• 专利附录
  • 专利说明
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    • 专利摘要:
    This method for determining the state of charge of a battery comprising several cells comprises: for each cell, the calculation (204) of a voltage difference between a measured value yk between its terminals at a time k and a value measured yk-x at a previous instant, - the allocation (206) to each cell of a priority level all the higher as the calculated deviation of voltages is important, - the planning (208) of instants of refreshing the estimation of the state of charge of each cell according to the assigned priority levels, this scheduling comprising the assignment, on the same time interval, to the cells whose priority level is higher by one more a large number of refresh instants than the number of refresh instants assigned to the other cells, and - for each cell, the complete execution (210) of an algorithm for estimating the state of charge of this cell at the same time. moments of refresh planning.
    公开号:FR3029299A1
    申请号:FR1461618
    申请日:2014-11-28
    公开日:2016-06-03
    发明作者:Vincent Heiries；Sylvain Leirens
    申请人:Renault SAS；
    IPC主号:
    专利说明:

    [0001] The invention relates to an automatic method for determining the state of charge of a battery as well as a recording medium and a management system. BACKGROUND OF THE INVENTION battery to implement this method. The invention also relates to a motor vehicle comprising this battery management system. [002] A battery comprises a plurality of cells for storing electrical energy. These cells are electrically connected to each other between two electrical terminals of the battery. [003] Known methods for determining the state of charge of a battery comprise: a) the acquisition, at each instant k and for each cell of the battery, of the measured value yk of the voltage between terminals of this cell, and the measured intensity ik 15 of the charging or discharging current of this cell, b) at least some of these instants k, the complete execution, by an electronic computer and for at least one of cells of the battery, an algorithm for estimating the state of charge SOCk of this cell, as a function of the value yk and intensity ik measured for this cell at this instant k, and c) the determination the state of charge of the battery from estimated charge states for each of the cells of this battery. [004] The state of charge of a cell is not a directly measurable physical quantity. It must therefore be estimated. Its estimation requires the implementation of estimation algorithm requiring a large computing power. For example, such estimation algorithms are described in Part 3 of the following article: L. Plett, et al. : "Extended Kalman filtering for battery management systems of LiPBbased HEV battery packs", Journal of Power Sources, 2004, page 252-292. Subsequently, this article is designated by the abbreviation "Plett 2004". In the known processes, at each instant k, the state of charge of each cell is estimated. Then, the state of charge of the battery is calculated from these estimates. These known methods work well, but require significant computing power, because it must be able to estimate at each moment k the state of charge of each cell. [006] The invention aims to propose a method for determining the state of charge of a battery which requires a reduced computing power while maintaining a precision on the state of charge determined identical or virtually identical to that of the known methods which estimate at each moment k the state of charge of each cell. It therefore relates to a method comprising: - for each cell, the calculation of a voltage difference between the measured value yk between the terminals of this cell at time k and a measured value yk_x between these same terminals at a previous instant kX, where X is an integer greater than or equal to one, then - the assignment to each cell of a priority level, the priority level of a cell being all the higher as the voltage difference calculated for this cell is important, - the scheduling of refresh times of the estimation of the state of charge of each cell according to the priority levels assigned to each of these cells, this planning comprising the assignment, on a same time interval, to cells with a higher priority level with a greater number of refresh times than the number of refresh times assigned to cells with a lower priority level. vé, and - for each cell, the complete execution of the algorithm for estimating the state of charge of this cell at each scheduled refresh time for this cell and the inhibition of the complete execution of this algorithm of estimate of its state of charge outside planned refresh times for this cell. [7] To limit the computing power required to determine the state of charge of the battery, the claimed method exploits the fact that the state of charge of a cell varies little when the voltage between its terminals varies little. [8] Therefore, by limiting the frequency with which the algorithm for estimating the state of charge of a cell whose voltage varies little, the calculation power required to determine the state of the cell is limited. charge of the battery 20 without degrading the accuracy of the state of charge thus determined. In addition, since the complete execution frequency of the algorithm for estimating the state of charge of a cell is decreased, this frees up time for the electronic computer to perform other tasks. [9] Embodiments of this automatic determination method may include one or more of the following features: - the assignment to each cell of a priority level also includes for each cell: - the comparison of the measured value yk between the terminals of this cell, at a predetermined threshold of voltages selected from the group consisting of a high threshold greater than or equal to 0.8μmax, and a low threshold of less than 1.2μm, where Umax and Um ,, are, respectively, the largest and the smallest possible voltages between the terminals of this cell, - if the measured value yk is between the high threshold and the low threshold, the assignment of a first priority level to this cell and if the measured value yk is below the predetermined threshold when the predetermined threshold is the low threshold or if the measured value yk is above the predetermined threshold when the predetermined threshold is the high threshold, the assigning to this cell a second level of priority strictly greater than the first priority level; For at least one of the cells of the battery: the complete execution of the algorithm for estimating the state of charge of this cell comprises the execution of the calculation of a prediction of the SOCk state of charge of this cell using a state model that connects its charge state SOCk at time k to a state of charge SOCk_i of the same cell at a previous instant k-1, then the correction of the calculated prediction of the state of charge SOCk as a function of the measured value yk at the instant k, and outside the scheduled refresh times for the complete execution of the estimation algorithm of the state of charge of this cell, the method comprises: the execution of the only calculation of the prediction of the state of charge of this cell, without executing the correction of this prediction so as to obtain, even outside the instants of planned refresh, an updated estimate of the state of charge of this cell e, and - the use of the updated estimate of the state of charge of this cell when determining the state of the charge of the battery; the method comprises: identifying a set of at least two twin cells, the twin cells having the same voltage difference and the same measured value yk at the same instant k, this identification comprising the comparison of the difference voltages and the measured value yk of a cell at voltage differences and measured values yk for the other cells at the same time in order to identify, among these other cells, the twin cell (s) of this cell, and for each set of twin cells, the complete execution of the state of charge estimation algorithm for only one of the cells of this set and, at the same time, the inhibition of the complete execution of the algorithm for estimating the state of charge for the other cells of this set, then estimating the state of charge of the other cells of this set by taking it equal to the state of charge of the single cell of this set together for which the algorithm estimate was completely executed. [0010] These embodiments of the determination method also have the following advantages: - Increase the refresh rate of the estimation of the state of charge of a cell whose voltage is higher than a high or low threshold at a low threshold makes it possible to limit the probability that this cell is damaged. Indeed, an incorrect estimate of the state of charge of the cell when its voltage is high or low can lead to over charging or, on the contrary, to over-discharge what damages it. By increasing in these situations the refresh rate of the estimation of the state of charge, the accuracy of this estimate is increased and thus the risk of damage to the cell is limited. 