Method and device for determining a stress profile representative of the future use of a battery for a given application

Abstract

The invention relates to a method and device for determining a stress profile representative of the future use of a battery for a given application, and more particularly a usage profile for lithium-ion batteries in stationary energy storage systems. Figure for the abstract: Fig. 1

Description

Title of the invention: Method and device for determining a stress profile representative of the future use of a battery for a given application Field of invention

[0001] The invention relates to the technical field of batteries, and relates to a method and a device making it possible to determine a profile representative of the future use of a battery for a given application, called a stress profile, and more particularly a profile of the use of Lithium-ion batteries. State of the art

[0002] Lithium batteries in their various variants such as for example "lithium-ion" or "lithium-ion-polymer" or "lithium-metal-polymer" batteries, are the batteries having one of the highest energy densities and one of the highest specific energies. This is the technology of choice for powering many applications such as for example electric or hybrid vehicles, portable devices.

[0003] However, these batteries show a degradation in their performance over time, in particular a degradation in capacity and electrical resistance, even during periods of non-use, and we speak of “calendar aging or degradation”.

[0004] Cycling degradation occurs when the battery is subjected to electrical stress, during charging or discharging, whereas calendar degradation occurs constantly during the life of the battery, whether it is subjected to electrical stress or not.

[0005] Taking into account the predicted evolution of the performance of a battery presents numerous advantages at different levels of a system which incorporates it, whether for its dimensioning, its maintenance, for the control strategies, among others.

[0006] Battery performance can be measured by implementing indicators such as the state of health or SOH (acronym for "State Of Health" according to the accepted Anglicism), indicators which are calculated via physical signals measured directly on the battery.

[0007] The predicted evolution of these indicators, and therefore of the battery performance for a battery stress profile and for a given application, can be simulated by means of an aging model or endurance model, which is previously parameterized.

[0008] For a given type of aging model, generally based on a simple-to-use empirical aging model, mappings of the model parameters are established using a database which is made up of endurance test results of the battery studied. The content of this database may be more or less rich depending on the number of endurance tests carried out. The extent of the endurance test campaign is determined based on a number of study parameters which can influence the performance of the battery.

[0009] Generally, the endurance conditions chosen for carrying out experimental tests are non-variable conditions - temperature stability, battery state of charge stability or SOC (acronym for "State of Charge" according to the accepted Anglicism), electrical current stability (i.e. the level of electrical stress) - in order to be able to identify the effects of these parameters on the degradation of the battery. The aging model used then makes it possible to link these different effects to each other, and it is then possible to estimate the degradations for more complex usage profiles, for which these conditions can be variable.

[0010] Since the parameter maps for a model are determined for non-variable conditions, it is then often interesting to have results of experimental tests carried out for variable endurance conditions representative of future battery usage conditions for a particular application, for the purposes of validating the model.

[0011] Battery tests with these stress profiles are carried out on electrical test benches for which it is possible to program different stress profiles, however with a limited number of points. However, a stress profile, even with a limited number of points, must remain representative of the actual use of the battery, which is generally not the case.

[0012] There are various approaches in the prior art for establishing battery stress profiles.

[0013] The paper by SB Vilsen and D.-I. Stroe, “Transfer Learning for Adapting Battery State-of-Health Estimation From Laboratory to Field Operation,” IEEE Access, vol. 10, pp. 26514-26528, 2022, doi: 10.1109 / ACCESS.2022.3156657, presents a method for extracting a forklift operating profile, taking as input 6-month operating data from three forklifts, and where rest periods have been removed. The resulting extracted 12-15 day profile is an accelerated test profile used for laboratory battery cycling, where measurement conditions do not closely replicate real-life operation. For example, the cycling bench did not allow going beyond 50 Amps while during real operation there were peaks of 350 Amps. To compensate these limitations, the discharge was extended until the laboratory aging profile at 50 Amps reached the same state of charge as the operating profile of the three forklifts. Also, in this approach, the profile is not representative of real-life usage. In addition, since Lithium-ion batteries also age during rest periods, these periods are not considered.

[0014] In patent application CN106980725B, the proposed method makes it possible to extract the operating phases of a vehicle's battery (in a parking lot, while driving and while starting), from a NEDC standard profile (acronym for "New European Driving Cycle" in English), to then carry out operating simulations and to size the battery. However, this profile can only be used for automotive applications, i.e. on-board batteries, and only for measuring the consumption and pollutant emissions of vehicles.

