Detection of a lithium deposit in a lithium-ion battery cell

Abstract

The invention relates to a method (100) for detecting lithium deposition on the negative electrode of a lithium-ion battery cell during charging. The method comprises, for at least one DC phase of a DC-CV charging cycle of the cell: - collecting (110) a plurality of cell surface temperature measurements, - generating (120) a temperature signal from the collected measurements, - decomposing the temperature signal into empirical modes (130), - determining (150) an intrinsic energy of the temperature signal for the DC phase considered, from the intrinsic components obtained by decomposition into empirical modes, - determining (160) an incidence value representative of the risk of lithium deposition as a function of the intrinsic energy, - evaluating (170) a lithium deposition detection criterion from the incidence value. Figure for the abstract: Fig. 1

Description

Title of the invention: Detection of a lithium deposit in a lithium-ion battery cell Scope of the invention

[0001] The present invention relates to the field of lithium-ion battery management. More particularly, a method and a system are proposed for detecting the formation of a lithium deposit on the negative electrode of a lithium-ion battery cell during cell charging. State of the art

[0002] Lithium plating is a phenomenon that can occur during the charging of lithium-ion batteries, particularly when they are charged quickly or at low temperatures.

[0003] A lithium-ion battery can comprise one or more cells. Each cell generally includes a negative electrode, a positive electrode, a liquid electrolyte, and a porous separator to prevent the electrodes from touching. The positive electrode contains metallic lithium (for example, in the form of a Lithium-Cobalt-Oxide alloy for an LCO type battery, a Lithium-Iron-Phosphate alloy for an LFP type battery, or a Nickel-Manganese-Cobalt alloy for an NMC type battery). The positive electrode provides lithium ions (Li+). The negative electrode generally contains graphite or another material that stores lithium ions. The use of these materials allows for "intercalation": lithium ions can easily enter or leave the electrodes.Lithium exists in ionic form when dissolved in the electrolyte and migrates between the electrodes during charge and discharge cycles. During these cycles, the lithium ions do not normally revert to metallic lithium; they are simply incorporated into the crystalline structures of the electrode materials when they are intercalated (inserted) into the electrodes.

[0004] If the charging current is too high, or if the battery temperature is too low, the lithium ions may not intercalate properly in the negative electrode. Instead, they may begin to deposit on the surface of the negative electrode in the form of metallic lithium.

[0005] This phenomenon of lithium deposition on the negative electrode during battery charging can be due to several factors. In particular, an excessively high charging rate can compromise the negative electrode's ability to effectively intercalate lithium ions. Indeed, if the charging current is too high, this can lead to an excessively high electrochemical potential at the negative electrode, which exceeds Then there is the potential at which lithium can be stably intercalated in the electrode material. Also, low temperatures slow down the mobility of lithium ions and the kinetics of the reactions.

[0006] Thus, under certain unfavorable conditions (too rapid charging or very low temperatures), lithium ions are deposited on the negative electrode in the form of pure metallic lithium, instead of intercalating normally into the electrode material.

[0007] Lithium deposition has several negative consequences for the battery. Lithium deposited on the negative electrode no longer contributes to the battery's charge-discharge cycle, thus reducing its overall capacity. The accumulation of metallic lithium on the negative electrode can increase the battery's internal resistance, which, in turn, reduces its efficiency and can affect its performance. Uneven lithium deposition can also damage the negative electrode by creating areas of concentrated stress, thereby reducing the battery's lifespan. Finally, lithium deposits can take the form of dendrites, elongated metallic structures that can grow over time until they pierce the separator and create a short circuit in the battery, leading to safety problems (risk of explosion or fire).

[0008] To prevent or reduce lithium deposition in a battery, it is important to be able to detect and potentially diagnose its occurrence. The formation of metallic lithium deposits is generally estimated using particularly complex electrochemical models that are not always compatible with real-time detection and / or are difficult to integrate into commercial battery management devices.

[0009] As explained above, lithium deposition can cause battery cell deterioration. In some cases, lithium deposition can also result from cell deterioration (for example, degradation of the material forming the negative electrode can promote lithium deposition). In all cases, detecting the formation of metallic lithium deposits on the negative electrode of a battery cell allows for the detection of cell degradation (reduced cell health and lifespan).

[0010] The document "Detecting undesired lithium plating on anodes for lithium-ion batteries - A review on the in-situ methods", Tian, ​​Y. et al., describes different methods for detecting the formation of metallic lithium plating on the anode of a lithium-ion battery cell. Description of the invention

[0011] The present invention aims to remedy all or part of the drawbacks of the prior art, in particular those described above, by proposing a solution innovative, particularly easy to implement, fast and inexpensive in terms of computing power, to detect the formation of a lithium deposit on an electrode of a lithium-ion battery cell.

