Method and device for sorting lithium-ion battery cells according to their type

Abstract

Method (100) for identifying the type of a lithium-ion battery cell among at least two battery types, each associated with a different full-charge voltage. The method includes at least one discrimination step (200) comprising one or more iterations (210). Each iteration comprises: - collecting (211) a plurality of float current measurements taken at the cell during constant-voltage charging at a voltage corresponding to the lowest full-charge voltage among those associated with the different battery types, - an EMD decomposition (213) of the float current derivative, - a calculation (215) of a spectral density from the intrinsic components of the EMD decomposition, - a search (216) for a minimum of the spectral density. The cell type is classified (220) according to the result of the minimum search. Figure for abstract: Fig. 1

Description

Title of the invention: Method and device for sorting lithium-ion battery cells according to their type. Field of the invention

[0001] The present invention relates to the field of lithium-ion battery management. More particularly, a method and a device are proposed for identifying the type of a lithium-ion battery cell among several battery types, each associated with a different full-charge voltage. State of the art

[0002] Sorting lithium-ion battery cells according to their specific chemistry is essential for recycling and end-of-life management of batteries. Indeed, battery cells with different chemistries will require different treatment processes for recycling, or different applications for reuse in a second life.

[0003] Among the different types of lithium-ion battery chemistry, examples include LFP (Lithium Iron Phosphate, LiFePO4), NCA (Nickel Cobalt Aluminum, LiNiCoAlO2), and NMC (Nickel Manganese Cobalt, LiNiMnyCoO2). These different battery types are used in particular in the industrial and automotive sectors. Other types of lithium-ion batteries exist, notably for powering smaller devices, such as LCO (Lithium Cobalt Oxide, LiCoO2) and LMO (Lithium Manganese Oxide, LiMn2O4) chemistry.

[0004] The different chemistries that make up the lithium-ion battery family generally have different full-charge voltages. For example, the full-charge voltage of an LFP type battery is 3.65 V; the full-charge voltage of an NCA type battery is 4.2 V; the full-charge voltage of an NMC type battery is 4.3 V.

[0005] These different full charge voltage values ​​are provided by the battery manufacturers, and they should be strictly observed to avoid safety problems such as thermal runaway.

[0006] To determine the health of a battery cell at the end of its first life, in order to decide, for example, whether the cell should be recycled or whether it can be reused in a second life, it is generally necessary to perform several charge and discharge cycles on the cell. To do this, it is necessary to know the type of cell in order to determine the appropriate charging voltage.

[0007] It is sometimes possible to identify the type of chemistry of a battery cell based on a visual identification (barcode, label or physical characteristic of the battery case for example).

[0008] When the type of a cell cannot be determined by simple visual identification, spectroscopic analysis can be used to identify the chemical elements present within the cell. For example, battery sorting methods exist using X-ray fluorescence spectrometry (XRF) or electrochemical impedance spectroscopy (EIS). However, these methods are relatively complex to implement. Description of the invention

[0009] The present invention aims to remedy all or part of the drawbacks of the prior art, in particular those set out above, by proposing an innovative and easy-to-implement solution for sorting lithium-ion battery cells according to their chemistry type.

[0010] To this end, and according to a first aspect, a method is proposed for identifying the type of a lithium-ion battery cell among at least two battery types, each associated with a different full-charge voltage. The method comprises at least one discrimination step comprising one or more iterations. Each iteration comprises: - a collection of a plurality of current measurements taken at the cell level during a constant voltage phase, or CV phase, of a "constant current - constant voltage" load, or CC-CV load, of the cell, the CV phase being carried out with a charging voltage corresponding to the lowest full charge voltage among the full charge voltages associated with said at least two types of battery, the plurality of measurements forming a "floating current" signal, - a derivative of the floating current signal to obtain a derivative signal of the floating current, - a decomposition into empirical modes of the floating-point current derivative signal in order to obtain a representation in the form of a sum of a residual signal and one or more intrinsic components, - a calculation of a spectral density from the intrinsic components obtained by decomposition into empirical modes, - a search for a minimum of the spectral density.

