficient and sustainable electric vehicles.

[0043] Moreover, the present invention finds a very clever application in the control, in the form of a feedback loop for example, of the battery management system called BMS, from the English acronym "Battery Management System".

[0044] Indeed, for example, by providing real-time predictions of the state of degradation of the electric battery, the present invention enables intelligent control of the BMS.

[0045] Indeed, for example, by regularly and / or continuously monitoring the battery's cycling conditions and by inputting the data into the present invention, i.e., into a mathematical model configured to mathematically represent the evolution of the battery's degradation state, the battery management system (BMS) can use the generated mathematical model to predict the battery's degradation state based on various parameters and thus adjust at least some of these parameters, such as the battery's charging and discharging parameters. This can help optimize the battery's lifespan and reduce the risk of premature failure.

[0046] Furthermore, the present invention can also help the BMS to plan maintenance and repair schedules based on the calculated and therefore predicted rate of degradation. By planning maintenance and repairs in advance, the BMS can reduce the risk of unexpected failures and minimize downtime.

[0047] Thus, according to a preferred embodiment, the present invention relates to a method for predicting the state of degradation of at least one electric battery. Said electric battery being, for example, configured to cooperate with an electric vehicle.

[0048] The method according to the present invention is advantageously configured to be executed by at least one electronic system. This electronic system, referred to as the prediction system, is preferably configured to communicate with at least one database and / or at least a first set of sensors as described below.

[0049] According to one embodiment, and as illustrated by [Fig. 1], the method for predicting the degradation state of an electric battery 10 100 comprises at least: a. A first collection step 110, performed by at least one first collection module 210 of said electronic system 200, of at least one piece of information relating to an initial cycling protocol, also called the initial charge / discharge protocol. Preferably, said initial cycling protocol corresponds to the current cycling protocol of said electric battery 10. As discussed below, the first initial cycling protocol is 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 120, executed by at least one selection module 220 of said electronic system 200, of at least one prediction cycling protocol from said plurality of cycling protocols; Advantageously, two prediction cycling protocols may be selected; c. A second collection step 130, executed by at least one second collection module 230 of said electronic system 200, 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 10; ii. A final state of charge of the electric battery 10; iii. A number N of charge-discharge cycles of said battery 10; iv. A temperature T of the electric battery 10; 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 140, executed by at least one computing module 240 of said electronic system 200, 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 said prediction cycling protocol. Advantageously, said processing step 140 comprises at least: i. A calculation step 141 of at least one descriptor by application of 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 classification step 142 of the state of degradation from said descriptor relative to a set of predetermined classes; iii. A calculation step 143 of 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: A. An initial state of charge of the electric battery 10; B. A final state of charge of the electric battery 10; C. A pressure applied to at least a part of the electric battery 10, said pressure preferably being predetermined and advantageously fixed; D. A number N' of charge-discharge cycles of said electric battery 10; E. A temperature T' of the electric battery 10; F. A first charge rate Cl relating to the first protocol of cycling; G. A second charge rate C2 relating to the second cycling protocol; H. A third C3 charge rate relating to the third cycling protocol; I. A first discharge rate DI relating to the first cycling protocol; J. A second discharge rate D2 relating to the second cycling protocol; K. A third discharge rate D3 relating to the third cycling protocol; L. A load limit; Mr. A pause time with zero current; N. Another cycling protocol; iv. A step of obtaining 144 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, preferably by variation of at least said variable.

[0050] This makes it possible to predict the state of degradation of the electric battery 10 according to the various variables.

[0051] Thus, the present invention makes it possible, from certain data of the electric battery, to generate a predictive mathematical model of the state of degradation of the electric battery 10. This predictive mathematical model then makes it possible to predict, by the generation of at least one predictive mathematical model, said state of degradation as a function of various variables, thus making it possible to predict the state of degradation of the electric battery as a function of these various variables.

[0052] Advantageously, the predictive mathematical model is a linear and / or non-linear combination of parameters, weighted or unweighted, allowing a mathematical representation in a multidimensional space of the state of degradation of the electric battery 10.

[0053] According to one embodiment, the first collection step 110 is configured to determine the initial cycling protocol of the electric battery 10, that is, its current protocol, i.e., the one currently applied to it or the one last applied to it. The term "cycling protocol" refers to the process used to charge and discharge an electric battery 10; it can also refer to the process used to test and evaluate the performance, lifespan, and safety of an electric battery 10. Preferably, a cycling protocol involves a series of repeated charges and discharges.

