Detecting a variation anomaly in an estimate of a level of charge of an electrical energy storage module

Abstract

The present disclosure is based on the implementation of a coulomb counting method using timestamped current measurement data to calculate the equivalent SOC, and then to calculate the variation in the equivalent SOC over a time interval, wherein this variation corresponds to the current delivered during that time period. The latter is then compared with one or more thresholds in order to qualify this variation as normal, or as abnormally slow ("SOC freeze") or fast ("SOC jump").

Description

i Description Title: Detection of an anomaly in the variation of an estimate of a charge level of an electrical energy storage module Technical field

[0001] This disclosure relates to the field of electrochemical energy storage devices, and more particularly to the diagnosis of batteries or accumulators. Prior art

[0002] The control of battery-based electrical energy storage systems, such as lithium-ion batteries, relies in part on estimating state variables, such as the battery's state of charge. This state of charge, also called SOC for "State Of Charge", is a unitless parameter, generally expressed as a percentage. The SOC is defined as the ratio of the actual available charge Qt of the tested element to the maximum charge Qmax.t of the same element in its current state of health. The equation below represents this parameter:

[0003] [Math. 1]

[0004] A lack of reliability of this key indicator is a risk factor for the effective control of electrical energy storage systems, also called BESS (for English "Battery Energy Storage System"). However, SOC jump phenomena (i.e., a sudden and unpredictable variation of the SOC indicator) and frozen SOC phenomena (i.e., maintaining the same SOC value while the battery is nevertheless exchanging current) have been observed on different real systems. The occurrence of these phenomena results in a loss of usable capacity of the battery, difficulties in operating the system, and finally loss of revenue for the operator.

[0005] It is therefore important for BESS operators to better understand the causes of these phenomena, to be able to identify and quantify them.

[0006] To achieve this, academic and industrial research focuses primarily on developing methods for estimating the state of charge, SOC, and / or methods for improving their accuracy. Thus, the documents "End of Discharge SOC Jump Elimination", Application Report, April 2020 by Texas Instruments and Orion BMS "Diagnosing State of Charge Calculation Jumps", 2018 by Ewert Energy Systems both address the issue of SOC indicator reliability. However, they primarily address the causes of the unreliability problem and propose solutions to avoid it.

[0007] To date, little work has focused on characterizing the reliability or accuracy of this battery state of charge indicator from a user's perspective. In the case of stationary storage, such a user may be a battery integrator, who receives the state of charge estimate from a battery supplier's control system. Such a user may also be the storage system operator, who receives the state of charge estimate from the lower control level of the BESS, who may have calculated it himself, or simply relayed it from another source.

[0008] However, documents WO2021136419A1 and CN108931739A disclose methods for determining the accuracy of an SOC estimate, or for determining the error in estimating the SOC of a battery system, particularly in an electric vehicle. Summary

[0009] This disclosure improves the situation, particularly in the detection of SOC estimation anomalies of the frozen SOC type, or in English "SOC Freeze" and of the SOC jump type, or in English "SOC jump".

[0010] A method is proposed for detecting an anomaly in variation of an estimate, by a battery management system, BMS, of a charge level, SOC, of ​​at least one electrical energy storage module. Such a method comprises: a. A collection of time-stamped data provided by the battery management system, comprising charge level estimation data and measurement data of a charge / discharge current of said at least one storage module; b. A calculation, from the time-stamped charge level estimation data, of a variation in estimated charge level over a set of determined time intervals of duration dt; c. A calculation, from the time-stamped current measurement data and a capacity of the electrical energy storage module, of an equivalent variation in charge level over the set of determined time intervals of duration dt; d. A grouping of the time intervals into a plurality of groups of time intervals associated with a variation in estimated charge level equal to a determined reference value, and, for each of the groups: i. a calculation of a sum of the equivalent variations in charge level over the intervals of said group; ii. a comparison with a threshold of a difference between the determined reference value and the sum and, depending on a result of the comparison, a detection of an anomaly in variation of the estimate.

