Method and device for assessing the health status of a Lithium-Ion battery

Abstract

Method (100) for estimating the health status of a Lithium-Ion battery. The method comprises, for at least one battery relaxation phase, and for each battery cell: a collection (110) of several cell voltage measurements, a formation (120) of a relaxation signal in the form of a logarithm of a normalized cell voltage value, a decomposition (130) into empirical modes (EMD) of the relaxation signal into intrinsic components. The method (100) also comprises: a calculation (150) of a cell energy variance from the intrinsic components obtained for the different cells, a determination (160) of a current battery health status (20) as a function of the cell energy variance for the considered relaxation phase. Figure for the abstract: Fig. 2

Description

Title of the invention: Method and device for estimating the health status of a Lithium-Ion battery Scope 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 estimating the State of Health (SOH) of a Lithium-Ion battery. State of the art

[0002] The energy storage sector, particularly with regard to the use of lithium-ion batteries, is currently experiencing rapid growth. This growth is largely driven by the development of electric vehicles, which generally incorporate so-called first-life batteries (batteries with a state of health, or SOH, close to 100% at the start of their use), but also by the stationary energy storage sector, especially for grid support applications, using new batteries as well as so-called second-life batteries (batteries with a lower state of health at the start of their use, for example, an SOH below 80%). Second-life batteries used for grid regulation often come from the reuse of electric vehicle batteries.

[0003] Batteries undergo degradation over their lifetime. It is important to be able to monitor the evolution of a battery's health over time in order to anticipate potential failures or safety issues. The state of health (SOH) of a lithium-ion battery is a measure that allows for the assessment of its degradation based on aging and usage. It is generally expressed as a percentage, with a new battery considered to have 100% SOH and a battery at the end of its life generally around 80% SOH.

[0004] There are different methods for estimating the SOH of a Lithium-Ion battery. The SOH can notably be estimated by comparing the values ​​of different battery parameters with nominal values ​​observed when the battery was new (capacity, depth of discharge, discharge rate, charge rate, internal resistance, voltage, current, etc.).

[0005] Conventional methods for estimating the SOH of a battery often require performing complete charge and discharge cycles, which can be time-consuming.

[0006] Some complex methods require data that is not always made available by commercially available battery management systems (BMS). Other, simpler methods are

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[0012] They are suitable for most battery management systems, but they do not always offer satisfactory reliability. As an example, US patent applications 2017 / 146608 Al and EP 3324197 Al describe methods for estimating the health status of a battery from battery voltage or current measurements. Description of the invention The present invention aims to overcome all or part of the drawbacks of the prior art, particularly those described above, by providing a method for assessing the health status of a lithium-ion battery. The proposed method offers good reliability and can be implemented by most existing battery management systems. To this end, and according to a first aspect, a method is proposed for assessing the health status of a lithium-ion battery. The battery comprises several cells. The method includes, for at least one relaxation phase following a battery discharge phase: - for each cell: • a collection of several voltage measurements at the cell level during said relaxation phase, • the formation of a relaxation signal by calculating, for each voltage measurement collected during said relaxation phase, a logarithm of a normalized value of the voltage measurement, • a decomposition into empirical modes of the relaxation 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 the variance of cell energies from the intrinsic components obtained for the different cells, - a determination of a current health state of the battery based on a previously calculated health state and the variance of cell energies for the relaxation phase considered. 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. In specific implementation modes, for a cell with index k, a value of the relaxation signal at time 1 can be written in the form: xkW = 'ln(^ j

[0013] where Vk(t) is a voltage measured across the terminals of the index cell is a voltage measured across the battery terminals, Nc is the number of battery cells, and lu is the natural logarithm operator.

[0014] In particular embodiments, the calculation of the variance of cell energies involves: For each intrinsic component of a cell, a calculation of the energy of said intrinsic component, For each cell, a calculation of the cell's energy corresponding to a sum of the energies of the cell's intrinsic components, a calculation of an average energy corresponding to an average of the energies of the battery cells, a calculation of the variance of cell energies relative to the average energy.

[0015] In particular embodiments, the determination of the current health status of the battery is further carried out based on one or more of the following parameters: an equivalent number of battery charge / discharge cycles, a battery charging speed, a battery discharging speed, a battery age.

[0016] In particular embodiments, the method further comprises, for each relaxation phase, a statistical reliability analysis of elements enabling the calculation of the variance of cell energies, and a filtering of at least one of the following elements if it is deemed unreliable: an intrinsic component of a cell, a battery cell, the variance of cell energies.