40 - Estimating the state of charge of a cell at each instant k by executing only the calculation of a prediction of this state of charge, without proceeding to its correction, makes it possible to benefit at each moment k from an updated estimate of the state of charge of each cell. This increases the accuracy of the determination of the state of charge of the battery. In addition, this does not substantially increase the computing power required because the calculation step of the prediction requires much less computing power than the step of correcting this prediction. - The identification of twin cells and the complete execution of the state of charge estimation algorithm for only one of the twin cells makes it possible to limit the calculation power required without deteriorating the accuracy of the determined state of charge. for the battery. The invention also relates to an information recording medium comprising instructions for executing the automatic method of determination above when these instructions are executed by an electronic computer. The invention also relates to a management system of a battery 15 equipped with several electric energy storage cells, these cells being electrically connected to each other between two electrical terminals of the battery, this system comprising an electronic computer programmed to: a) acquire, at each instant k and for each cell of the battery, the measured value yk of the voltage between terminals of this cell, and the measured intensity ik of the charge or discharge current 20 of this cell, b) at at least some of these instants k, perform completely, for at least one of the cells of the battery, an algorithm for estimating the state of charge SOC k of this cell, as a function of the value yk and intensity ik measured for this cell at this instant k, and c) determining the state of charge of the battery from the estimated charge states for each of the cells of this battery, in the that the electronic computer is also programmed for: for each cell, calculate a voltage difference between the measured value yk between the terminals of this cell at time k and a measured value yk_x between these same terminals at a previous instant kX , where X is an integer greater than or equal to one, then - assigning each cell a priority level, the priority level of a cell being all the greater the greater the calculated voltage difference for this cell, plan refresh times of the estimation of the state of charge of each cell according to the priority levels assigned to each of these cells, this planning comprising the assignment, over the same time interval, to the cells of which the priority level is higher by a greater number of refresh times than the number of refresh times assigned to cells whose priority level is less high, and 40 - for each cell, completely perform the algorithm for estimating the state of charge of this cell at each scheduled refresh time for this cell and inhibit the complete execution of this estimation algorithm sound state of charge outside planned refresh times for this cell. Finally, the invention also relates to a motor vehicle comprising: - at least one driving wheel, - an electric motor capable of driving in rotation this drive wheel to move the motor vehicle, - a battery comprising at least one cell adapted to store electrical energy and, alternately, to restore electrical energy to power the electric motor, this cell having two terminals through which it is electrically connected to the electric motor, - an electrically connected voltmeter between the terminals of the cell for measuring the voltage between these terminals, - an ammeter connected in series with the electric cell for measuring the intensity of the charge or discharge current of that cell, and 15 - the claimed system for managing the battery connected to the voltmeter and to the ammeter, this management system comprising a programmable electronic calculator suitable for timer the state of charge of the battery cell from the voltmeter and ammeter measurements. The invention will be better understood on reading the description which will follow, given solely by way of nonlimiting example and with reference to the drawings, in which: FIG. 1 is a partial schematic illustration of a motor vehicle equipped with an electric battery; FIG. 2 is a schematic illustration of an electric model of a cell of the battery of the vehicle of FIG. 1; FIG. 3 is a schematic illustration of an estimator arrangement used for estimating the state of charge of a cell of the vehicle battery of FIG. 1; FIGS. 4 to 9 represent equations of different models of FIG. state and observation used by the estimators of Figure 3; Figure 10 is a flowchart of a method of estimating the state of charge of a cell using the estimators of Figure 3; FIG. 11 is a flowchart of a method for determining the state of charge of the battery of the vehicle of FIG. 1; FIG. 12 is a flowchart of a method for scheduling refresh times of the estimates of charge states of different cells of a battery; FIG. 13 is a timing chart illustrating different refresh times planned using the method of FIG. 12; FIG. 14 is a schematic illustration of another estimator arrangement 40 used to estimate the state of charge of a battery cell of the vehicle of FIG. 1; FIGS. 15 and 16 show, respectively, a state model and an observation model used by the estimators of FIG. 14; FIG. 17 is a flowchart of a method for estimating the state of charge of a cell using the estimators of FIG. 14; FIG. 18 is an illustration of another possible state model for predicting the capacity and internal resistance of a cell of a battery. In these figures, the same references are used to designate the same elements. In the remainder of this description, the features and functions well known to those skilled in the art are not described in detail. In this description, the term "computing power" designates the number of operations to be performed by an electronic computer. Thus, reducing the computing power means reducing the number of operations to achieve to achieve the same result or a result of the same nature. [0017] Figure 1 shows a motor vehicle 2 with electrical traction plus known as the "electric vehicle". Electric vehicles are well known and only the structural elements necessary to understand the rest of this description are presented. The vehicle 2 comprises: - an electric motor 4, able to drive in rotation the driving wheels 6 to roll the vehicle 2 on a roadway 8, and 20 - a battery 10 which supplies electrical energy to the engine 4. [0018] battery 10 comprises two terminals 12, 14 of electrical connection and several electrical cells electrically connected between these terminals 12 and 14. The terminals 12 and 14 are connected to the electrical loads to be powered. Here, they are therefore in particular connected to the electric motor 4. [0019] To simplify FIG. 1, only four electric cells 18 to 21 are shown. Typically, these electric cells are grouped into several stages and these stages are connected in series between the terminals 12 and 14. Here, only two stages are represented. The first stage comprises cells 18 and 19, and the second stage comprises cells 20 and 21. Each stage has several branches connected in parallel. Each branch of a stage comprises an electric cell or several electric cells in series. Here, the first stage has two branches, and each branch has a single electric cell. The second stage is structurally identical to the first stage in the example shown in FIG. 1. Here, all the cells of the battery 10 are structurally identical to the manufacturing tolerances. Therefore, only the cell 18 is now described in more detail. The cell 18 comprises two electrical connection terminals 30, 32 which electrically connects it to the other cells and terminals 12 and 14 of the battery 10. 40 The cell 18 is also mechanically fixed, without any degree of freedom to the other cells of the battery 10 to form what is often called a "pack" of cells. Cell 18 is capable of storing electrical energy when not in use. This stored electrical energy is then used to power the motor 4, which discharges the cell 18. In alternation, the cell 18 can also receive electrical energy which charges it. The complete discharge of a cell followed by its complete recharge constitutes what is called a charge / discharge cycle, which is simply called a "cycle of a cell". The cell 18 is a known type of cell, for example, it is a LiPB cell (Lithium-ion Polymer Battery) or other. The cell 18 is characterized by an initial nominal capacity Cn'n ', an initial internal resistance RO' ', a current intensity Imax current, a maximum voltage Umax, a minimum voltage Um, n and a function OCV (SOCk ). The capacity Cn'n 'is the initial capacity of the cell 18. The capacity of a cell represents the maximum amount of electrical energy that can be stored in that cell. This capacity is expressed in Ah. As the cell 18 ages, i.e. as the number of charge and discharge cycles increases, the capacity of the cell decreases. At the moment k, the nominal capacity of the cell 18 is noted Cn, k thereafter. The initial internal resistance RO '' is the value of the internal resistance of the cell 18 before it begins to age. The internal resistance of a cell is a physical quantity found in most electrical models of an electric cell. As the cell ages, typically, the internal resistance increases. At time k, the internal resistance of cell 18 is denoted ROk. Imax is the maximum intensity of the current that can be delivered by the cell 18 without damaging it. Umax is the maximum voltage that can be permanently present between the terminals 30 and 32 of the cell without damaging it. The voltage Uni is the minimum voltage between the terminals 30 and 32 when the cell 18 is completely discharged. Subsequently, lm., Umax, Um, n are considered constant physical quantities that do not change over time. OCV (SOCk) is a predetermined function which returns the empty voltage of the cell 18 according to its state of charge SOCk. The no-load voltage is the measurable voltage between terminals 30 and 32 after the cell 18 has been electrically isolated from any electrical load for two hours. The state of charge at the instant k of the cell 18 is SOCk noted. The state of charge represents