[0011] According to another aspect, there is provided a battery management system, BMS, of at least one electrical energy storage module, which comprises a memory and a processor configured to execute the aforementioned method of detecting an anomaly in variation of an estimate of the charge level, SOC, of ​​this or these electrical energy storage module(s).

[0012] According to another aspect, a method is proposed for managing an electrical energy storage system comprising a plurality of electrical energy storage modules, which implements, for each of said modules, the aforementioned method for detecting an anomaly in variation of an estimate of charge level, SOC, and which comprises a determination of a performance indicator of the storage system, as a function of a number of anomalies in variation of the estimate detected for each of the modules.

[0013] According to another aspect, there is provided a computer program comprising instructions for implementing all or part of a method as defined herein when this program is executed by a processor. According to a Another aspect is that a non-transitory, computer-readable recording medium is provided on which such a program is recorded.

[0014] The features set out in the following paragraphs may, optionally, be implemented, independently of each other or in combination with each other:

[0015] Such a method for detecting an anomaly in variation of an SOC estimate also comprises returning to a user an indicator of detection of an anomaly in variation of the estimate and of the sum of equivalent variations in the calculated load level.

[0016] Such a method for detecting an anomaly in variation of an SOC estimate also comprises a determination of a state of health, SOH, of said at least one electrical energy storage module.

[0017] The capacity of said at least one electrical energy storage module taken into account for the calculation of the equivalent variation in charge level is a residual capacity of said at least one electrical energy storage module calculated according to the following formula: [Math. 2] in which C residueUe denotes the residual capacity, C nom denotes a nominal electrical capacity of said at least one module and SOH is a unitless parameter expressed as a percentage which denotes the state of health of said at least one module.

[0018] The calculation of the equivalent variation of load level over a time interval of duration dt is calculated according to the following formula: [Math. 3] dSOC éauivalent= ^~ dtIdt x 100 100 , ^residual

[0019] in which I denotes the charge / discharge current of said at least one electrical energy storage module and C residueUe denotes the residual capacity of the electrical energy storage module.

[0020] The threshold can be set by the user, according to his needs, for example in order to verify the announced precision performance of the SOC. In a mode of realization, this threshold is an error in the SOC estimation of the battery management system, for example the maximum error announced by the manufacturer of the battery management system, or the sum of this maximum error and the resolution announced by the manufacturer, or the sum of this maximum error and a known error in current measurement by the sensors. Brief description of the drawings

[0021] Other features, details and advantages will become apparent upon reading the detailed description below, and upon analyzing the attached drawings, in which: Fig. 1

[0022] [Fig. 1] shows a block diagram of an electrical storage system comprising one or more electrical energy storage modules and a corresponding battery management system. Fig. 2

[0023] [Fig. 2] shows an exemplary embodiment of a method for detecting an anomaly in the estimation of the variation of SOC of an electrical energy storage module, for example one of the modules in Figure 1. Fig. 3A

[0024] [Fig. 3A] shows a graphical representation of a SOC charge level as a function of time, with grouping of time intervals according to one embodiment. Fig. 3B

[0025] [Fig. 3B] shows the graphical representation of the SOC load level of Figure 3A, on which an anomaly detection threshold S has been represented. Fig. 3C

[0026] [Fig. 3C] shows the graphical representation of SOC charge level of Fig. 3A and Fig. 3B, on which the result of the anomaly detection according to one embodiment is shown. Fig. 4A SHEET INCORPORATED BY REFERENCE (RULE 20.6)

[0027] [Fig. 4A] shows a graphical representation of a SOC charge level as a function of time, with grouping of time intervals according to another embodiment. Fig. 4B

[0028] [Fig. 4B] shows the graphical representation of the SOC load level of Figure 4A, in which an anomaly detection threshold S and the result of the anomaly detection according to this embodiment are shown. Fig. 5A

[0029] [Fig. 5A] shows a graphical representation of a SOC charge level as a function of time, with grouping of time intervals according to another embodiment and using a zero reference value D. Fig. 5B

[0030] [Fig. 5B] shows the SOC charge level plot of Figure 5A, with an alternative grouping of time intervals and use of a non-zero reference value D. Description of the embodiments

[0031] The general principle of the method described is based on the implementation of a coulombic counting method from time-stamped current measurement data for a calculation of equivalent SOC, then of a variation of equivalent SOC over a time interval, which corresponds to the image of the current delivered during this time interval. The latter is then compared to one or more thresholds to qualify this variation as normal, or abnormally slow ("SOC freeze") or fast ("SOC jump").

