CONTROLLED ESTIMATION OF THE STATE OF CHARGE OF A CELLULAR BATTERY IN A SYSTEM

Abstract

A method is implemented in a system comprising a cellular battery. This method includes a step (10-120) in which a predicted state of charge is determined using an estimation algorithm based on a battery model that is a function of estimated cell impedance parameters and the voltages across each cell, and a function of the cumulative current consumption of the battery. A corrected state of charge is then determined using this estimation algorithm, and it is verified whether the battery usage, the estimated impedance parameters, and the corrected state of charge satisfy certain criteria. If so, a new estimated state of charge equal to the corrected state of charge is provided; if not, a new estimated state of charge equal to the predicted state of charge is provided. Figure 3

Description

Title of the invention: CONTROLLED ESTIMATION OF THE STATE OF CHARGE OF A CELLULAR BATTERY IN A SYSTEM Technical field of the invention

[0001] The invention relates to systems comprising at least one cellular battery, and more specifically to the real-time estimation of the current state of charge (or SoC (“State of Charge”)) of such a cellular battery. State of the art

[0002] In many systems, such as for example in vehicles (possibly automobiles), rechargeable batteries are used which have cells designed to store electrical energy intended to power at least one electrical machine (possibly a motor) and / or an electrical power supply circuit.

[0003] For example, the cells can be electrochemical (in particular lithium-ion (or Li-ion) or Ni-Mh or Ni-Cd). Furthermore, in the case of a vehicle, the rechargeable battery can be either a so-called "main" battery (or traction or power battery) when it is responsible for supplying electrical current to an on-board network, via a converter, and at least one electric drive unit of the powertrain (or powertrain), or a so-called "service" battery when it is of the very low voltage type (typically between 12 V and 48 V) and responsible for supplying electrical current to the on-board network in the absence of a main battery (and therefore of an electric drive unit) or in place of or in addition to the main battery.

[0004] In what follows and what precedes, "on-board network" means an electrical power supply network to which electrical (or electronic) equipment (or components) consuming electrical energy are coupled.

[0005] Typically, the (rechargeable) cellular battery is associated with a battery case that includes means for measuring internal voltage / current / temperature and a battery calculator. The latter centralizes the current measurements, voltage measurements and internal temperature measurements (inside the cellular battery), and determines or estimates parameters of this cellular battery based on these measurements, and in particular its internal resistance, its minimum voltage and its current state of charge (or SoC).

[0006] This state of charge (or SoC) is an important parameter in a vehicle with at least a partially electric powertrain because it allows, in particular, for the real-time estimation of at least part of the vehicle's driving range, but also for optimization the use of the cellular battery in order to slow down its degradation (and therefore increase its lifespan).

[0007] At least three techniques can be implemented to estimate the SoC of a cellular battery in real time.

[0008] A first technique consists of estimating the SoC of the cellular battery using only the current consumption (in Ah) counts from the cellular battery. However, relying solely on current information is not optimal due to the accumulation of errors in the current sensors over time and the impossibility of correcting the capacity error as the cellular battery ages.

[0009] A second technique consists of estimating the SoC of the cell battery using an extended Kalman filter (EKF) estimation algorithm applied only to the minimum and maximum cell voltages. In this case, the cell voltages of the cell battery are composed of the open circuit voltage (OCV) and the contribution of the direct current resistance (DCR). Given aging and dispersion within the cell battery early in its life, it is rare for the minimum voltage (respectively the maximum voltage) to lead to a minimum SoC (respectively a maximum SoC). Therefore, this second solution is not accurate and can lead to poor prediction of the functional limitations for the cell voltages of the cell battery.

[0010] A third technique consists of estimating the SoC of the cellular battery using an extended Kalman filter (EKF) estimation algorithm and the so-called recursive least squares (RLS) technique applied to an equivalent electric circuit (EEC) battery model, optionally combined with a hysteresis model for batches of cellular battery cells, with a sequential approach to adapt to the computational constraints of the battery computer. More precisely, it allows for the determination of a predicted state of charge using the estimation algorithm, which employs a battery model based on estimated cell impedance parameters and the voltages across each cell, and as a function of the cumulative current consumption by the cellular battery, followed by a corrected state of charge using this estimation algorithm.The results obtained at each sampling period with this third technique are not always correct because the estimation algorithm needs some time (several iterations) before converging to the correct value. Therefore, the intermediate estimates that have not yet converged must be filtered before use.