the filling rate of cell 18. It is equal to 100% when the amount of electrical energy stored in cell 18 is equal to its capacity Cn, k. It is equal to 0% when the amount of energy stored in the cell 18 is zero, that is to say that it can no longer be extracted from the electrical energy of the cell 18 to supply an electric charge. The parameters Cnn, ROn, Imax, U., Umin and OCV function (SOCk) are known parameters of the cell. For example, they are given by the constructor of the cell or are determined experimentally from measurements made on this cell. The battery 10 also comprises for each cell: - a voltmeter which measures the voltage between the terminals of this cell, and - an ammeter which measures the intensity of the charging or discharging current of this cell. To simplify Figure 1, only a voltmeter 34 and an ammeter 36 of the cell 18 have been shown. In contrast to the various parameters of the cell 18 introduced previously, the state of SOCk charge of the cell 18 is not measurable. It must therefore be estimated. For this purpose, the vehicle 2 comprises a battery management system 40, better known by the acronym BMS ("Battery Management System"). This system 40 has the particular function of determining the state of charge of the battery 10 as well as the state of health of this battery. To determine this state of charge and this state of health, the system 40 is able to estimate the state of charge and the state of health of each cell of the battery 10. The state of health of a cell represents the state of progress of the aging of this cell. Here, the state of health of a cell, at instant k, is denoted SOHk. Subsequently, it is measured by the ratio Cn, k / Cn'n '. To calculate the state of health of a cell, the system 40 is thus also capable of estimating the capacity Cn, k of this cell at the current instant k. To achieve these different estimates, the system 40 is electrically connected to each voltmeter and each ammeter of the battery 10 to acquire the measurements of the voltage and the intensity of the current between the terminals of each cell. Here, the system 40 comprises a memory 42 and a programmable electronic calculator 44, able to execute instructions stored in the memory 42. For this purpose, the memory 42 includes the instructions necessary for carrying out the methods of the figures. 10 to 12 and / or Figure 17. This memory 42 also includes the initial values of the various parameters necessary for the execution of these methods. The structure of the system 40 is therefore identical or similar to those of known battery management systems and is not described in more detail. Figure 2 shows an electric model 50 of the cell 18. This model is known by the term "Thevenin model of the first order" or "Lumped parameter model". It comprises successively connected in series from terminal 32 to terminal 30: a generator 52 of the empty voltage OCV (SOCk), a parallel circuit 54, and an internal resistor 56 called by the next, at the instant k, "internal resistance ROk". The circuit 54 has a capacitor CD connected in parallel with a resistance RD value. Subsequently, it is considered that these two parameters CD and RD of the model 50 are known and constant over time. The voltage at instant k across the circuit 54 is denoted VD, k. The value at the instant k of the voltage between the terminals 30 and 32 of the cell 18 is denoted yk and the intensity, at the same instant, of the charging or discharging current of the cell 18 is denoted ik. FIG. 3 represents a first embodiment of an estimator arrangement implemented in the system 40 for estimating the state of charge and the state of health of the cell 18. Each estimator is implemented in the form of an estimation algorithm executed by the calculator. Thus, we will not talk about the sequence of "execution of an estimator" as "execution of an estimation algorithm". In this first embodiment, the system 40 comprises an estimator 60 of the charge state SOCk and the voltage VD, k from the measured value yk of the voltage and the measured intensity ik. The estimator 60 is here implemented in the form of a Kalman filter. It therefore uses a state model 62 (FIG. 4) and an observation model 64 (FIG. 5). In these figures 4 and 5, the equations of these models are represented using the previously defined notations. Notations R0k2 and Cn, k3 represent, respectively, the capacity and internal resistance of cell 18, respectively, at times k2 and k3. These moments k2 and k3 are defined later. Moreover, in the model 62, xk designates the state vector [SOCk, vp, k] r at time k. In this description, the symbol "T" designates the transposed mathematical operation. The multiplication operation is represented by the operator ". " or " * ". Subsequently, it is considered that the origin of the time corresponds to the zero value of the instant k. Under these conditions, the current instant k is equal to kTe, where T is the sampling period of the ammeter and voltmeter measurements of the battery 10. Thus, Te is the period of time that separates any two successive instants k and k-1 acquisition of the voltage and current intensity by the system 40. The period Te is typically a constant between 0.1 s and 10 s. Here, the period Te is equal to 1 s to plus or minus 20%. For example, Te is equal to one second. In the model 62, wk is a state noise vector. Here, the noise wk is a Gaussian white noise centered. This noise represents the uncertainty on the model used. The covariance matrix, at the instant k, of the noise wk is denoted by Qk It is defined by the following relation: Qk = E (Wk * WkT), where E (...) is the expected expectation function. Model 62 is also written as Xk + 1 = FkXk Bkik Wk, where - Fk is the state transition matrix at time k, - Bk is the control vector at time k. The model 62 allows in particular to predict the state of charge SOCk + i at time k + 1 from the state of charge preceding SOCk. The model 64 can predict the value yk of the voltage at time k from SOCk charge state, the voltage VD, k and the measured intensity ik. In this model, vk is a centered Gaussian white measurement noise. The covariance matrix of the noise vk at time k is noted Rk thereafter. In the particular case described here, this matrix Rk is a single-column, single-row matrix. It is defined by the relation Rk = E (Vk * VkT). This noise vk is independent of the noise wk and the initial state vector xo. It will be noted that the model 64 is non-linear, since the OCV function (SOCk) is generally nonlinear. Because of this, the estimator 60 implements the extended version of the Kalman filter, better known by the acronym EKF (Extended Kalman Filter). In this extended version, we return to a linear observation model of the form yk = Hkxk + ROk2.ik + vk by linearizing the model 64 in the vicinity of the vector xk. Typically, the model 64 is developed in Taylor series in the vicinity of the vector xk. Then we neglect the contributions of derivatives from the second order. Here, the matrix H k is therefore equal to the first derivative of the OCV function in the vicinity of the state of charge SOCk. This linearization of the model 64 is typically performed for each new value of the state of charge SOC k. The estimator 60 needs to know the capacitance C n, k3 and the internal resistance R0k2 to be able to estimate the state of charge SOCk.i. The capacity and internal resistance of cell 18 vary as it ages. To account for this aging, the capacity and the internal resistance of the cell 18 are estimated, respectively, at times k3 and k2. Here, an estimator 66 estimates the internal resistance R0k2 from the measured value yk2, the measured intensity ik2 and the state of charge SOCk2. Another estimator 68 estimates the capacitance C n, k3 from the intensity i k3 and the state of charge SOCk3. The internal resistance and the capacity of the cell 18 vary more slowly than its state of charge. Thus, in order to limit the computing power required to estimate the state of charge of the cell without degrading the accuracy of this estimate, the estimators 66 and 68 are executed less frequently than the estimator 60. Thereafter, the Execution times of the estimators 66 and 68 are noted, respectively, k2 and k3 to distinguish them from the instants k. Here, the set of instants k2 and the set of instants k3 are subsets of the set of instants k. Between two successive instants k2 and k2-1 and between two successive instants k3 and k3-1, several periods Te and several instants k elapse. These estimators 66 and 68 are also implemented each in the form of a Kalman filter. The estimator 66 uses a state model 70 (FIG. 6) and an observation model 72 (FIG. 7). In these models, the noises 2 w -, k2 and v 2, k2 are Gaussian white noise centered. The covariances of the noise W - 2, k2 and V2, k2 are noted, respectively, 0 -2, k2 and R2, k2 thereafter. The observation model 72 makes it possible to predict the value of a directly measurable physical quantity uk2. The physical quantity uk2 is here the sum of the last N measured values yk. It is defined by the following relation: N is an integer strictly greater than one which is counted as will be described later. In the relation above and in the model 72, the instant k is equal to the instant k2. The model 72 takes into account not only the charge state SOCk, the voltage VD, k and the measured intensity ik at the instant k = k2 but also the previous N estimates of the estimator 60. and of the preceding N measured intensities, between the times k2 and k2-1. Taking into account the measurements and intermediate estimates between the instants k2 and k2-1, makes it possible to increase the precision of the estimation of the internal resistance