Method for determining the state of charge (SOC) of a battery

Abstract

1. A method for determining a state of charge (SOC) of a battery, comprising: (1) receiving at least one parameter corresponding to a percentage of an initial state of charge of the battery based on at least one open circuit voltage value and according to a landing zone of an open circuit voltage (OCV) - state of charge (SOC) function, wherein the landing zone of the OCV - SOC function is separated into at least two zones; (2) determining at least one estimate of a state of charge and at least one output voltage value for each model based on the received at least one parameter and at least two separate models; and (4) providing an estimate of the state of charge of the battery based on the determined state of charge having a minimum error rate.

Description

[Technical Field]

[0001] The present invention relates to a method for determining the state of charge (SOC) of a battery, and also to a system for determining the state of charge (SOC) of a battery. [Background technology]

[0002] The lifespan and safety of sodium-ion batteries are crucial for practical applications. However, typical challenges faced include optimal energy utilization and minimizing the effects of degradation. The challenges of safety management, charge and discharge control, performance degradation, and capacity fade of sodium-ion batteries have made state-of-charge and state-of-health estimation challenging and challenging. In fact, the design and implementation of diagnostic models is considered key to addressing the issue of battery durability. The introduction of diagnostic solutions makes it possible to anticipate and avoid failures, assess health, and estimate state-of-charge. Based on such information, it is possible to foresee control and / or maintenance actions to ensure the battery's continued operation. However, various battery states, such as state-of-charge and state-of-health, cannot be directly observed, which requires estimation and prediction algorithms, such as diagnostics and prognostics.

[0003] State of health is a critical aspect of battery management systems (BMSs) because it is considered a measure of lifespan. Therefore, poor state of health estimation can ultimately damage the battery and reduce its expected service life. Like other chemistry-based energy storage systems, battery use causes irreversible physical and chemical changes, and therefore battery performance tends to gradually deteriorate over the battery's lifetime. Several aging experimental designs have been developed to test sodium-ion batteries for aging, storage aging, and cycle aging, showing internal resistance increase and capacity loss. Therefore, the definition of battery life relies on these aging indicators, capacity, and resistance, which are the same degradation as lithium-ion batteries. However, because these aging indicators cannot be measured, the primary method for tracking battery aging during operation without system interruption is to estimate these indicators using diagnostic models. Summary of the Invention [Problem to be solved by the invention]

[0004] The object of the present invention is to overcome at least one of these drawbacks. [Means for solving the problem]

[0005] This object is achieved by a method for determining the state of charge (SOC) of a battery, the method comprising the following steps: - receiving at least one parameter corresponding to a percentage of the initial state of charge of the battery based on at least one off-load voltage value and according to a pace zone of an open circuit voltage (OCV) - state of charge (SOC) function, the pace zone of the OCV-SOC function being separated into at least two zones; - determining, based on the received at least one parameter and at least two distinct models, at least one estimate of a state of charge and at least one output voltage value for each of the models; - determining the state of charge of the battery based on the determined state of charge with a minimum error rate; providing an estimate of said state of charge; Equipped with.

[0006] The method of the present invention allows for the determination of the state of charge (SOC) of a battery. The state of charge is defined as a percentage of the total capacity and is used to reflect battery performance. The OCV-SOC function is defined as the open circuit voltage (OCV) in volts relative to the state of charge (SOC) in percentage.

[0007] The battery referred to in the method has a non-linear OCV-SOC function. That is, a battery whose OCV-SOC function has at least two "plateaus." The term "plateaus" refers to the fact that the function has two sections consisting of distinct curves that follow two different affine functions. For example, - Na3V2(PO4)2F3 - Na2CoFe(CN)6 - Na 0.6 Ni 0.22 Al 0.11 Mn 0.66 O2 - Na 0.6 Ni 0.45 Zn 0.05 Mn 0.4 Ti 0.1 O2 - R-Na 1.92 Fe[Fe(CN)6 - Na2VTi(PO4)3 - P2-Na 2 / 3 Ni 1 / 3 Mn 2 / 3 O2 Regarding.