[0032] Reference is now made to Figure 1, which presents in block diagram form an example of an electrical energy storage system 10, or BESS (for the English “Battery Energy Storage System”). Such a BESS 10 comprises a plurality of devices 11j, four of which have been shown as an example in Figure 1. Each of these devices 111 to 114 comprises an electrical energy storage module, for example in the form of a pack of electrochemical battery cells, and a battery management system, also called BMS (for the English “Battery Management System”). SHEET INCORPORATED BY REFERENCE (RULE 20.6)

[0033] The BMS has multiple functions, including assessing the SOH state of health of the electrical energy storage module, estimating its SOC charge level, controlling the balance between the different cells in the pack, and estimating the remaining autonomy time for the module.

[0034] A bidirectional power flow P is established between the devices 111 to 114 and a conversion electronics module 12, which enables the power supply of an electrical network to which the BESS 10 is connected.

[0035] The BESS 10 also comprises a control module 13, which sends control signals to the conversion electronics module 12, and receives from the latter voltage and flux intensity measurement data P. The control module 13 can also interrogate the BMS to receive from the latter estimated or calculated values ​​of SOC, SOH, temperature, or even charge / discharge current of the storage modules of the devices 11 1 to 114.

[0036] The control module 13 is in communication with an upper control stage 14, which controls the acquisition of data from the BESS 10, monitors its operation and controls it. This upper control stage 14 is for example controlled by the operator of the BESS 10, or an integrator of this battery storage system.

[0037] We now describe, in relation to Figure 2, an example of implementation of a method for detecting an anomaly in estimating a variation in SOC of an electrical energy storage module. This module is for example a battery or a battery cell pack of one of the devices 11 i of Figure 1. Such a method is based on an analysis of data from operational systems, such as that illustrated in Figure 1. It is therefore simple to implement, and does not require hardware modification of existing BESSs.

[0038] In one example, this method is implemented in the control module 13 of the BESS 10. In another example, this method is implemented in the upper control stage 14 of the BESS 10. Indeed, a user of such a method could for example be, in the case of stationary storage, a battery integrator who receives the estimate of the state of charge by the control system of the battery supplier, or even a storage system operator. SUBSTITUTION SHEET (RULE 26) which receives the state of charge estimate from the lower control level (whether calculated at the lower level or simply relayed).

[0039] In a first operation 21, measurement or estimation data from the BMS of one of the devices 11 i are collected. These data are time-stamped: thus, they can then be processed according to the date on which they were measured or calculated. These time-stamped data include at least measurement data of a charge / discharge current l(t) of the electrical energy storage module, and data for estimating the charge level SOC by the BMS. Alternatively, other data can also be collected, such as the voltage U(t) at the terminals of the storage module, its state of health SOH, or the temperature T(t).

[0040] The collected data are acquired during a period of uninterrupted operational operation of the device 11 i. Thus, each type of data takes the form of a time series, here of the SOC and the current of the device in operational operation. Here, operational operation is understood to be distinguished from a maintenance or test phase for which the operating conditions can be chosen to correspond to test conditions. In operational operation, on the contrary, the device follows usual operating conditions, not imposed specifically for the measurements.

[0041] It is preferable that each collected time series be usable. Also, the collection operation 21 may include, or be preceded by, a selection, or filtering, eliminating from the collection the time series including an unusable time step, for example in the event of an absence of value, an error message, poor alignment of the measurements, etc. At the end of the selection, the collected time series are all continuous and usable.

[0042] Thus, only time-stamped records of charge / discharge current and SOC estimation are required to deploy this approach, making it simple to implement and accessible for most existing and already deployed systems.