R0k2. The estimator 68 uses a state model 74 (FIG. 8) and an observation model 76. In the models 74 and 76, the noises W3, k3 and V3, k3 are centered Gaussian white noises. The covariances of the noises W3, k3 and V3, k3 are noted respectively Q3, k3 and R3, k3 thereafter. It will be appreciated that the model 76 is a linear model so that a simple Kalman filter can be used for the estimator 68 instead of an extended Kalman filter. The observation model 76 makes it possible to estimate a directly measurable physical quantity zk3. The physical quantity zk3 is here the sum of the N last measured intensities ik. It is defined by the following relation: = k- I re P'02 = k - N [0051] In the above relation and in the model 76, the instant k is equal to the instant k3. This physical quantity zk3 takes into account not only the measured intensity ik-1 at the instant k-1 preceding the instant k3 but also of N preceding intensities measured between the instants k3 and k3-1. Here, N is an integer strictly greater than one which is counted as will be described later. It is not necessarily equal to the 25 N introduced in the model 72. The fact of taking into account measurements and intermediate estimates between the instants k3 and k3-1, makes it possible to increase the precision of the estimate of the capacity Cn. , k 3. The operation of the estimators 60, 66 and 68 will now be described using the method of FIG. 10 and in the particular case of estimating the state of charge of the cell 18. [0053] The method starts with a phase 100 of adjusting the different covariance matrices necessary to execute the estimators 60, 66 and 68. More specifically, during an operation 102, the covariance matrices Q k and Rk of the estimator 60 are automatically adjusted by the following relations: Qk = [NoGo, k (No)] - 1 and Rk = I, where - No is a predetermined integer strictly greater than 1, - I is the identity matrix, and - Go, k (No) is defined by the following relation: (Fk) [0054] No is generally chosen during the design of the system 40 and then fixed once and for all. Generally, No is less than 100. For example, No is between 5 and 15. Here, No is chosen equal to 10. Using the above relationships greatly simplifies the setting of matrices Qo and Ro as well as the setting of matrices Qk and Rk as we shall see later. Indeed, the only parameter to choose is the value of the integer No. [0056] During an operation 104, the covariances Q2,0 and R2,0 are also set. For example, Q2,0 is chosen equal to [(13 * ROini) / (3 * NCeoms)] 2, where - [3 is a chosen constant greater than or equal to 0.3 or 0.5 and, preferably, greater than at 0.8 and generally less than three, - Nced is the expected number of cycles of charge and discharge of cell 18 before it reaches its end of life, and - Ns is the number of times the internal resistance is estimated per charge and discharge cycle of the cell18. The constant [3 represents, expressed as a percentage divided by 100, the difference between the value of the initial internal resistance RO '' and its end-of-life value. Typically, [3 is set by the user or measured experimentally. No is a number of cycles that can be measured experimentally or obtained from the manufacturer's data of cell 18. Ns is set by the state of charge estimation method implemented by calculator 44. In this embodiment, as will be seen below, the internal resistance is estimated only once per cycle. Therefore, Ns is set equal to 1. By way of illustration, the covariance R2.0 is chosen to be equal to (2cniUmax / 300) 2, where Cm 30 is the maximum error of the voltmeter 34 expressed as a percentage. Subsequently, the covariances 0 -2, k2 and R2, k2 are considered to be constant and equal, respectively, to Q2, o and R2,0. In an operation 106, the covariances Q3,0 and R3,0 are set. For example, the covariance Q3.0 is taken as [y * Cn1f '/ (3 * Nced * Ns)] 2, where y is, expressed as a percentage divided by 100, the difference between the capacity Cn'n' and the capacity of the cell 18 at the end of its life. y is a constant selected by the user between 0.05 and 0.8 and preferably between 0.05 and 0.3. Here, y = 0.2. The covariance R3.0 is for example chosen to be equal to [2 * E, m * Imax / 300] 2, where En is the maximum error of the ammeter 36 expressed as a percentage. Subsequently, the covariances Q3, k3 and R3, k3 are considered to be constant and taken equal, respectively, to Q3,0 and R3,0. Once the covariance matrices are set, the estimation of the state of charge of the cell 18 can begin. During a phase 110, at each instant k, the voltmeter 34 and the ammeter 36 measure, respectively, the value yK and the intensity ik and these measurements are immediately acquired by the system 40 and stored in the memory 42. Phase 110 is reiterated at each moment k. In parallel, the estimator 60 executes a phase 114 for estimating the state of charge at the instant k of the cell 18. For this, during a step 116, the estimator 60 calculates a SOCkik-i prediction and a prediction VD, k / ki of, respectively, the state of charge of the cell 18 and the voltage VD across the circuit 54 at the instant k. In the notations used here, the index k / k-1 indicates that this prediction is made taking into account only the measurements made between the instants 0 and k-1. This is called prediction a priori. The index kik indicates that the prediction at the instant k takes into account all the measurements made between the instants 0 and k. This is called a posteriori prediction.
    [0002] The predictions SOCkik_i and VD, k / k-1 are calculated using the model 62, the measured intensity ik-1 and the capacity Cn, k3. It will be noted that in the model 62, the state transition matrix F k-i is constant regardless of k and therefore does not need to be re-evaluated at each instant k. In a step 117, the estimator 60 also calculates the prediction P k / k-i of an estimation error covariance matrix on the state vector xk. Typically, this is done using the following relationship: Pk / k-1 = Fk-1Pk-1 / k-1Fk-1T + Qk-1 [0068] These different matrices Fk-1, Pk-1 / k -1 and Qk-1 have already been defined previously. Then, in a step 118, the estimator 60 constructs the matrix H k by linearizing the model 64 around predictions SOCkik_i and VD, k / k-1. In a step 120, the covariance matrices Q k and Rk are automatically updated. Here, for this, the step 120 is identical to the operation 102 taking into account this time the matrix H k constructed during the step 118. [0071] After this, during a step 122, the estimator 60 corrects the predictions SOCkik-i and VD, k / k-1 as a function of a difference between the measured value yk and a value j / k predicted from the model 64. This difference is known as "Innovation". This step 122 typically comprises: an operation 124 for calculating the prediction j / k, then an operation 126 for correcting the predictions SOCkik-i and VD, k / k-1 and the matrix Pk / k-1 for obtain the corrected predictions SÔCk / k, VD, k / k and Pk / k. In the operation 124, the prediction S / k is calculated using the model 64 in which the value of the state of charge is taken equal to SOCkik_i and the value of the voltage VD, k is taken equal to VD, k / ki. The difference between the measured value yk and its prediction j / k is noted Ek thereafter. Many methods exist to correct the estimates a priori SOCkik-i and VD, k / k-1 from the innovation E k. For example, in step 126, these estimates are corrected using the Kalman Kk gain. The gain Kk is given by the following relation Kk = Pkik-1Erk (HkPkik-1Wk + Rk) -1. Then, the predictions a priori are corrected with the following relation: xkik = KkEk. The matrix Pk / ki is corrected using the following relation: Pk / k = Pk / k-1 -KkF1 kPk / k-1 - [0075] Steps 116 to 122 are repeated at each instant k where new estimates of the state of charge of the cell 18 must be made. During each new iteration, the state vector xk_i is initialized with the values obtained during the previous iteration of the phase 114 for the cell 18. In parallel, during a step 130, the computer 44 compares each new measurement of the intensity ik at a threshold SH, of predetermined current. As long as the measured intensity does not exceed this threshold SH 'the execution of the estimator 66 and inhibited. On the other hand, as soon as the measured intensity ik exceeds this threshold SH 'then the estimator 66 is immediately executed. The threshold SH, is generally greater than I max / 2 and advantageously greater than 0.81. or 0.91max. The estimator 66 executes a phase 140 for estimating the internal resistance R0k2 at the instant k2. Here, the instant k2 is equal to the instant k where the intensity ik crosses the threshold SH ,. For this, during a step 142, the estimator 66 calculates the a priori prediction RO of the internal resistance from the model 70. [0079] Then, during a step 144, the estimator 66 calculates the prediction P - 2, k2 / k2-1 of the covariance matrix of the estimation error on the internal resistance. For example, this prediction is calculated using the following relationship: P - 2, k2 / k2-1 = P2, k2 - 1 / k2-1 ± Q2.0. It will be noted that here, the model 72 is a linear function of the state variable. It is therefore not necessary to linearize it in the vicinity of the prediction R k2 / k2-1 to obtain the matrix H2, k2. Here, this matrix H2, k2 is equal to -N. In a step 148, the estimator 66 corrects the prediction RO k2 / k2-1 as a function of the difference between the measured physical quantity uk2 and a prediction Ûk2 of this same physical quantity. Here, N is a predetermined constant selected strictly at one and, preferably, greater than 10 or 30. The magnitude uk2 is acquired by the