[0015] Other approaches such as the one described in the article by A. Bhatt, W. Ongsakul, N. Madhu Manjiparambil, and J.G. Singh, “Machine learning-based approach for useful capacity prediction of second-life batteries employing appropriate input selection,” International Journal of Energy Research, vol. 45, no. 15, pp. 21023-21049, 2021, doi: 10.1002 / er.7160, use the public database “NASA Prognostics Data Repositry” to obtain simulation profiles, used for training battery health prediction algorithms. The profiles in the database are laboratory test profiles, and are therefore not subject to constraints of real-life systems.

[0016] As a result, according to known approaches, the electrical stress profiles used in system aging simulations are not representative of real battery usage, and this for each operational system. As a result, the resulting battery life projection is not sufficiently reliable and precise.

[0017] Also, there is a need for a method that allows the establishment of stress profiles that overcomes the drawbacks of known approaches.

[0018] The present invention meets this need. Brief description of the invention

[0019] The subject of the present invention is a method for determining stress profiles, representative of future uses of a battery for specific applications.

[0020] Advantageously, the method of the invention makes it possible to generate stress profiles which are compatible with experimental constraints linked to laboratory endurance tests of Li-ion batteries.

[0021] The method of the invention makes it possible to define stress profiles representative of the use of a battery or representative of a sequence of restricted use of a battery, from data originating from monitoring of a battery in real operation, on a long term (annual or longer).

[0022] According to a particular aspect, the method of the invention makes it possible to determine an electrical stress profile which is representative of the use of batteries in battery storage systems.

[0023] Advantageously, the invention makes it possible, from the analysis of operational data from stationary systems, recorded over a long period, to determine profiles representative of the use of batteries in stationary battery storage systems.

[0024] Advantageously, the profiles established are profiles with a limited number of points, which are representative of real future use of the battery for a given application.

[0025] The established stress profiles can, among other things, be used for the validation of aging models in simulations. They can also allow experimental testing of the endurance of a system (with an adapted stress profile), and also allow optimal sizing of a battery system for a given application.

[0026] The stress profiles can also be used in battery modeling to produce a health prognosis.

[0027] For this purpose, a method is proposed for determining stress profiles for a battery storage system. The method is implemented by computer and comprises at least steps consisting of:

[0028] - calculate an operating time of a battery storage system, from a history of data collected over a monitoring period of said system, the data comprising at least values ​​of current I(t), temperature T(t), voltage U(t) and state of charge SOC values;

[0029] - carry out, for the duration of the operation, analyses of usage statistics of the battery as to state of charge SOC and depth of discharge DOD;

[0030] - cutting said operation duration into n time sequences, the duration of a sequence being representative of the operation of said storage system for a given application;

[0031] - carry out statistical analyses for each of the n time sequences battery usage regarding state of charge SOC and depth of discharge DOD;

[0032] - determine among the n time sequences, a plurality m of sequences which have states of charge and depths of discharge comparable to states of charge and depths of discharge for the duration of operation;

[0033] - determine among the m comparable sequences, symmetric sequences cor corresponding to sequences having a state of charge at the end of the period equal to the state of charge at the beginning of the period; and

[0034] - select from the symmetrical sequences, a sequence representative of a stress profile for said storage system.

[0035] Advantageously, the method of the invention comprises differentiated or combined embodiment variants.

[0036] Thus, the method may comprise an initial step of filtering the data, consisting of determining over the data collection period, a monitoring sub-period, corresponding either to a period with the fewest data gaps, or to a period where there is the least non-digital data.

[0037] Preferably, the operating duration corresponds to an average of the operating hours over the monitoring period of said storage system.

[0038] The step of analyzing statistics for the duration of operation is preferably done on histograms of the time spent per state of charge interval and on histograms of the time spent per depth of discharge interval.

[0039] In one embodiment, the step of statistical analyses for each of the n time sequences is carried out on histograms, for each of the n sequences, of the time spent per state of charge interval and on histograms, for each of the n sequences, of the time spent per depth of discharge interval.