[0008] Furthermore, at least two models are used in parallel to estimate the state of charge. This allows for good accuracy in estimating the state of charge. Each model satisfactorily estimates the state of charge in a specific range of the state of charge. By combining at least two models in parallel, the model with the lowest voltage gives a state of charge with low accuracy. Comparing the error rates of at least two makes it possible to select the most accurate estimation.

[0009] The step of receiving at least one parameter comprises the steps of: - receiving at least one open circuit voltage value; - dividing the OCV-SOC function into the at least two zones, each zone having a separate function; - determining to which of said zones said at least one open circuit voltage value belongs; - providing the percentage of the initial state of charge of the battery from the function according to the area; It can be equipped with:

[0010] This step makes it possible to obtain a first estimate of the state of charge as a function of the OCV-SOC function curve area, which is more accurate than the estimate obtained as a function of the entire curve, and in fact makes it possible to further limit the error rate when calculating the state of charge.

[0011] For the at least two ranges of the OCV-SOC function, the first range may be set from 0 volts to 3.5 volts and the second range may be set from 3.5 volts to 4.5 volts, which correspond to the OCV values ​​in the OCV-SOC function.

[0012] At least two zones are defined according to voltage values ​​or according to the percentage of the state of charge. In this case, the threshold value dividing the curve of the OCV-SOC function into at least two zones is 3.5 volts (OCV value) or 40% (state of charge value). This separation occurs before the second plateau defined above.

[0013] A first of the at least two separate models may use an observer.

[0014] The observer used may be a Kalman filter.

[0015] A Kalman filter is used to estimate the state variables of a continuous nonlinear system linearized around an equilibrium point and represented using a state function. The Kalman filter gives better estimates in the linear part of the open circuit voltage, from 0% to 35% and from 45% to 100%.

[0016] The first of the at least two separate models may use a sliding mode observer.

[0017] A first of the at least two separate models may use an adaptive mode observer.

[0018] The second of at least two separate models may use a counting function. State of charge estimation between 35% and 45% is difficult. Therefore, in addition to estimation using a state function and an observer, additional validation of state of charge estimation has been added using Coulomb counting.

[0019] The state of charge may be updated with each iteration of the at least two models.

[0020] Thus, the state of charge estimate is updated at each iteration.

[0021] According to yet another aspect of the present invention, there is provided a system for determining the state of charge (SOC) of a battery, the system comprising: a calculation module; - one or more processors; - one or more computer-readable media that, when executed by one or more processors, cause the system to: receiving at least one parameter corresponding to a percentage of an initial state of charge of the battery based on at least one open circuit voltage value and according to a pace section of an open circuit voltage (OCV)-state of charge (SOC) function, the pace section of the OCV-SOC function being separated into at least two sections; - determining, for each model, at least one estimate of a state of charge and at least one output voltage value based on the received at least one parameter and the at least two distinct models; providing an estimate of the state of charge of the battery based on said determined state of charge with a minimal error rate; one or more computer-readable media storing instructions; Equipped with.

[0022] The system is termed a "state of charge module" and is configured to estimate and / or calculate the state of charge of the battery.

[0023] The one or more computer-readable media: - receiving at least one open circuit voltage value; - cutting the OCV-SOC function into at least two regions, each region having a separate function; - determining which zone at least one open circuit voltage value belongs to; - Providing the percentage of the initial state of charge of the battery as a function of said area It may be configured as follows.

[0024] For the at least two ranges of the OCV-SOC function, a first range may be set from 0 volts to 3.5 volts, and a second range is set from 3.5 volts to 4.5 volts.

[0025] A first of the at least two separate models may use an observer.

[0026] The observer used may be a Kalman filter.

[0027] A first of the at least two separate models may use a sliding mode observer.

[0028] A first of the at least two separate models may use an adaptive observer.

[0029] A second of the at least two separate models may use a counting function.

[0030] The counting function used may be coulomb counting.

[0031] The one or more computer-readable media may be configured to update the state of charge with each iteration of the at least two models.