estimator 66 as the values yk are measured and acquired. More specifically, during an operation 150, the computer 44 acquires the measured quantity uk2 and calculates the prediction Û k2. The acquisition of the size uk2 is performed by summing the last N measures of the measured value yk. The prediction Ûk2 is calculated using the model 72. In this model 72, the value RO k2 is taken equal to the value ROk2 / k2_1 previously calculated. Then, in an operation 152, the estimator 66 corrects the prediction R0 k2 / k2-1 as a function of the innovation E k2. The innovation E k2 is equal to the difference between the measured quantity uk2 and the predicted quantity Ûk2. For example, during the operation 152, the same method as that implemented during the operation 126 is used. Thus, this operation 152 is not described here in more detail. Then, the new estimate R 0 k2 / k2 is used in subsequent executions of the estimator 60 in place of the previous estimate R0k2-1 / k2-1. Triggering the execution of the estimator 66 only when the measured intensity ik is high makes it possible to increase the accuracy of the estimate of the internal resistance while at the same time decreasing the computing power required. to implement this method. Indeed, the accuracy of the ammeter measurement is higher when the intensity ik is higher. Also in parallel with the phases 110 and 114, the method comprises a step 160 in which at each instant k, the estimate SOCk is compared with a predetermined high threshold SHs'. If the estimate SOCk falls below this threshold SH0, then the process is continued immediately by steps 162 and 164. In the opposite case, step 160 is repeated at the next instant k. Typically, the threshold S Flsoc is between 90% and 100%. In step 162, the computer 44 starts by initializing a counter to zero and then increments it by 1 to each new measure of the intensity ik since the beginning of this step. Moreover, at each instant k, the measured intensity ik and the estimate SOCk generated at the same time are recorded, associated with this instant k, in a database. In parallel with step 162, during step 164, the calculator 44 compares each new estimate SOCk with a predetermined threshold SLs'. The threshold SLs' is for example between 0% and 10%. As long as the estimate SOCk remains above this threshold SLs', step 162 is repeated at the instant k following. In the opposite case, as soon as the estimate SOCk for the cell 18 falls below this threshold SLs' then, the computer 44 immediately triggers the execution of the estimator 68 and stops incrementing the counter. Thus, as long as this threshold SLs' is not crossed, the execution of the estimator 68 is inhibited. The estimator 68 estimates, during a phase 166, capacity C n, k3 at time k3. The instant k3 is therefore equal to the instant k when the execution of the estimator 68 is triggered. As for phase 140, since the estimator 68 is not executed at each instant k, the instant k3-1 does not correspond to the instant k-1. In contrast, the instants k3 and k3-1 are separated by a time interval greater than or equal to NT, where N is the number counted in step 162. The parameters of the Kalman filter of the estimator 68 are initialized with the previous 40 values of these parameters obtained at the end of the previous iteration at time k3-1 of phase 166. [0090] Phase 166 comprises: 302 92 99 16 - calculation, when a step 170, of the prediction Cn, k3 / k3-1 using the model 74, the calculation, during a step 172, of the prediction P-3, k3 / k3-1 of the covariance matrix the error of estimation of the capacity, and the correction, during a step 174, of the predictions Cn, k3 / k3-1 and P-3, k3 / k3-1. [0091] During the steps 172 and 174, the observability matrix 113, k3 is equal to [(SOCk - SOCk_N)] * 3600 / (NTe). Here is the number of times k elapsed between the moment when the estimated state of charge has dropped below the threshold SHs 'and the moment when the estimated state of charge has dropped below the threshold SLs'. The value N is equal to the value counted in step 162. [0092] Step 174 comprises an operation 176 for acquiring the measured physical quantity zk3 and calculating the prediction 2k3 of the quantity zk3. The acquisition of the magnitude zk3 here consists in calculating the sum of the N last intensities measured between the instants k-1 and k-N. The prediction 2k3 is obtained from the model 76. [0093] Then, during an operation 178, the estimator 68 corrects the prediction C n, k3 / k3-1 as a function of the difference between the measured quantity zk3 and the predicted magnitude k3 to obtain the posterior estimate of the capacity C n, k3 / k3. This correction is for example carried out as described in step 126. Next, the capacity Cn, k3 / k3 is transmitted to the estimator 60 which uses it to estimate the state of charge of the cell 18. at the following moments. [0095] Triggering the execution of the estimator 68 only after the cell 18 has been largely discharged makes it possible to increase the precision of the estimate while at the same time decreasing the computing power necessary to this process. At the end of phase 166, during a step 180, the calculator calculates the state of health SOHk3 at time k3 using the following formula: SOHk3 = Cn, k3 / Cn ' not'. FIG. 11 represents a method for determining the state of charge of the battery 10. At instant k, the state of charge of the battery 10 is determined from the state of charge of each of the cells of this battery. For example, this is done as follows. During a step 190, the computer 44 determines the state of charge of each stage of the battery by adding the state of charge of each cell of this stage. Then, during a step 192, the state of charge of the battery is taken equal to the smallest of the states of charge of a given stage during step 190. As illustrated by FIG. In the method of FIG. 11, determining the charge state of the battery at each instant k requires having an estimation of the state of charge for each of the cells at time k. A first solution therefore consists in executing in parallel, for each of the cells, the estimation method of FIG. 10 by executing at each instant k phase 114. However, in order to limit the computing power required, without degrading the accuracy of FIG. determined state of charge for the battery, it is also possible to plan the execution of the estimations of the states of charge of the cells as described with reference to the method of FIG. 12. The method of FIG. 12 is described in FIG. Simplified case where only three levels of priority are used, called high level, medium level and low priority level respectively. Moreover, it is assumed that the state of charge of a cell whose priority level is high must be estimated at each instant k and therefore at a frequency fe. The state of charge of a cell whose priority level is average must only be estimated with a frequency three times lower and therefore at a frequency fe / 3. Finally, the state of charge of the cells of low priority level must be estimated at a frequency ten times lower is therefore at a frequency fe / 10. In this example, for the high and medium priority levels, there are a limited number of known places in advance. In other words, the number of cells assigned to the high priority level is limited to a predetermined maximum number in advance. The same is true for the number of cells assigned to the medium priority level. To plan the times at which the estimates of the state of charge of each of the cells must be refreshed, the computer first assigns, in a step 198, a priority level to each cell. Step 198 begins with an operation 200 in which the system 40 acquires the measured value yk of the voltage between the terminals of each of the cells. Then, during an operation 202, if the measured value yk is above a high threshold SHy or, conversely, below a low threshold SLy, then the computer 44 assigns to this cell the high priority as long as there is enough room in this level. The threshold SHy is greater than or equal to 0.9 * Umax and, preferably, greater than 0.95 * Umax. The threshold SLy is greater than or equal to Umni, and less than 1.1 * Um ,, 'or 1.05 * Umni. It is important to frequently refresh the estimation of the state of charge of cells whose voltage is close to Umax or on the contrary close to Umm. Indeed, an error in the estimation of the state of charge of a cell in such a situation can lead to a degradation of the electrical and mechanical properties of this cell. Then, for the other cells, during an operation 204, the computer 44 calculates the voltage difference between the current measured value yk and a previous value yk_x, where X is a predetermined integer greater than or equal to one and generally, less than 5 or 10. Here, X = 1. [00105] In an operation 205, the calculator 44 identifies twin cells. Cells are considered to be "binocular" if, at the same instant k, they have the same voltage difference and the same measured value yk. For this, during step 205, the computer 44 compares the voltage difference and the measured value yk for a cell at the voltage differences and at the measured values yk for the other cells at the same instant in order to identify, among these other cells, the cell or cells of this cell. The identifier of this cell and the identifiers of the cell or cells identified as being its binoculars are then grouped together in a set that is stored in the memory 42. The above comparison is for example made for each of the cells of the battery 10. whose identifier has not already been incorporated into one of the registered twin cell sets. Subsequently, a priority level is assigned to only one of the cells in each set of twin cells.