[0040] The method may comprise an initial step of verifying the reliability of the state of charge values, and if necessary a step of calculating a reliable state of charge.

[0041] The method may comprise a step of calculating depth of discharge values.

[0042] In one embodiment, the step of selecting a sequence representative of a stress profile for said storage system consists of: - recovering current profiles or power profiles corresponding to the symmetrical sequences; - calculating a point-by-point difference between each recovered current or power profile and an average profile; and - selecting a profile corresponding to that having a minimal difference with the average profile.

[0043] The method may comprise a step consisting of scaling the stress profile selected as a representative profile.

[0044] Another object of the invention relates to a device for determining stress profiles for a battery storage system, which comprises means for implementing the steps of the method of the invention.

[0045] In one embodiment, the device of the invention is applied to a stationary storage system, preferably a “BESS” battery storage system.

[0046] Preferably, the storage system comprises at least one lithium-ion battery.

[0047] Another object of the invention is a device for simulating the aging of batteries of a battery storage system, the device comprising at least one processor for executing a battery aging model, said model being applied on the basis of a stress profile obtained by the method of the invention.

[0048] The invention also addresses a computer program comprising code instructions which, when the program is executed by a computer, cause the latter to implement the method for determining stress profiles for a battery storage system of the invention. Brief description of the figures

[0049] Other characteristics, details and advantages of the invention will emerge from reading the description given with reference to the appended drawings given by way of example and which represent:

[0050] [Fig. 1] is a flow diagram of the steps in the method of determining electrical stress profiles, according to one embodiment of the invention;

[0051] Figures 2a and 2b illustrate examples of histograms of the times spent respectively, by state of charge (SOC) range and by depth of discharge (DOD) range of a battery over a period of 4 years of data recording;

[0052] Figures 3a and 3b illustrate examples of histograms of the times spent respectively, by state of charge (SOC) range and by depth of discharge (DOD) range of a battery over a period of 7 days for representative sequences;

[0053] [Fig.4] is a flow diagram of the steps in the method of determining electrical stress profiles, according to embodiments of the invention including optional steps. Detailed description of the invention

[0054] First, a reminder of different principles and definitions known in the field of batteries is given, and repeated in the rest of the description.

[0055] The state of charge (SOC) of a battery represents the amount of stored electric charge q(t) relative to the current capacity C(t), and is expressed by equation (1):

[0056] stored electric charge _ qU) V / current capacity C(t)

[0057] The state of charge of a battery is expressed as a percentage and usually varies between 0% designating a completely discharged battery (empty state) and 100% designating a completely charged battery (full state). The state of charge level with the most time elapsed influences calendar aging and cycling aging.

[0058] The depth of discharge (DOD) (acronym for "Depth Of Discharge" in English) of a cycle, corresponds to the percentage of the quantity of charge removed from the battery starting from a given state of charge. The depth of discharge with which a battery is cycled influences its aging in cycling.

[0059] Electrochemical battery storage system: this is a system which contains one or more batteries capable of storing electrical energy (in a form chemical) and return it in a timely manner. There are two types of electrochemical battery storage systems: stationary battery storage systems and on-board battery storage systems.

[0060] Stationary storage makes it possible, in particular, to ensure the balance between the production and consumption of electricity on an electricity distribution network, and in particular to compensate for the variability of the production of renewable energies (solar, wind, etc.). For example, excess electricity produced on a very sunny day can be returned in the evening when demand is higher. Stationary storage also helps to guarantee the quality of the electricity distribution network by limiting fluctuations caused by the intermittency of renewable energy production. Finally, stationary storage makes it possible to meet the needs of isolated sites, which are difficult to supply or not supplied by the distribution networks.

[0061] Stationary battery storage systems are mainly large-scale, medium or high-power storage systems (of the order of several hundred kilowatts (kW) to several tens of megawatts (MW=), having high energies (of the order of several hundred kilowatt-hours (kWh) to several tens of megawatt-hours (MWh)).

[0062] Conversely, on-board storage systems are for smaller amounts of energy (from a few Watt-hours (Wh) to a few tens of kWh) and have lower power (from a few watts (W) to a few hundred kW). These systems are dedicated to mobile applications. They are mainly used in transport, particularly in electric and rechargeable hybrid vehicles, and in portable electronic devices (phones, tablets, computers, etc.).