[0011] The discrimination step includes a classification of the cell type according to a criterion determined from a result of the minimum search for said at least one iteration.

[0012] In particular modes of implementation, the method may further include one or more of the following characteristics, taken individually or in all technically possible combinations.

[0013] In particular embodiments, said at least one discrimination step comprises several iterations, and the criterion is determined based on the results of the minimum searches obtained for the different iterations.

[0014] In particular embodiments, the criterion is satisfied if a minimum fmin of the spectral density PSD for which there exists f < such that PSI)(fj is found for a predetermined number or ratio of iterations.

[0015] In particular embodiments, the method is used to identify the cell type among at least three battery types, each associated with a different full-charge voltage. The method includes a first discrimination step in which the CV phase is performed for each iteration with a charging voltage corresponding to the lowest full-charge voltage among the full-charge voltages associated with said at least three battery types. If the criterion is met for the first discrimination step, the cell type corresponds to the type associated with the lowest full-charge voltage.If the criterion is not met for the first discrimination, the cell type corresponds to a different type than the one associated with the lowest full-charge voltage, and the method includes a second discrimination in which the CV phase is performed for each iteration with a charge voltage corresponding to the second lowest full-charge voltage among the full-charge voltages associated with said at least three battery types. If the criterion is met for the second discrimination, the cell type corresponds to the type associated with the second lowest full-charge voltage. If the criterion is not met for the second discrimination, the cell type corresponds to a different type than the one associated with the second lowest full-charge voltage.

[0016] In particular embodiments, the method includes a preliminary step of comparing a cell voltage with the lowest full charge voltage among the full charge voltages associated with said at least three battery types, and the first discrimination is implemented only if the cell voltage is less than or equal to the lowest full charge voltage.

[0017] In particular embodiments, the different types of battery considered for the cell include the LFP (Lithium-Iron-Phosphate), NCA (Nickel-Cobalt-Aluminium) and NMC (Nickel-Manganese-Cobalt) types.

[0018] In particular embodiments, the spectral density corresponds to a spectral density of a sum of the intrinsic components obtained by decomposition into empirical modes.

[0019] In particular embodiments, the spectral density corresponds to a sum of spectral densities calculated respectively for each of the intrinsic components obtained by the decomposition into empirical modes.

[0020] In particular embodiments, each iteration of said at least one discrimination step includes a statistical reliability analysis of the CV phase as a function of the intrinsic components of the derivative signal of the floating current.

[0021] In particular embodiments, the reliability analysis includes, for each intrinsic component of the derivative signal of the floating current, a calculation of an entropy of the intrinsic component.

[0022] In particular embodiments, the reliability analysis includes an entropy calculation of a sum of the intrinsic components of the derivative signal of the floating current.

[0023] According to a second aspect, a device is proposed for identifying the type of a lithium-ion battery cell among at least two battery types, each associated with a different full-charge voltage. The device comprises a sensor adapted to provide current measurements taken at the cell during a CV phase of a CC-CV charging cycle of the cell, and a computing unit configured to implement the method according to any one of the embodiments described above. Presentation of the figures

[0024] The invention will be better understood upon reading the following description, given by way of non-limiting example, and made with reference to the following figures:

[0025] [Fig. 1] a schematic representation of the main steps of an example of implementation of the method according to the invention for sorting a lithium-ion battery cell according to its type,

[0026] [Fig.2] a graph representing the current flowing in a lithium-ion battery cell during four successive CC-CV charging cycles,

[0027] [Fig.3] a graph representing a floating current signal flowing in a lithium-ion battery cell during a CV phase of a CC-CV charging cycle of the cell,

[0028] [Fig.4] a graph representing a derivative signal of the floating current,

[0029] [Fig.5] a graph representing a decomposition into empirical modes of the derivative signal of the floating current shown in [Fig.4],

[0030] [Fig.6] a schematic representation of a particular embodiment of the invention with at least two discrimination steps,

[0031] [Fig.7] a graph representing the spectral densities calculated respectively for two LFP type cells, two NCA type cells and two NMC type cells during a discrimination step using a load voltage corresponding to the full load voltage of an LFP type cell (3.65 V),

[0032] [Fig.8] a graph representing the spectral densities calculated respectively for two NCA type cells and two NMC type cells during a discrimination step using a load voltage corresponding to the full load voltage of an NCA type cell (4.2 V),

[0033] [Fig.9] a schematic representation of a device according to the invention allowing identification of the type of a lithium-ion battery cell.