[0012] To this end, and according to a first aspect, a method is proposed for detecting a lithium deposit on a negative electrode of a lithium-ion battery cell during cell charging. The method comprises, for at least one constant current charging phase, or CC phase, of a "constant current - constant voltage" charging cycle, or CC-CV charging cycle, of the cell: - the collection of a plurality of cell surface temperature measurements using a temperature sensor positioned on a cell surface, - the formation of a temperature signal by normalizing each cell surface temperature measurement relative to an ambient cell temperature measured during the considered CC phase, - a decomposition into empirical modes of the temperature signal in order to obtain a representation in the form of a sum of a residual signal and one or more intrinsic components, - a determination of the intrinsic energy of the temperature signal, for the CC phase considered, from the intrinsic components obtained by the decomposition into empirical modes of the temperature signal, - a determination of an incidence value representative of the risk of lithium deposition as a function of intrinsic energy, - an evaluation of a lithium deposition detection criterion based on the incidence value thus determined.

[0013] 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.

[0014] In particular embodiments, the determination of the intrinsic energy of the temperature signal for the CC phase considered involves, for each intrinsic component obtained by the decomposition into empirical modes, a calculation of an energy of said intrinsic component, and the intrinsic energy corresponds to a sum of the energies of the intrinsic components.

[0015] In particular embodiments, the incidence value is determined to be equal to the intrinsic energy of the temperature signal, or to a moving average of the intrinsic energy for the CC phase considered and the intrinsic energies determined respectively for a predetermined number of previous CC phases.

[0016] In particular embodiments, the evaluation of the lithium deposition detection criterion involves a comparison of the incidence value with an incidence threshold.

[0017] In particular embodiments, in which the incidence value or incidence threshold is determined as a function of an average value of the intrinsic energies determined respectively for a plurality of previous CC phases for the cell.

[0018] In particular embodiments, the incidence value or incidence threshold is determined according to a load regime used for the CC phase considered and / or according to the ambient temperature measured for the CC phase considered.

[0019] In particular embodiments, the method further includes a statistical reliability analysis of the CC phase considered to filter out a CC phase deemed unreliable.

[0020] In particular embodiments, the reliability analysis includes a comparison of the residual signal obtained by the decomposition into empirical modes of the temperature signal with an interpolated signal of the temperature signal.

[0021] In particular embodiments, the comparison of the residual signal with the interpolation signal involves a comparison of a mean squared deviation between the residual signal and the interpolation signal with a predetermined threshold.

[0022] In particular embodiments, the interpolation signal is obtained in the form of a trigonometric polynomial.

[0023] In particular embodiments, the reliability analysis includes, for each intrinsic component of the temperature signal, a calculation of an entropy of the intrinsic component.

[0024] In particular embodiments, the reliability analysis includes a calculation of an entropy of a sum of the intrinsic components of the temperature signal.

[0025] In particular embodiments, the reliability analysis includes a comparison of an energy of an intrinsic component of the temperature signal with a first energy threshold or with the energies of the other intrinsic components of the temperature signal, and / or a comparison of the intrinsic energy of the temperature signal with a second energy threshold or with intrinsic energies determined for previous CC phases.

[0026] In particular embodiments, when a lithium deposit is detected, the method includes a memorization of an occurrence of a cell failure, for a posteriori analysis of a cell health state.

[0027] In particular embodiments, when a lithium deposit is detected, the method includes a corrective action relating to the charging and / or environmental characteristics of the battery. The corrective action may, for example, consist of reducing the charging rate, increasing the ambient temperature of the battery, and / or prohibiting charging below a certain temperature, etc.

[0028] According to a second aspect, the present invention relates to a system for detecting lithium deposits on the negative electrode of a lithium-ion battery cell during cell charging. The system comprises: - a temperature sensor intended to be positioned on a cell surface to provide surface temperature measurements of the cell, - a temperature sensor intended to measure the ambient temperature of the cell, - a computing unit configured to implement the method according to any one of the preceding implementation modes. Presentation of the figures

[0029] 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:

[0030] [Fig-1] a schematic representation of the main steps of an example of Implementation of the method according to the invention for detecting a lithium deposit on the negative electrode of a lithium-ion battery cell,

[0031] [Fig.2] a schematic representation of an example of implementation of the temperature signal formation step,

[0032] [Fig.3] a graph representing the evolution over time of an incidence value representative of a lithium deposition risk for two similar battery cells subjected to different temperature and charging speed conditions,

[0033] [Fig.4] a schematic representation of an example of implementation of the reliability analysis step,

[0034] [Fig.5] to [Fig.8] graphs representing the evolution over time of an incidence value representative of a risk of lithium deposition for four similar battery cells subjected to different temperature and charging conditions,

[0035] [Fig.9] a schematic representation of a system according to the invention allowing the detection of a lithium deposit on the negative electrode of a lithium-ion battery cell.