[0063] The present description is made for an embodiment of the method of the invention applied to the case of stationary storage systems, designated by the acronym BESS (for “Battery Energy Storage System” according to the accepted Anglicism), having Li-ion batteries.

[0064] However, those skilled in the art will be able to apply the principles described to other types of systems and for other applications.

[0065] A BESS is a large-scale stationary storage system, generally coupled to a renewable energy production unit (solar energy, wind energy, etc.). The system comprises several battery sections, each battery section comprising battery assemblies called "racks", each rack comprising a plurality of modules connected together, themselves made up of electrochemical cells assembled in series and in parallel.

[0066] During its use, the storage system (whether stationary or on-board) is subject to constraints of its environment called “terrain constraints” and is subject to conditions of use.

[0067] The constraints of the field environment are, for example, the presence of connection elements and protection elements, between the electrochemical cells, between the different batteries of a rack, between the different racks of a battery section (in the case of a BESS). They can also result from the presence of a housing around the electrochemical cells of each battery, from the presence of electronic management systems, such as a BMS battery management system and / or an EMS energy management system. These constraints linked to the actual use of the storage system have an impact on the performance of the system, in particular in terms of internal resistance (and therefore heating and power), capacity and lifetime.

[0068] The usage conditions are defined by different parameters such as the charging and discharging regimes to which the electrochemical cells are subjected, the cycling and rest phases of the electrochemical cells and the surrounding temperature. These usage conditions may be different between the batteries that make up the storage system, or even between the electrochemical cells of the same battery (with regard to temperature).

[0069] “Cycling phases” means the phases during which the electrochemical cells of the storage system undergo charge and discharge cycles, i.e. the phases during which the storage system is used. In this case, the storage system is said to be “in cycled mode”.

[0070] “Rest phases” are understood to mean the phases during which the electrochemical cells of the storage system are not used. In this case, the storage system is said to be “in calendar mode”.

[0071] [Fig.l] is a flow diagram of the steps of the method 100 for determining electrical stress profiles according to an embodiment of the invention applied to a BESS system.

[0072] In a first step 102, after having recovered a history of reliable monitoring data of the operation of a BESS system, the method makes it possible to calculate an average number of hours of operation of the system.

[0073] It should be noted that the relevance of the results, and therefore of the stress profile resulting from the method 100, is linked to the quality of the data made available and used as inputs to a device making it possible to implement the steps of the method of the invention.

[0074] Such a device (not illustrated) comprises appropriate means for receiving input data; for processing them according to the different steps of the method of the invention in all their variants; and for restoring the results obtained in a form available for their exploitation (for parameterization of a model of aging for example).

[0075] Thus, such a device comprises at least one data reception module configured to receive data and, if necessary, adapt it (filter it to retain reliable data) for processing by a data processing module. The data processing module comprises a microprocessor for operating code instructions of a software program making it possible to carry out the different steps of the method of the invention. The results obtained by the data processing module can be adapted to a human-machine interface for their exploitation (in battery test simulations for example).

[0076] The input data which are a prerequisite for implementing the steps of the method are in particular a history of operational data collected on a storage system, and corresponding to voltage U(t), current I(t), temperature T(t), state of charge SOC(t) data.

[0077] Data collection corresponds to recordings made of this data for a battery energy storage system (for example stationary BESS), over a very long period, from at least several months to several years.

[0078] The initial input data may have been collected by one or more BMS management systems associated with the BESS, and have been stored in a dedicated database 101 or be filtered among other data recorded in a more general database of a BMS management system.

[0079] In the case where the initial data does not have any gaps and all of the modules of the battery of the BESS system are permanently connected (i.e. no rack is non-operating over certain time ranges), the data is considered reliable and the calculation 102 of the number of hours of operation of the system (or the calculation of a percentage of hours) is made directly on the initial data received.

[0080] Otherwise (the data has holes), a cleaning or filtering step of the received data is carried out to improve the quality of the data, and the filtered data, corresponding to a new period, is used to calculate the number of hours of operation.

[0081] [Fig. 4] illustrates optional steps (402, 406, 410) of the method 100 of the invention, which can be carried out independently of each other, including a step 402 of preparing reliable data. Thus, the method of the invention can be carried out according to different variants combining required steps ([Fig. 1]) and one or more optional steps (illustrated in dotted lines in [Fig. 4]).