    [0003] Thus, step 206 and subsequent steps 208 and 210 are performed only for cells that do not have a twin cell and for a single cell in each set of twin cells. In an operation 206, the computer classifies the cells in decreasing order of absolute value of the difference calculated during the operation 204. Then, it assigns 10 to the first cells of this classification the remaining places associated with high priority levels. Then, it assigns the remaining places associated with an average priority level to the next cells in that ranking. Finally, it assigns to the last cells of this classification the low priority level. [00107] Once a priority level has been assigned to each cell, in a step 208, the computer 44 schedules the refresh instants of the estimates of the states of charge of the cells as a function of their priority level. Step 208 is performed so as to respect the refresh frequency of the estimates associated with each of the priority levels. For this purpose, for example, the computer 44 first reserves the times at which the estimates of the high priority level cells are to be refreshed. Then, it reserves the moments at which the estimates of the state of charge of the cells of average priority level must be refreshed taking into account the times of refreshment already reserved. Finally, it does the same with cells assigned a low priority level. [00108] To illustrate this, it is assumed that a high priority level has been assigned to cell 18, that a medium priority level has been assigned to cells 19 and 20 and that a low priority level has been assigned. assigned to the cell 21. In addition, it is imposed that during a period T ,, the computer executes at most twice the phase 114 of the method of FIG. 10. The result obtained with these hypotheses is represented in FIG. figure, the instants k to k + 11 have been represented on the abscissa. Above each of these instants k, two boxes symbolize the fact that the computer 44 can, at each instant k, execute twice the phase 114 of the method of FIG. 10. In each of these boxes, the number of the cell for which phase 114 is executed. When no number is in this box, this means that the method of FIG. 10 is not executed and therefore the saved computing power can be used for other purposes such as, for example, the execution of the estimators. Finally, in a step 210, for each cell assigned a priority level, the computer 44 executes the phase 114 at the scheduled time for this cell. Outside these planned times, the computer inhibits the complete execution of the phase 114 for this cell. Similarly, the execution of phase 114 for twin cells to which no priority level has been assigned is also inhibited. In parallel, during a step 212, for each twin cell to which no priority level has been assigned, the estimate of the state of charge of this cell is taken equal to the last estimate calculated during of step 210 for a twin cell of this cell. Thus, phase 114 is executed for only one of the twin cells. This reduces the computing power required to determine the state of charge of battery without degrading the accuracy of this determination. Optionally, in parallel with step 210, at each instant k, the computer 44 also executes a step 214 for predicting the state of charge for each of the cells that are not processed during step 210 to this moment k. Step 214 consists in executing only the prediction step 116, without performing the correction step 122, for all the cells for which, at the same time, the complete estimation phase 114 is not executed. Indeed, the prediction step 116 is much less computationally intensive than the step 122 and can therefore be executed, for example, at each instant k. Thus, when step 214 is implemented, there is at each moment k of a new estimation of the state of charge for each of the cells of the battery. Steps 198 and 208 are repeated at regular intervals to update the priority level assigned to each of these cells and therefore the refreshing frequencies of the estimate of the state of charge of these cells. This method for scheduling refresh times of the estimates of the charge states of the cells makes it possible to limit the computing power required without degrading the accuracy of the state of charge determined for the battery. Indeed, the method of Figure 12 exploits the fact that the cells whose voltage differences are low are cells that discharge or load little and therefore whose state of charge does not change quickly. It is therefore possible to estimate the state of charge of these cells at a lower frequency without degrading the accuracy of the state of charge determined for the battery. During the execution of the methods of FIGS. 10 and 11, whenever the state of charge SOCk of a cell at a given instant must be used for a calculation, the state of charge SOCk is taken. equal to the last estimated state of charge or predicted for this cell. In other words, it is considered that the state of charge remains constant between two successive instants where it is estimated or predicted. It will also be noted that each time the computer 44 executes the estimation phase 114 for a cell, it retrieves the information necessary for this execution from the values obtained at the end of the previous execution of this phase. for the same cell. This is the case, for example, for state variables. Note, however, that the previous execution time is not necessarily the time k-1 but may be the time k-3 or k-10 depending on the priority level assigned to this cell. [00115] Many other embodiments of the method for estimating the state of charge of a cell are possible. For example, Fig. 14 shows another arrangement of estimators. This other arrangement is identical to that of FIG. 3 except that the estimators 66 and 68 are replaced by a single estimator 230. The estimator 230 simultaneously estimates the capacity and the internal resistance of the cell 18. The estimator 230 is executed less It is frequently the estimator 60. Here, we write k4 the execution times of the estimator 230 and hence C, -, k4 and ROka the capacity and the estimated internal resistance at time k4. The set of instants k4 is a subset of instants k. The estimator 230 estimates at the same time the capacity Cn, k4 and the internal resistance ROka. This estimator 230 implements a Kalman filter that uses a state model 232 (FIG. 15) and an observation model 234 (FIG. 16). The operation of this estimator 230 will now be described with reference to the method of FIG. 17 and in the particular case of the cell 18. This method of FIG. 17 is identical to the preceding one of FIG. 10 except that the steps 130 to 174 are replaced by steps 240, 242, 244 and a phase 246 for estimating the capacitance and the internal resistance. In step 240, the computer 44 compares at each instant k the measured value yk to a high threshold SHy2. Typically, this threshold SHy2 is greater than or equal to 0.8 * Umax or 0.9 * Umax. Steps 242 and 244 are executed only if measured value yk falls below this threshold SHy2. In step 242, the computer 44 starts by initializing a counter to zero and then increments this counter by 1 at each new instant k. Moreover, at each of these instants k, the measured intensity ik, the value yk, the state of charge SOCk and the estimated voltage VD, k are recorded, associated at this instant k, in a database. [00120] In parallel with step 242, during step 244, the computer 44 compares, at each instant k, the new measured value yk with a low voltage threshold SLy2. This threshold SLy2 is less than or equal to 1.2 * Um ,, or 1.1 * Um, n and greater than or equal to Unn. As soon as the measured value yk goes below the threshold SLy2, the incrementation of the counter during step 242 is stopped and the execution of the estimator 230 is triggered. On the other hand, as long as the measured value yk remains above this threshold SLy2, the execution of the estimator 230 is inhibited. The estimator 230 executes the phase 246. As previously, it is noted that the instants ka and k4-1 are separated by a time interval greater than or equal to NTe, where N is the value of the counter incremented during the Step 242. The operation of the estimator 230 is deduced from the operation described above for the estimators 66 and 68. It will therefore not be described here in more detail. Other electrical models and therefore other state models can be used to estimate the state of charge of the cell 18. For example, in a simplified variant 40, the circuit 54 is omitted. Conversely, a more complex electric model may contain several parallel RC circuits electrically connected in series with each other. The state model of the cell 18 must then be modified accordingly to correspond to this new electrical model of the cell. However, everything described above applies without difficulty with such a modified state model. For examples of modified state models, the reader may refer to application WO2006057468. [00124] The RD and CD parameters of the model 50 can also be estimated instead of being considered as predetermined constant parameters. For this purpose, these two parameters RD and CD are for example introduced into the state vector xk which then becomes [SOCk, VD, k, RD, k and CD, k] T. For example, the state model is modified to incorporate the two following RD equations, k + 1 = RD, k and CD, k + 1 = CD, k. The state vector xk may also be supplemented by the temperature of the cell so as to estimate this temperature at the same time as the state of charge of this cell. The cell may also be equipped with additional sensors such as a temperature sensor. In this case, the observation model is modified to take into account these additional measured physical quantities. The reader can refer to application WO2006057468 for examples of modified observation models. [00127] Other possible electric models for modeling the electric cell are also presented in Part 2 of Plett 2004, Chapter 3.3. The continuous automatic adjustment of the covariance matrices Rk and Qk can be performed differently. For example, the so-called "Covariance Matching" method described in the following article can be applied: Mehra, RK: "On the identification of variances and adaptive Kalman Filtering", Automatic Control, IEEE Transaction on, Volume 15, No. 2 , pages 175-184, April 1970. This method applies after an initial setting of the matrices Ro and Qo, for example, as described during the operation 102. [00129] In another variant, the matrices Qo, Ro, Qk and Rk are not set as described with reference to operations 102 and 120. For example, these matrices are set using a conventional method. In a simplified case, they are constant. For example, the matrix Ro is then set from the data provided by the sensor manufacturer or from tests performed on these sensors and the matrix Qo by successive tests. The step 122 for correcting the prediction can be performed differently. For example, in a preferred method, the correction of the prediction of the state of charge and the voltage VD, k, is achieved by minimizing a quadratic cost function J which has two terms: a term related to prediction error of the measured value, and - another term related to the estimation error of the state vector. This method is described in detail in Chapter 10.5.2 of the following book: Y. Bar-40 Shalom, et al .: "Estimation With Applications to Tracking and Navigation, Theory Algorithms and Software", Wiley