[0011] Generally speaking, SoC estimation errors when using cellular batteries (and therefore in real time) are very difficult to avoid because they can have several origins, such as an accumulated error of the sensor measuring the current, an inaccuracy in the measurement of the cell battery capacity (since the SoC is the ratio between the remaining charge and the total capacity of the cell battery), an error in modeling the cell battery, real-time estimates of certain parameters of the battery model (such as impedances), or the chemical characteristics of the cells which affect their open circuit voltage (or OCV).

[0012] Furthermore, under certain critical operating conditions, such as driving with violent accelerations that impose very abrupt changes in the current or power of the cellular battery over very short periods, the battery model used for SoC estimation cannot react as quickly as the actual behavior of the cellular battery. Consequently, the EKF algorithm can become unstable and provide estimated SoCs that are not representative of reality.

[0013] Furthermore, most existing versions of EKF estimation algorithms use the open-circuit voltage (OCV) to dynamically correct the SoC during the wake-up phase after a sufficiently long battery relaxation time. These SoC corrections are entirely dependent on the OCV voltages, which are themselves a function of the SoC. Therefore, when the evolution of the OCV = f(SoC) curve is significant almost everywhere, the SoC correction is qualitative. However, when the OCV = f(SoC) curve has a significant, relatively flat portion, the SoC correction is poor (this is particularly the case for LFP (Lithium Iron Phosphate or LiFePO4) or LS (Lithium Sulfur) type batteries). It is therefore very difficult to use voltage information to correct the SoC when an error occurs.

[0014] The invention therefore aims in particular to improve the situation. Presentation of the invention

[0015] In particular, it proposes for this purpose an estimation method, on the one hand, intended to estimate a state of charge of a rechargeable battery comprising cells and equipping a system, and, on the other hand, comprising a step in which a predicted state of charge is determined by means of a chosen estimation algorithm and using a battery model as a function of estimated impedance parameters of the cells and voltages across each of the cells, and as a function of a cumulative current consumption by the rechargeable battery, then a corrected state of charge is determined by means of this estimation algorithm.

[0016] This estimation method is characterized by the fact that in its step it is verified whether the current use of the rechargeable battery meets a normality criterion, whether the estimated impedance parameters meet a chosen reliability criterion, and whether the state of corrected load satisfies a chosen robustness criterion, then, if all (three) criteria are satisfied, a new estimated load state equal to this corrected load state is provided, or, if at least one of the criteria is not satisfied, a new estimated load state equal to the predicted load state is provided.

[0017] Thanks to this increased robustness of the state of charge estimates, we are guaranteed to obtain more accurate estimates of cellular battery parameters and to avoid divergence of the state of charge (and therefore to reduce battery failures).

[0018] The estimation method according to the invention may include other features which may be taken separately or in combination, and in particular:

[0019] - in its step, the estimation algorithm may use an extended Kalman filter (or EKF);

[0020] - in its stage, the battery model can be an electrical circuit model of type called n-RC, with n > 1;

[0021] - in its step, the normality criterion can be a variation over a chosen period of a current flowing in the rechargeable battery or of an electrical power delivered or received by the rechargeable battery that is less than a chosen threshold;

[0022] - in its stage, the reliability criterion may be membership in a first range of chosen values ​​for each of the estimated impedance parameters;

[0023] - in its step, the robustness criterion may be membership in a second range of selected values ​​of the corrected state of charge;

[0024] - in its step, after a complete recharge of the rechargeable battery, one can correct the new estimated state of charge based on a target state of charge based on full charging and an estimated maximum state of charge of one of the cells, when current values ​​of at least two selected parameters of the rechargeable battery and a charging potential current used during full charging respectively meet normality criteria.