[0034] In these figures, identical reference numerals from one figure to another designate identical or analogous elements. For clarity, the elements shown are not necessarily to the same scale, unless otherwise stated. Detailed description of the invention

[0035] A lithium-ion battery cell charging cycle typically comprises two phases: a first charging phase at constant current, or CC phase (for "Constant Current"), and a second charging phase at constant voltage, or CV phase (for "Constant Voltage"). This is referred to as a CC-CV charging cycle. In the present invention, we are interested in the current flowing through the cell during the CV phase. This current is generally called the "floating current."

[0036] Although the first charging phase (CC phase) is typically carried out at constant current, more elaborate charging schemes can exist during this first phase, with, for example, a variation in current to maximize the charging rate while remaining within the charging range compatible with the cell. The end of the first charging phase is then marked by reaching a voltage threshold that triggers the switchover to the second charging phase at constant voltage (CV phase). For simplicity, these particular cases of the first charging phase will be considered as also covered by the term CC-CV charging, "constant current - constant voltage," used in this application.

[0037] Figure 2 is a graph representing four successive DC-CV charging cycles of a lithium-ion battery cell. The current flowing through the cell is represented on the ordinate (in amperes) and time is represented on the abscissa (in seconds). Each cycle comprises a CC phase 31 and a CV phase 32. As illustrated in the graph in [Fig. 2], the current flowing through the cell during the CC phase 31 is approximately constant, and it exhibits an exponential decrease during the CV phase 32. [Fig. 3] is a graph representing in more detail the floating current of a CV phase 32 of the cell.

[0038] Figure 1 schematically represents the main steps of an example of implementing method 100 according to the invention for sorting a lithium-ion battery cell according to its type. The cell is identified from among at least two battery types, each associated with a different full-charge voltage.

[0039] Method 100 comprises at least one discrimination step 200. The maximum number of discrimination steps 200 to be performed depends on the number of battery types to which the cell is likely to belong. Each discrimination step 200 comprises one or more iterations 210. Each iteration 210 comprises: - a collection 211 of a plurality of floating current measurements acquired at the cell level during a CV phase carried out with a charging voltage corresponding to the lowest full charge voltage among the full charge voltages associated with the different types of battery considered; the plurality of measurements form a "floating current" signal; - a 212 derivative of the floating current signal to obtain a derivative signal of the floating current; - an empirical mode decomposition 213 (or EMD decomposition, for "Empiricical Mode Decomposition" in English) of the derivative signal of the floating current, in order to obtain a representation in the form of a sum of a residual signal and one or more intrinsic components; - a calculation 215 of a spectral density from the intrinsic components obtained by the EMD decomposition; - a search 216 for a minimum of the spectral density.

[0040] The discrimination step 200 further includes a classification 220 of the cell type according to a criterion determined from the result of the minimum search 216 obtained for said iteration 210. When the discrimination step 200 includes several iterations 210, the criterion can be determined according to the results of the minimum searches 216 obtained for the different iterations 210. For example, the criterion can be satisfied as soon as a minimum is found for one iteration 210, or as soon as a minimum is found for a predetermined number of successive iterations, or if a minimum is found for a predetermined ratio of iterations. However, nothing would prevent us from considering other criteria for classifying the cell.

[0041] When there are several iterations, it is preferable to return the cell to its initial voltage with a discharge cycle before each new iteration.

[0042] By way of non-limiting example, the charge or discharge rate may be C / 2. The rate is often expressed in units of "C", where IC represents a charge current that would fully charge the cell in one hour (for example, a charge rate of C / 2 for a 2Ah cell corresponds to a charge current of IA). The charge current to be used during the CV phase can be estimated based on the cell format (for example, a charge current of IA can be used for 18650 and 21700 format cells and for small cylindrical or prismatic cells. A higher charge current can be used for larger cells (for example, cells with a capacity greater than or equal to 5Ah).