[0036] 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

[0037] 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 "skin temperature" of the battery cell (temperature measured at the cell surface) during the CC phase.

[0038] Although the first charging phase (CC phase) is typically carried out at a constant current, more elaborate charging schemes are possible during this initial phase, with, for example, a variation in current to maximize the charging rate while remaining within the charging range compatible with the battery. 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 specific cases of the first charging phase will be considered as also covered by the term CC-CV "constant current - constant voltage" used in this application.

[0039] Fig. 1 schematically represents the main steps of an example of implementation of the method according to the invention for detecting a lithium deposit on the negative electrode of a lithium-ion battery cell.

[0040] As illustrated in [Fig. 1], method 100 comprises the following steps, for at least one CC phase of a CC-CV charging cycle of the cell: - a collection of 110 measurements of the surface temperature (or skin temperature) of the cell, - a 120 temperature signal formation by normalizing each cell surface temperature measurement relative to an ambient cell temperature, - an empirical mode decomposition of the temperature signal (EMD for "Empirical Mode Decomposition" in English), - optionally, a statistical reliability analysis of the CC phase under consideration, - a determination of an intrinsic energy of the temperature signal, for the CC phase considered, from the intrinsic components obtained by the EMD decomposition, - a determination of 160 of an incidence value representative of a lithium deposition risk as a function of intrinsic energy, - an evaluation 170 of a lithium deposition detection criterion from the incidence value.

[0041] As explained previously, the formation of a lithium deposit on the negative electrode of the cell is indicative of cell degradation. The proposed method 100 therefore makes it possible to detect cell degradation: when the lithium deposition detection criterion is met, this means that the cell has undergone degradation.

[0042] According to a first example, all the steps of Method 100 illustrated in [Fig. 1] can be implemented for the CC phase of each CC-CV charging cycle undergone by the cell during its lifetime, or for the CC phase of a subset of all charging cycles (for example, only for one out of every two charging cycles, or even at a lower frequency). Lithium deposition can then be detected at the time it occurs (in real time). Corrective actions can then be implemented to preserve the battery's health. For example, if feasible for the battery's intended use, the charging rate should be reduced, the ambient temperature of the battery increased, or charging prohibited below a certain temperature.

[0043] According to another example, it is possible to store in a computer file the incidence value determined for the CC phase of each CC-CV charging cycle undergone by the cell during its lifetime, or for the CC phase of a subset of all charging cycles. The detection of lithium deposit formation can then be performed retrospectively, using the values ​​stored in the computer file, for example, to assess the cell's health at the end of its first life (for example, to decide whether the cell should be recycled or whether it can be reused in a second life, possibly with a different application). In particular, the incidence values ​​stored in the computer file can provide information on the number of cell charging cycles for which lithium deposits occurred, and on the extent of these deposits.If a cell has undergone too many charging cycles with lithium deposition, or if there has been at least one very significant lithium deposition during the battery's first life, it may be decided that the cell degradation is too great to consider its use in a second life.

[0044] During the collection step 110, the surface temperature of the cell is measured using a temperature sensor positioned at the cell surface (in contact with the cell). Preferably, the temperature sensor is positioned in the immediate vicinity of the negative electrode of the cell (i.e., opposite the negative electrode), but simple positioning A sensor placed on the cell surface (i.e., on the cell casing) may suffice. This could be, for example, a thermocouple or an RTD (Resistance Temperature Detector, also called a platinum resistance sensor). It could be a PTC (Positive Temperature Coefficient, meaning its resistance increases as its temperature rises) or an NTC (Negative Temperature Coefficient, meaning its resistance decreases as its temperature rises) type sensor. The method is applicable to any type of temperature sensor. For example, an infrared sensor could be used.

[0045] The measurements are, for example, carried out with an acquisition frequency of between five and sixty seconds, for an acquisition duration of twenty to ninety minutes. However, there is nothing preventing the measurements from being carried out with a different acquisition frequency and / or for a different acquisition duration. It is advantageous to use between forty and three hundred measurements per CC 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 performance of the method).