[0032] According to yet another aspect of the present invention, a method for generating a plurality of digital signals includes the steps of: - receiving at least one parameter corresponding to a percentage of an initial state of charge of the battery based on at least one open circuit voltage value and according to a landing zone of an open circuit voltage (OCV) - state of charge (SOC) function, the landing zone of the OCV-SOC function being separated into at least two zones; - determining, for each model, at least one estimate of a state of charge and at least one output voltage value based on the received at least one parameter and the at least two distinct models; - providing an estimate of the state of charge of the battery based on the determined state of charge having a minimum error rate;

[0010] One or more non-transitory computer-readable media storing instructions that cause a system to perform a method for determining the state of charge (SOC) of a battery, comprising:

[0033] Other advantages and uniqueness of the invention will become apparent from the accompanying drawings, which follow and upon reading the detailed description of implementations and embodiments, which are in no way limiting. [Brief explanation of the drawings]

[0034] [Figure 1] 1 illustrates a battery management system (BMS) according to the present invention. [Figure 2] A computational model according to the present invention will now be described. [Figure 3] 1 illustrates a model of a SOC module according to the present invention. [Figure 4a] The curves of OCV-SOC function for lithium-ion NMC are described. [Figure 4b] The curves of the OCV-SOC function for lithium-ion LFP are described. [Figure 5] The curve of OCV-SOC function for sodium ion NVPF-HC ((Na3V2(PO4)2F3)-(hard carbon)) is described. [Figure 6] A model using a Kalman filter in accordance with the present invention is described. [Figure 7] A model using coulomb counting according to the present invention is described. [Figure 8a] The NVPF-HC electrical model according to the present invention is described. [Figure 8b] The electrical circuit is described along with the electrochemical impedance spectroscopy (EIS) test results. DETAILED DESCRIPTION OF THE INVENTION

[0035] In particular, but not limited to, these embodiments, variations of the invention comprising only the described and illustrated characteristic selections thereafter separated from other described and illustrated characteristics may be considered, where this characteristic selection provides a technical advantage or is sufficient to distinguish the invention from the prior state of the art (even when this selection is separated within a sentence with these other characteristics), including at least one feature of a functional selection without structural details and / or with only a portion of the structural details, where this portion alone provides a technical advantage or is sufficient to distinguish the invention from the prior art.

[0036] First, the battery management system (BMS) will be described with reference to Figure 1. In a typical sodium-ion battery implemented in a practical application, it is communication link 102 that is coupled to external communication 101. Communication link 102 may be used to obtain configuration updates from external data sources and to communicate various information related to the sodium-ion battery to the user, such as state of health, state of charge, and state of functionality.

[0037] Communication links 103 may be used to provide communication between each cell module and the battery management system. Data extraction 104 may read various measurement data. Using these data, a computational model 105 calculates the state of charge. The computational model 105 comprises a battery model and an estimation and calculation module. The output state of charge is communicated to a balancing algorithm 109 using communication links 106. This balancing algorithm 109 allows for balancing of the various sodium-ion cell modules. Cell module balancing is a way to compensate for these weaker cells by equalizing the charge across all cell modules in the chain, thereby extending the battery life. A good balancing algorithm 109 can be used to extend the life of a sodium-ion battery.

[0038] Sodium-ion batteries have important advantages compared to other technologies. They can be fully discharged down to 0V. In case of an abnormal event, the communication link 108 is used to activate the safety protocol 110. The safety protocol 110 also controls the semiconductor devices, which in this case fully discharge all cells or all batteries to achieve 0V. Therefore, the cell polarity does not show any potential. In this way, the battery can be removed and safely transported. It is noted that even in case of a fault alarm, the sodium-ion battery charges normally without any problems.

[0039] Since the proposed technique is used to monitor the various cells of a sodium-ion battery, the detection of aging cells or thermal runaway events becomes easy, thereby aiding in preventive maintenance.