[0043] In step 211, floating current measurements during the CV phase are performed by a current sensor connected to the cell. For example, the measurements are taken with an acquisition frequency of between one and sixty seconds, for an acquisition duration of five to thirty minutes. However, there is nothing preventing the measurements from being taken with a different acquisition frequency and / or for a different acquisition duration. It is advantageous to use between twenty and one hundred measurements per CV phase (using a larger number of measurements does not necessarily imply a significant improvement in the method; using a smaller number of measurements may, on the other hand, limit the method's performance). All the measurements collected during the collection step 110 form a "floating current" signal. The graph in [Fig. 3] represents an example of a floating current signal obtained in the collection step 211.

[0044] In the derivation step 212, the floating current signal obtained in step 211 is differentiated to obtain a floating current derivative signal. The graph in [Fig. 4] shows an example of a floating current derivative signal obtained in the derivation step 212 (it is the derivative of the floating current signal shown in the graph in [Fig. 3]).

[0045] In step 213, the floating current derivative signal is decomposed using an empirical mode decomposition (EMD decomposition). It should be noted that the floating current signal is generally too "smooth" and does not easily lend itself to empirical mode decomposition; this is why we are interested in its derivative, even though nothing might initially suggest that this floating current derivative signal could contain information allowing the discrimination of the cell chemistry type based on the charging voltage that was applied during the CV phase.

[0046] Empirical mode decomposition consists of decomposing a signal into a sum of functions, in a similar way to what Fourier series decomposition or wavelet decomposition does.

[0047] One of the particularities of decomposition into empirical modes is that the basis of functions into which the signal is decomposed is not known a priori, but is constructed adaptively according to the properties of the signal.

[0048] Empirical mode decomposition corresponds to the first part of the Hilbert-Huang Transform (HHT). Empirical mode decomposition consists of decomposing a signal into a sum of a residual signal and intrinsic mode functions (IMFs). In this application, these intrinsic mode functions are also referred to as "intrinsic components".

[0049] As previously stated, the intrinsic components are not defined analytically. Rather, they are determined adaptively according to the properties of the signal.

[0050] An intrinsic component (IMF) resulting from an empirical mode decomposition (EMD) must satisfy the following requirements: - the number of extrema (i.e., the sum of the number of local maxima and the number of local minima) and the number of zero crossings of the intrinsic component must be equal or differ by a maximum of one; - at every point of the intrinsic component, the average value of the envelope defined by the local maxima and of the envelope defined by the local minima is zero.

[0051] A signal s(t) decomposed by EMD can then be written in the form:

[0052] £ ( / ) = r ( ^ ) +

[0053] In this expression, corresponds to the residual signal, N is the number of intrinsic components of the EMD decomposition, and ct is the intrinsic component with index . Each successive intrinsic component contains oscillations of a frequency lower than that of the preceding one. The residual signal corresponds to a general trend of the signal

[0054] The decomposition into empirical modes involves a succession of sifting processes. The first sifting process takes the signal s(t) directly as input. The sifting process consists of identifying all the local extrema of the input signal and linking the local maxima and minima, respectively, by cubic spline interpolation, in order to obtain a upper envelope, and respectively a lower envelope. An average between the upper and lower envelopes can then be calculated and subtracted from the input signal. If the resulting intermediate signal (subtracting the average of the upper and lower envelopes from the input signal) is not an intrinsic component, the sieving process is repeated on the intermediate signal (which thus becomes the input signal for a new sieving process) until an intrinsic component is obtained. The sieving processes are repeated until the last intrinsic component is obtained, i.e., for example, until the intermediate signal becomes monotonic or has only one local extremum. The remaining signal then corresponds to the residual signal.

[0055] A stopping criterion can be defined for the sieving process. For example, the stopping criterion is satisfied if the standard deviation between the results of two successive sieving processes is less than or equal to a predetermined stopping threshold. The stopping threshold can typically be between 0.2 and 0.3.

