Method and device for detecting a failure of a lead-acid battery

Abstract

The invention relates to a method (100) for detecting a failure of a lead-acid battery. The method comprises, for each segment of a plurality of segments of a "constant voltage" phase, or CV phase, of a battery charging cycle: a collection (110) of several current measurements to form a "floating current" signal for the segment considered, a decomposition into empirical modes (120) of the floating current signal, a calculation (140) of an intrinsic energy for each intrinsic component obtained by the decomposition, a calculation (150) of a total intrinsic energy normalized as a function of the intrinsic energies and of a maximum intrinsic energy determined among the intrinsic energies of the different components, an evaluation (160) of a criterion for detecting a failure of the battery as a function of the total normalized intrinsic energy. Figure for the abstract: Fig. 1

Description

Title of the invention: Method and device for detecting a failure of a lead-acid battery Field of invention

[0001] The present invention belongs to the field of lead-acid battery management. More particularly, a method and a device are provided for detecting a failure of a lead-acid battery. State of the art

[0002] Data centers are responsible for storing, processing, and transmitting large amounts of data. They are critical infrastructure in today's digital world. Data center servers must remain operational at all times, so it is imperative that they are powered by a reliable electrical system. This is why data centers typically use uninterruptible power supply (UPS) systems.

[0003] Lead-acid batteries are a critical component of uninterruptible power supply systems for a data server. Batteries store energy and, in the event of fluctuations or outages in the power supplied by the electrical grid, they can provide backup power to ensure that the system remains operational. Batteries play an important role in protecting equipment, improving power quality, and meeting the power requirements of data centers.

[0004] Lead-acid batteries are widely used in these uninterruptible power supply systems due to their low cost, long life, and high reliability. They are also well suited for use in relatively high-temperature environments, which is crucial for data centers.

[0005] Lead-acid batteries, however, require regular maintenance and inspection to ensure proper operation and avoid impacting the data center.

[0006] There are various methods for detecting a failure of a lead-acid battery. For example, it is known to monitor the voltage, internal resistance, capacity or temperature of the battery (low voltage, high internal resistance, loss of capacity or high temperature may indicate a defective battery). It is also known to monitor the presence of corrosion in the battery, which can lead to premature failure.

[0007] Charging a lead-acid battery is generally carried out in two successive phases. During a first phase called "CC" (acronym for "Constant Current"), the current flowing in the battery is maintained at a substantially constant value. During this first phase, the voltage across the battery terminals increases as the battery recharges. During a second phase called "CV" (acronym for "Constant Voltage"), the voltage across the battery terminals is maintained at a substantially constant value. During this second phase, the current generally follows a decreasing function of time. The current flowing in the battery during the CV phase is often called "floating current." It prevents the natural discharge of the lead-acid battery.

[0008] In the field of lithium batteries, patent application EP 3324197 A1 describes a method for determining the state of health of a battery cell as a function of a ratio between a charge variation and a current difference measured between two instants of a CV phase (constant voltage recharge phase) of a CC-CV cycle (charge cycle comprising a constant current recharge phase followed by a constant voltage recharge phase).

[0009] The reliability of current methods for detecting a failure of a lead-acid battery is not always fully satisfactory. Also, these methods generally do not allow a battery failure to be anticipated sufficiently early. Presentation of the invention

[0010] The present invention aims to remedy all or part of the drawbacks of the prior art, in particular those set out above.

[0011] To this end, and according to a first aspect, the present invention provides a method for detecting a failure of a lead-acid battery. The method comprises, for each segment of a plurality of segments of a “constant voltage” phase (CV phase) of a battery charging cycle: - collecting a plurality of measurements of current flowing in the battery during said segment, said plurality of measurements forming a “floating current” signal for said segment, - a decomposition into empirical modes of the floating current 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 an intrinsic energy for each intrinsic component, - a determination of a maximum intrinsic energy among the intrinsic energies of the different intrinsic components, - a calculation of a total intrinsic energy normalized as a function of the energies intrinsic and maximum intrinsic energy, - an evaluation of a battery failure detection criterion based on the normalized total intrinsic energy.

[0012] The present invention finds a particularly advantageous, although in no way limiting, application in the monitoring of a lead battery of an uninterruptible power supply system of a data server. However, nothing would prevent the present invention from being applied in other fields (lead batteries are notably widely used in industry and in the equipment of railway and automobile vehicles).