[0082] In one embodiment, the optional step 402 of cleaning the initial data consists of choosing, over the period of the history of the received data, a new long-term period for the availability of data. This new long period can meet a criterion called “period with the fewest gaps” "data" can mean either a period with the fewest sub-periods with missing data, or a period where there is the least non-numeric data (NaN) acronym for "Not a Number" according to the Anglicism used to designate invalid data or unavailable data.

[0083] Indeed, availability is a criterion which qualifies three indicators.

[0084] A first indicator concerns the correct uploading of data (the fact that the data is valid, i.e. is digital data), and the fact that the data values ​​are within the operating limits (i.e. neither too large nor too small). This indicator is characterized by a daily output of a parameter here designated by “Data_hours”. Data outside the limits is not taken into account in the estimation of the Data_hours parameter.

[0085] A second indicator relates to the number of hours per day of operational operation of the system (excluding “Idle” rest periods). This indicator is characterized by a parameter here designated by “Operation_hours”. The value of the Operation_hours parameter is in principle less than or equal to the value of the Data_hours parameter.

[0086] A third indicator relates to the proportion of racks connected for a given day, designated here by the “AvailableRacks” parameter.

[0087] Returning to step 102, the operating periods which are taken into account to enable a representative stress profile to be determined, must have an 'operation hours' parameter "Operation_hours" corresponding to an average of the operating hours of the entire monitoring period of the storage system.

[0088] In the case of an optional preparation step 402, the operating periods which are taken into account to determine a representative demand profile must also have a maximized “Data_hours” parameter and a maximized “AvailableRacks” parameter.

[0089] After the step of calculating the operating hours (the total number of operating hours excluding inactivity), either in 102 on directly reliable data, or in 404 after a step 402 of preparing reliable data, the method makes it possible, in a following step 104, to carry out an analysis of the battery usage statistics over the entire data period.

[0090] The method of the invention can perform, before step 104, an optional step 406 which consists of verifying whether the state of charge indicator SOC which was received and which had been calculated by the battery management system (BMS) is reliable.

[0091] The reliability of the SOC value is estimated according to a relationship between the capacity values ​​(Amp-Hours Ah) of the charged and discharged accumulators, and the difference in state of charge between the start and end of each charge or discharge period. If this relationship is linear, the state of charge provided by the BMS is considered as reliable. Otherwise, the state of charge provided by the BMS is not considered reliable, and the method allows to construct a reliable state of charge, i.e. determine a new indicator allowing to define a reliable state of charge.

[0092] In one embodiment, a reliable state of charge SOC can be obtained by applying an Ah integration method supplemented by a recalibration method. This is a conventional method for calculating the state of charge of a battery. We start from a given state of charge (even by the BMS including if it is considered unreliable). Then we integrate the battery current to obtain Ah, the Ah are then divided by the battery capacity to obtain a state of charge. Then, if the system detects a moment suitable for a reliable estimation of the state of charge (for example, reaching a fully charged condition, or a fully discharged condition, or a sufficiently long rest, etc.), the calculation recalibrates the state of charge to a value determined by these recalibration conditions. An example can be found at http: / / liionbms.com / php / wp_soc_estimate.php, "State Of Charge estimate with Li-Ion batteries" by Davide Andrea.

[0093] Returning to [Fig. 1], and step 104, the method makes it possible to carry out an analysis of the battery usage statistics, in relation to a cumulative duration.

[0094] Cumulative time refers to the time spent per state of charge (or depth of discharge) interval. The state of charge of the battery is divided into a plurality of intervals, and the time spent in each interval is accumulated. The cumulative time of each interval is then divided by the total time of the period, to obtain a time spent value.

[0095] Thus in step 104, the method makes it possible to construct histograms over the entire collection period, one for the states of charge SOC and another for the depths of discharge DOD. The histograms are constructed from the history of the collected data, over the entire monitoring period (with reliable initial data), or over a sub-period (with filtered data, made reliable).

[0096] A first usage histogram is constructed for the cumulative times spent per interval for the battery charge state, corresponding to global statistics over the entire period retained (initial period or recalculated period).