[0056] The document "The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time serial analysis", Norden E. Huang et al., Proc. R. Soc. Lond. A (1998) 454, p. 903-995, describes in detail the decomposition into empirical modes, particularly in its sections 4 and 5.

[0057] Algorithms for decomposing into empirical modes are available in programming libraries, for example in MATLAB or Python.

[0058] The graph in Figure 5 represents an example of the empirical mode decomposition of the floating-point current derivative signal shown in Figure 4. In the example considered and illustrated in Figure 5, the empirical mode decomposition yielded a single intrinsic component 34 (ci) and a residual signal 33. It should be noted that, in other examples, a larger number of intrinsic components can be obtained. However, the number of intrinsic components generally remains below five. It is advantageous to set the stopping threshold at a relatively low level, on the order of 0.2, to extract a maximum of information from the floating-point current derivative signal. Using a lower stopping threshold imposes particularly long computation times.

[0059] As will be seen later, it is also possible to filter out certain intrinsic components that would be deemed irrelevant, for example based on an entropy of the intrinsic component (an entropy that is too low may mean that the component does not contain enough relevant information, an entropy that is too high may correspond to an outlier resulting from an error in the EMD decomposition).

[0060] Step 215 corresponds to calculating a spectral density from the intrinsic components obtained by EMD decomposition. Different methods can be considered for calculating this spectral density.

[0061] According to a first example, the spectral density corresponds to a spectral density of a sum of the intrinsic components obtained by the EMD decomposition.

[0062] According to a second example, the spectral density corresponds to a sum of spectral densities calculated respectively for each of the intrinsic components obtained by the EMD decomposition.

[0063] The spectral density can in particular be calculated by the Welch method. However, nothing prevents the use of other spectral density estimation methods, such as Bartlett's or Blackman-Tukey's methods. Spectral density can also be calculated as the square of the magnitude of the Fourier transform of the signal (intrinsic component or sum of intrinsic components) divided by the integration time.

[0064] The spectral density can optionally be calculated from a Hilbert transform of the signal under consideration. The Hilbert transform extends a real signal into the complex domain. The transformed signal then exhibits zero amplitude responses at zero frequencies. This avoids artifacts when exploiting the information contained in the processed signal. The Hilbert transform thus optimizes the spectral density calculation.

[0065] The spectral density can optionally be normalized, for example with respect to a maximum value or an average value of the spectral density, or with respect to a predetermined theoretical value.

[0066] Algorithms enabling these Hilbert transformation calculations and spectral density estimation are available in programming libraries, for example in MATLAB or in Python.

[0067] Figures 7 and 8 show examples of spectral densities calculated for six battery cells (two LFP type cells, two NCA type cells and two NMC type cells). Each spectral density is calculated by the Welch method from a signal corresponding to the sum of the intrinsic components obtained by the EMD decomposition of the floating current derivative signal from a CV phase of the cell.

[0068] Figure 7 shows spectral densities calculated for the six cells after a CV phase with a charging voltage of 3.65V. This voltage value corresponds to the full-charge voltage of an LFP-type battery cell. It is the lowest full-charge voltage among the three battery types considered here (the full-charge voltage of an NCA-type cell is 4.2V; the full-charge voltage of an NMC-type cell is 4.3V). In Figure 7, curve 41 represents the spectral density calculated for the first LFP type cell; curve 42 represents the spectral density calculated for the second LFP type cell; curve 43 represents the spectral density calculated for the first NCA type cell; curve 44 represents the spectral density calculated for the second NCA type cell; curve 45 represents the spectral density calculated for the first NMC type cell; curve 46 represents the spectral density calculated for the second NMC type cell.

[0069] Figure 8 shows the spectral densities calculated for the two NCA-type cells and the two NMC-type cells after a CV phase with a charging voltage of 4.2V. This voltage value corresponds to the full-charge voltage of an NCA-type cell (it is lower than the full-charge voltage of an NMC-type cell). In Figure 8, curve 51 represents the spectral density calculated for the first NCA-type cell; curve 52 represents the spectral density calculated for the second NCA-type cell; curve 53 represents the spectral density calculated for the first NMC-type cell; and curve 54 represents the spectral density calculated for the second NMC-type cell.