Inter-science, 2001. [00131 In another variant, the estimator 60 is not implemented in the form of a Kalman filter. For example, the state of charge is estimated by modeling its evolution over time in the form of an Infinite Impulse Response (IRN) filter whose coefficients are estimated by the Recursive Least Square (RLS) method. Other state models can be used to estimate the internal resistance and the capacity of the cell. For example, the model 232 may be replaced by a model 250, shown in FIG. 18. In the model 250, a, 13 and y are constants whose values are obtained from data of the cell constructor or measured experimentally. . Typically: - a is equal to 1 to plus or minus 30% or 10%, -13 is also 1 to plus or minus 30% or 10%, and - y is typically between 0.1 and 0, 5. For example, y is 0.2 to plus or minus 30% or 10%. In the model 250, Nck is equal to the number of charge / discharge cycles of the cell performed before the instant k. This number of cycles is for example measured by counting the number of times the state of charge of the cell falls below the high threshold SHs 'and then below the low threshold SLs'. wad, k is a centered Gaussian white noise. y is the difference, expressed as a percentage divided by 100, between the initial capacity Cn '' of the cell and its end-of-life capacity. This model takes into account the fact that: - the internal resistance increases as the cell ages, and - the capacity of the cell decreases as the cell ages. [00134] Similarly, the state model 70 can be replaced by the following state model: R0k2 + 1 = (a + 13Nck2 / N) R0 W CEOL, - - k2 + - 2, k2, where the different symbols of this model have already been previously defined. The state model 74 can be replaced by the following state model: Cn, k3 + 1 = (1 -YNCk3 / NCEOL) Cn, k3 ± V3, k3 where the different symbols of this model have already been previously defined. According to the observation model used by the estimator 68, the magnitude zk3 can be calculated differently. For example, the magnitude zk3 is equal to the sum of the N last intensities measured between the instants k and k-N + 1. In this case, when N is 1, zk3 = 1k3. What has been previously described for the initialization of the covariance matrices Qk and Rk can also be applied for the initialization of the covariance matrices of the estimators 68 and 230. [00138] As a variant, the estimator 68 n is not implemented as a Kalman filter. For example, the capacity is estimated by modeling its evolution over time in the form of an Infinite Impulse Response (IRR) filter whose 40 coefficients are estimated by the Recursive Least Square (RLS) method. The methods of FIGS. 10 and 17 can be simplified by taking N equal to a predetermined constant. In this case, N is not counted and steps 160, 162, 240, 240 and 242 may be omitted. For example, N is chosen equal to one or, conversely, strictly greater than 1 or 5 or 10. In another variant, at each instant k between instants k3 and k3-1, only step 170 of calculation of a prediction Cn, k is executed but the correction step 174 of this prediction is not executed. Thus, we obtain a new prediction of the capacity of the cell at each of these instants k while limiting the computing power required. Similarly, at each instant k between the instants k4 and k4-1, only the step of calculating the predictions of the capacitance and the internal resistance is executed without performing the step of correcting these predictions. Thus, in these variants, the capacity of the cell is predicted at each instant k but this prediction is corrected only at times k3 or k4. The algorithm for estimating this capacity is therefore only partially executed between the instants k3 and k3-1 or k4 and k4-1 and completely executed only at the instant k3 or k4. At each instant k between the instants k3 and k3-1 or between the instants k4 and k4-1, the capacity can be estimated by executing a first algorithm, and then at the instant k3 or k4, the capacity is estimated by executing a second algorithm different from the first algorithm and requiring a larger computing power. The first and second algorithms do not necessarily correspond, as previously described, respectively to step 170 and phase 166 or 246 of a Kalman filter. It can also be two completely different estimation algorithms. The step 166 or 246 for estimating the capacity of the cell can be triggered in response to crossing a threshold on the state of charge, as described with reference to FIG. 10, or in response to the crossing. A threshold on the voltage as described with reference to FIG. 17. These steps 166 and 246 can also be triggered in response to crossing a current amount threshold. For this, from the moment when the voltage or the state of charge of the cell has fallen below a predetermined high threshold, at each instant k, the calculator 44 calculates the quantity QCk of the current delivered with the aid of the following relationship: QCk = QCk_i + kTe. As soon as QCk exceeds a high threshold SHQ, then phase 166 or 246 is executed. On the other hand, as long as the quantity QCk remains greater than the threshold SHQ, the execution of the phases 166 or 246 is inhibited. Alternatively, the quantity QCk can also be calculated on a sliding window containing the last N instants k, where N is a predetermined constant. In another embodiment, the triggering of the estimates of the capacitance and / or of the internal resistance in response to the crossing of a threshold is omitted. For example, these estimates are triggered at regular intervals. This regular interval is equal to Te if the available computing power is sufficient to estimate this capacity and this internal resistance at each instant k. [00144] Many other embodiments of the method of FIG. 12 are possible. For example, operation 205 may be omitted. In this case, no twin cell is identified and step 212 is also omitted. Operation 202 may be performed differently. For example, only one of the high and low thresholds is used. Operation 202 can also be omitted. [00146] The number of priority levels may be any and greater than at least two or three. Other methods for assigning a priority level to the cells are possible. For example, the priority level of a cell can be calculated using a formula that relates its priority level to its voltage difference and voltage. In the latter case, the comparison operations are omitted. The method described for associating refresh instants to cells according to their priority levels is only one example. Any other known method of scheduling tasks according to the priority level of these tasks can be adapted to the case described here of the scheduling of the refresh times of the estimations of the states of charge of the cells. The planning of the refresh instants of the estimation of the state of charge of each of the cells described with reference to FIG. 12 can be omitted.
    [0004] For example, this will be the case if the computing power needed to estimate the state of charge of each of the cells at each instant k is available. [00149] In a variant, the computer 44 comprises several programmable sub-computers capable of executing each and in parallel the estimation method of FIG. 10 or 17 for respective cells. [00150] The state of health of a cell can also be calculated using the following relation: SOHK = ROK / RO ". [00151] The battery 10 can be replaced by any type of battery, as by For example, a lead-acid battery, a super-capacitor, or a fuel cell, in which case the state model and / or the observation model of the estimator 60 may eventually be adapted to take into account the technology. of the battery [00152] What has been described above also applies to the hybrid vehicle, that is to say the vehicle whose drive wheel drive is at the same time or, alternatively, provided by an engine The vehicle 2 may also be a truck, a motorcycle or a tricycle and generally any vehicle capable of moving by driving driving wheels with the aid of a powered electric motor. for example, it may be a freight elevator The battery 10 can be recharged by means of an electrical outlet which makes it possible to connect it electrically to an electricity distribution network. The battery 10 can also be recharged by an internal combustion engine.
    权利要求:
    Claims (7)
    [0001]
    REVENDICATIONS1. An automatic method of determining the state of charge of a battery comprising a plurality of electric energy storage cells, said cells being electrically connected to one another between two battery terminals, said method comprising: a) the acquisition (110), at each instant k and for each cell of the battery, the measured value yk of the voltage between the terminals of this cell, and the measured intensity ik of the charging or discharging current of this cell, B) at least some of these instants k, the complete execution (210), by an electronic computer and for at least one of the cells of the battery, of an algorithm for estimating the state of charge SOCk of this cell, as a function of the value yk and intensity ik measured for this cell at this instant k, and c) the determination (190, 192) of the state of charge of the battery from the states of 15 estimated loads for each of the cells of this battery, characterized in that the method comprises: - for each cell, the calculation (204) of a voltage difference between the measured value yk between the terminals of this cell at time k and a measured value yk_x between these same terminals at a previous instant kX, where X is an integer greater than or equal to one, then - the assignment (206) to each cell of a priority level, the priority level of a cell being as much higher than the calculated voltage difference for this cell is important, - scheduling (208) refresh times of the estimation of the state of charge of each cell according to the priority levels assigned to each of these cells, this planning comprising assigning, on the same time slot, to the cells whose priority level is higher by a greater number of refresh times than the number of refresh times allocated to the cells d have the priority level is lower, and 30 - for each cell, the complete execution (210) of the algorithm for estimating the state of charge of this cell at each planned refresh time for this cell and the inhibition of the complete execution of this algorithm for estimating its state of charge outside the scheduled refresh times for this cell. 35
    [0002]
    2. Method according to claim 1, wherein the assignment to each cell of a priority level also comprises for each cell: the comparison (202) of the measured value yk between the terminals of this cell, to a predetermined threshold selected from the group consisting of a high threshold greater than or equal to 0.8Umax, and a low threshold less than 1.2Um ,,, where Umax and Um ,, are, respectively, the largest and the most small voltages possible between the terminals of this cell, - if the measured value yk is between the high threshold and the low threshold, the assignment (202) of a first priority level to this cell, and - if the measured value yk is below the predetermined threshold when the predetermined threshold is the low threshold or if the measured value yk is above the predetermined threshold when the predetermined threshold is the high threshold, the assignment (202) to that cell of a second level of priority strictly superior to the first first level of priority.