[0040] According to FIG. 2, the computational model 105 includes: - NVPF / HC((Na3V2(PO4)2F3)-(Hard Carbon)) Model Module 10: Cell model that estimates cell voltage based on current and temperature measurements. A State of Health (SOH) module 11 that estimates various battery pack state of health parameters based on measurements and voltage estimation errors. - A State of Charge (SOC) module 12 that estimates the state of charge based on measurements and health parameters.

[0041] The input parameters of the NVPF / HC model module 10 are Vcell corresponding to the cell voltage, Tcell corresponding to the cell surface temperature, and Tamb corresponding to the operating temperature. Besides, V_pack corresponds to the voltage of the total battery pack, SOH_R corresponds to the state of health based on the resistance, and SOH_Q corresponds to the state of health based on the capacity.

[0042] Based on measurements of current, operating temperature, and open circuit voltage (the voltage just before connecting the cell to a load or charger), the NVPF-HC model module 10 estimates the cell voltage. The NVPF-HC model module 10 is based on an RC open circuit as shown in Figure 8a. Uoc is the open circuit voltage and i is the current (positive for charging, negative for discharging). V - is the negative terminal of the battery. V + is the positive terminal of the battery. Rs is the equivalent series resistance, which represents all the resistance contributions of the cell (contact resistance, electron movement in the current collectors and electrodes, and ion movement in the electrolyte). R surf is the surface resistance, which corresponds to the voltage drop at the interface between the particles of active material and the electrolyte. This parameter is related to the charge transfer between the two electrodes and any passivation layers present on the surfaces of these electrodes. C surf is the "surface time constant" τsurf=R, which is used to approximate the rapid kinetics (often less than 1 second) associated with interfacial phenomena between two electrodes (possibly kinetics associated with charge transfer, double layer capacitance, passivation layers with capacitive and / or diffusive effects). surf ×C surf Corresponding to V surf is R surf C surf is the voltage across the circuit. Zd is the diffusion impedance that groups together the overvoltages related to the phenomena of atomic diffusion within the active material particles of each electrode and ionic diffusion within the electrolyte. Vd is the voltage across the impedance Zd.

[0043] [Number 1] V cell =V + -V - JPEG2025529506000002.jpg36150V cell =U OC +R s i+V surf +V d

[0044] The open circuit voltage depends on the state of charge, temperature, and operating stage (charging or discharging). Based on the values ​​of the open circuit voltage and operating temperature, parameters are specified. Parameters corresponding to the initial state of charge (SOC0) are specified. This initial value is important for the state of charge estimation and is considered as an input for the state of charge estimation module. The result of the state of charge estimation becomes the input for the model. At each iteration, the open circuit voltage is calculated using the estimated state of charge coupled with the operating temperature.

[0045] Referring to Figure 3, a model of the SOC module 12 according to the present invention will be described. In this embodiment, the battery is a sodium-ion NVPF-HC battery. The method is applied by the state-of-charge module 12. The method comprises: - receiving at least one parameter corresponding to a percentage of an initial state of charge of the battery based on at least one open circuit voltage value and according to a landing zone of an open circuit voltage (OCV) - state of charge (SOC) function, wherein the landing zone of the OCV-SOC function is separated into at least two zones; - determining (2 and 3) for each model at least one estimate of the state of charge and at least one output voltage value based on the at least one parameter and the at least two models received; - providing an estimate of the state of charge of the battery based on said determined state of charge having a minimum error rate (4); Equipped with.

[0046] State of Charge (SOC) is the charge level of a battery compared to its capacity. The formula for SOC is:

[0047] [Number 2] JPEG2025529506000003.jpg22170JPEG2025529506000004.jpg15170

[0048] η is the Coulomb coefficient, i(t) is the current (positive for charge, negative for discharge), and SOC0 is the percentage of the battery's initial state of charge. Q can be thought of as the capacity available in the actual battery at a given aging condition. As such, Q is updated after each diagnostic procedure and is understood to be equal to the actual capacity the battery provides at each charge / discharge measurement. Capacity also degrades as the battery ages.