[0043] During an operation referenced 22, the time-stamped charge level estimation data, SOC(t), is used to calculate a variation SUBSTITUTION SHEET (RULE 26) of estimated charge level, over a set of time intervals of duration dt determined. For example, we choose a time step dt = 1 second, and we calculate, over contiguous time intervals of 1 s, the variation of charge level estimated by the BMS, according to the following formula:

[0044] [Math. 4] d S0 C estimated = SOC t) - SOC (t ~ dt)

[0045] This gives the SOC increment estimated by the battery management system (BMS).

[0046] In one embodiment, this time step dt is that of the data sampling frequency (often identical between SOC and current). The sampling period should preferably be an order of magnitude lower than the period of change of the SOC value, so as to properly capture each of the value increments. For example, for a discharge at 1 C with 0.5% SOC increments, the SOC change period will be of the order of 18s and sampling every 1-2s would allow all the value increments to be comfortably captured.

[0047] During an operation referenced 23, which may take place before, after, or in parallel with the operation referenced 22, the time-stamped current measurement data collected during operation 21 are used to calculate an equivalent variation in charge level over the same set of time intervals of duration dt. This equivalent variation is calculated by an integral calculation according to the following formula:

[0048] [Math. 5] 100

[0049] in which I denotes the charge / discharge current measured by the BMS and collected during operation 21, and C denotes the capacity of the electrical energy storage module.

[0050] We thus calculate the increment of the equivalent SOC corresponding to the battery current between t-dt and t. SUBSTITUTION SHEET (RULE 26)

[0051] The capacity C can also be provided by the BMS. In one example, the nominal electrical capacity C is usednom of the storage module. In another example, for a more precise calculation, we also take into account the health status SOH of the storage module, and we replace in the equation the nominal capacity Cnom by the residual capacity C residueUe of the storage module, calculated according to the following formula:

[0052] [Math. 2] '-'residual — H '-'nom-

[0053] It is recalled that SOH is a unitless parameter expressed as a percentage which designates the state of health of the electrical energy storage module, and which can be part of the data collected during operation 21: the state of health indicator provided by the BMS is then used. Alternatively, it is also possible to use a third-party state of health indicator. In an advantageous embodiment, the method can comprise an operation for diagnosing the SOH state of health, based on reference tests, also called capacity tests, according to which a complete charge and discharge cycle is carried out on site, for which the discharged capacity in Ah is calculated (C residueUe ). In this case, we can either directly use C residuelle , or go through the product SOH x C nom .

[0054] Considering time intervals of sufficiently short duration dt, and taking into account the health state SOH, the integral calculation of the equivalent variation of load level can be approximated according to the following formula:

[0055] [Math. 3] 100 ,

[0056] In an operation 24, the successive time intervals of duration dt are grouped into a plurality of groups of time intervals each associated with a variation in estimated load level equal to a determined reference value dsoc estimé = D . This reference value D can be set by a user of the electrical energy storage module.

[0057] In a first embodiment, D=0, and the successive time intervals of duration dt are then grouped into a plurality of groups of time intervals each associated with a variation in the estimated zero load level. Thus, SUBSTITUTION SHEET (RULE 26) if the variation in load level calculated during the operation referenced 22 is zero over the successive time intervals [to; to+dt], [to+dt; to+2dt] and [to+2dt; to+3dt], these three time intervals are grouped into a first group of time intervals, noted G1. We thus construct several groups Gj, each of which groups together a set of consecutive time intervals over which the variation in load level dsoc estimé calculated during the operation referenced 22 is zero.

[0058] Figure 3A illustrates such a grouping of time intervals, within the framework of a graphical representation of the charge level SOC as a function of time t. In this figure 3A, three groups of time intervals referenced G1, G2 and G3 are represented as an example. For each of these groups G1 to G3, the estimated charge level variation dsoc estiméfrom the BMS (step 21) is zero, as symbolized by the horizontal solid line. The variation in the equivalent charge level dSOC équivaient on the other hand is non-zero, and illustrated, for each of the groups G1 to G3, by an ascending dotted line.