[0046] It should be noted that it is not essential that the CC phase during which the measurements are carried out corresponds to a full charge of the cell (it may be a partial charge).

[0047] The measurements thus collected allow a temperature signal to be formed (step 120). The temperature signal is obtained by normalizing each surface temperature measurement (fj) of the cell with respect to an ambient temperature (Tx) of the cell. If we denote Ts as the surface temperature, Tx as the ambient temperature, and sk as a value taken by the temperature signal, we have:

[0048] e - Tx

[0049] Temperature values ​​are expressed in degrees Kelvin.

[0050] The ambient temperature is measured using another sensor The ambient temperature sensor is located at a sufficient distance from the cell so as not to be affected by the cell's temperature fluctuations. For example, the ambient temperature sensor can be located at least four times the cell height when convection is natural, and at least once the cell height if convection is forced (these values ​​may be lower if the fluid has a Prandtl number higher than that of air). The ambient temperature corresponds, for example, to an average temperature of the cell's environment, measured during the considered CC phase. The ambient temperature could, however, also correspond to a median ambient temperature during the CC phase under consideration, or to an average of a maximum and minimum ambient temperature measured during the CC phase (and other indicators of the central tendency of the cell's ambient temperature during the CC phase under consideration could be considered). The different ways of defining an ambient cell temperature for a CC phase are merely variations of the invention. The ambient cell temperature can be measured at the same frequency as the cell surface temperature, or at a different frequency.

[0051] Figure 2 illustrates in detail an example of implementing the temperature signal formation 120 from the surface temperature measurements collected in step 110. As illustrated in Figure 2, the temperature signal formation step 120 comprises a determination 121 of the ambient temperature T' of the cell during the CC phase considered, a normalization 122 of each surface temperature measurement Ts collected during the CC phase with respect to the ambient temperature, and a temperature signal formation 123 from the normalized measurements. The temperature signal represents the evolution over time of the normalized values ​​of the cell surface temperature during the CC phase considered.

[0052] In step 130, the temperature signal is decomposed according to an empirical mode decomposition (EMD decomposition), in order to obtain a representation in the form of a sum of a residual signal and one or more intrinsic components.

[0053] 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.

[0054] 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.

[0055] 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".

[0056] As previously stated, the intrinsic components are not defined analytically. Rather, they are determined adaptively according to the properties of the signal.

[0057] 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.

[0058] A gfy signal decomposed by EMD can then be written in the form:

[0060] In this expression, rfy corresponds to the residual signal, N is the number of intrinsic components of the EMD decomposition, and ci is the intrinsic component with index '. Each successive intrinsic component c contains oscillations of a frequency lower than that of the preceding one. The residual signal corresponds to a general trend of the signal $( / ).

[0061] The decomposition into empirical modes involves a succession of sifting processes. The first sifting process takes the signal directly as input. This process involves identifying all local extrema of the input signal and linking the local maxima and minima, respectively, by interpolation using cubic splines, in order to obtain an upper and lower envelope, respectively. An average of the upper and lower envelopes can then be calculated and subtracted from the input signal. If the intermediate signal obtained (subtracting the average of the upper and lower envelopes from the input signal) is not an intrinsic component, the sifting process is repeated on the intermediate signal (which thus becomes the input signal for a new sifting 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 rfy-.

[0062] 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 processes of The concentration of successive sievings is less than or equal to a predetermined stopping threshold. The stopping threshold can typically be between 0.2 and 0.3.

[0063] 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.

[0064] Algorithms for decomposing into empirical modes are available in programming libraries, for example in MATLAB or Python.

[0065] The number of intrinsic components obtained by EMD decomposition can vary from one CC phase to another. The number of intrinsic components is generally less than five. It is advantageous to set the stopping threshold at a relatively low level, on the order of 0.2, to extract maximum information from the temperature signal. Using a lower stopping threshold results in relatively long computation times.

[0066] The intrinsic components obtained in step 130 are then used to determine an incidence value representative of the risk of lithium deposition on the negative electrode of the cell during the CC phase considered. This incidence value is determined as a function of an intrinsic energy of the temperature signal.

[0067] As illustrated in [Fig.1], the intrinsic energy of the temperature signal, for the CC phase considered, is determined in step 150 from the intrinsic components obtained by the EMD decomposition.