[0049] Based on the NVPF-HC electrical model in Fig. 8a, we obtain:

[0050] [Number 3] JPEG2025529506000005.jpg22170

[0051] And the battery voltage is V cell =U OC +R s i+V surf +V d is.

[0052] In the battery voltage type, the battery voltage V cell and current i can be measured. The parameters of a resistance-capacitance (RC) circuit (R s , R surf , C surf , and Z d ) is determined using electrochemical impedance spectroscopy (EIS) and galvanostatic intermittent titration technical (GITT) tests (presented in FIG. 8b) at various states of charge and temperatures. This stage corresponds to the calibration stage of the method, at the beginning of the battery life. These data are initial and not even accurate, and with each iteration of the method, these data are updated. In this way, the cell model converges to the measured voltage value and the error is reduced to reach the minimum possible value. Therefore, the voltage equation V cell So the last parameter to be specified is the open circuit voltage U oc U ocdepends on the state of charge, temperature, and operation stage (charging or discharging).

[0053] At least one parameter (1) corresponding to the percentage of the initial state of charge of the battery is provided by the NVPF-HC model module 10 to the SOC module 12 as an input parameter.

[0054] As shown in Figures 4a and 4b, the open circuit voltage of a lithium-ion battery exhibits a linear function with only one stable plateau P0. This plateau P0 is linear for NMC (Figure 4a) and stable for LFP (Figure 4b). The construction of the open circuit voltage of lithium-ion batteries is useful for presenting the open circuit voltage evolution using several assumptions. The following assumptions are commonly used for lithium-ion cells:

[0055] [Number 4] JPEG2025529506000006.jpg10170JPEG2025529506000007.jpg12170

[0056] This assumption does not apply to the sodium-ion NVPF-HC embodiment. For the sodium-ion NVPF-HC, the open-circuit voltage evolution is not linear, as can be seen in FIG. 5. The open-circuit voltage evolution encompasses two stable plateaus, P1 and P2: a first plateau P1 from 0% to 40% state of charge and a second plateau P2 from 40% to 100% state of charge. Thus, the first region is set from 0 V to 3.5 V (0% to 40%), and the second region is set from 3.5 V to 4.5 V (40% to 100%).

[0057] The OCV-SOC function is defined based on the region where the characteristic is (approximately) linear. In NVPF-HC cells, the open circuit voltage depends on the state of charge and temperature. The two OCV-SOC functions (corresponding to the P1 and P2 regions) correspond to two regions defined as follows: {If SOC≦40%, U OC (SOC)=a1·SOC+b1 If SOC>40%, U OC (SOC)=a2·SOC+b2

[0058] The subdivision of this OCV-SOC function depends on the state-of-charge interval applicable to all active materials with different plateaus.

[0059] The measurable conditions of the system are the cell voltage Vcell, the current i, the cell surface temperature Tcell, and the operating temperature Tamb.

[0060] According to the method, at least one estimate of the state of charge and at least one output voltage value are calculated from the initial state of charge percentage provided by the NVPF-HC model module 10 and from at least two separate models. Two calculations are performed from the at least two separate models. The calculations from the at least two models are performed simultaneously in parallel. In the presented embodiment, the first model comprises a state of charge estimation using an observer, and more specifically, an extended Kalman observer. For the state of charge estimation for an NVPF-HC sodium-ion cell, the state function is:

[0061] [Number 5] JPEG2025529506000008.jpg12170JPEG2025529506000009.jpg17170JPEG2025529506000010.jpg14170JPEG2025529506000011.jpg19170In the formula,

[0062] [Number 6] Based on the JPEG2025529506000012.jpg18170 state function, the state vector for the Kalman observer is

[0063] [Number 7] JPEG2025529506000013.jpg16170, where V2=V surf +V d and Y k =V kis the output.

[0064] The noise is assumed to be white, i.e., Gaussian. In fact, this model must combine all deterministic system information, and the system variables are continuous. In the model of the present invention, the main goal of this extended Kalman is to estimate the state-of-charge characteristics. The parameters that the observer must estimate, along with the input and output Y, are as follows:

[0065] [Number 8] Consider JPEG2025529506000014.jpg18170. Therefore, X k+1 =A·X k +B·i k and in the formula

[0066] [Number 9] JPEG2025529506000015.jpg18142.