[0059] In an operation 25, we calculate, for each of the groups Gj formed during the operation referenced 24, a sum of the equivalent variations in charge level dSOC équivaient on the intervals of the group. Thus, for the group G1 for example, we calculate this sum according to the following formula:

[0060] [Math. 6] tg + 3dt to

[0061] In an operation 26, the sum thus calculated for each group Gj is compared to an anomaly detection threshold, noted S in FIG. 3B. In one embodiment, for example, the error E of charge level estimation, as specified in the construction parameters of the BMS, generally expressed in the form ±E% of SOC, is used as anomaly detection threshold S. More generally, this threshold S is defined by the user. It is of course appropriate to distinguish this error threshold from the resolution.

[0062] For example, if for group G1, the sum of the equivalent variations in load level dSOC équivaient (Gl') is not zero but is SUBSTITUTION SHEET (RULE 26) less than the SOC estimation error specified by the BMS manufacturer, it is considered that there is a priori no SOC estimation anomaly, and that the difference between dSOC équivaient (Gl)' and dSOC estimé(Gl) = 0 is within the operating error margin of the BMS. Note that the minimum anomaly detection threshold value that makes sense is the resolution of the BMS SOC estimate. It would not be relevant to define an anomaly detection threshold of 0.2% for a resolution of the SOC variable of 0.5% because, by construction, this threshold would be exceeded most of the time.

[0063] If, on the other hand, for group G1, the sum of the equivalent variations in the dSOC load level équivaient(Gl)' is not zero but, moreover, is greater than a threshold of interest defined by the user (e.g., the SOC estimation error specified by the BMS manufacturer), a SOC estimation anomaly is detected. Such an anomaly is of the "SOC freeze" type: in fact, according to the estimation provided by the BMS, the variation in SOC is zero, and the SOC remains constant. On the other hand, according to the time-stamped current measurement data collected during operation 21 , the charge level has changed over the time interval [to; to+3dt], according to an equivalent increment large enough not to fall within the threshold of interest defined by the user (e.g., within the margin of error of the BMS). This increment corresponds to an exchange of current in Ah over the time interval [to; to+3dt] which should have triggered a SOC increment: if this is not the case, an anomaly is detected.

[0064] In the embodiment illustrated in Figure 3C, no anomaly is detected. Indeed, for each of the groups of time intervals G1 to G3, we have:

[0065] [Math. 7]

[0066] Thus, the difference between the estimated SOC variation and the equivalent SOC variation is always lower than the threshold S, in absolute value, for each of the groups G1 to G3: no SOC estimation anomaly is therefore detected, as symbolized by the mention “OK” for each interval G1 to G3. SUBSTITUTION SHEET (RULE 26)

[0067] In another embodiment, the reference value D is non-zero, in order to allow the operation of the BESS 10 to be analyzed over larger sets of time steps.

[0068] As illustrated by the curves in Figures 4A and 4B, the use of a non-zero reference value D makes it possible, for example, to identify the cumulative error which would not be identified with a grouping of time intervals associated with an estimated zero load level variation, taken individually.

[0069] In Figure 4A, as part of a graphical representation of the charge level SOC as a function of time t, a group of time intervals referenced G'1 is shown as an example, formed from the three groups of time intervals G1, G2 and G3 illustrated in Figures 3A to 3C. In Figure 4A, the estimated charge level variation dsoc estimé from the BMS (step 21) is symbolized by a solid line, and the variation in the equivalent charge level dSOC équivaient is illustrated by an upward dotted line.

[0070] Over the group of time intervals G'1, the sum of the estimated load level variations <iSOC estimé (G'l) is equal to D.

[0071] In an operation 25, we calculate, for the group G'1, a sum of the equivalent variations of charge level dSOC équivaient G'l') on the intervals of the group. In operation 26, the difference, in absolute value, between dSOC éqidvaient (G'l)~ and dSOC estimé (G'l) is compared to the anomaly detection threshold, noted S in Figure 4B, as defined by the user in the example of Figure 4B, an anomaly is detected (symbol "NOK") because

[0072] Thus, by comparing Figures 3C and 4B, we understand that the grouping of time intervals by dSOC esiimé of non-zero reference value D (group G'1) makes it possible to identify a cumulative error which is not identified on each group G1, G2, G3 taken individually for which dSOC esiimé = 0.