[0068] The determination 150 of the intrinsic energy of the temperature signal may in particular include, for each intrinsic component obtained by the EMD decomposition, a calculation of an energy of the intrinsic component. The intrinsic energy of the temperature signal then corresponds to a sum of the energies of the intrinsic components.

[0069] The energy E{ of an intrinsic component cî corresponds, for example, to the integral of the square of the amplitude of the intrinsic component ci over the acquisition time of the CC phase considered: t0070'

[0071] The intrinsic energy E of the temperature signal for the CC phase considered can then be written in the form: [°072]

[0073] It should be noted that nothing would prevent, in a variant, calculating the intrinsic energy E by summing the energies of only a subset of the intrinsic components obtained by the EMD decomposition (for example by considering only a predefined maximum number of the first intrinsic components obtained by EMD decomposition, or, as will be seen later, by filtering out certain intrinsic components considered aberrant. Nothing would prevent defining the energy of an intrinsic component differently. The choice of a particular method for determining the intrinsic energy of the temperature signal is simply a variant of the invention.

[0074] The incidence value can then be determined, in step 160 of [Fig. 1], as being equal to the intrinsic energy of the temperature signal. According to another example, the incidence value can correspond to a moving average of the intrinsic energy for the CC phase under consideration and the intrinsic energies determined respectively for a predetermined number of previous CC phases.

[0075] For example, the incidence value for a DC phase with index & can correspond to the intrinsic energy Ek calculated for this DC phase with index &:

[0076] (pk = Ek

[0077] According to another example, the incidence value can correspond to the moving average of the intrinsic energies E^ EkA and Ek~2 of the three CC phases of index &, k-1 and k - 2;

[0079] According to yet another example, the incidence value ^k can be determined as a function of an average value Ë of the intrinsic energies determined respectively for a plurality of previous CC phases for the cell (for example for all the CC phases during the life of the cell for which the intrinsic energy of the cell is determined, or only for a part of these CC phases, for example the last thirty):

[0080] ■k~Ë

[0081] or

[0082] ^"ë

[0083] with 100841 È =

[0085] As illustrated in [Fig.1], method 100 includes an evaluation step 170 of a lithium deposition detection criterion as a function of the incidence value.

[0086] For example, the incidence value can be compared with an incidence threshold, and if the incidence value exceeds the threshold, then it is considered that a lithium deposit has formed on the negative electrode of the cell during the CC phase considered.

[0087] The incidence threshold can be predetermined empirically in the laboratory, for example by comparing incidence values ​​with other measurements from other methods of detecting lithium deposition (cell capacity loss, high float current, abnormal electrochemical impedance measurement, etc.).

[0088] It is also possible to determine the incidence threshold based on the average value ε of the intrinsic energies determined respectively for a plurality of previous CC phases. For example, the detection criterion is satisfied if the intrinsic energy Ek (or the moving average ε^3 of the intrinsic energy) is greater than five times the average value ε of the intrinsic energies.

[0089] The incidence value or incidence threshold can also be determined based on a charging regime used for the DC phase considered and / or based on the ambient temperature measured for the DC phase considered. The charging regime may, in particular, correspond to the current value used during the DC phase of charging. It is often given in units of "C", where IC represents a charging current that would fully charge the cell in one hour (for example, a charging regime of 0.5C for a 2Ah cell corresponds to a charging current of IA). A correction factor that depends on the charging regime used and / or the ambient temperature can be applied to the incidence value or incidence threshold.

[0090] It is possible to consider several incidence thresholds to provide information on the extent of lithium deposition that occurred during the CC phase under consideration. The higher the threshold, the greater the extent of lithium deposition (and the greater the cell degradation). Exceeding a relatively low initial threshold may simply indicate a risk of accelerated cell aging, while exceeding a second threshold higher than the first may indicate a critical risk (for example, a risk of thermal runaway of the cell) and the need to stop using the cell.

[0091] Figure 3 illustrates, by way of example, the evolution over time of an incidence value representative of the risk of lithium deposition for two similar battery cells subjected to different temperature and charging rate conditions. When the incidence value exceeds the incidence threshold S, this means that lithium deposition has likely occurred during the corresponding DC charging phase. A first cell, represented by curve 31, is subjected to favorable conditions (temperature of around 20°C, charging rate of 0.5C). It can be seen in the graph of Figure 3 that the incidence values ​​of the first cell remain below the incidence threshold S. A second cell, represented by curve 32, is subjected to unfavorable conditions (low temperature of around 0°C, high charging rate of 2C). The incidence values ​​of the second cells often exceed the incidence threshold S, meaning that a lithium deposit has formed over a large number of cell charges during the observation period considered.