[0017] In particular embodiments, the statistical reliability analysis includes, for each intrinsic component of the relaxation signal of a cell, a calculation of an entropy of the intrinsic component.

[0018] In particular embodiments, the statistical reliability analysis includes, for each cell, a calculation of an entropy of a sum of the intrinsic components of the relaxation signal of the cell.

[0019] In specific implementation modes, the statistical reliability analysis includes: - a comparison of the energy of an intrinsic component of a cell with a threshold or with the energies of the other intrinsic components of the cell, and / or - a comparison of the energy of one cell with a threshold or with the energies of the other cells in the battery, and / or - a comparison of the variance value calculated for the relaxation phase considered with a threshold or with other variance values ​​calculated for previous relaxation phases.

[0020] According to a second aspect, a device is proposed for assessing the health status of a Lithium-Ion battery. The battery comprises several cells. The device comprises: - a measurement system configured to provide voltage measurements taken at each cell during at least one relaxation phase following battery discharge, - a computing unit connected to the measurement system.

[0021] The computing unit is configured to implement the method according to any one of the preceding implementation modes.

[0022] According to a third aspect, a battery management system, or BMS, is proposed, comprising a device as mentioned above. Presentation of the figures

[0023] 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:

[0024] [Fig-1] a graph representing the evolution over time of the voltage measured across the terminals of a Lithium-Ion battery, as well as the voltages measured across the terminals of each cell of the battery,

[0025] [Fig.2] a schematic representation of the main steps of an example of implementation of the method according to the invention for estimating the health status of a Lithium-Ion battery,

[0026] [Fig.3] a graph representing a relaxation signal, as well as the residual signal resulting from the empirical decomposition of this relaxation signal,

[0027] [Fig.4] a graph representing four empirical components obtained by the empirical decomposition of the relaxation signal shown in [Fig.3],

[0028] [Fig.5] a particular method of implementing the calculation of a variance of cell energies from the intrinsic components of each cell, for a given relaxation phase,

[0029] [Fig.6] a graph representing the evolution over time of SOH values estimated for different Lithium-Ion batteries subjected to different environmental conditions and different uses,

[0030] [Fig.7] a graph representing the value of the variance of cell energies of a battery at different times for a first set of batteries illustrated in [Fig.6],

[0031] [Fig.8] a graph representing the value of the variance of cell energies of a battery at different times for a second set of batteries illustrated in [Fig.6],

[0032] [Fig.9] a schematic representation of a device according to the invention allowing to assess the health status of a Lithium-Ion battery.

[0033] 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

[0034] To estimate the state of health (SOH) of a lithium-ion battery, the method according to the invention uses the voltage signals of the different battery cells. The voltage signals are measured during battery relaxation phases. A relaxation phase follows a more or less complete discharge phase of the battery.

[0035] Figure 1 is a graph showing the evolution over time of the voltage measured across the terminals of a lithium-ion battery, as well as the voltages measured across each cell of the battery. In the example considered and illustrated in Figure 1, the battery has twelve cells. Figure 1 represents four relaxation phases. Each relaxation phase follows a battery discharge phase and precedes a battery recharge phase. However, there is nothing preventing the inclusion of a relaxation phase between two discharge phases. During each relaxation phase, the voltage signals of the different cells can be observed to take on different values ​​(they follow a similar trend but with different values). During the discharge and recharge phases, the voltage signals of the different cells are essentially identical to the battery voltage signal.

[0036] From a physics perspective, the work published in the document "Anisotropic ionic transport properties in solid PEO based electrolytes" by R. Jeanne-Brou et al., makes it possible to link the evolution of voltage over time to the diffusion of electroactive species towards the charge transfer sites. The state of health (SOH) of a battery can have an impact on this evolution. This is why The inventors decided to focus on the relaxation voltage of the cells of a Lithium battery, and more specifically on the logarithm of a normalized voltage value.

[0037] Fig. 2 schematically represents the main steps of an example of implementation of a method 100 according to the invention for estimating the health status of a Lithium-Ion battery.

[0038] As illustrated in [Fig.2], method 100 comprises, for at least one relaxation phase: - for each cell: • a collection of 110 several voltage measurements at the cell level during the relaxation phase, • a 120 formation of a relaxation signal by calculating, for each voltage measurement collected during the relaxation phase, a logarithm of a normalized value of the voltage measurement, • a decomposition 130 into empirical modes of the relaxation 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 150 of the variance of cell energies from the intrinsic components obtained for the different cells, - a determination 160 of a current health state of the battery based on a previously calculated health state and the variance of cell energies for the relaxation phase considered.