[0025] The invention also proposes a computer program product comprising a set of instructions which, when executed by processing means, is suitable for implementing an estimation method of the type presented above, in a system comprising a rechargeable battery including cells, to estimate a state of charge of the rechargeable battery.

[0026] The invention also provides an estimation device, firstly, suitable for inclusion in a system comprising a rechargeable battery with cells, and secondly, comprising at least one processor and at least one memory arranged to perform the operations of determining a predicted state of charge by means of a chosen estimation algorithm and using a battery model based on estimated cell impedance parameters and voltages across each cell, and based on a cumulative current consumption by the battery rechargeable, then to determine a corrected state of charge using this estimation algorithm.

[0027] This estimation device is characterized by the fact that its processor and memory are also arranged to perform the operations of checking whether a current use of the rechargeable battery meets a normality criterion, whether the estimated impedance parameters meet a chosen reliability criterion, and whether the corrected state of charge meets a chosen robustness criterion, and then, if all these (three) criteria are met, to provide a new estimated state of charge equal to the corrected state of charge, or, in the event of non-satisfaction of at least one of the criteria, to provide a new estimated state of charge equal to the predicted state of charge.

[0028] The invention also proposes a system comprising, on the one hand, a rechargeable battery comprising cells, and, on the other hand, an estimation device of the type of that presented above.

[0029] For example, this system can be a vehicle, possibly of the automobile type. Brief description of the figures

[0030] Other features and advantages of the invention will become apparent from an examination of the detailed description below, and the accompanying drawings, in which:

[0031] [Fig. 1] schematically and functionally illustrates an example of an embodiment of a vehicle comprising a powertrain with an electric drive unit powered by a cellular battery associated with a battery computer, and an estimation device according to the invention,

[0032] [Fig.2] schematically and functionally illustrates an example of the realization of a battery calculator comprising an estimation device according to the invention, and

[0033] [Fig.3] schematically illustrates an example of an algorithm implementing a estimation method according to the invention. Detailed description of the invention

[0034] The invention aims in particular to provide an estimation method, and an associated DE estimation device, intended to allow the estimation of the current state of charge (or SoC (State of Charge)) of a BC cellular battery of an S system.

[0035] In what follows, system S is considered, by way of non-limiting example, to be a motor vehicle, such as a car, as illustrated in [Fig. 1]. However, the invention is not limited to this type of system. It relates to any type of system comprising at least one cell battery. Thus, it relates to vehicles (land, sea (or river), and air), mobile machinery (including those that perform a lifting function), electronic devices (possibly household appliances and / or possibly mobile), fixed or stationary installations (possibly industrial), such as installations of electrical power supply, and buildings, for example. As a purely illustrative example, the BC cellular battery of an S system can be connected to a renewable energy source (in particular photovoltaic or wind).

[0036] Furthermore, in what follows, by way of non-limiting example, system S (here a vehicle) is considered to comprise a powertrain (or PWM) of the all-electric type (and therefore whose propulsion is provided exclusively by at least one electric motor). However, the PWM could be of the hybrid type (thermal and electric) or purely thermal.

[0037] Furthermore, in the following, by way of non-limiting example, the cellular battery BC is considered to be a main (or traction or power) battery suitable for supplying electrical energy to at least one electric motor. However, the cellular battery that is the subject of these estimates could be a service battery (possibly rechargeable via a converter powered by electrical energy from a main battery).

[0038] A system S (here a vehicle) comprising an estimation device DE according to the invention and an electric GMP transmission chain (and therefore an electric motor machine MME), an on-board network RB, a power supply group comprising a service battery BS and (here) a CV converter associated with a cellular battery BC (itself associated with a battery computer CB).

[0039] The RB on-board network is an electrical power supply network to which electrical (or electronic) equipment (or components) that consume electrical energy are coupled.

[0040] The auxiliary battery BS is responsible for supplying electrical power to the vehicle's electrical system RB, supplementing that supplied by the CV converter powered by the cellular battery BC, and sometimes replacing this CV converter (particularly when the engine is off and the CV converter is inactive). For example, this auxiliary battery BS can be configured as a very low voltage type battery (typically 12 V, 24 V, or 48 V). It is rechargeable at least by the CV converter. In the following, for the sake of non-limiting example, the auxiliary battery BS is considered to be a 12 V lithium-ion type.