[0092] The intercalation of lithium ions during the charging cycles of a battery cell is associated with changes in the volume of the cell electrodes. While these volume changes do not affect the amount of heat exchanged from a heat transfer perspective, they do significantly impact thermal fluctuations by generating electrolyte displacement (internal convective movement) (see, for example, "Heat Flux and Entropy Produced by Thermal Fluctuations," S. Ciliberto et al.). Monitoring an incidence value representative of the intrinsic energy of the temperature signal, calculated from the EMD decomposition of this signal, is therefore particularly well-suited for detecting thermal fluctuations caused by electrolyte displacements related to a volume change in the negative electrode of the cell during charging.This volume variation can be disrupted by the formation of metallic lithium deposits on the electrode, and that is why the proposed method makes it possible to detect cell degradation linked to this lithium deposit.

[0093] If a lithium deposit is detected, it is possible to record the occurrence of cell degradation in memory, for example with the associated date and time, and possibly information on the extent of the detected deposit (which is representative of the extent of the degradation suffered by the cell). This makes it possible, at the end of a given period, to assess the number and extent of the degradations detected during that period, and to evaluate the cell's condition at the end of that period (for example, at the end of the cell's first life). This evaluation of the cell's condition can, for example, influence a choice between recycling or reusing the cell in a second life.

[0094] As previously mentioned, another option is to record the incidence values ​​in memory and to detect the degradation suffered by the cell at the end of a given period (a posteriori), based on the stored incidence values. In this case, the evaluation step 170 of the lithium deposit detection criterion is not performed at the end of a CC phase, but is performed at a later date using the stored incidence value corresponding to said CC phase.

[0095] As illustrated in [Fig. 1], the method 100 according to the invention may also include an optional step 140 of statistically analyzing the reliability of a CC phase or an intrinsic component of the temperature signal for the CC phase under consideration. An intrinsic component or a CC phase deemed unreliable can then be discarded. This statistical reliability analysis step 140 allows, in particular,

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103]

[0104]

[0105]

[0106] to avoid false positives (i.e., to avoid incorrectly indicating a detection of lithium deposit). In particular, the reliability analysis step 140 may include a comparison of the residual signal obtained by the EMD decomposition of the temperature signal with an interpolated signal f(t) of the temperature signal. Such provisions allow for verification of the validity of the EMD decomposition. The interpolation signal can notably be obtained in the form of a trigonometric polynomial: f(t) = -i-^siixwDl with 2.7 where L is the number of measurement points of a DC phase. The coefficients ai and are obtained, for example, by a least-squares distance minimization method with respect to the temperature signal $( / ). Ne corresponds to the order of the trigonometric polynomial, for example Nc “ 2. Comparison of the residual signal with the interpolation signal can be performed, in particular, by comparing a root mean squared error (RMSE) between the residual signal and the interpolation signal with a predetermined threshold: RMSE = If the mean square deviation is greater than a threshold (e.g. 3.103), then the EMD decomposition is considered invalid, and the corresponding CC phase is discarded (i.e., it is not taken into account for lithium deposition detection). Figure 4 illustrates an example of the implementation of the reliability analysis step 140 of a CC phase. It includes an interpolation 141 of the temperature signal, a calculation 142 of a mean square deviation between the interpolated signal and the residual of the EMD decomposition of the temperature signal for the CC phase considered, and a comparison 143 of the mean square deviation with a predetermined threshold. Using an interpolation signal in the form of a trigonometric polynomial is particularly advantageous in terms of computation time. However, there is nothing preventing the use of other types of interpolation (polynomial interpolation, piecewise polynomial interpolation, etc.). Nor is there anything preventing the use of a parameter other than the root mean square deviation to compare the interpolation signal and the residual signal (for example, an absolute deviation).

[0107] Alternatively, or in addition, the statistical reliability of an intrinsic component or a CC phase can also be estimated as a function of an entropy calculated for the intrinsic component, or as a function of an entropy calculated for a sum of the intrinsic components of the temperature signal.

[0108] Various methods for calculating entropy can be considered, such as a Shannon entropy calculation or a Kolmogorov entropy calculation. For example, DC phases for which the calculated entropy 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 of the intrinsic components is too low, it is not possible to distinguish dynamic fluctuations of the signal from background noise. The temperature signal is then unusable for lithium deposition detection.

[0109] Alternatively, or in addition, the statistical reliability of an intrinsic component or a CC phase can also be estimated by statistical tests (Peirce test, Chauvenet test, Grubbs test, etc.) relating to the energy of each intrinsic component of the temperature signal and / or to the intrinsic energy of the temperature signal.