[0070] In Figures 7 and 8, the spectral densities are normalized, both in value (along the y-axis) and in frequency (along the x-axis). Artifacts can be observed at the minimum and maximum frequency values. As mentioned previously, these artifacts can be avoided by using a Hilbert transform. It is also possible to ignore them by reducing the relevant frequency range (for example, by using a window set back three to five frequency points within the spectrum).

[0071] As illustrated in Figures 7 and 8, it is possible to classify (step 220 of [Fig.1]) the type of a cell by searching (step 216 of [Fig.1]) for a minimum of the spectral density calculated for that cell.

[0072] More specifically, if the spectral density exhibits a significant minimum, then this means that the associated cell corresponds to the type of cell whose full-charge value was used for the CV phase considered. Otherwise, this means that the associated cell corresponds to another type.

[0073] In [Fig. 7], a significant minimum can indeed be observed in curves 41 and 42, which correspond to the spectral densities of LFP-type cells (the curves in [Fig. 7] were obtained with a full-load voltage corresponding to the LFP type). In contrast, curves 43, 44, 45, and 46, which correspond to the spectral densities of NCA or NMC-type cells (for which the full-load voltage is higher than that of an LFP-type cell), do not show a significant minimum.

[0074] Similarly, in [Fig. 8], a significant minimum can be observed in curves 51 and 52, which correspond to the spectral densities of NCA-type cells (the curves in [Fig. 8] were obtained with a full-load voltage corresponding to the NCA type). In contrast, curves 53 and 54, which correspond to the spectral densities of NMC-type cells (for which the full-load voltage is higher than that of an NCA-type cell), do not show a significant minimum.

[0075] A significant minimum of the spectral density corresponds, for example, to a minimum of the spectral density PSD for which there exists f < such that ) < PSDÿ) ■ However, nothing would prevent using a different condition for the search 216 for the minimum spectral density. The appropriate criterion to use can, for example, be determined empirically in the laboratory, possibly depending on the types of battery likely to be encountered.

[0076] Figure 6 schematically represents the main steps of a particular embodiment of the invention for identifying the type of a battery cell among three different types. The method may require up to two discrimination steps to identify the cell type.

[0077] In the example considered and illustrated in [Fig. 6], the first type (“Type 1”) corresponds to the LFP type, associated with a full-load voltage UFi of 3.65V. The second type (“Type 2”) corresponds to the NCA type, associated with a full-load voltage UF2 of 4.2V. The third type (“Type 3”) corresponds to the NMC type, associated with a full-load voltage UF3 of 4.3V. Therefore, UFi < UF2 < UF3.

[0078] As illustrated in [Fig. 6], method 100 comprises a first discrimination step 200-1 in which the CV phase is performed for each iteration with the UFi charging voltage corresponding to the lowest full-charge voltage among the full-charge voltages associated with the different battery types considered. There may be several iterations, for example, between three and ten iterations. However, nothing would prevent performing only a single iteration.

[0079] At the end of the iterations of the first discrimination 200-1, an identification criterion makes it possible to determine (at the classification step 220-1 of the first discrimination step) whether the type of the cell corresponds to the first type associated with the full load voltage UFi (type LFP, when the criterion is satisfied) or to one of the other types considered (type NCA or NMC, when the criterion is not satisfied).

[0080] For example, the criterion is satisfied if a significant minimum is found for the power spectral density for at least one iteration, or for a predetermined number of iterations (possibly successive) or for a particular ratio of iterations.

[0081] If the criterion is not met, method 100 includes a second discrimination step 200-2 in which the CV phase is performed for each iteration with a charging voltage UF2 corresponding to the second lowest full-charge voltage among the full-charge voltages associated with the different battery types considered. Again, the second discrimination step 200-2 may include one or more iterations. The number of iterations used in the first discrimination step 200-1 may be the same as, or different from, the number of iterations used in the second discrimination step 200-2.