[0097] [Fig.2a] shows an example of a usage histogram for cumulative times spent (“Time Spent” on the ordinate) per state of charge (SOC) interval of a battery for 10 intervals, over a period of 4 years. It can be noted for this example, that the battery spends most of the time in a low state of charge.

[0098] The state of charge for constructing the histogram may be that provided directly by the BMS or be a reconstructed state of charge (step 406).

[0099] A second usage histogram is constructed for the cumulative times spent per interval for the battery discharge depth, corresponding to sta overall statistics over the entire period retained (initial period or recalculated period).

[0100] [Fig.2b] shows an example of a usage histogram for cumulative times past ("Time Spent" on the ordinate) by depth of discharge (DOD) interval of a battery for 10 intervals, over a period of 4 years. It can be noted for this example, that the battery spends a lot of time in an inactive state (depth of discharge less than 10%), and when it is discharged, most of the cycles have a depth of discharge between 70 and 80% and then between 60 and 70%.

[0101] The discharge depth is calculated by algorithm, because it is not given directly by the BMS. In one embodiment, the algorithm is based on a known counting method, such as for example the “Rainflow” counting method.

[0102] The Rainflow counting method, originally developed in the field of materials for cyclic fatigue estimations, is now used in many applications where cyclic behavior can be observed, including wind power, rotating machinery, and battery charging. The Rainflow counting method consists of pairwise associating the increasing minima and decreasing maxima of an initial curve, and it can be implemented by many known algorithms.

[0103] For each selected cycle, a discharged Ah quantity is estimated by integrating the current. Then the estimated Ah quantity is divided by the nominal capacity to calculate the discharge depth of the cycle concerned.

[0104] Returning to [Fig.l], the method makes it possible, in a following step 106, to define a sequence duration which is representative of the use of the BESS in the given application.

[0105] A sequence duration that is representative of the use of the BESS is often equal to one day (24 h), but other durations can be considered depending on the application for which it is used.

[0106] The choice of the sequence duration can be progressive. The method can start by checking whether a short period (for example a day for a stationary storage application used with a solar system), assumed to be representative of the battery usage, is valid, and increase the duration until reaching a correct representativeness.

[0107] According to alternative embodiments, if a sequence duration of one day is not representative, the sequence durations can be tested by durations of one week.

[0108] To determine whether a duration is representative, the method makes it possible to establish histograms of the SOC and DOD for all durations over the data collection period. For example, for a tested duration of 24 hours for a data collection made over a year, a histogram of SOC (and DOD) is established for all days over a year.

[0109] If sequences of the tested duration (for the example 24 hours) are identified in the histograms which have histograms similar to those of the entire data collection period, this sequence duration is retained.

[0110] If no such "similar" sequences are identified, the duration of the sequence can be progressively increased, for example by duration of 24 h, going to 2 days, then to 3 days, etc., until sequences with histograms similar to those of the entire period are found.

[0111] When a representative sequence duration is determined, the method makes it possible to divide the total data collection period into a plurality n of sequences of duration equal to the representative duration.

[0112] The method then continues with a step 108 consisting of carrying out an analysis of the battery usage statistics, in particular the analysis of the SOC and the DOD, for each of the sequences of the plurality of n defined time sequences.

[0113] The method initially allows two new types of histograms to be constructed for performing usage statistical analyses.

[0114] A first type of histogram is constructed for the cumulative times spent per interval for the state of charge of the battery, over the defined representative duration.

[0115] [Fig.3a] shows an example of a histogram of the cumulative times spent per interval for the state of charge (SOC) of a battery, over a period of one week, for one sequence among the n sequences identified.

[0116] A second type of histogram is constructed for the cumulative times spent per interval for the battery discharge depth, over the defined representative duration.

[0117] [Fig.3b] shows an example of a histogram of the cumulative times spent per interval for the depth of discharge (DOD) of a battery, over a period of one week for one of the n sequences identified.

[0118] Thus, n histograms are constructed for the state of charge, and n histograms for the depth of discharge.

[0119] The analysis of the usage statistics then consists, in the following step 110, of comparing the n histograms of the cumulative times spent per interval for the state of charge and the depth of discharge, with the histograms established for the entire data period (initial period or recalculated period) in step 104.