[0073] Figures 5A and 5B illustrate another exemplary embodiment, in which the effect on the detection of SOC estimation anomalies of using a zero or non-zero reference value D for grouping the time intervals is compared. CORRECTED SHEET (RULE 91) ISA / EP

[0074] In Figure 5A, we consider four groups of time intervals referenced G1 to G4, which were constructed using a reference value D=0. Thus, on each group G1 to G4, we have dSOC estimé (Gi) = 0, as represented by the horizontal solid line. We consider a user-defined threshold S. The variation dSOC éqidvalent (G ), for each of the groups G1 to G3 is negative (the SOC decreases over each of these groups of time intervals), as represented by the dotted lines, but remains below the threshold S for each of these groups. For group G4, the variation dSOC équivaient(G4) is positive, but, again, remains below the threshold S. However, there is a significant jump in SOC between the time interval groups G3 and G4, which is therefore not detected as an anomaly in this case of Figure 5A where the time intervals associated with a variation in the estimated charge level of zero (dSOC estimé = 0) are grouped.

[0075] In Figure 5B, we consider a non-zero reference value D, which leads to constructing a group of time intervals G'1 including the groups G1 to G4 of Figure 5A. On the group G'1, we have dSOC estimé (G'l) — D.

[0076] In an operation 25, we calculate, for the group G'1, a sum of the equivalent variations of charge level dSOC équivaient (G'l)' on the intervals of the group. In operation 26, the difference, in absolute value, between dSOC équivaient (G'l~) and dSOC estimé(G'l) is compared to the anomaly detection threshold, noted S in figure 5B: an anomaly is then detected (symbol "NOK") because

[0077] This allows us to identify excessively rapid SOC variation rates (also called “SOC jumps”).

[0078] Thus, the use of a non-zero reference value D for grouping time intervals makes it possible to give a lower weight, in anomaly detection, to the resolution of the SOC variable, as announced for example by the manufacturer of the BESS 10.

[0079] Returning to the flowchart of Figure 2, in an optional operation 27, the anomaly detection results of the referenced operation 26 can be used to develop a performance indicator of the BESS system 10. Such an indicator can take the form of a frequency of occurrence of SUBSTITUTION SHEET (RULE 26) frozen SOC phenomena, or even a distribution of the amplitude of these frozen SOCs. In another example, when installing the BESS 10, the performance indicator is initialized to a value of 10, and decremented by one point each time an anomaly is detected during operation 26. In particular, it is possible to monitor the number or frequency of occurrences over time of the detection of anomalies, and observe whether this number or frequency of occurrences is the same for all the elements of the battery. This performance indicator can be relayed, via the upper control stage 14, to the operator of the BESS 10.

[0080] This anomaly detection indicator can be returned to a user on a human-machine interface of the BESS 10. In one embodiment, this indicator is returned in association with the value of the sum of the equivalent variations in load level calculated, which allows the user to refine the anomaly detection diagnosis, and provides him with more complete and richer information on the identified problem.

[0081] The methods and variants described above may be implemented by computer means, in particular a computer on which a program is recorded for implementing such a method when this program is executed by a processor. Such a program may also be stored on a non-transitory recording medium readable by a computer.

[0082] The present disclosure for the detection of anomalies in the estimation of SOC of an electrical energy storage system in operation differs from known techniques in particular by: - the use of data traditionally available within the BMS, which can be accessed without hardware modification of existing systems. The process can thus be directly implemented on all electrical energy storage systems already deployed and operational; - the ability to operate in real time, on a running system. This allows BESS operators to detect SOC estimation anomalies as soon as they appear, and therefore to quickly implement the necessary corrective measures for optimized system operation; - the grouping of equivalent increments of variation of SoC over a period over which the estimate of the SoC by the BMS remains unchanged or equal to a determined reference value and the comparison of the total increment obtained with a SUBSTITUTION SHEET (RULE 26) user-defined threshold, which can be, for example, the SoC estimation error of the BMS. We thus propose a simple calculation method, which consumes little resources and is directly applicable to the detection of anomalies of the “SOC freeze” and “SOC jump” type; - taking into account the state of health of the battery to define the comparison thresholds. This makes it possible to increase the accuracy of the detection of SOC estimation anomalies, by relying on the particular state of the battery at the time the calculations are carried out; - the possibility of carrying out the steps of the anomaly detection method described in the present disclosure by using sliding time windows for grouping the equivalent increments of SOC variation.