[0013] The proposed method clearly differs from conventional methods in that it is based on the analysis of the floating current during the CV phase of a battery charging cycle. Nothing suggests at first glance that this signal contains information relevant to monitoring the state of health of the battery.

[0014] The decomposition into empirical modes is particularly well suited to the analysis of this signal. This decomposition also has the advantage of being relatively fast and not very demanding in terms of computing capacity.

[0015] Monitoring the normalized total intrinsic energy of the battery allows for early and efficient detection of failure.

[0016] In particular embodiments, the invention may further comprise one or more of the following characteristics, taken individually or in any technically possible combination.

[0017] In particular embodiments, the normalized total intrinsic energy is equal to a ratio between a sum of the intrinsic energies of the different components and the maximum intrinsic energy.

[0018] In particular embodiments, the evaluation of the battery failure detection criterion comprises a comparison of the normalized total intrinsic energy with a predetermined failure threshold.

[0019] In particular embodiments, the evaluation of the battery failure detection criterion comprises a verification whether the normalized total intrinsic energy is greater than or equal to the failure threshold for a predetermined number of consecutive segments.

[0020] These different conditions can be used individually or in combination to detect a battery failure.

[0021] In particular embodiments, when a failure is detected, the method further comprises a verification whether the detected failure is linked to the environment in which the battery has evolved.

[0022] In particular embodiments, the verification whether the detected failure is linked to the environment comprises a comparison, for a period data comprising at least one segment, of the normalized total intrinsic energy calculated for the battery for said at least one segment with a normalized total intrinsic energy calculated for at least one other battery subjected to the same environment during said period.

[0023] In particular embodiments, the verification whether the detected failure is linked to the environment comprises a comparison, for a given period comprising at least one segment, of environmental measurements carried out during said period with a predetermined threshold.

[0024] It may indeed be advantageous to know whether a detected failure is linked to the environment in which the battery operates. If the failure is linked to the environment, it is possible to take corrective action at the level of the battery's environment, and the failure will potentially have a limited impact over time. A failure that is not linked to the environment is potentially more serious since it presages an intrinsic failure of the battery, such as for example a failure linked to a manufacturing defect or premature degradation of the battery. An intrinsic failure of the battery may require maintenance to repair or replace the faulty battery.

[0025] In particular embodiments, the method further comprises, for each segment of the plurality of segments, an estimation of a statistical reliability of the segment, as a function of the intrinsic components of the floating current signal of the segment. The segment is then filtered if it is deemed unreliable.

[0026] In particular embodiments, the statistical reliability of the segment is estimated as a function of an entropy calculated for a sum of the intrinsic components of the floating current signal of the segment.

[0027] In particular embodiments, for each segment of the plurality of segments, a total intrinsic energy is calculated in the form of a sum of the intrinsic energies of the different intrinsic components of the segment, and the statistical reliability of a segment is estimated by comparing the total intrinsic energy of the segment with a predetermined energy threshold, or with the total intrinsic energies calculated for at least one previous segment.

[0028] This filtering step makes it possible to exclude segments with outliers (segments deemed statistically unreliable).

[0029] In particular embodiments, the battery is part of an uninterruptible power supply system for a data server.

[0030] According to a second aspect, the present invention provides a device for detecting a failure of a lead-acid battery. The device comprises a battery management system configured to provide measurements of current flowing in the battery during a "constant voltage" phase (CV phase) of a charge cycle of the battery. The device also comprises a computing unit connected to the battery management system, said computing unit being configured to implement a method according to any of the previously described implementation modes. Presentation of the figures

[0031] The invention will be better understood on reading the following description, given by way of non-limiting example, and made with reference to the following figures 1 to 5:

[0032] [[Fig.l] a schematic representation of the main steps of an example of implementation of the method according to the invention for detecting a failure of a lead battery,

[0033] [Fig.2] a graph representing the evolution over time of the normalized total intrinsic energy for seven different batteries subjected to a temperature above a maximum temperature threshold recommended for normal operation,

[0034] [Fig.3] a graph representing the evolution over time of the normalized total intrinsic energy for four different batteries subjected to a temperature below the maximum temperature threshold,

[0035] [Fig.4] a graph representing the evolution over time of the normalized total intrinsic energy for a battery exhibiting an intrinsic failure (design defect or premature deterioration of the battery), a non-failing battery subjected to an abnormal environment, and a non-failing battery subjected to a normal environment,

[0036] [Fig.5] a schematic representation of a device according to the invention for detecting a failure of a lead battery.