[0039] These different steps can be implemented for each relaxation phase that follows a battery discharge phase during its lifetime. However, in an alternative, nothing would prevent these steps from being implemented only for a subset of the relaxation phases.

[0040] The inventors observed that the longer the discharge phase preceding the relaxation phase, the more relevant the battery health estimate obtained for that relaxation phase. Therefore, it is possible, for example, to consider only relaxation phases following a discharge phase with a depth of discharge (DOD) of at least 60%. A depth of discharge of 60% means that the battery's state of charge (SOC) is 40% (SOC = 1 - DOD). Even more optimally, one could consider only relaxation phases following a discharge phase with a depth of discharge of at least 90%.

[0041] Voltage measurements during the relaxation phase are, for example, performed by a battery management system (BMS) connected to the battery cells. The measurements are, for example, taken with an acquisition frequency of between five and sixty seconds, for an acquisition duration of thirty to sixty 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 forty and two hundred measurements per relaxation phase (using a larger number of measurements does not necessarily imply a significant improvement in the method, while using a smaller number of measurements may limit the method's performance).

[0042] The relaxation signal formation step 120 comprises, for each voltage measurement collected during the relaxation phase, a calculation of a logarithm of a normalized value of the voltage measurement. For example, the relaxation signal of a cell with index & can be written in the form:

[0043] ,, , 1 / v»(>) \ Xk(t) = - In (¾ /

[0044] where y^ is the voltage measured across the terminals of the cell with index k, is the voltage measured across the terminals of the battery, Nc is the number of cells in the battery, and In is the natural logarithm operator.

[0045] In variations, nothing would prevent the relaxation signal from being defined differently, in particular by changing the way the voltage measured across a cell is normalized, or by using a logarithm in a different base (for example, in a base equal to Nc). For normalization, one could, for example, consider using a polynomial characteristic of the average relaxation voltage of the battery (for example, of the form — Clt + b^) or simply using an average value (UB) of the battery voltage during the relaxation phase.

[0046] In step 130, the relaxation signal is decomposed according to an empirical mode decomposition (EMD for "Empirical Mode Decomposition" in English).

[0047] 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.

[0048] 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.

[0049] The decomposition into empirical modes corresponds to the first part of the Hilbert-Huang transform (HHT for "Hilbert-Huang Transform" in English). Empirical mode decomposition involves 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".

[0050] As previously stated, the intrinsic components are not defined analytically. Rather, they are determined adaptively according to the properties of the signal.

[0051] 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.

[0052] A signal decomposed by EMD can then be written in the form:

[0054] In this expression, 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 contains oscillations of a frequency lower than that of the preceding one. The residual signal corresponds to a general trend of the signal

[0055] The decomposition into empirical modes comprises 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 interpolation using cubic splines, in order to obtain an upper envelope and a lower envelope, respectively. An average between the upper and lower envelopes can then be calculated and subtracted from the input signal. If the intermediate signal obtained (subtracting the input signal from the average of the upper and lower envelopes) 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 signal. The intermediate signal becomes monotonic or consists of only one local extremum. The remaining signal then corresponds to the residual signal.

[0056] 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.

[0057] The document "The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis", Norden E. Huang et al., Proceedings of the Royal Society of London Series A (1998) 454, p. 903-995, describes in detail the decomposition into empirical modes, particularly in its sections 4 and 5.

[0058] Algorithms for decomposing into empirical modes are available in programming libraries, for example in MATLAB or Python.

[0059] The graphs in Figures 3 and 4 represent an example of the decomposition into empirical modes of a relaxation signal from one of the twelve cells of the battery. More specifically, [Fig. 3] represents the cell relaxation signal 31, and the residual signal 32 from the EMD decomposition of this signal 31. [Fig. 4] represents four intrinsic components (components Ci to C4, represented respectively by curves 41 to 44) obtained by the EMD decomposition of the signal 31.

[0060] It should be noted that, in other examples, a different number of intrinsic components could be obtained. In particular, the number of intrinsic components obtained during the EMD decomposition of the relaxation signal can vary from one cell to another. 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 relaxation signal. Using a lower stopping threshold imposes particularly long computation times.

[0061] The intrinsic components obtained in step 130 are then used in step 150 to calculate a variance of cell energies from the intrinsic components obtained for the different cells.