[0054] According to one embodiment, we will consider three distinct charging protocols, however it will be noted that the present invention can perfectly be implemented for other types of charging protocols existing to date or not.

[0055] Thus, according to a non-limiting embodiment, the first data collection step 110 allows for the collection of at least one piece of data relating to the nature of the initial cycling protocol of a given electric battery 10. Advantageously, this initial cycling protocol corresponds to at least one of the following three protocols: i. A first cycling protocol, also called a constant current and constant voltage (CC-CV) cycling protocol. This first cycling protocol is a widely used protocol that involves initially charging the battery with a predetermined constant current until it reaches a predetermined voltage, and then switching to a predetermined constant voltage to complete the charging process. This first cycling protocol aims to balance charging rate and voltage control to prevent overcharging. This first cycling protocol is commonly used in various applications and provides efficient and controlled charging. ii. A second cycling protocol, also called the pulsed current cycling protocol. This second cycling protocol consists of applying short bursts of high current to the battery with intermediate rest periods, i.e., pause times (see, for example, the following article: "Lv, H., Huang, X. & Liu, Y. Analysis on puise charging-discharging strategies for improving capacity retention rates of lithium-ion batteries. lonics 26, 1749-1770 (2020)."). It is estimated by those skilled in the art that this charging technique improves the battery's capacity and performance, and also reduces its degradation rate. iii. A third cycling protocol, also called a multi-stage current cycling protocol. This third cycling protocol is more complex than the previous two and involves several charging stages with different current levels (see, for example, the following article: “Romain Mathieu, Olivier Briat, Philippe Gyan, Jean-Michel Vinassa. Fast charging for electric vehicles applications: Numerical optimization of a multi-stage charging protocol for lithium-ion battery and impact on cycle life. Journal of Energy Storage, 2021, 40, pp. 102756. (10.1016 / j.est.202L 102756). (hal-04087488)”).

[0056] It should be noted that the cycling protocols described above have a different impact on the state of degradation, and therefore the health, of an electric battery 10 and in particular of a LiB. The present invention, during its development, has shown that this state of degradation is strongly correlated with the operating conditions of said electric battery 10.

[0057] Cleverly, each protocol can be characterized by several specific parameters. Thus, the degradation of electric batteries 10 and in particular LiBs depends, preferably, on a wide set of cycle parameters, i.e. charge-discharge cycle parameters.

[0058] Next, the selection step 120 allows the selection of a predictive cycling protocol, that is, a cycling protocol that may be different from the initial cycling protocol and which will serve as a parameter for the future prediction. Thus, the user can decide to predict the degradation state of an electric battery 10 if it is subjected to a particular, i.e., selected, cycling protocol.

[0059] Advantageously, the second data collection step 130 allows for the collection of at least one data point, and preferably a plurality of data points, relating to at least one parameter, and preferably to several parameters. This step allows for the collection of information, data, on the initial state of the electric battery 10, and on its current state. This data then serves to define the state of the electric battery 10.

[0060] We will now describe an example of parameters considered by the present invention, and in particular during the second collection step 130. These various parameters have been cleverly selected because they represent the parameters of an electric battery 10 that can have the most significant impact on its degradation rate: a. The initial state of charge of the electric battery 10: The initial state of charge of the electric battery 10 corresponds to the amount of electrical charge it holds in relation to its maximum capacity before carrying out a charge-discharge cycle, i.e. a cycling; b. The final state of charge of the electric battery 10: The final state of charge of the electric battery 10 corresponds to the amount of electrical charge it holds relative to its maximum capacity after having completed a charge-discharge cycle, i.e., a cycling; c. The number N of charge-discharge cycles of said electric battery 10: The number N of charge / discharge cycles, i.e., cycling, is a parameter that can strongly impact the degradation of a LiB. Indeed, the higher this number, the higher the rate of battery degradation. In fact, the number of repeated cycles of a battery can lead to several degradation mechanisms reducing the capacity of the electric battery 10 and its performance over time; d. The temperature T of the electric battery 10: During the development of the present invention, it was observed that the operating temperature of the electric battery 10 can play an important role in understanding its degradation rate. For example, by varying the temperature within a specific range, it is possible to evaluate the performance and behavior of the electric battery 10 under different thermal conditions. e. The charge rate C: The charge rate C corresponds to the charging speed, that is, the rate at which the electric battery 10 is charged. Preferably, this charge rate is a function of the cycling protocol considered. During the development of the present invention, it was observed that the charge rate can have a significant impact on the behavior and degradation patterns of a LiB. Thus, it was observed that higher charge rates can accelerate the aging process and reduce the overall lifespan of the electric battery 10. The development work of the present invention yielded valuable information on the performance of the electric battery 10 under different charging conditions and thus enabled the development of optimization strategies.