[0037] In these figures, identical references from one figure to another designate identical or similar elements. For reasons of clarity, the elements represented are not necessarily on the same scale, unless otherwise stated. Detailed description of the invention

[0038] A lead-acid battery is an electrochemical accumulator whose electrodes are lead-based and the electrolyte is a mixture of water and sulfuric acid. The battery may comprise one or more cells in series assembled in the same housing. The electrodes are generally plates or grids made of a hardened lead alloy (for example using tin, cadmium and strontium, at a rate of a few percent of the alloy).

[0039] In the remainder of the description, the case of managing one or more lead batteries of an uninterruptible power supply system of a data server is considered in a non-limiting manner. However, nothing would prevent the present invention from being applied in other fields (for example to detect a failure of a lead-acid battery of a motor vehicle).

[0040] [Fig.l] schematically represents the main steps of an example of implementation of a method 100 according to the invention for detecting a failure of a lead battery.

[0041] As illustrated in [Fig.l], the method 100 comprises the following steps for each of a plurality of segments of a "constant voltage" phase, or CV phase, of a battery charge cycle: - a collection 110 of several battery floating current measurements during the segment considered, - a decomposition into empirical modes 120 (EMD for “Empirical Mode Decomposition” in English) of the floating current signal formed by the measurements obtained, - a calculation 140 of an intrinsic energy for each intrinsic component obtained by the EMD decomposition, - a determination of a maximum intrinsic energy among the intrinsic energies of the different intrinsic components, - a calculation 150 of a total intrinsic energy normalized according to the intrinsic energies and the maximum intrinsic energy, - an evaluation 160 of a criterion for detecting a battery failure based on the normalized total intrinsic energy.

[0042] The different segments correspond to a time division of the CV phase considered. The floating current measurements are for example carried out by a battery management system (BMS) connected to the battery. The measurements are for example carried out with an acquisition frequency of between fifteen and sixty seconds in order to obtain between two hundred and eight hundred measurements per segment (in this case the duration of a segment is then between fifty and eight hundred minutes). However, nothing would prevent the measurements from being carried out with a different acquisition frequency, and / or with a different number of points per segment.However, it is advantageous to use a number of measurements between two hundred and eight hundred measurements per segment (using a larger number of measurements does not necessarily imply a significant improvement in the method, and this results in relatively long calculation times; using a smaller number of measurements can, however, limit the performance of the method). All of the measurements collected during the collection step 110 form a floating current signal.

[0043] In step 120, the floating current signal is decomposed according to a decomposition into empirical modes. It should be noted that nothing could suggest that this floating current signal could contain relevant information on the state of cell health.

[0044] Empirical mode decomposition consists of decomposing a signal in the form of a sum of functions, in a similar way to what Fourier series decomposition or wavelet decomposition does.

[0045] One of the particularities of the decomposition into empirical modes is that the basis of functions into which the signal is decomposed is not known a priori, but it is constructed adaptively according to the properties of the signal.

[0046] The empirical mode decomposition corresponds to the first part of the Hilbert-Huang transform (HHT). The empirical mode decomposition consists of decomposing a signal in the form of a sum of a residual signal and intrinsic mode functions (IMFs). In the present application, these intrinsic mode functions are also called “intrinsic components”.

[0047] As previously indicated, the intrinsic components are not defined analytically. Rather, they are determined adaptively based on the properties of the signal.

[0048] 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 any 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.

[0049] A signal s(t) decomposed by EMD can then be written in the form: S(f)=r(,)+E^)

[0051] In this expression, r(t) corresponds to the residual signal, N is the number of intrinsic components of the EMD decomposition, and c^t) is the intrinsic component of index 6. Each successive intrinsic component C contains oscillations of a frequency lower than that of the previous one. The residual signal corresponds to a general trend of the signal s(f).

[0052] 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 corresponds to identifying all the local extrema of the input signal, and to connecting the local maxima, respectively the local minima, by an interpolation by cubic splines, in order to obtain an upper envelope, respectively a lower envelope. An average between The upper and lower envelopes can then be calculated and subtracted from the input signal. If the intermediate signal obtained (subtraction of the input signal with the average of the upper and lower envelopes) is not an intrinsic component, the sieving process is repeated on the intermediate signal (which therefore 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 f(l).

[0053] A stopping criterion may 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 may typically be between 0.2 and 0.3.

[0054] The paper "The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis", Norden E. Huang et al., Proc. R. Soc. Lond. A (1998) 454, pp. 903-995, describes empirical mode decomposition in detail, particularly in sections 4 and 5.