[0062] Different methods can be considered for calculating the variance of cell energies. For example, and as illustrated in [Fig. 5], the calculation of the variance of cell energies may involve: - for each intrinsic component of a cell, a calculation of 151 of the energy of said intrinsic component, - for each cell, a calculation of 152 of a cell energy corresponding to a sum of the energies of the intrinsic components of the cell, - a calculation of 153 of an average energy corresponding to an average of the energies of the battery cells, - a calculation 154 of the variance of cell energies relative to the average energy.

[0063] The energy Ej of an intrinsic component C{ corresponds, for example, to the integral of the square of the amplitude of the intrinsic component C, over the acquisition time of the relaxation phase considered: 100641 E.^c^dt

[0065] The energy Ek of a cell with index & for the relaxation phase considered can then be written in the form:

[0066]

[0067] where Mk is the number of intrinsic components obtained during the EMD decomposition of the relaxation signal associated with the cell of index k for the relaxation phase considered.

[0068] It should be noted that nothing would prevent, in a variant, calculating the energy Ek of a cell of index k 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 the EMD decomposition or, as will be seen later, by discarding certain intrinsic components deemed unreliable on a statistical point of view).

[0069] The average energy of the battery cells, for the relaxation phase considered, can then be written in the form:

[0070] Nc f^k

[0071] It should be noted that nothing would prevent, in variants, calculating the average of the cell energies in a different way, for example in the form of a weighted average of the energies of the different cells that make up the battery.

[0072] The variance V of the cell energies with respect to the mean energy can then be written in the form: 100731 V =

[0074] As indicated above, the variance of cell energies could be calculated in different ways. The particular choice of a method for calculating the variance of cell energies is only one variant of the invention.

[0075]

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[0079] In step 160, the variance of cell energies calculated for the considered relaxation phase is used to determine a current battery health state. The current health state is estimated based on a previously calculated health state and the variance of cell energies for the considered relaxation phase. Current health status can also be estimated based on one or more of the following parameters: - an equivalent number of battery charge / discharge cycles, - a battery charging speed, - a battery discharge rate, - a battery age. As a non-limiting example, the current state of health of the battery can be estimated in the following form: SOH^ — SOHprec - &EMD ' ' N Eq where SOHcur is the current health state of the battery (as a percentage), SOHprec is a previously calculated health state, N Eq is the equivalent number of charge / discharge cycles of the battery, and aEMD is a parameter whose value can be determined empirically in the laboratory. In the example considered, = 25- 109- During the first estimation of SOHcur, SOHprec corresponds to an initial value of the state battery health. This initial value can be calculated using a conventional SOH estimation method. SOHprec calibration may be considered. at any point during the battery's life by a capacity assessment. It is then It is possible to charge the battery to 100% SOC and then perform a discharge at 0% SOC: a coulometric measurement then allows access to the actual capacity or measured either SOH = Capa measured Capa. .. , fmualc *100 Other partial methods of charging and discharging

[0080]

[0081]

[0082] could also be used. Figure 6 shows the evolution over time of the estimated SOH values ​​for different Lithium-Ion batteries subjected to different environmental conditions and for various uses. The x-axis represents the number of equivalent charge / discharge cycles (Ngq). The y-axis represents the SOH as a percentage. The battery marked "la", whose SOH values ​​are represented on the graph of [Fig.6] by diamonds, was tested in a climatic chamber at 45°C with an initial use similar to that of an electric vehicle battery with a sequence of driving phases and daily full recharges. The battery labeled "1b", whose SOH values ​​are represented by crosses on the graph in [Fig. 6], was tested in a climatic chamber at 45°C with a The second use is similar to regulating the frequency of the electrical network, with full recharges much less frequently (only one full recharge per week).

[0083] The battery marked “2a”, whose SOH values ​​are represented on the graph in [Fig.6] by empty circles, was tested in a climatic chamber at 25°C with the first use.

[0084] The battery marked “2b”, whose SOH values ​​are represented on the graph in [Fig.6] by empty squares, was tested in a climatic chamber at 25°C with the second use.

[0085] The battery marked "3a", whose SOH values ​​are represented on the graph in [Fig.6] by solid circles, was tested with the first use in outdoor climatic conditions (high temperatures in summer and cold in winter).

[0086] The battery marked “3b”, whose SOH values ​​are represented on the graph in [Fig.6] by filled squares, was tested in outdoor climatic conditions with the second use.

[0087] It appears in [Fig. 6] that the batteries 1a and 1b, which were tested in a climatic chamber at 45°C, exhibit a particularly rapid decrease in their SOH. The decrease in SOH is faster for battery 1b, for which the frequency of full recharges is lower.