    [0003]
    3. Method according to any one of the preceding claims, in which for at least one of the cells of the battery: the complete execution of the algorithm for estimating the state of charge of this cell comprises the execution of: - calculating (116) a prediction of the SOCk state of charge of this cell using a state model which relates its state of charge SOCk at time k to a state of charge SOCk_i of this same cell at a previous instant k-1, then - the correction (122) of the calculated prediction of the state of charge SOCk as a function of the measured value yk at time k, and - outside the instants of scheduled refresh for the complete execution of the algorithm for estimating the state of charge of this cell, the method comprises: - the execution (214) of the only calculation of the prediction of the state of charge of this cell , without executing the correction of this prediction so as to obtain even outside the moments of ra planned cool down, an updated estimate of the state of charge of this cell, and - the use of the updated estimate of the state of charge of this cell during the determination of the state of the charge of the battery.
    [0004]
    A method according to any one of the preceding claims, wherein the method comprises: - identifying (205) a set of at least two twin cells, the twin cells having the same voltage difference and the same value measured yk at the same instant k, this identification comprising the comparison of the difference in voltages and the measured value yk of a cell at the voltage differences and at the measured values yk for the other cells at the same instant in order to identify, among these other cells, or the twin cells of this cell, and - for each set of twin cells, the complete execution (210) of the state of charge estimation algorithm for only one of the cells of this cell. together and, at the same time, the inhibition (210) of the complete execution of the state of charge estimation algorithm for the other cells of this set, then the estimation (212) of the state of charge of the other cells of this set taking it equal to the state of charge of the single cell of this set for which the estimation algorithm has been completely executed. 5
    [0005]
    5. Information recording medium (42), characterized in that it comprises instructions for the execution of a determination method according to any one of the preceding claims, when these instructions are executed by a calculator electronic. 10
    [0006]
    6. Management system of a battery equipped with several electric energy storage cells, these cells being electrically connected to each other between two electrical terminals of the battery, this system comprising an electronic calculator (44) programmed to: a) acquiring, at each instant k and for each cell of the battery, the measured value yk of the voltage between the terminals of this cell, and the measured intensity ik of the charging or discharging current of this cell, b) at at least some of these instants k, perform completely, for at least one of the cells of the battery, an algorithm for estimating the state of charge SOC k of this cell, as a function of the value yk and of the ik intensity measured for this cell at this instant k, and c) determine the state of charge of the battery from the estimated charge states for each of the cells of this battery, characterized in that the electronic calculator that (44) is also programmed for: - for each cell, calculating a voltage difference between the measured value yk between the terminals of this cell at time k and a measured value yk_x between these same terminals at a previous instant kX , where X is an integer greater than or equal to one, then - assigning each cell a priority level, the priority level of a cell 30 being all the higher as the voltage difference calculated for this cell is important plan refresh times of the estimation of the state of charge of each cell according to the priority levels assigned to each of these cells, this planning comprising the assignment, on the same time interval, to the cells the priority level is higher by a greater number of refresh times than the number of refresh times assigned to cells with lower priority, and - for each cell, perform completely the algorithm for estimating the state of charge of this cell at each scheduled refresh time for this cell 302 92 99 28 and inhibit the complete execution of this algorithm for estimating its state of charge. loads outside scheduled refresh times for this cell. 5
    [0007]
    7. Motor vehicle comprising: - at least one drive wheel (6), - an electric motor (4) capable of driving this driving wheel in rotation to move the motor vehicle, - a battery (10) comprising at least one cell (18). 21) adapted to store electrical energy and, alternately, to restore electrical energy to power the electric motor, this cell having two terminals (30, 32) through which it is electrically connected to the electric motor; - a voltmeter (34) electrically connected between the terminals of the cell for measuring the voltage between these terminals; - an ammeter (36) connected in series with the electric cell for measuring the intensity of the charging or charging current; discharge of this cell, and a system (40) for managing the battery connected to the voltmeter and to the ammeter, this management system comprising a programmable electronic calculator (44) capable of estimating the charging unit of the battery cell from the voltmeter and ammeter measurements, characterized in that the battery management system (40) is in accordance with claim 6.
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    同族专利:
    公开号 | 公开日
    CN107003360B|2020-01-03|
    US10267863B2|2019-04-23|
    JP6827417B2|2021-02-10|
    EP3224633A1|2017-10-04|
    US20170276734A1|2017-09-28|
    CN107003360A|2017-08-01|
    JP2018506017A|2018-03-01|
    KR20170090442A|2017-08-07|
    WO2016083757A1|2016-06-02|
    EP3224633B1|2019-04-24|
    FR3029299B1|2016-12-09|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    US20100169033A1|2007-07-02|2010-07-01|Alf Isaksson|State Of Charge Determination|
    US20140272653A1|2013-03-14|2014-09-18|Robert Bosch Gmbh|Flow Battery System and Method of SOC Determination|
    BR0308950A|2002-03-29|2007-01-30|Estco Battery Man Inc|fuel cell health management system|
    US7458433B2|2003-07-31|2008-12-02|Arvinmeritor Technology, Llc|Electric motor and gear drive assembly for driving a vehicle wheel|
    US8103485B2|2004-11-11|2012-01-24|Lg Chem, Ltd.|State and parameter estimation for an electrochemical cell|
    CA2588856C|2004-11-29|2012-11-13|Lg Chem, Ltd.|Method and system for battery state and parameter estimation|
    JP5050325B2|2005-07-12|2012-10-17|日産自動車株式会社|Battery control device|
    FR2976676B1|2011-06-15|2014-12-12|Michelin Soc Tech|DEVICE FOR MONITORING AN ELECTRIC ACCUMULATION BATTERY AND ASSOCIATED METHOD|
    JP2013132147A|2011-12-22|2013-07-04|Sony Corp|Electric storage device, electronic apparatus, power system and electric vehicle|
    JP6111275B2|2013-02-05|2017-04-05|日立オートモティブシステムズ株式会社|Battery control device|
    US8972091B2|2013-03-12|2015-03-03|Ford Global Technologies, Llc|Reduced central processing unit load and memory usage battery state of charge calculation|
    US20140266059A1|2013-03-14|2014-09-18|Ford Global Technologies, Llc|Selective updating of battery parameter estimations|
    CN103414224B|2013-08-14|2016-02-10|惠州市亿能电子有限公司|A kind of high-tension integrated battery group system of Large Copacity and control method thereof|
    GB201407805D0|2014-05-02|2014-06-18|Dukosi Ltd|Battery condition determination|
    WO2015180743A1|2014-05-28|2015-12-03|Volvo Truck Corporation|Method for determining the reliability of state of health parameter values|FR3029296B1|2014-11-28|2016-12-30|Renault Sa|AUTOMATIC METHOD OF ESTIMATING THE CHARGING STATE OF A CELL OF A BATTERY|
    CN107250822B|2015-12-03|2020-09-01|深圳市大疆创新科技有限公司|System and method for determining an operating state of a battery|
    KR20180037722A|2016-10-05|2018-04-13|삼성전자주식회사|Apparatus, method, and system for managing battery|
    CN110049896A|2016-12-01|2019-07-23|沃尔沃卡车集团|Select the system and method for balancing the energy storage monomer of power storage group|
    KR20190099904A|2018-02-20|2019-08-28|에스케이이노베이션 주식회사|Battery management system|
    JPWO2019243950A1|2018-06-22|2021-07-26|株式会社半導体エネルギー研究所|Abnormality detection method of power storage device and control device of power storage device|
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1461618A|FR3029299B1|2014-11-28|2014-11-28|AUTOMATIC METHOD FOR DETERMINING THE CHARGING STATE OF A BATTERY|FR1461618A| FR3029299B1|2014-11-28|2014-11-28|AUTOMATIC METHOD FOR DETERMINING THE CHARGING STATE OF A BATTERY|
    PCT/FR2015/053243| WO2016083757A1|2014-11-28|2015-11-26|Automatic method for determining the state of charge of a battery|
    JP2017528440A| JP6827417B2|2014-11-28|2015-11-26|An automated method for determining the battery charge status of a battery|
    US15/528,573| US10267863B2|2014-11-28|2015-11-26|Automatic method for determining the state-of-charge of a battery|
    CN201580064361.5A| CN107003360B|2014-11-28|2015-11-26|Method for automatically determining the state of charge of a battery|
    EP15807960.8A| EP3224633B1|2014-11-28|2015-11-26|Automatic method for determining the state of charge of a battery|
    KR1020177016678A| KR20170090442A|2014-11-28|2015-11-26|Automatic method for determining the state of charge of a battery|
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