[0055] Empirical mode decomposition algorithms are available in programming libraries, for example in MATLAB or Python language.

[0056] In step 140 an intrinsic energy is calculated for each intrinsic component obtained by the EMD decomposition. The energy E{ of an intrinsic component ci corresponds for example to the integral of the square of the amplitude of the intrinsic component ci over the duration of the segment considered: [°°571 E.-rikrfdr

[0058] A maximum intrinsic energy Emax can then be determined from among the intrinsic energies Ej of the different intrinsic components:

[0059] E„a = max(Ef)

[0060] In step 150, a normalized total intrinsic energy E is calculated as a function of the intrinsic energies Et and the maximum intrinsic energy Emax. The normalized total intrinsic energy E is for example equal to the ratio between the sum of the intrinsic energies Et of the different intrinsic components ci and the maximum intrinsic energy Emax:

[0061] E-~ë— ■^tnax

[0062] Nothing would prevent, however, in a variant, from considering in the sum of the intrinsic energies only a subset of the intrinsic components obtained by the EMD decomposition (for example by considering only one predefined maximum number of first intrinsic components obtained by EMD decomposition).

[0063] In step 160, a criterion for detecting a battery failure is evaluated as a function of the normalized total intrinsic energy E.

[0064] Different conditions can be evaluated, individually or in combination, to detect a battery failure from the normalized total intrinsic energy value for the segment considered.

[0065] The evaluation 160 of the battery failure detection criterion may in particular comprise a comparison of the normalized total intrinsic energy with a predetermined failure threshold. For example, a battery failure is detected if the normalized total intrinsic energy is greater than or equal to the failure threshold.

[0066] The evaluation 160 of the battery failure detection criterion may also include a check whether the normalized total intrinsic energy is greater than or equal to the failure threshold for a predetermined number of consecutive segments. For example, a battery failure is detected if the normalized total intrinsic energy remains greater than or equal to the failure threshold for at least five consecutive segments.

[0067] It is particularly interesting to use the normalized total intrinsic energy. The maximum value of normalized intrinsic energy for a component is equal to one (maximum value of normalized intrinsic energy = 1). If, for a given segment, several components have a particularly high intrinsic energy, the normalized total intrinsic energy will be significantly greater than one (normalized total intrinsic energy > 1).

[0068] It is considered that the normalized total intrinsic energy value is linked to the corrosion processes of the battery. Indeed, the corrosion processes involve a multitude of events which, during normalization, involve a non-negligible contribution of the majority of the intrinsic components (IMFs) and thus a value significantly greater than one (for example at least equal to two).

[0069] The failure threshold can in particular be determined empirically in the laboratory. The failure threshold can be specific to a particular type of battery.

[0070] [Fig.2] shows a graph representing the evolution over time of the normalized total intrinsic energy for seven different batteries subjected to a temperature above 50°C. This temperature corresponds to a maximum temperature threshold recommended by the battery manufacturer for normal operation of the batteries. The y-axis of this graph represents the normalized total intrinsic energy value. The x-axis represents the segment number.

[0071] It can be observed from the graph in [Fig.2] that the seven batteries tested have a normalized total intrinsic energy greater than two from the fourth segment onwards. This graph shows that it seems appropriate to use a value of two for the failure threshold.

[0072] [Fig. 3] shows a graph representing the evolution over time of the normalized total intrinsic energy for four different batteries subjected to a temperature below the maximum threshold of 50°C. The normalized total intrinsic energy value is represented by the y-axis; the x-axis represents the segment number.

[0073] It can be seen from the graph in [Fig.3] that only one of the four batteries tested has a total normalized intrinsic energy greater than two. This graph highlights a failure of the “XPV08” battery for which the total normalized intrinsic energy is greater than or equal to the failure threshold during the ten segments considered. The other three batteries do not show any failure during this period.

[0074] It may be advantageous to know whether a detected failure is related to the environment in which the battery has operated. If the failure is related to the environment (for example, if the failure is due to an abnormal increase in the temperature of the location where the battery is located), it is possible to take corrective action at the level of the battery environment, and the failure will potentially have a limited impact over time. A failure that is not related to the environment is potentially more serious since it presages an intrinsic failure of the battery, such as, for example, a failure related to a manufacturing defect or premature degradation of the battery. An intrinsic failure of the battery may require maintenance to repair or replace the faulty battery.

[0075] For this purpose, and as illustrated in [Fig. 1], the method 100 may include an optional verification step 170 if a detected failure is linked to the environment in which the battery has evolved.