[0083] Furthermore, the ability of the BESS operator to have reliable real-time information on the SOC estimation accuracy allows it to make documented market-oriented arbitrage choices to meet a given service.

[0084] Such a process also improves the operational safety of BESSs. Indeed, given the alerts generated by the detection of SOC estimation anomalies, it is possible for the BESS operator to detect that maintenance is necessary on the electrical energy storage system, for example to replace one or more electrical energy storage modules for which anomalies are frequently detected. Examples

[0085] For example, consider a battery with a capacity of 200Ah. According to the current measurement data collected during the operation referenced 21, this battery was charged at a current of 100A for a duration of 15min. On the other hand, according to the result obtained during the operation referenced 22, the SOC indicator remained unchanged during this period: dSOC estimé (15min) = 0. The equivalent variation in charge level over this quarter-hour period, as calculated during operations 23 and 25, gives a charge increment of 25Ah, or an equivalent SOC increment of 12.5%: SUBSTITUTION SHEET (RULE 26) x 100 = 12.5%

[0086] Depending on the anomaly detection threshold chosen for operation 26, this increment, not considered by the BMS SOC gauge, could trigger the identification of a frozen SOC, or “SOC freeze”. Industrial application

[0087] The present technical solutions allow to identify the abnormal behaviors of the SOC variable proposed by a BMS based on limited operational data of the studied BESS battery storage system.

[0088] The estimation of the indicators is done without modifying the behavior of the electrical energy storage system. The service value of the system is therefore not impacted by such processes. No additional tools or equipment are required (probes, sensors). Only basic physical quantities such as the SOC estimate and current are used. However, these parameters are usually measured and available in existing systems.

[0089] Operators or integrators of battery storage systems can use the proposed method to challenge the reliability of the state of charge indicator provided by a third party (typically by the lower control level). This method can be used as a performance indicator for storage systems in operation to identify problems related to the estimation of the state of charge. It then uses "online" data, and can be used at any level of battery architecture for which the monitoring system calculates a state of charge (cell, module or rack ...).

[0090] This method can also be used offline on historical data. Thus, Battery Management System providers can use this approach to characterize the behavior of their state of charge estimator a posteriori.

[0091] There are two ways of implementing the process: - For real-time applications (stationary storage or onboard storage system). The algorithmic method can be integrated into the SUBSTITUTION SHEET (RULE 26) battery management system (or BMS for "Battery Management System"). The sufficient raw data then corresponds to the instants (to, to + dt, , to + n*dt). Operations 21 to 26 can be executed continuously over a sliding window, or periodically. Anomaly detection can be done at each execution (iteration) of operations 21 to 26. The method then uses the anomalies detected locally, or can export them. - For a posteriori analysis applications (stationary or on-board energy storage). The algorithmic method can be deported from the BESS storage system itself, for example integrated into a more general data processing tool in which part or all of the collected data is processed.

[0092] This disclosure is not limited to the examples of methods, systems, programs and recording media described above, only by way of example, but it encompasses all variations that may be envisaged by those skilled in the art within the framework of the protection sought. List of reference signs

[0093] - 10: Electrical energy storage system; - 11 i: electrical energy storage device; - 12: conversion electronics module; - 13: control module; - 14: upper control floor; - 21 to 27: operation. List of cited documents Patent documents

[0094] For convenience, the following patent documents are cited: - patcitl: WO2021136419A1 (publication number); - patcit2: CN108931739A (publication number). Non-patent literature