[0041] The transmission system has a powertrain which, in this case, is purely electric, and therefore includes, in particular, an electric drive machine MME, a drive shaft AM, and a transmission shaft AT. The term "electric drive machine" here refers to an electric machine arranged to supply or recover torque to move the system S (here, a vehicle). The operation of the powertrain is supervised by a control unit CS.

[0042] The electric drive unit MME (here an electric motor) is coupled to the cell battery BC, in order to be supplied with electrical energy, and also possibly to supply this cell battery BC with electrical energy (for example, during a regenerative braking phase). It is coupled to the motor shaft AM, to provide it with torque by rotational drive. This motor shaft AM is here coupled to a reduction gear RD which is also coupled to the transmission shaft AT, itself coupled to a first set of wheels Tl, preferably via a differential Dl.

[0043] This first train Tl is here located in the front part PVV of the vehicle S. But in a variant this first train Tl could be the one which is here referenced T2 and which is located in the rear part PRV of the vehicle S.

[0044] The MME driving machine is, here, also coupled to the CV converter which is also indirectly coupled to the BS auxiliary battery, in particular to recharge it with electrical energy from the BC cellular battery and converted.

[0045] This CV converter is an electrically coupled current converter, here, for example, to a CN charging connector of vehicle S. It is also responsible for supplying the on-board network RB with electrical energy from the cellular battery BC, converted when the engine is running or when the engine is asleep but vehicle S is in a charging phase for its cellular battery BC, in addition to charging the auxiliary battery BS.

[0046] For example, the BC cellular battery may comprise CE electrochemical energy storage cells. Also, for example, each CE (electrochemical energy storage) cell may be of the lithium-ion (or Li-ion) type. But this is not mandatory. Indeed, it could be of the Ni-MH or Ni-Cd type, for example. Also, for example, the BC cellular battery may be of the low-voltage type (typically 450 V, 600 V, or 800 V, for illustrative purposes). But it could also be of the medium-voltage or high-voltage type.

[0047] It should be noted, as illustrated in Figure 1, that CE cells can be part of MC modules which are coupled together, for example in series, within the BC cell battery. Here, "MC module" means a group of at least one CE cell. When an MC module comprises several CE cells, these cells (CE) can be coupled together in series and / or in parallel.

[0048] It should also be noted that the BC cellular battery is associated with a BB battery housing which includes, in particular, means for measuring voltage, current and internal temperature (not shown) and the CB battery calculator. The latter (CB) centralizes current measurements, determined voltage measurements (and in particular the first ucl voltages across the terminals of the various CE cells), the measurement of the current flowing through the BC cellular battery, the measurement of the open-circuit voltage (OCV) of the BC cellular battery, and temperature measurements. internal (particularly those relating individually to each of the CE cells). In addition, the CB battery calculator can estimate parameters of the BC cell battery based on these measurements, including its internal resistance, minimum voltage, and current capacity.

[0049] It should also be noted that in the example illustrated, but not limited to, in [Fig. 1], the vehicle S also includes a distribution box BD to which the auxiliary battery BS, the CV converter, and the on-board network RB are coupled. This distribution box BD is responsible for distributing the electrical energy produced by the CV converter or stored in the auxiliary battery BS into the on-board network RB to power the electrical components (or equipment) connected to the on-board network RB, according to power demands received (in particular from the powertrain control unit CS).

[0050] As mentioned above, the invention proposes in particular an estimation method intended to allow the estimation of the state of charge SoCB R of the cellular battery BC of the system S (here a vehicle).

[0051] This estimation method can be implemented at least partially by the DE estimation device (illustrated at least partially in Figures 1 and 2), which for this purpose comprises at least one PR1 processor, for example a digital signal processor (or DSP), and at least one MD memory. This DE estimation device can therefore be implemented as a combination of electrical or electronic circuits or components (or "hardware") and software modules (or "software"). For example, it could be a microcontroller.