[0067] The input is the current i k And the output Y k =V k =a·SOC k +b+V surf,k +R·i k Y k =[a 1]·X k +R·i k +b and where D=[a 1].

[0068] If SOC≦40%, a=a1 and b=b1.

[0069] If SOC>40%, a=a2 and b=b2.

[0070] In an estimation model using a Kalman filter, the model estimates or predicts the state of charge 2 (21). Full details of the Kalman filter model are presented in Figure 6. A Kalman filter in a discrete setting is a recursive estimator. This means that to predict the current state (21), only previous state estimates 20 and current measurements are needed. Therefore, past observations and predictions are not required. The initial state of health is the input data to the model. The model outputs an estimate of the state of charge. From the estimate of the state of charge, the output voltage is calculated. The model repeats the above steps at each time step or iteration. Thus, the value of the state of charge estimate and the output voltage are updated at each iteration (22).

[0071] The state of the observer is represented by two variables.

[0072] [Number 10] JPEG2025529506000016.jpg12170JPEG2025529506000017.jpg13170

[0073] The Kalman filter has two distinct stages: prediction 20 and update 21. The prediction step 21 uses the estimated state at a previous instant to produce an estimate of the current state. The update step 22 corrects the predicted state using the observations at the current time to obtain a more accurate estimate. In other embodiments, the first model consists of a state of charge estimation using a sliding mode observer or an adaptive observer or a fuzzy observer.

[0074] In addition to the first model, a second model is used to calculate a second estimate and a second output voltage in parallel with the first model. In the present embodiment, the second model uses a coulomb counting model. The coulomb counting model is presented in FIG. 7.

[0075] For the coulomb counting model, the model calculates the number of coulombs that charge / discharge the battery. The amount of charge transferred by the current is measured in coulombs, C c = i × dt where Cc is the amount of charge transferred and dt is the time in seconds that the current flows.

[0076] The charge thus calculated is compared to the total capacity of the cell, taking into account the Coulomb coefficient η, to obtain the state of charge transferred during dt. The sum of the SOC transferred in each iteration gives the total SOC.

[0077] [Number 11] JPEG2025529506000018.jpg14170

[0078] The same inputs of the Kalman filter, such as current and voltage measurements, are considered in the Coulomb counting model.

[0079] Therefore, the output of the second model is also a state of charge estimate and an output voltage 3. From the output voltage of each model, an error rate is calculated. The error rate is calculated by comparing the model output voltage and the measured output voltage. When two error rates are calculated, one error rate per output voltage, the two error rates are compared to each other (4). The state of charge estimate with the smallest error rate corresponds to the state of charge of the battery.

[0080] Typically, at least one of the means of the device according to the invention described above, preferably each of the means of the device according to the invention described above, is a technical means.

[0081] Typically, each of the means of the apparatus according to the invention described above may comprise at least one computer, central unit or calculation unit, (preferably dedicated) analog electronic circuitry, (preferably dedicated) digital electronic circuitry, and / or (preferably dedicated) microprocessor, and / or software means.

[0082] Naturally, the invention is not limited to the embodiments described, and many modifications can be made to these embodiments without going beyond the scope of the invention.

[0083] It will be appreciated that the various features, forms, modifications, and embodiments of the present invention can be associated with one another in various combinations, provided that they are not mutually exclusive or inconsistent with one another. In particular, all modifications and embodiments described above can be combined with one another.

Claims

1. A method for determining the state of charge (SOC) of a battery, comprising the following steps: - Step (1) of receiving at least one parameter corresponding to the percentage of the initial charge state of the battery, based on at least one open-circuit voltage value and according to a pace region of the open-circuit voltage (OCV)-state of charge (SOC) function, wherein the pace region of the OCV-SOC function is separated into at least two regions, - Steps (2 and 3) of determining, for each model, at least one estimate regarding the charge state and at least one output voltage value based on the received at least one parameter and at least two separate models, - Step (4) to provide an estimate of the charge state of the battery based on the determined charge state having the smallest error rate. A method for providing this.