[0110] In particular, the reliability analysis 140 may include a comparison of the energy of an intrinsic component of the temperature signal with a threshold or with the energies of the other intrinsic components of the temperature signal. For example, intrinsic components that have a level that is too high are considered aberrant and are discarded.

[0111] The reliability analysis 140 may also include a comparison of the intrinsic energy of the temperature signal with a threshold or with intrinsic energies determined for previous CC phases. CC phases exhibiting aberrant intrinsic energy are discarded.

[0112] Figures 5 to 8 are graphs showing the evolution over time of an incidence value representative of the risk of lithium deposition for four similar cells subjected to different temperature and charging conditions. The graph in [Fig. 5] corresponds to a cell subjected to a high ambient temperature of 40°C and charging cycles with an IC charging regime. The graph in [Fig. 6] corresponds to a cell subjected to a moderate ambient temperature of 25°C and charging cycles with a 0.3C charging regime. The graph in [Fig. 7] corresponds to a cell subjected to a low ambient temperature of 0°C and charging cycles with an IC charging regime. The graph in [Fig. 8] corresponds to a cell subjected to a low ambient temperature of 0°C and charging cycles with a 2C charging regime.

[0113] For the examples illustrated in Figures 5 to 8, the incidence value V>k is calculated in the form:

[0114] Ek3

[0115] In other words, the incidence value is a ratio between a moving average of the intrinsic energies of the last three CC phases and an average of the intrinsic energies of all the previous CC phases.

[0116] When <pk>If pK > 50 (first incidence threshold of five), lithium deposition likely to cause accelerated cell aging is considered to have occurred during the CC phase under consideration. When pK > 50 (second incidence threshold of fifty), lithium deposition likely to cause a critical risk (thermal runaway, explosion) is considered to have occurred during the CC phase under consideration.

[0117] It is clear from the graphs in Figures 5 to 8 that a low temperature and a fast charging rate (high charging regime) are favorable factors for the deposition of metallic lithium. In particular, it can be observed in the graphs of Figures 5 and 6 that the incidence value always remains below the first incidence threshold (these graphs correspond to moderate or warm temperature conditions, with a reasonable charging rate). On the other hand, it can be observed in the graph of [Fig. 7] that the incidence value relatively often exceeds the first incidence threshold (this graph corresponds to a low temperature but a moderate charging rate). In the graph of [Fig. 8], it can be observed that the incidence value takes on particularly high values, and that it exceeds the second incidence threshold during charging (this graph corresponds to a low temperature and a fast charging rate). For the cell of [Fig.8], a rupture of the cell casing was observed shortly after exceeding the second incidence threshold.

[0118] Fig. 9 schematically represents an example of an embodiment of a system 10 for detecting a lithium deposit on the negative electrode of a cell 21 of a lithium-ion battery 20.

[0119] The system 10 includes in particular a temperature sensor 13 intended to be positioned on the surface of the cell 21, preferably at the negative electrode of the cell 21, to provide surface temperature measurements (skin temperature, Ts) of the cell.

[0120] The system 10 also includes a temperature sensor 14 for measuring an ambient temperature (71") of the cell 21 (temperature of the environment in which the cell is located).

[0121] The system 10 also includes a computing unit 12 configured to implement method 100 according to any one of the implementation modes described above.

[0122] The system 10 may also include a computer memory 11 for storing incidence values ​​(or measurements for calculating these incidence values) for a plurality of cell loads during the life of the cell.

[0123] Communication between sensors 13, 14 and the computing unit 12 (for transmitting temperature measurements) can be implemented by wired communication means or by wireless communication means.

[0124] The memory 11, the processing unit 12, and the sensors 13 and 14 can be part of a battery management system (BMS). In another example, the processing unit 12 can belong to a remote server to which temperature measurements are transmitted.