[0052] The MD memory is random access memory (RAM) to store instructions for the implementation by the PR1 processor of at least part of the estimation process. The PR1 processor may comprise integrated (or printed) circuits, or several integrated (or printed) circuits connected by wired or wireless connections. An integrated (or printed) circuit is defined as any type of device capable of performing at least one electrical or electronic operation.

[0053] In the example illustrated, but not limited to, in Figures 1 and 2, the DE estimation device is part of the CB battery computer. However, this is not mandatory. Indeed, the DE estimation device could comprise its own dedicated computer, which is then coupled to the CB battery computer, or it could be part of another computer embedded in the S system, for example.

[0054] As illustrated, but not limited to, in [Fig. 3], the (estimation) method according to the invention comprises a step 10-120 which is implemented at least every time the vehicle V is used (and in particular every time its powertrain is running) and an estimation of the state of charge SoCBc is requested, for example by the battery computer CB (but this could be requested by the CS monitoring computer or by a server that can access the S system via radio waves).

[0055] This step 10-120 includes a substep 10 in which one (for example the estimation device DE) begins by determining a predicted state of charge ecp by means of a chosen estimation algorithm and using a battery model as a function of estimated impedance parameters pie of the cells CE and voltages across each of the cells, and as a function of a cumulative current consumption (in Ah) by the cell battery BC.

[0056] This step 10-120 also includes a substep 80 in which a corrected state of charge ecc is determined by means of this estimation algorithm using the predicted state of charge ecp (determined in substep 10).

[0057] In addition, in step 10-120 one (for example the estimation device DE) checks, firstly, whether the current use of the cellular battery BC meets a normality criterion, secondly, whether the estimated impedance parameters pie meet a chosen reliability criterion, and, thirdly, whether the corrected state of charge ecc meets a chosen robustness criterion.

[0058] Then, if all three of the above-mentioned criteria are met, step 10-120 also includes a substep 110 in which a new estimated state of charge SoCBr (for example, the DE estimation device) is provided, which is equal to the corrected state of charge ecc. The overall estimation of the new state of charge SoCBr is considered robust because it has successfully passed the three checks (by satisfying the three criteria).

[0059] Conversely, if at least one of these three criteria is not met, step 10-120 also includes a substep 40 in which a new estimated state of charge SoCBR (for example, the DE estimation device) is provided, which is equal to the predicted state of charge ecp. This is because the overall estimation of the new state of charge SoCBR is considered not robust since at least one of the three checks was not successfully passed, and therefore the corrected state of charge ecc cannot be retained as the new state of charge SoCBC, which necessitates using the predicted state of charge ecp instead.

[0060] The invention offers several advantages, including:

[0061] - the failure to take into account the corrected state of charge ecc in the presence of use abnormal BC cell battery (such as during very high current or power dynamics) helps to avoid SoCBC state of charge divergence, and therefore to reduce battery failures;

[0062] - the guarantee of obtaining more accurate estimates of battery parameters cellular BC (such as State of Health (SoH)), due to the increased robustness of State of Charge (SoCBc) estimates,

[0063] - the possibility of better matching the specific conditions of use of drivers, thanks to usage filtering;

[0064] - it allows for automatic verification of the algorithm's activation conditions estimation,

[0065] - it allows for a more complete correction of errors in estimating the state of charge SoCBc,

[0066] - it makes it possible to avoid unstable estimates and therefore to guarantee the stability of the state SoCBR charging in all use cases of the system (and in particular when it is a vehicle).

[0067] For example, in step 10-120, the estimation algorithm that is chosen may be a recursive algorithm using an extended Kalman filter (or EKF) with the chosen battery model.

[0068] In this case, and as illustrated non-limitingly in [Fig. 3], step 10-120 may also include a substep 50 in which one (for example, the estimation device DE) can determine, during each sampling period k, the voltage uBR(k) across the terminals of the cell battery BC. Then, in a substep 60 of step 10-120, one (for example, the estimation device DE) can predict an error ueiT(k) on this determined voltage uBR(k). Then, in a substep 70 of step 10-120, one (for example, the estimation device DE) can determine a covariance matrix P and a correction gain K, taking into account the update of the covariance matrix resulting from the determination of the corrected state of charge ecc(k - 1) during the previous sampling period (k - 1).Then, in substep 80 of step 10-120, the corrected load state ecc(k) is determined (for example, by the DE estimation device) as a function of the predicted load state ecp(k) and the covariance matrix P and correction gain K determined in substep 70 (for example, ecc(k) = ecp(k) + K*ueiT(k)). Then, the covariance matrix P is updated, and substep 10 is performed again after incrementing the index k by one.