[0076] This verification 170 may in particular comprise a comparison, for a given period comprising at least one segment, of the normalized total intrinsic energy calculated for the battery for said at least one segment with a normalized total intrinsic energy calculated for at least one other battery subjected to the same environment during said period. If the normalized total intrinsic energy values ​​calculated for one or more other batteries subjected to the same environment highlight a similar abnormal evolution during a given period, then it is highly probable that this abnormal behavior is linked to the environment (for example because of an exceptional increase in the temperature experienced by the different batteries during this period).

[0077] Alternatively or in addition, the verification 170 may comprise a comparison, for a given period comprising at least one segment, of environmental measurements carried out and stored during said period with a predetermined threshold. These may for example be measurements of the temperature experienced by the battery. If the environmental measurements highlight an abnormal change in the environment during the period considered (for example an exceptional increase in the temperature experienced by the battery during this period), then it is highly probable that a failure detected during this period is linked to the environment.

[0078] By way of illustration, and in no way limiting, [Fig. 4] graphically represents the evolution over time of the normalized total intrinsic energy for three different batteries. The y-axis represents the normalized total intrinsic energy value; the x-axis represents the segment number.

[0079] Curve 31 represents the evolution over time of the normalized total intrinsic energy for a non-failing battery subjected to a normal environment. This curve 31 remains continuously below the failure threshold S.

[0080] Curve 32 represents the evolution over time of the normalized total intrinsic energy for a battery exhibiting an intrinsic failure (for example a design defect or premature deterioration of the battery). This curve 31 remains continuously above the failure threshold S. The method 100 according to the invention can make it possible to detect this intrinsic failure of the battery corresponding to curve 32.

[0081] Curve 33 represents the evolution over time of the normalized total intrinsic energy for a non-failing battery subjected to an abnormal environment for a certain period of time. During this period, the battery is for example exposed to an exceptionally high temperature, which results in abnormal operation of the battery. As illustrated by curve 33 on the graph of [Fig. 4], the normalized total intrinsic energy of the battery is greater than the failure threshold S during the period of time when the temperature is abnormally high. Outside of this period of time, the normalized total intrinsic energy of the battery is less than the failure threshold S. The method 100 according to the invention can make it possible to detect the failure of the battery corresponding to curve 33.The method 100 according to the invention can also make it possible to detect whether this failure is due to abnormal conditions in the environment in which the battery operates. For example, abnormally high temperature measurements measured during the period of time when the failure is detected suggest that this failure is due to abnormal conditions in the environment. According to another example, similar abnormal behavior observed during the same period of time for other . batteries subjected to the same environment also suggests that this failure is due to abnormal environmental conditions.

[0082] As illustrated in [Fig.l], the method 100 according to the invention may also comprise an optional step 130 of estimating the statistical reliability of the segment considered, and filtering the segment if it is deemed unreliable (the segment and the associated measurements are then ignored, i.e. they are not taken into account in the evaluation 160 of the failure criterion).

[0083] This filtering of a segment deemed unreliable makes it possible to avoid taking into account aberrant values ​​in the analysis of the state of health of the battery.

[0084] The statistical reliability of a segment is estimated based on the intrinsic components of the floating current signal obtained for the segment.

[0085] According to a first example, the statistical reliability of the segment is estimated as a function of an entropy calculated for a sum of the intrinsic components of the floating current 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). Different methods of calculating entropy can be envisaged, such as for example a Shannon entropy calculation, or a Kolmogorov entropy calculation. For example, the segments for which the calculated entropy is too low (less than a predetermined entropy threshold) are filtered. A Shannon entropy threshold of between 0.25 and 0.5 can in particular be envisaged.

[0086] According to a second example, a total intrinsic energy equal to a sum of the intrinsic energies of the different intrinsic components of the segment is calculated (here again, it is possible to consider the set of all the intrinsic components obtained by the EMD decomposition, or only a subset of these intrinsic components). The statistical reliability of a segment is estimated by comparing the total intrinsic energy calculated for the segment with a predetermined energy threshold. For example, segments which have an aberrant total intrinsic energy value (greater than the energy threshold) are filtered.

[0087] According to yet another example, the statistical reliability of a segment is estimated by comparing the total intrinsic energy calculated for the segment with the total intrinsic energies calculated for all or part of the previous segments (for example, the total intrinsic energies can be compared with each other, or the total intrinsic energy of the current segment can be compared with an average value of the total intrinsic energies of previous segments). Different statistical tests can be envisaged for this purpose (Pierce test, Pierson test, etc.).