[0120] The result of the comparison makes it possible to identify among the plurality of n time sequences, a subset of m sequences called closest sequences, i.e. sequences which are in past time levels which are similar to those of the entire period, and this in terms of charge state intervals and charge intervals. depth of discharge.

[0121] In a following step 112, the method makes it possible to identify in the plurality of m sequences, representative sequences which are symmetrical, that is to say sequences having a final charge state which is equal to the initial charge state.

[0122] It is important that the state of charge does not drift over time (the profile returns to its starting point from the point of view of its state of charge). Indeed, the stress profile (representative of the actual use of the battery for a given application) will have to be repeated in order to be able to test or simulate the aging of the battery (i.e. the representative sequence will be repeated over time to estimate the degradation according to this sequence over time).

[0123] In one embodiment, the identification of symmetrical periods (or sequences) is done by considering the state of charge at the start of the period and at the end of the period.

[0124] In an alternative embodiment, the identification of the symmetrical periods is done by considering a symmetrical cycle over a part of the representative period.

[0125] In another embodiment variant, the identification of the symmetrical periods is done by reconstructing symmetrical cycles.

[0126] In one embodiment, the reconstruction of symmetrical cycles can be done by converting all of the charging and discharging operations of the measurement period into an equivalent set of symmetrical reconstructed cycles, each symmetrical reconstructed cycle comprising a charging operation and a discharging operation of the same amplitude of the state of charge indicator (the same charging or discharging amplitude).

[0127] The symmetrical cycles resulting from this conversion are said to be “reconstituted” because they come from a reorganization, with possible approximations, of real cycles.

[0128] For certain applications, whose charge / discharge cycles are simple and regular, a relatively simple algorithm takes care of this conversion.

[0129] For applications where the charge-discharge cycles are complex and irregular, more sophisticated algorithms are employed for the conversion operation, such as the rainflow counting method.

[0130] In the context of the present invention, the conversion into symmetrical reconstituted cycles is carried out by associating in pairs the increasing minima and the decreasing maxima of the charging and discharging operations of the measurement period.

[0131] Returning to [Fig.l], step 112 therefore allows the selection of a plurality p of sequences corresponding to symmetrical periods, from among the plurality of m sequences defined in the previous step 110.

[0132] At the end of step 112, the method has produced an equivalent set of symmetrical reconstructed cycles, relating to the measurement period.

[0133] Thus, each selected sequence corresponds to a state of charge profile which is representative of the operation of the battery for the given application.

[0134] However, given that to cycle a battery, it is necessary to apply current or power profiles, the method makes it possible in a following step 114 to recover from the initial database, the current profiles or the power profiles corresponding to the p selected sequences.

[0135] Then, the method makes it possible to calculate the point-by-point difference between each recovered current (or power) profile and an average profile, then makes it possible to select a single profile (final profile or typical profile) corresponding to the one having a minimal difference with the average profile.

[0136] The average profile is obtained by taking the average, for a given time step, between the currents (or the powers) of the p selected sequences obtained previously. Indeed, at step 114, there are several current profiles I=f(t) (or several power profiles P= f(t)) and the average profile is calculated by taking the average of I (or of P) for each time step.

[0137] The selection in step 114 of the typical stress profile can be done by selecting the profile having the least differences with the average profile, by applying the following equation (2):

[0138] min(Eabs(diff(seq(i), seqAvg))-

[0139] In one embodiment, the method may comprise an optional step 410 after the selection of the typical profile, which consists of scaling the typical profile and adapting it to the specifications, battery limits, or test means that are intended to be used.

[0140] Indeed, depending on the initial BESS monitoring data and the sizing thereof, it may be necessary to adapt the typical stress profile from the previous step 114 to obtain 412 a final profile to scale, which can be used in an aging model for the battery (or a sample) that one wishes to test. This adaptation is made according to the constraints and limitations specified in the battery specification, and / or according to the test means.

[0141] Thus, for example for certain batteries, the maximum charge currents may be different from the maximum discharge currents. It will then be necessary to saturate the current profile with adequate values, in charge and in discharge according to these limits.

[0142] The balance of the profile in the loaded state can then be maintained by applying the following equations (3) and (4):

[0143] r _ Ahd-Ahch^ (3) and = hch ' f (4),

[0144] in the case of a maximum charging current which is less than the maximum current discharge (classically encountered case).