[0061] These five parameters have been identified as having an important effect on the state of degradation of an electric battery 10.

[0062] Next comes the processing step 140. This processing step uses information relating to the initial cycling protocol, the data collected regarding the operating parameters of the electric battery 10, and the choice of the predictive cycling protocol. With these elements and a pre-trained mathematical model, the present invention makes it possible to generate at least one descriptor. This descriptor is then capable of classifying different degradation states of the electric battery 10 according to various variables and parameters. Advantageously, the descriptor classifies the degradation state of the battery 10 according to a set of predetermined classes, such as: a first class called "high loss," a second class called "very high loss," a third class called "low loss," and a fourth class called "moderate loss." The first class can thus correspond to a high degradation rate.The second class may correspond to a very high rate of degradation. The third class may correspond to a low rate of degradation. The fourth class may correspond to a moderate rate of degradation.

[0063] Advantageously, the present invention, by exploiting at least some of these parameters and preferably other so-called secondary parameters, makes it possible to generate a so-called pre-trained mathematical model. Indeed, by using data from manufacturers, integrators, or even users, the development of the present invention has made it possible to design what is called artificial intelligence, that is to say, a pre-trained mathematical model, capable of processing data from parameters relating to an electric battery 10 in order to then design a predictive mathematical model of the degradation state of said electric battery 10, preferably via a prediction of said degradation state as a function of various variables.

[0064] It should be noted that a variable and a parameter can refer to the same data or physical quantity. However, we will speak of a parameter when it is a collected data item and of a variable when it is a data item intended to vary for the calculation of a prediction, for example.

[0065] Preferably, data collection was an important step in building a predictive mathematical model based on machine learning during the development of the present invention. In this type of approach, the quality and quantity of the data collected have a direct impact on the accuracy and effectiveness of the predictive mathematical model thus generated. Preferably, the data collected includes a wide range of parameters to provide a comprehensive view of the performance of the electric battery 10 under different conditions, allowing the machine learning model to make accurate predictions, that is to say to generate a predictive mathematical model.

[0066] The parameters taken into consideration for the design of the pre-trained mathematical model include the parameters described above and also additional or secondary parameters. These additional parameters are advantageously the following: a. A pressure P applied to at least a part of the electric battery 10, said pressure preferably being predetermined and advantageously fixed. This parameter relates to the application of a pressure P on the electric battery 10 during a cycle. This parameter adds a dimension of mechanical constraints to the mathematical model during its design, relative to the performance of the electric battery 10 and its degradation under conditions of mechanical stress; b. A discharge rate D related to the cycling protocol of the electric battery 10: During the development of the present invention, different discharge rates were applied to evaluate their impact on the aging process of the electric battery 10 and its overall performance. It appears that the discharge rate D has a significant impact on the performance degradation of an electric battery. c. A charge limit L: This involves imposing a limit on the charging of an electric battery, such as 80% of its total charging capacity. It appears that using a charge limit L has a significant impact on the degradation of an electric battery's performance. d. A rest period Tp with zero current: This involves imposing a pause during the charging and / or discharging of an electric battery 10. During this rest period, the electric battery 10 has the opportunity to stabilize and recover from the stress imposed during the previous charge or discharge cycle. The duration of the rest period, i.e., the pause time, can influence the performance of the electric battery 10. It should be noted that rest periods that are too short can lead to increased stress on the electric battery 10, potentially accelerating degradation mechanisms. Conversely, long rest periods can allow for better relaxation of chemical stresses, and thus improve the performance and lifespan of the electric battery 10.

[0067] We will now present the variables that can be used to predict the degradation state of an electric battery 10 by the present invention. These variables include parameters that have been at least partially used in the design of the pre-trained mathematical model. These parameters The training parameters also include the parameters discussed previously. These training parameters become prediction variables when predicting the degradation state of the electric battery 10. These prediction variables are intended to be used as adjustable variables, preferably by a user, when predicting the degradation state of the electric battery 10. These variables are preferably the following: a. An initial state of charge of the electric battery 10; b. A final state of charge of the electric battery 10; c. A pressure P' applied to at least a part of the electric battery 10, said pressure preferably being predetermined and advantageously fixed; d. A number N' of charge-discharge cycles of said electric battery 10; e. A temperature T' of the electric battery 10; f. A first charge rate Cl relating to the first cycling protocol; g. A second charge rate C2 relating to the second cycling protocol; h. A third C3 charge rate relating to the third cycling protocol; i. A first discharge rate DI relating to the first cycling protocol; j. A second discharge rate D2 relating to the second cycling protocol; k. A third discharge rate D3 relating to the third cycling protocol; 1. A load limit L'; m. A pause time Tp' with zero current; n. Another cycling protocol;