[0069] Also, for example, in step 10-120, the battery model chosen (and used to determine the predicted state of charge ecp and the corrected state of charge ecc) can be an electrical circuit model of the so-called n-RC type (for each CE cell), with n > 1, well known to those skilled in the art. Thus, one can use an equivalent 1-RC electrical circuit model or, to improve accuracy, an equivalent 2-RC electrical circuit model, for example. One can also add to an equivalent n-RC electrical circuit model (with n > 1) a simple hysteresis model for increase the accuracy of state of charge estimates, especially in the presence of flat SoC = f(OCV) curves around 90% to 30% SoC, for example for LFP (Lithium Iron Phosphate or (LiFePO4)) type cells.

[0070] It is recalled that in an equivalent 1-RC electrical circuit model, the CE cell is electrically represented by impedance parameters (R0, RI, Cl), where R0 is a resistor connected in series with a set comprising the resistor RI connected in parallel with the capacitance Cl. For example, these impedance parameters can be estimated for each CE cell using a Forgetting Factor Recursive Least Squares (FFRLS) method. Two states, SoC(k) and Up(k), can then be estimated with the following state equations:

[0071] SoC(k+l) = SoC(k) + I(k)*dt / capacity, where I(k) is the current consumed by the cellular battery BC, and

[0072] Up(k+1) = [Up(k)*(l- (dt / (Rl*Cl)))] - [Rl*(I(k)*dt) / (Rl*Cl)].

[0073] It should be noted, as illustrated (though not exhaustively) in [Fig. 3], that the verification of the normality criterion by the ongoing use of the BC cellular battery can be carried out immediately after substep 10 of determining the predicted state of charge ecp. It is considered unnecessary to determine the corrected state of charge ecc if the ongoing use of the BC cellular battery is abnormal.

[0074] But in an alternative embodiment (not illustrated) the verification of the normality criterion could be done after substep 80 of determination of the corrected load state ecc.

[0075] Also, for example, the normality criterion may be a variation over a chosen time period (dt) of the current (I(k)) flowing in the cellular battery BC or of the electrical power (P(t)) delivered or received by the cellular battery BC less than a chosen threshold.

[0076] In this case, as illustrated non-limitingly in [Fig. 3], step 10-120 may also include a substep 20 in which the variation over a chosen period of the current flowing through the cell battery BC or of the electrical power delivered or received by the cell battery BC can be determined (for example, by the estimation device DE). Then, in a substep 30 of step 10-120, this determined variation can be compared (for example, by the estimation device DE) to the chosen threshold. If the determined variation is less than the chosen threshold, the current use of the cell battery BC is normal, and therefore the corrected state of charge ecc can be determined. Conversely, if the determined variation is greater than or equal to the chosen threshold, the current use of the cell battery BC is abnormal, and therefore substep 40 is performed.

[0077] Also, for example, the reliability criterion may be membership in a first range of chosen values ​​of each of the estimated pie impedance parameters for the different CE cells.

[0078] It should be noted, as illustrated (though not exhaustively) in [Fig. 3], that the verification of the reliability criterion using the estimated impedance parameters pie can be performed in a substep 90 of step 10-120, immediately following substep 80 for determining the corrected state of charge ecc(k), since these estimated impedance parameters pie are used in the battery model, which is itself used by the estimation algorithm (here EKF) to determine the corrected state of charge ecc(k). Indeed, the corrected state of charge ecc, just determined in substep 80, cannot be retained if at least one of the estimated impedance parameters pie is unreliable.

[0079] But in an alternative embodiment (not illustrated) the verification of the normality criterion could be done before substep 80.