[0125] The foregoing description clearly illustrates that, through its various features and their advantages, the present invention achieves the stated objectives. In particular, monitoring the intrinsic energy of the temperature signal, calculated from the EMD decomposition of the signal, is particularly well-suited for detecting the formation of metallic lithium deposits on the negative electrode of the cell, and thus for detecting cell degradation. This detection can be performed in real time, to take corrective measures as needed to optimize the cell's lifespan, or retrospectively, at the end of the cell's first life, to assess the feasibility of reusing the cell in a second life. The proposed solution is relatively easy to implement, fast, and inexpensive in terms of computing power. In particular, it can be easily integrated into a battery management system.< / pk>

Claims

Demands

1. Method (100) for detecting a lithium deposit on a negative electrode of a cell (21) of a lithium-ion battery (20) during charging of the cell (21), the method (100) comprising, for at least one constant current charging phase, or CC phase, of a "constant current - constant voltage" charging cycle, or CC-CV charging cycle, of the cell (21): - a collection (110) of a plurality of surface temperature measurements of the cell using a temperature sensor positioned on a surface of the cell (21), - a formation (120) of a temperature signal by normalizing each surface temperature measurement of the cell (21) with respect to an ambient temperature of the cell (21) measured during the CC phase considered,- a decomposition into empirical modes (130) of the temperature signal in order to obtain a representation in the form of a sum of a residual signal and one or more intrinsic components, - a determination (150) of an intrinsic energy of the temperature signal, for the CC phase considered, from the intrinsic components obtained by the decomposition into empirical modes (130) of the temperature signal, - a determination (160) of an incidence value representative of a risk of lithium deposition as a function of the intrinsic energy, - an evaluation (170) of a lithium deposition detection criterion from the incidence value thus determined.

2. Method (100) according to claim 1 wherein the determination (150) of the intrinsic energy of the temperature signal for the CC phase considered comprises, for each intrinsic component obtained by the decomposition into empirical modes (130), a calculation of an energy of said intrinsic component, and the intrinsic energy corresponds to a sum of the energies of the intrinsic components.

3. Method (100) according to any one of claims 1 to 2 wherein the incidence value is determined to be equal to the intrinsic energy of the temperature signal, or to an average sliding of the intrinsic energy for the CC phase considered and of the intrinsic energies determined respectively for a predetermined number of previous CC phases.

4. Method (100) according to any one of claims 1 to 3 wherein the evaluation (170) of the lithium deposition detection criterion includes a comparison of the incidence value with an incidence threshold.

5. Method (100) according to claim 4 wherein the incidence value or incidence threshold is determined as a function of an average value of the intrinsic energies determined respectively for a plurality of previous CC phases for the cell (21).

6. Method (100) according to any one of claims 4 to 5 wherein the incidence value or incidence threshold is determined as a function of a load regime used for the CC phase considered and / or as a function of the ambient temperature measured for the CC phase considered.

7. Method (100) according to any one of claims 1 to 6 further comprising a statistical reliability analysis (140) of the CC phase considered for filtering out a CC phase deemed unreliable.

8. Method (100) according to claim 7 wherein the reliability analysis (140) comprises a comparison of the residual signal obtained by the decomposition into empirical modes (130) of the temperature signal with an interpolation signal of the temperature signal.

9. Method (100) according to claim 8 wherein the comparison of the residual signal with the interpolation signal comprises a comparison of a mean squared deviation between the residual signal and the interpolation signal with a predetermined threshold.

10. Method (100) according to claim 9 wherein the interpolation signal is obtained in the form of a trigonometric polynomial.

11. Method (100) according to any one of claims 7 to 10 wherein the reliability analysis (140) comprises, for each intrinsic component of the temperature signal, a calculation of an entropy of the intrinsic component.

12. Method (100) according to any one of claims 7 to 11 wherein the reliability analysis (140) comprises a calculation of a entropy of a sum of the intrinsic components of the temperature signal.

13. Method (100) according to any one of claims 7 to 12 wherein the reliability analysis (140) comprises a comparison of an energy of an intrinsic component of the temperature signal with a first energy threshold or with the energies of other intrinsic components of the temperature signal, and / or a comparison of the intrinsic energy of the temperature signal with a second energy threshold or with intrinsic energies determined for previous CC phases.

14. Method (100) according to any one of claims 1 to 13 wherein, when a lithium deposit is detected, the method (100) includes a memorization of an occurrence of a failure of the cell (21), for an a posteriori analysis of a health state of the cell (21).

15. Method (100) according to any one of claims 1 to 14 wherein, when a lithium deposit is detected, the method (100) includes a corrective action relating to the charging and / or environmental characteristics of the battery.

16. System (10) for detecting a lithium deposit on a negative electrode of a cell (21) of a lithium-ion battery (20) during charging of the cell (21), said system (10) comprising: - a temperature sensor (13) for being positioned at a surface of the cell (21), to provide surface temperature measurements of the cell, - a temperature sensor (14) for measuring an ambient temperature of the cell (21), - a computing unit (12) configured to implement the method (100) according to any one of claims 1 to 14.

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