[0082] At the end of the iterations of the second discrimination step 200-2, an identification criterion determines (in the classification step 220-2 of the first discrimination) whether the cell type corresponds to the second type associated with the full-load voltage UF2 (NCA type, when the criterion is met) or whether it corresponds to the third type (NMC type, when the criterion is not met). The identification criterion used at the end of the first discrimination step 200-1 may be the same as or different from that used at the end of the second discrimination step 200-2 (for example, the condition used for the search 216 for a significant minimum of the spectral density and / or the number of iterations for which the condition must be met may vary).

[0083] As illustrated in [Fig. 6], Method 100 may further include a preliminary step 110 of comparing a cell voltage with the full-load voltage UFi. If the cell voltage is greater than UFi, then the first discrimination 200-1 can be omitted and the second discrimination 200-2 is sufficient to determine the cell type. Indeed, if the cell voltage is greater than UFi, then it cannot be of the LFP type, and it is necessarily of the NCA or NMC type. Method 100 could also include a comparison of the cell voltage with the full-load voltage UF2 (this is not shown in [Fig. 6]). If the cell voltage is greater than UF2, then it is necessarily of the NMC type because it cannot be of the LFP or NCA type.

[0084] When the number of battery types to which the cell is likely to belong is greater than three, Method 100 described above can be extended by increasing the maximum number of discrimination steps that may be required to identify the battery type. When the number of battery types to which the cell is likely to belong is equal to NT, Method 100 may require up to |j discrimination steps to identify the type of the cell.

[0085] As illustrated in [Fig. 1], the method 100 according to the invention may also include an optional step 214 of statistical reliability analysis of the phase CV associated with an iteration, or an intrinsic component of the floating current derivative signal for the considered CV phase. An intrinsic component or a CV phase deemed unreliable can then be discarded.

[0086] In particular, the statistical reliability of an intrinsic component or of a CV phase can be estimated as a function of an entropy calculated for each intrinsic component, or as a function of an entropy calculated for a sum of the intrinsic components of the temperature signal.

[0087] Various methods for calculating entropy can be considered, such as a Shannon entropy calculation or a Kolmogorov entropy calculation. For example, CV phases for which the entropy of the sum of the intrinsic components is too low (below a predetermined threshold) are filtered out. A Shannon entropy threshold between 0.25 and 0.5 can be considered. When the entropy is too low, it may mean that it is not possible to distinguish dynamic fluctuations of the signal from background noise.

[0088] Figure 9 schematically represents a device 10 for identifying the type of a lithium-ion battery cell from among at least two battery types, each associated with a different full-charge voltage. The device 10 includes a sensor 13 adapted to provide current measurements taken at the cell 21 during a CV phase of a CC-CV charging cycle of the cell 21. The device 10 also includes a computing unit 12 configured to implement method 100 according to any one of the embodiments described above.

[0089] Communication between the sensor 13 and the calculation unit 12 (for the transmission of current measurements) can be implemented by wired communication means or by wireless communication means.

[0090] The processing unit 12 and the sensor 13 can be part of a battery management system (BMS). According to another example, only the sensor 13 is part of a BMS and the processing unit 12 belongs to a computer to which the current measurements are transmitted.

[0091] The above description clearly illustrates that, by its various characteristics and their advantages, the present invention achieves the stated objectives. In particular, the analysis of a spectral density calculated from the intrinsic components of the EMD decomposition of the floating current derivative offers an innovative and relatively easy-to-implement solution for sorting lithium-ion battery cells according to their chemistry type.

[0092] The solution was presented using LFP, NCA, and NCM types as examples. However, there is nothing preventing the solution from being applied to other types of lithium-ion battery cells.

Claims

1.