[0080] For example, in substep 90, one (for example, the estimation device DE) can determine whether the value of each estimated impedance parameter pie for a cell CE is within the first range of selected values. If all the values ​​of the estimated impedance parameters pie are within the first range of selected values, then they are all reliable and therefore the robustness criterion can be verified. On the other hand, if at least one of the estimated impedance parameter values ​​pie is not within the first range of selected values, then they are considered unreliable, and therefore the corrected load state ecc just determined in substep 80 cannot be retained. Consequently, substep 40 is performed.

[0081] Also, for example, the robustness criterion can be membership in a second interval of chosen values ​​of the corrected load state ecc(k).

[0082] It should be noted, as illustrated (though not exhaustively) in [Fig. 3], that the verification of the robustness criterion by the corrected load state ecc(k) can be carried out in a substep 100 of step 10-120, immediately following substep 90 for verifying the reliability criterion. Indeed, the corrected load state ecc, just determined in substep 80, cannot be retained if it does not fall within the second range of selected values, even if the estimated impedance parameters pie are reliable.

[0083] But in an alternative embodiment (not illustrated) the verification of the normality criterion could be done just before the verification of the reliability criterion.

[0084] For example, in substep 100, one (for example, the DE estimation device) can determine whether the corrected state of charge ecc(k) is within the second range of chosen values. If so, this means that it is robust and therefore can become the new state of charge SoCBr in substep 110. On the other hand, If the value is negative (ecc(k) not included in the second interval of chosen values), it is considered not robust, and therefore cannot be retained. Consequently, substep 40 is performed.

[0085] Also, for example, and as illustrated non-limitingly in [Fig. 3], step 10-120 may also include a substep 120 in which, after a complete recharge of the cell battery BC, one (for example, the estimation device DE) can correct the new estimated state of charge SoCBr based on a target state of charge ecb, which is a function of the complete recharge, and an estimated maximum state of charge SoCmax of one of the cells CE. This correction is only performed when current values ​​of at least two selected parameters of the cell battery BC and a charging potential current used during the complete recharge of the latter (BC) respectively satisfy normality criteria. When one of these two normality criteria is not met, the correction of the new estimated state of charge SoCBc is not carried out, and therefore it remains as is.

[0086] To perform this correction after a full charge, one can, for example, proceed as follows. First, the variable SoCmax is initialized with the raw SoC value of the first CE cell. Then, the SoC of each CE cell is compared to the variable SoCmax at each iteration, in order to find the CE cell with the maximum SoC in the entire BC cell battery. Once this maximum SoC is found, the SoC corresponding to the found CE cell is set to the target state of charge value ecb. Then, the SoCs of the other CE cells are adjusted with a distance proportional to the maximum SoC. But other methods can be used, such as, for example, adjusting the SoC of each cell with respect to the target state of charge ecb.

[0087] This ability to reset the error of the SoCmax variable is particularly useful when the cell chemistry induces a relatively flat OCV = f(SoC) curve, as it helps to start a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) cycle without any risk of underestimation or overestimation. It should be noted that overestimation is problematic for the authorization of regenerative (or recuperative) energy, leading to higher energy consumption (kWh / km), while underestimation is detrimental due to safety and sustainability risks, as it results in a higher than expected regeneration current and therefore greater exposure to the risk of lithium metallic deposition (Li-plating).

[0088] It should also be noted, as illustrated but not limited to [Fig. 2], that the battery calculator CB (or the dedicated calculator of the DE estimation device) may also include a mass memory MM1, in particular for the temporary storage of each quantity of ampere-hours (Ah) consumed, parameters estimated impedance values ​​pi, each open-circuit voltage, each current capacitance, voltage, current, and possibly internal temperature measurements, and any intermediate data used in all its calculations and processing. Furthermore, this battery calculator CB (or the dedicated calculator of the estimation device DD) may also include an input interface IE for receiving at least each quantity of ampere-hours (Ah) consumed, the estimated impedance parameters pi, each open-circuit voltage, each current capacitance, voltage, current, and possibly internal temperature measurements, for use in calculations or processing, possibly after shaping and / or demodulating and / or amplifying them, in a manner known per se, by means of a digital signal processor PR2.In addition, this CB battery calculator (or the dedicated calculator of the DD estimation device) may also include an IS output interface, notably to deliver each new state of charge (possibly corrected) SoCBc.