[0088] For batteries 2a and 2b, which have been operating under favorable environmental conditions (temperature of 25°C), it can be observed that the SOH decreases significantly more slowly. However, the SOH decrease is slightly faster for battery 2b, for which the frequency of full recharges is lower than for battery 2a.

[0089] The results presented in [Fig.6] show that the deterioration of the health of a battery is particularly significant when the battery is subjected to adverse environmental conditions (high or low temperatures) and / or when the battery is subjected to an insufficient frequency of full recharges.

[0090] Generally, a battery management system rebalances the battery cells at the end of the charging cycle, when the state of charge (SOC) is close to 100%. When the battery is not fully recharged, rebalancing of the battery cells cannot occur. When battery cell rebalancing is not performed frequently enough, the battery may suffer from premature aging.

[0091] The graphs in Figures 7 and 8 show that it is possible to correlate the variance of the energies of the battery cells with the evolution of the health state of the battery.

[0092] In particular, [Fig. 7] shows the variance value of the cell energies of batteries 2a, 2b, 3a and 3b at different times during the period in which the The batteries were tested. Figure 8 shows the variance values ​​of the cell energies for batteries 1a and 1b. It can be observed that the variance values ​​for batteries 1a and 1b are approximately ten times higher than the variance values ​​for batteries 2a, 2b, 3a, and 3b. Thus, the greater the variance of the battery cell energies (or in other words, the greater the dispersion of cell energies), the more the battery's health deteriorates. Figure 8 also shows that the variance values ​​for battery 1b are generally higher than the variance values ​​for battery 1a. This can be explained by the fact that the more frequent cell rebalancing in battery 1a promotes better preservation of the battery's health. A similar phenomenon can be observed in Figure 7 for batteries 2a and 2b.

[0093] As illustrated in [Fig. 2], the method 100 according to the invention may also include an optional step 140 of statistically analyzing the reliability of the elements used to calculate the variance of the cell energies. This statistical reliability analysis 140 may, in particular, make it possible to estimate whether the energy of an intrinsic component of a cell has an aberrant value, or whether the intrinsic energy of a cell has an aberrant value. If so, the intrinsic component and / or the intrinsic energy deemed unreliable may be filtered out (i.e., not taken into account) in the calculation 150 of the variance of the cell energies. Alternatively, it is possible to consider that the variance value itself is not sufficiently reliable. In this case, the relaxation phase is discarded and is not considered for monitoring the battery's health status.

[0094] According to a first example, the statistical reliability analysis 140 may include, for each intrinsic component of the relaxation signal of a cell, a calculation of the entropy of that intrinsic component. For example, an intrinsic component whose entropy is too low (below a predetermined entropy threshold) is filtered out.

[0095] According to another example, the statistical reliability analysis 140 may include, for each cell, a calculation of an entropy of a sum of the intrinsic components of the cell's relaxation signal (for example, for the sum of all the intrinsic components obtained by the EMD decomposition, or for the sum of a predefined maximum number of the first intrinsic components obtained by the EMD decomposition). For example, a cell for which the sum of the intrinsic components has too low an entropy is filtered out.

[0096] Different methods for calculating entropy can be considered, such as a Shannon entropy calculation or a Kolmogorov entropy calculation. A Shannon entropy threshold between 0.25 and 0.5 can in particular be considered.

[0097] According to yet another example, statistical reliability analysis 140 may include: - a comparison of the energy of an intrinsic component of a cell with a threshold or with the energies of the other intrinsic components of the cell, and / or - a comparison of the energy of one cell with a threshold or with the energies of the other cells in the battery, and / or - a comparison of the variance value calculated for the relaxation phase considered with a threshold or with other variance values ​​calculated for previous relaxation phases.

[0098] The statistical reliability analysis 140 may also include a combination of the examples presented above (the different criteria for filtering an intrinsic component, a cell, or even the variance value may be used jointly).

[0099] Figure 9 schematically represents an example of an embodiment of a device 10 for estimating the health status of a Lithium-Ion battery 20. The device 10 includes, in particular, a memory 11, a measurement system 13, and a processing unit 12 connected to the memory 11 and the measurement system 13.

[0100] The measurement system 13 is configured to provide voltage measurements taken at each cell 21 of the battery 20 during at least one relaxation phase following a battery discharge.

[0101] The calculation unit 12 is configured to implement method 100 according to any one of the implementation modes described above.