[0068] It should be noted that for the design of the pre-trained mathematical model, the variables / parameters considered were carefully controlled and varied within a predetermined range to ensure that the data were representative of real-world conditions. This data collection process involved running each protocol multiple times and collecting data at regular time intervals. This data was then stored in a database for processing and training at least one machine learning model.To achieve this, techniques well-known to professionals in the field (see, for example, the book: "An Introduction to Statistical Learning," Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Springer New York, NY, 2013, 978-1-4614-7138-7), such as k-means and support vector machines (SVMs), were used to predict the degradation rate of the electric battery 10 as a function of given parameters. This allowed for the design of a pre-trained mathematical model serving as the basis for [the following]. the creation of the predictive mathematical model according to the present invention. This predictive mathematical model is specifically designed for a given electric battery 10 and not for a so-called "generic" electric battery as in the case of the pre-trained mathematical model.

[0069] Creating a predictive mathematical model can be long and complex, which is why the present invention uses a pre-trained mathematical model as a starting point for developing a predictive mathematical model specific to an electric battery 10, this approach allows for considerable savings in time and system resources.

[0070] During the development of the present invention, a data preprocessing step proved very useful. Indeed, once the data were collected, they were preprocessed to adapt them to machine learning algorithms. This included cleaning the data, removing all irrelevant or redundant features, and transforming the data into a digital format that could be fed into the machine learning models.

[0071] Next, the preprocessed data were divided into training and test sets. The k-means technique was used to group similar data points, and then SVM support vector machines were trained on each group or cluster to predict the degradation rate of the electric batteries 10 for a particular group of data.

[0072] As a reminder, unsupervised k-means analysis aims to identify k groups in the dataset by optimizing the distances between each point and data exhibiting similarities and by maximizing cluster separations using predefined metrics known to the person of the trade.

[0073] The advantage of using a multi-parameter classification is that it can handle data and capture the non-linear relationships between the input parameters and the degradation state of the electric battery 10. It provides a flexible and adaptable framework for integrating different functionalities and optimizing decision boundaries to accurately classify the degradation of the electric battery 10.

[0074] Once the SVM support vector machine is created, it can be used to predict the classification of new data points. To facilitate understanding and decision-making for a user, a decision diagram, such as the one illustrated in [Fig. 4], can be created to illustrate the boundaries between the different classes in a dataset of the SVM model. The decision diagram is a visual representation of the decision boundaries produced by the SVM classifier. In one embodiment, the decision diagram can be generated by the predictive mathematical model.

[0075] A decision plot can show the decision boundaries that the SVM has learned from the training data. Points on one side of the boundary are classified as belonging to one class, while points on the other side are classified as belonging to a different class. Advantageously, and as described below, this data can be visualized based on different pairs of input parameters, i.e., according to various parameters such as those described above. Therefore, the user can control the plot axis, i.e., choose the input variables they wish to visualize.

[0076] Thus, advantageously, the method according to the present invention may also include at least one display step, by at least one display module 260 of said electronic system 200, of at least one graphical representation 60 of said second mathematical model. This display step is, for example, very useful for representing the calculated state of degradation to a user via a screen, for example. It is easy to imagine that the present invention could be fitted to an electric vehicle and that the driver could, via the dashboard, know when the battery 10 is likely to fail and therefore know when maintenance of the battery 10 should be scheduled.

[0077] Preferably, the method according to the present invention may also include a step of interaction by a user with said predictive mathematical model via a user interface 260 of said electronic system 200. In this situation, the user could, for example, vary a variable of the prediction and thus see in real time the evolution of the degradation state of the electric battery 10 over time. For example, the user could charge their battery 10 according to the first cycling protocol, but when they make this prediction and switch to the second cycling protocol, they could then realize that the second cycling protocol might be more advantageous for their battery 10, taking into account the battery and its specific parameters.