2. The step of receiving at least one parameter is as follows: - A step of receiving at least one open-circuit voltage value, - A step of dividing the OCV-SOC function into at least two regions, wherein each region has a separate function, - A step of determining which of the above-mentioned areas the at least one open-circuit voltage value belongs to, - A step of providing the percentage of the initial charge state of the battery from the function according to the area. The method according to claim 1, comprising:

3. The method according to claim 1, wherein, for the at least two regions of the OCV-SOC function, the first region is set from 0 volts to 3.5 volts, and the second region is set from 3.5 volts to 4.5 volts.

4. The method according to claim 1, wherein the first of the at least two distinct models uses an observer.

5. The method according to claim 4, wherein the observation instrument used is a Kalman filter.

6. The method according to claim 1, wherein the first of the at least two distinct models uses a slip mode observer.

7. The method according to claim 1, wherein the first of the at least two distinct models uses an adaptive observer.

8. The method according to claim 1, wherein the second of the at least two distinct models uses a counting function.

9. The method according to claim 8, wherein the counting function used is Coulomb counting.

10. The method according to claim 1, wherein the charging state is updated in each iteration of the at least two models.

11. A system for determining the state of charge (SOC) of a battery, - Calculation module and, - One or more processors, - One or more computer-readable media, when executed by the one or more processors, to the system: - Based on at least one open-circuit voltage value and according to the open-circuit voltage (OCV) function-state of charge (SOC) plateau region, the battery is given at least one parameter corresponding to the percentage of its initial charge state (1), wherein the OCV-SOC function plateau region is divided into at least two regions, - Based on the received at least one parameter and at least two distinct models, for each model, determine at least one estimate regarding the charge state and at least one output voltage value (2 and 3), - Provide an estimation of the charge state of the battery based on the determined charge state having the smallest error rate (4) One or more computer-readable media for storing instructions and A system equipped with these features.

12. The aforementioned one or more computer-readable media are - Receive at least one open-circuit voltage value, - The OCV-SOC function is cut into at least two of the regions, each of which has a separate function. - Determine which of the above-mentioned areas the at least one open-circuit voltage value belongs to, - Provides the percentage of the initial charge state of the battery from the function according to the aforementioned region. The system according to claim 11, configured as follows.

13. The system according to claim 11, wherein, for the at least two regions of the OCV-SOC function, the first region is set from 0 volts to 3.5 volts, and the second region is set from 3.5 volts to 4.5 volts.

14. The system according to claim 11, wherein the first of the at least two distinct models uses an observer.

15. The system according to claim 14, wherein the observation instrument used is a Kalman filter.

16. The system according to claim 11, wherein the first of the at least two distinct models uses a slip mode observer.

17. The system according to claim 11, wherein the first of the at least two distinct models uses an adaptive observer.

18. The system according to claim 11, wherein the second of the at least two distinct models uses a counting function.

19. The system according to claim 18, wherein the counting function used is Coulomb counting.

20. The system according to claim 11, wherein the one or more computer-readable media are configured to update the charge state in each iteration of the at least two models.

21. When one or more non-temporary computer-readable media are executed by one or more processors, the following steps are taken: - Step (1) of receiving at least one parameter corresponding to the percentage of the initial charge state of the battery, based on at least one open-circuit voltage value and according to a plateau region of the open-circuit voltage (OCV)-state of charge (SOC) function, wherein the plateau of the OCV-SOC function is separated into at least two regions, - Steps (2 and 3) of determining, for each model, at least one estimate regarding the charge state and at least one output voltage value based on the received at least one parameter and at least two separate models, - Step (4) to provide an estimate of the charge state of the battery based on the determined charge state having the smallest error rate. One or more non-temporary computer-readable media that store instructions causing a system to execute a method for determining the charge state (SOC) of the battery.

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