[0102] The variance values ​​and / or SOH values ​​calculated for relaxation phases observed by the battery 20 during its lifetime can be stored in memory 11.

[0103] Communication between the measuring system 13 and the processing unit 12 (particularly for transmitting voltage measurements) can be implemented by wired or wireless means. The measuring system 13 and the processing unit 12 can form a single physical entity (i.e., they can be integrated into the same housing, for example, in a battery management system (BMS)). However, there is nothing to prevent the measuring system 13 and the processing unit 12 from each being part of a separate physical entity. It is even conceivable that the processing unit 12 could be integrated into a remote server configured to collect data from several batteries.

[0104] The foregoing description clearly illustrates that, through its various features and their advantages, the present invention achieves the stated objectives. In particular, calculating the variance of the battery cell energies during a relaxation phase makes it possible to estimate the battery's current health status. Regular monitoring of The variance in the energy of the battery cells can thus allow monitoring of the evolution of the battery's health over time.

Claims

Demands

1. Method (100) for estimating the health status of a Lithium-Ion battery (20), said battery (20) comprising several cells (21), the method (100) comprising, for at least one relaxation phase following a discharge phase of the battery: - for each cell (21): • a collection (110) of several voltage measurements at the cell (21) during said relaxation phase, • a formation (120) of a relaxation signal by calculating, for each voltage measurement collected during said relaxation phase, a logarithm of a normalized value of the voltage measurement, • a decomposition (130) into empirical modes of the relaxation 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 (150) of a variance of cell (21) energy from the intrinsic components obtained for the different cells (21),- a determination (160) of a current health state of the battery (20) based on a previously calculated health state and the variance of cell energies for the relaxation phase considered.

2. Method (100) according to claim 1 wherein, for a cell (21) of index k, a value of the relaxation signal at an instant z can be written in the form: where is a voltage measured across the terminals of the cell (21) of index k, is a voltage measured across the terminals of the battery, and Nc is the number of cells (21) of the battery (20).

3. Method (100) according to any one of claims 1 to 2 wherein the calculation (150) of the variance of the cell energies (21) comprises: - for each intrinsic component of a cell (21), a calculation (151) of an energy of said intrinsic component, - for each cell (21), a calculation (152) of an energy of the cell corresponding to a sum of the energies of the intrinsic components of the cell (21), - a calculation (153) of an average energy corresponding to an average of the energies of the cells (21) of the battery (20), - a calculation (154) of a variance of the energies of the cells (21) with respect to the average of the energies.

4. Method (100) according to any one of claims 1 to 3 wherein the determination (160) of the current state of health of the battery (20) is further carried out as a function of one or more of the following parameters: - an equivalent number of charge / discharge cycles of the battery, - a charging rate of the battery, - a discharging rate of the battery, - an age of the battery.

5. Method (100) according to any one of claims 1 to 4 further comprising, for each relaxation phase, a statistical reliability analysis (140) of elements enabling the calculation (150) of the variance of the cell energies, and a filtering of at least one of the following elements if it is deemed unreliable: - an intrinsic component of a cell (21), - a cell (21) of the battery (20), - the variance of the cell energies.

6. Method (100) according to claim 5 wherein the statistical reliability analysis (140) comprises, for each intrinsic component of the relaxation signal of a cell (21), a calculation of an entropy of the intrinsic component.

7. Method (100) according to any one of claims 5 to 6 wherein the statistical reliability analysis (140) comprises, for each cell (21), a calculation of an entropy of a sum of the intrinsic components of the relaxation signal of the cell (21).

8. Method (100) according to any one of claims 5 to 7 wherein the statistical reliability analysis (140) comprises: - a comparison of an energy of an intrinsic component of a cell (21) with a threshold or with the energies of the other intrinsic components of the cell (21), and / or - a comparison of an energy of a cell (21) with a threshold or with the energies of the other cells (21) of the battery (20), and / or - a comparison of the variance value calculated for the relaxation phase considered with a threshold or with other variance values ​​calculated for previous relaxation phases.

9. Device (10) for estimating the health status of a Lithium-Ion battery (20), said battery (20) comprising several cells (21), said device (10) comprising: - a measuring system (13) configured to provide voltage measurements taken at each cell (21) during at least one relaxation phase following a discharge of the battery, - a computing unit (12) connected to the measuring system (13), said computing unit (12) being configured to implement the method (100) according to any one of claims 1 to 8.

10. Battery management system, or BMS, comprising a device (10) according to claim 9.

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