[0078] Thus, for example, the development of a graphical user interface (GUI) 260 is a step in the development of the present invention which has made the second predictive mathematical model more accessible to a wider audience, including researchers, engineers and designers working on battery systems, and especially to the end user, for example the driver of an electric vehicle 10. The graphical interface 260 preferably provides an intuitive and user-friendly interface which allows the user to easily control the various input parameters which affect the aging process of lithium-ion batteries 10, i.e. which affect the prediction.

[0079] This tool allows the user to enter their own cycling data and obtain a prediction of the degradation rate of their electric battery 10. The user can The expected results can also be visualized in the form of decision graphs 60. This type of graph can include various zones 61, 62, 63, and 64 indicating different scenarios based on input parameters. Thus, the present invention also allows for improved decision-making. Figure 4 shows an example of the implementation of such a graphical interface 260. In this figure, the first class 61 is illustrated, as well as the second class 62, the third class 63, and the fourth class 64, as previously described.

[0080] According to an advantageous embodiment, the method may also include at least one feedback control step, via the BMS 40 for example, of at least one parameter of the battery 10. This embodiment is illustrated in [Fig. 3]. In this figure, a feedback control loop 50 is shown. Indeed, since the electronic system 200 has generated the predictive mathematical model via the method 100, the BMS 40 can then use this predictive mathematical model to adjust one or more parameters of the electric battery 10, preferably in real time, so as to optimize its performance.

[0081] It should be noted that according to one embodiment, the first collection step 110 of said information relating to an initial cycling protocol may include a step of acquiring said information from at least one database 20 and / or from at least one set of sensors 30.

[0082] According to one embodiment, the electronic system is in communication, via a communication module 260, with a database 20 and / or a set of sensors 30. This set of sensors 30 is configured to allow the measurement, preferably in real time, of various parameters of an electric battery 10.

[0083] The present invention also relates to a computer program product comprising a plurality of instructions which, when executed by at least one processor, execute method 100 according to the present invention.

[0084] The present invention also relates to a non-transient memory medium comprising a computer program product according to the present invention.

[0085] We will now describe, through [Fig. 2], an electronic system 200 according to an embodiment of the present invention. Preferably, this electronic system 200 comprises at least one processor and advantageously at least one non-transient memory.

[0086] This electronic system 200 is preferably configured to perform process 100. This electronic system 200 advantageously comprises at least: a. A communication module 260. This communication module 260 is preferably configured to communicate with at least one database 20 and / or at least one set of sensors 30; b. A first collection module 210. This first collection module 210 is advantageously configured to collect, preferably via the communication module 260, at least one piece of information relating to the initial cycling protocol of an electric battery 10 under consideration; c. A selection module 220. This selection module 220 is preferably configured to select at least one prediction cycling protocol from a plurality of cycling protocols; d. A second collection module 230. This second collection module 230 may be identical to the first collection module 210 according to one embodiment. This second collection module 230 is advantageously configured to collect, preferably via the communication module 260, at least one data point relating to a set of parameters; e. A computing module 240. This computing module 240 advantageously includes at least one pre-trained mathematical model. This computing module 240 is advantageously configured to process said information, said data, and said prediction cycling protocol by machine learning. This computing module 240 is preferably configured to predict a degradation state of said electric battery 10 by generating at least one predictive mathematical model configured to mathematically represent at least one evolution of the degradation state of said electric battery 10.

[0087] It should be noted that a module can include one or more other modules.

[0088] According to one embodiment, the electronic system 200 may include a display module. This display module, as previously stated, may be configured to display at least one graphical representation of said predictive mathematical model. This graphical representation may be an aid to decision-making.

[0089] Preferably, the electronic system 200 may include a user interface 260. This user interface 260 may be configured to allow at least one interaction by a user with said predictive mathematical model. This allows the user, for example, to select the parameters they wish to take into account in the prediction. It also allows them to vary the value(s) of certain variables to see the impact this has on the prediction and therefore on the future state of degradation of the electric battery 10.

[0090] Advantageously, the electronic system 200 may include a control module. This control module is preferably configured to retroactively control at least one parameter of said electric battery 10. According to one embodiment, this control module may be a BMS 40.

[0091] The present invention thus allows an improvement in the performance of an electric battery by anticipating its degradation rate through the generation of a predictive mathematical model allowing predictions.

[0092] The invention is not limited to the embodiments described above and extends to all embodiments covered by the claims.

[0093] All publications referred to in this description are incorporated by reference in their entirety for all purposes of aiding in the understanding of the present invention.