[0145] The parameters of equations (3) and (4) are defined as: - Ahch: this is the cumulative number of Ah charged (the phases of the profile for which the current is positive); - Ahch sat: this is the cumulative number of Ah charged when the current is saturated at a value lower than the charging current of the initial profile.

[0146] By applying the coefficient Kf to the discharge current profile Idch, its level is reduced proportionally to the saturated charge current profile. The result obtained is thus a "balanced" profile, i.e. we charge as much as we discharge, while maintaining the dynamics.

[0147] This profile can be repeated cyclically on a sample without having to define a profile for the duration of the desired electrical test. Indeed, generally test benches do not allow very long profiles to be defined or profiles to be defined on a very high number of points.

[0148] In an alternative embodiment, the correction coefficient Kf can be applied only to the necessary cycles, i.e. if there is saturation in charge, the coefficient is applied to the following discharge of the same cycle (and vice versa if saturation in discharge). The detection of the cycles can be done via the Rainflow counting method.

[0149] In another embodiment, the correction of the SOC profile is made by applying a different initial SOC.

Claims

Claims

1. Method for determining stress profiles for a battery storage system, the method being implemented by computer and comprising at least steps consisting of: - calculating (102) an operating duration of a battery storage system, from a history of data collected over a monitoring period of said system, the data comprising at least values ​​of current I(t), temperature T(t), voltage U(t) and state of charge SOC values; - carrying out (104) for the operating duration, analyses of battery usage statistics with regard to the state of charge SOC and the depth of discharge DOD; - cutting (106) said operating duration into n time sequences, the duration of a sequence being representative of the operation of said storage system for a given application;- performing (108) for each of the n time sequences, statistical analyses of battery usage with regard to the state of charge SOC and the depth of discharge DOD; - determining (110) from among the n time sequences, a plurality m of sequences which have states of charge and depths of discharge comparable to states of charge and depths of discharge for the duration of operation; - determining (112) from among the m comparable sequences, symmetrical sequences corresponding to sequences having a state of charge at the end of the period equal to the state of charge at the start of the period; and - selecting (114) from among the symmetrical sequences, a sequence representative of a stress profile for said storage system.;

2. The method according to claim 1 comprising an initial step of filtering the data, consisting of determining over the data collection period, a monitoring sub-period, corresponding either to a period with the fewest data gaps, or to a period where there is the least non-digital data.

3. The method of claim 1 or 2 wherein the operating time corresponds to an average of the operating hours over the monitoring period of said storage system.

4. The method according to any one of claims 1 to 3 wherein the step of analyzing statistics for the operation duration is done on histograms of time spent per state of charge interval and on histograms of time spent per depth of discharge interval.

5. The method according to any one of claims 1 to 4 in which the step of analyzing statistics for each of the n time sequences is done on histograms, for each of the n sequences, of the time spent per state of charge interval and on histograms, for each of the n sequences, of the time spent per depth of discharge interval.

6. The method according to any one of claims 1 to 5 comprising an initial step of checking the reliability of the state of charge values, and if necessary a step of calculating a reliable state of charge.

7. The method of any one of claims 1 to 6 comprising a step of calculating depth of discharge values.

8. The method according to any one of claims 1 to 7 wherein the step of selecting a sequence representative of a stress profile for said storage system, consists of: - recovering current profiles or power profiles corresponding to the symmetrical sequences; - calculating a point-by-point difference between each recovered current or power profile and an average profile; and - selecting a profile corresponding to that having a minimal difference with the average profile.

9. The method of any one of claims 1 to 8 further comprising a step of scaling the selected stress profile as the representative profile.

10. A computer program comprising code instructions for executing the steps of the method according to any one of claims 1 to 9, when said program is executed by a processor.

11. A device for determining stress profiles for a battery storage system comprising means for implementing the steps of the method according to any one of claims 1 to 9.

12. The device according to claim 11 wherein the storage system is stationary, preferably a “BESS” battery storage system.

13. The device according to claim 12 wherein the storage system comprises at least one lithium-ion battery.

14. Device for simulating the aging of batteries in a system battery storage, the device comprising at least one processor for executing a battery aging model as well as a second device according to claim 11, said model being applied on the basis of a stress profile determined by said second device, said profile therefore being obtained by the method according to any one of claims 1 to 9.

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