2. Demands Method (100) for identifying the type of a lithium-ion battery cell (21) among at least two battery types, each associated respectively with a different full-charge voltage, the method (100) comprising at least one discrimination step (200) comprising one or more iterations (210), each iteration (210) comprising: - a collection (211) of a plurality of current measurements taken at the cell (21) during a constant voltage phase, or CV phase, of a "constant current - constant voltage" charge, or CC-CV charge, of the cell (21), the CV phase being carried out with a charge voltage corresponding to the lowest full charge voltage among the full charge voltages associated with said at least two types of battery, the plurality of measurements forming a "floating current" signal, - a derivative (212) of the floating current signal to obtain a derivative signal of the floating current, - a decomposition into empirical modes (213) of the derivative signal of the floating current in order to obtain a representation in the form of a sum of a residual signal and one or more intrinsic components, - a calculation (215) of a spectral density from the intrinsic components obtained by decomposition into empirical modes (213), - a search (216) for a minimum of the spectral density, the discrimination step (200) comprising: - a classification (220) of the type of cell (21) according to a criterion determined from a result of the search (216) of minimum for said at least one iteration (210). Method (100) according to claim 1 in which said at least one discrimination step (200) comprises several iterations (210), and the criterion is determined according to the results of the minimum searches (216) obtained for the different iterations (210).

3. Method (100) according to any one of claims 1 to 2 wherein the criterion is satisfied if a minimum f of the spectral density PSD for which there exists f < such that PSri f ) < is found for a predetermined number or ratio v* min) — 2 of iterations.

4. Method (100) according to any one of claims 1 to 3 for identifying the type of cell (21) among at least three types of battery, each associated respectively with a different full-charge voltage; the method (100) comprises a first discrimination (200-1) for which the CV phase is carried out for each iteration with a charge voltage corresponding to the lowest full-charge voltage among the full-charge voltages associated with said at least three types of battery; if the criterion is satisfied for the first discrimination (200-1), the type of cell (21) corresponds to the type associated with the lowest full-charge voltage;If the criterion is not met for the first discrimination (200-1), the cell type (21) corresponds to a different type than the type associated with the lowest full-charge voltage, and the method (100) includes a second discrimination (200-2) for which the CV phase is carried out for each iteration with a charge voltage corresponding to the second lowest full-charge voltage among the full-charge voltages associated with said at least three battery types; if the criterion is met for the second discrimination (200-2), the cell type (21) corresponds to the type associated with the second lowest full-charge voltage; if the criterion is not met for the second discrimination (200-2), the cell type corresponds to a different type than the type associated with the second lowest full-charge voltage.

5. Method (100) according to claim 4 comprising a preliminary step of comparing (110) a cell voltage (21) with the lowest full-charge voltage among the full-charge voltages associated with said at least three battery types, and the first discrimination (200-1) is implemented only if the cell voltage (21) is less than or equal to the lowest full load voltage.

6. Method (100) according to any one of claims 1 to 5 wherein the different battery types considered for the cell (21) include the types LFP (Lithium-Iron-Phosphate), NCA (Nickel-Cobalt-Aluminium) and NMC (Nickel-Manganese-Cobalt).

7. Method (100) according to any one of claims 1 to 6 wherein the spectral density corresponds to a spectral density of a sum of the intrinsic components obtained by the decomposition into empirical modes (213).

8. Method (100) according to any one of claims 1 to 6 wherein the spectral density corresponds to a sum of spectral densities calculated respectively for each of the intrinsic components obtained by the decomposition into empirical modes (213).

9. Method (100) according to any one of claims 1 to 8 wherein each iteration (210) of said at least one discrimination step (200) comprises a statistical reliability analysis (214) of the CV phase as a function of the intrinsic components of the floating current derivative signal.

10. Method (100) according to claim 9 wherein the reliability analysis (214) comprises, for each intrinsic component of the floating current derivative signal, a calculation of an entropy of the intrinsic component.

11. Method (100) according to any one of claims 9 to 10 wherein the reliability analysis (214) comprises an entropy calculation of a sum of the intrinsic components of the derivative signal of the floating current.

12. A device (10) for identifying the type of a lithium-ion battery cell (21) from among at least two battery types, each associated with a different full-charge voltage, the device (10) comprising: a sensor (13) adapted to provide current measurements taken at the cell (21) during a CV phase of a CC-CV charging cycle of the cell (21), a computing unit (12) configured to implement method (100) according to any one of claims 1 to 11.

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