[0095] For all useful purposes, the following non-patent element(s) is (are) cited: SUBSTITUTION SHEET (RULE 26) - nplcitl: “End of Discharge SOC Jump Elimination”, Application Report, April 2020 by Texas Instruments; - nplcit2: Orion BMS “Diagnosing State of Charge Calculation Jumps”, 2018 by Ewert Energy Systems. SUBSTITUTION SHEET (RULE 26)

Claims

Claims

1. Method for detecting an anomaly in the variation of an estimate, by a battery management system, BMS, of a charge level, SOC, of ​​at least one electrical energy storage module, characterized in that it comprises: a. A collection (21) of time-stamped data provided by said battery management system, comprising data for estimating said charge level and measurement data of a charge / discharge current of said at least one storage module; b. A calculation (22), from said time-stamped data for estimating said charge level, of a variation in the estimated charge level over a set of determined time intervals of duration dt; c. A calculation (23), from said time-stamped data for measuring current and a capacity of said electrical energy storage module, of an equivalent variation in the charge level over said set of determined time intervals of duration dt; d.A grouping (24) of said time intervals into a plurality of groups of time intervals associated with an estimated charge level variation equal to a determined reference value, and, for each of the groups: i. a calculation (25) of a sum of the equivalent charge level variations over said intervals of said group; ii. a comparison (26) with a threshold of a difference of said determined reference value and of said sum and, depending on a result of said comparison, a detection of an anomaly of variation of said estimate.

2. Method for detecting an anomaly according to claim 1, characterized in that it also comprises a restitution to a user of an indicator of detection of an anomaly of variation of said estimate and of said sum of equivalent variations of calculated load level.

3. Method for detecting an anomaly according to any one of claims 1 and 2, characterized in that it also comprises a determining a state of health, SOH, of said at least one electrical energy storage module.

4. Method for detecting an anomaly according to claim 3, characterized in that the capacity of said at least one electrical energy storage module taken into account for the calculation of said equivalent variation in charge level is a residual capacity of said at least one electrical energy storage module calculated according to the following formula: [Math. 2] '-'residual — H ^-black in which C residueUe designates said residual capacity, C nom denotes a nominal electrical capacity of said at least one module and SOH is a unitless parameter expressed as a percentage which denotes the state of health of said at least one module.

5. Method for detecting an anomaly according to claim 4, characterized in that the calculation (23) of said equivalent variation in charge level over a time interval of duration dt is calculated according to the following formula: [Math. 3] 100 , in which I denotes the charge / discharge current of said at least one electrical energy storage module and C residueUe designates the residual capacity of said electrical energy storage module.

6. Method for detecting an anomaly according to any one of claims 1 to 5, characterized in that said threshold is defined by a user of said battery management system.

7. Computer program comprising instructions for implementing the method according to one of claims 1 to 6 when this program is executed by a processor.

8. A non-transitory computer-readable recording medium on which a program for implementing the method according to one of claims 1 to 6 when this program is executed by a processor.

9. Method for managing an electrical energy storage system (10) comprising a plurality of electrical energy storage modules, characterized in that it implements, for each of said modules, the method for detecting an anomaly according to any one of claims 1 to 6, and in that it comprises a determination (27) of a performance indicator of said storage system (10), as a function of a number of anomalies of variation of said estimate detected for each of said modules.

10. Battery management system, BMS, of at least one electrical energy storage module, characterized in that it comprises a memory (M) and a processor (PROC) configured to execute: a. A collection (21) of time-stamped data, comprising data for estimating said charge level and data for measuring a charge / discharge current of said at least one storage module; b. A calculation (22), from said time-stamped data for estimating said charge level, of a variation in estimated charge level over a set of determined time intervals of duration dt; c. A calculation (23), from said time-stamped data for measuring current and a capacity of said electrical energy storage module, of an equivalent variation in charge level over said set of determined time intervals of duration dt; d.A grouping (24) of said time intervals into a plurality of groups of time intervals associated with an estimated charge level variation equal to a determined reference value, and, for each of the groups: i. a calculation (25) of a sum of the equivalent charge level variations over said intervals of said group; ii. a comparison (26) with a threshold of a difference of said determined reference value and of said sum and, depending on a result of said comparison, a detection of an anomaly of variation of said estimate.

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