[0089] It will also be noted that the invention also proposes a computer program product (or computer program) comprising a set of instructions which, when executed by processing means of the type of electronic circuits (or hardware), such as for example the PR1 processor, is suitable for implementing the estimation process described above to estimate the state of charge SoCBr of the BC cellular battery of the S system.

Claims

Demands

1. A method for estimating the state of charge of a rechargeable battery (BC) comprising cells (CE) and equipping a system (S), said method comprising a step (10-120) in which a predicted state of charge is determined by means of a chosen estimation algorithm and using a battery model as a function of estimated impedance parameters of said cells (CE) and the voltages across each of said cells (CE), and as a function of a cumulative current consumption by said rechargeable battery (BC), and then a corrected state of charge is determined by means of said estimation algorithm, characterized in that in said step (10-120) it is verified i) whether a current use of said rechargeable battery (BC) satisfies a normality criterion, ii) whether said estimated impedance parameters satisfy a chosen reliability criterion, and iii) whether said corrected state of charge satisfies a chosen robustness criterion, and then,If all of the aforementioned criteria are met, a new estimated load state equal to the corrected load state is provided, or, if at least one of the aforementioned criteria is not met, a new estimated load state equal to the predicted load state is provided.

2. Method according to claim 1, characterized in that in said step (10-120) said estimation algorithm uses an extended Kalman filter.

3. Method according to claim 1 or 2, characterized in that in said step (10-120) said battery model is an electrical circuit model of type said n-RC, with n > 1.

4. A method according to any one of claims 1 to 3, characterized in that in said step (10-120) said normality criterion is a variation over a chosen time of a current flowing in said rechargeable battery (BC) or of an electrical power delivered or received by said rechargeable battery (BC) below a chosen threshold.

5. A method according to any one of claims 1 to 4, characterized in that in said step (10-120) said reliability criterion is membership in a first range of chosen values ​​of each of said estimated impedance parameters.

6. A method according to any one of claims 1 to 5, characterized in that in said step (10-120) said robustness criterion is a belonging to a second range of chosen values ​​of said corrected charge state.

7. A method according to any one of claims 1 to 6, characterized in that in said step (10-120), after a complete recharge of said rechargeable battery (BC), said new estimated state of charge is corrected as a function of a target state of charge as a function of said complete recharge and an estimated maximum state of charge of one of said cells (CE), when current values ​​of at least two selected parameters of said rechargeable battery (BC) and a charging potential current used during said complete recharge respectively satisfy normality criteria.

8. Product computer program comprising a set of instructions which, when executed by processing means, is suitable for implementing the estimation method according to any one of claims 1 to 7, in a system (S) comprising a rechargeable battery (BC) comprising cells (CE), to estimate a state of charge of said rechargeable battery (BC).

9. Estimating device (ED) suitable for being part of a system (S) comprising a rechargeable battery (BC) comprising cells (CE), and comprising at least one processor (PR1) and at least one memory (MD) arranged to perform the operations of determining a predicted state of charge by means of a selected estimation algorithm and using a battery model as a function of estimated impedance parameters of said cells (CE) and voltages across each of said cells (CE), and as a function of a cumulative current consumption by said rechargeable battery (BC), and then determining a corrected state of charge by means of said estimation algorithm, characterized in that said processor (PR1) and memory (MD) are further arranged to perform the operations of verifying i) whether a current use of said rechargeable battery (BC) satisfies a normality criterion,ii) if said estimated impedance parameters satisfy a chosen reliability criterion, and iii) if said corrected state of load satisfies a chosen robustness criterion, then, if all said criteria are met, to provide a new estimated state of load equal to said corrected state of load, or, if at least one of said criteria is not met, to provide a new estimated state of load equal to said predicted state of load. 17

10. System (S) comprising a rechargeable battery (BC) comprising cells (CE), characterized in that it further comprises an estimation device (DE) according to claim 9.

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