[0088] [Fig. 5] schematically represents a device 10 for detecting a failure of a lead battery 21. The device 10 comprises in particular a memory 11, a battery management system 13 and a computing unit 12 connected to the memory 11 and the battery management system 13.

[0089] The battery management system 13 is configured to provide current measurements made at the battery 21 during at least one segment of a CV phase of the battery.

[0090] The calculation unit 12 is configured to implement the method 100 according to any one of the implementation modes described above.

[0091] The device 10 may further comprise a sensor configured to measure the environment (for example a temperature sensor).

[0092] The battery management system 13 may also be configured to provide floating current measurements of one or more other batteries 22 subjected to the same environment as the battery 21.

[0093] The above description clearly illustrates that, through its various characteristics and their advantages, the present invention achieves the set objectives. In particular, monitoring the normalized total intrinsic energy of the battery effectively makes it possible to detect a failure early.

Claims

Claims

1. Method (100) for detecting a failure of a lead-acid battery (21), the method (100) comprising, for each segment of a plurality of segments of a "constant voltage" phase, or CV phase, of a battery charging cycle: - collecting (110) a plurality of measurements of current flowing in the battery (21) during said segment, said plurality of measurements forming a "floating current" signal for said segment, - decomposing the floating current signal into empirical modes (120) in order to obtain a representation thereof in the form of a sum of a residual signal and one or more intrinsic components, - calculating (140) an intrinsic energy for each intrinsic component, - determining a maximum intrinsic energy among the intrinsic energies of the different intrinsic components,- a calculation (150) of a total intrinsic energy normalized as a function of the intrinsic energies and the maximum intrinsic energy, - an evaluation (160) of a criterion for detecting a failure of the battery (21) as a function of the total normalized intrinsic energy.,

2. A method (100) according to claim 1 wherein the normalized total intrinsic energy is equal to a ratio between a sum of the intrinsic energies of the different components and the maximum intrinsic energy.

3. Method (100) according to one of the preceding claims in which the evaluation (160) of the criterion for detecting a failure of the battery (21) comprises a comparison of the normalized total intrinsic energy with a predetermined failure threshold.

4. Method (100) according to one of the preceding claims in which the evaluation (160) of the criterion for detecting a failure of the battery (21) comprises a verification whether the normalized total intrinsic energy is greater than or equal to the failure threshold for a predetermined number of consecutive segments.

5. Method (100) according to any one of claims 1 to 4 wherein, when a failure is detected, the method further comprises a verification (170) whether the detected failure is linked to the environment in which the battery (21) has evolved.

6. Method (100) according to claim 5, wherein the verification (170) whether the detected failure is related to the environment comprises a comparison, for a given period comprising at least one segment, of the normalized total intrinsic energy calculated for the battery (21) for said at least one segment with a normalized total intrinsic energy calculated for at least one other battery (22) subjected to the same environment during said period.

7. Method (100) according to any one of claims 5 to 6, wherein the verification (170) whether the detected failure is related to the environment comprises a comparison, for a given period comprising at least one segment, of environmental measurements carried out during said period with a predetermined threshold.

8. Method (100) according to any one of claims 1 to 7, further comprising, for each segment of the plurality of segments, an estimation (130) of a statistical reliability of the segment, as a function of the intrinsic components of the floating current signal of the segment, and a filtering of the segment if it is judged to be unreliable.

9. The method (100) of claim 8, wherein the statistical reliability of the segment is estimated based on an entropy calculated for a sum of the intrinsic components of the segment's floating current signal.

10. A method (100) according to any one of claims 8 to 9 wherein, for each segment of the plurality of segments, a total intrinsic energy is calculated as a sum of the intrinsic energies of the different intrinsic components of the segment, and the statistical reliability of a segment is estimated by comparing the total intrinsic energy of the segment with a predetermined energy threshold, or with the total intrinsic energies calculated for at least one previous segment.

11. A method (100) according to any one of claims 1 to 10, wherein the battery (21) is part of an uninterruptible power supply system for a data server.

12. Device (10) for detecting a failure of a lead battery (21), said device (10) comprising: - a battery management system (13) configured to provide measurements of current flowing in the battery (21) during a “constant voltage” phase, or CV phase, of a charging cycle of the battery (21), - a computing unit (12) connected to the battery management system (13), said computing unit (12) being configured to implement a method according to any one of claims 1 to 11.

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