[0094] Numerical references

[0095] 10 Electric battery

[0096] 20 Database

[0097] 30 Sensor assembly

[0098] 40 Electric Battery Management System (BMS)

[0099] 50 Feedback loop

[0100] 60 Decision Graph

[0101] 61 First class

[0102] 62 Second class

[0103] 63 Third class

[0104] 64 Fourth class

[0105] 100 Prediction method

[0106] 110 First collection step

[0107] 120 Selection step

[0108] 130 Second collection stage

[0109] 140 Machine learning processing step

[0110] 141 Calculation step [YES] 142 Classification step

[0112] 143 Calculation step

[0113] 144 Step of obtaining a predictive mathematical model

[0114] 200 Electronic system

[0115] 210 First collection module

[0116] 220 Selection Module

[0117] 230 Second collection module

[0118] 240 Calculation Module

[0119] 250 Communication Module

[0120] 260 Graphical User Interface

Claims

1. Demands Method for predicting (100) the degradation state of at least one electric battery (10), said method (100) being configured to be executed by at least one electronic system (200), said method (100) comprising: a. A first step of collecting (110) 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 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 (120) of at least two prediction cycling protocols taken from said plurality of cycling protocols, including the first and second cycling protocols; c. A second collection step (130) 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 (140) by machine learning from at least one pre-trained mathematical model, from said at least one piece of information, from said at least one data point, and from For each selected prediction cycling protocol, said processing step includes: i. A computation step (141) of at least one descriptor by applying said pre-trained mathematical model in a multidimensional computation space, said multidimensional computation space being configured such that each parameter of said parameter set defines a dimension of said multidimensional computation space; ii. A classification step (142) of the state of degradation from said descriptor relative to a set of predetermined classes; iii. A calculation step (143) of 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); • Pressure applied to at least 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 level Cl relative to the first cycling protocol; • A second C2 charge rate relating to the second cycling protocol; • A third C3 charge rate relating to the third cycling protocol; • A first discharge rate DI 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 load limit; • A pause time with zero current; • Another cycling protocol; iv. A step of obtaining (144) 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 feedback control, by at least one management system (40) of the electric battery (10), of 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 display step, by at least one display module of said electronic system (200), of at least one graphical representation (60) of said predictive mathematical model.

3. Method (100) according to any one of the preceding claims comprising at least one step of interaction by a user with said predictive mathematical model via a user interface (260) of said electronic system (200).

4. Method (100) according to any one of the preceding claims, wherein in the feedback control step, the management system (40) of the electric battery (10) adjusts the charging and discharging parameters of the battery.

5. Method (100) according to the preceding claim wherein the feedback control step is carried out in real time.

6. Method (100) according to any one of the preceding claims wherein at least one of respectively the first collection step (110) and the second collection step (130) comprises the acquisition respectively of said information and said data from at least one database (20) and / or at least one set of sensors (30).

7. Product computer program, preferably stored on a non-transient memory medium, comprising instructions which, when executed by at least one of a processor, a computer, executes the method (100) according to any one of the preceding claims.

8. Non-transient memory carrier comprising a computer program product according to claim 7.

9. Electronic system (200) configured to perform the method (100) according to any one 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 of sensors (30); b. A first collection module (210) configured to collect, preferably via the communication module (250), information relating to the initial cycling protocol of the electric battery (10); c. A selection module (220) configured to select at least two predictive cycling protocols from a plurality of cycling protocols and including the first and second cycling protocols; d. A second collection module (230) configured to collect, preferably via the communication module (250), data relating to a set of parameters; e. A computing module (240) configured to: i.i. Process, using machine learning from at least one pre-trained mathematical model, at least one piece of information, at least one data point, and each selected prediction cycling protocol; ii. Calculate at least one descriptor by applying said pre-trained mathematical model in a multidimensional computational space; iii. Classify the degradation state of said electric battery (10) from 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 a part of the electric battery (10).

10. • A number N' of charge-discharge cycles of said electric battery (10); • A temperature T' of the electric battery (10); • A first charge level Cl relative to the first cycling protocol; • A second C2 charge rate relating to the second cycling protocol; • A third C3 charge rate relating to the third cycling protocol; • A first discharge rate DI 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 load limit; • A pause time with zero current; • Another cycling protocol; f. Generate at least one predictive mathematical model, said predictive mathematical model being configured to mathematically represent the evolution of the state of degradation of said electric battery (10) as a function of at least said variable, perform at least one feedback control step, by at least one management system (40) of the electric battery (10), of at least one variable as a function of said predictive mathematical model and / or at least one predetermined setpoint. 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 permit at least one interaction by a user with said predictive mathematical model.

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