Method for estimating the charge state of an electrochemical element and associated devices

Description

[0001] The present invention relates to a method for estimating the state of charge of at least one electrochemical element of a battery. The present invention also relates to an associated calculator, management system and battery.

[0002] Typically, a battery comprises one or more current accumulators, also called electrochemical generators, cells, or elements. An accumulator is an electricity-generating device in which chemical energy is converted into electrical energy. The chemical energy comes from electrochemically active compounds deposited on at least one face of electrodes arranged in the accumulator. The electrical energy is produced by electrochemical reactions during a discharge of the accumulator. The electrodes, arranged in a container, are electrically connected to current output terminals that ensure electrical continuity between the electrodes and an electrical consumer with which the accumulator is associated.

[0003] In order to increase the electrical power delivered, several sealed accumulators can be combined together to form a battery. Thus, a battery can be divided into modules, each module being composed of one or more accumulators connected together in series and / or in parallel. Thus, a battery can, for example, comprise one or more parallel branches of accumulators connected in series and / or one or more parallel branches of modules connected in series.

[0004] A charging circuit is usually provided to which the battery can be connected to recharge the accumulators.

[0005] Furthermore, an electronic management system comprising measurement sensors and an electronic control circuit, more or less advanced depending on the applications, can be associated with the battery. Such a system makes it possible in particular to organize and control the charging and discharging of the battery, to balance the charging and discharging of the different accumulators in the battery in relation to each other.

[0006] The state of charge is useful information for the electronic battery management system to optimize its use and lifespan. The state of charge is often referred to by the abbreviation SOC, which refers to the English term "State of Charge."

[0007] To obtain the state of charge SOC, it is known to use two calculation techniques using continuous measurements of the evolution of voltage, current and temperature.

[0008] The first technique can be called "coulometric" since it uses the fact that the state of charge SOC depends on the load (counting Ampere-hours) and the capacity C of the battery.

[0009] In fact, the following formulas come: SOC = SOC 0 + ∫ 0 t I × dt C

[0010] Or : SOC 0 is the initial value of the state of charge SOC at time t=0.

[0011] However, this first technique is very sensitive to current measurement error as well as to capacity estimation. Therefore, using this technique alone leads to accumulation of current measurement error, which leads to an erroneous estimation of the state of charge.

[0012] The second technique is based on open circuit voltage (OCV) measurements and uses a pre-established lookup table to obtain the state of charge (SOC) as a function of the open circuit voltage. Open circuit voltage is often referred to by the abbreviation OCV, which refers to the English term for "Open Circuit Voltage".

[0013] Since the function that relates the open-circuit voltage OCV to the state of charge SOC is a function of the voltage minus the product of the resistance and the current, the second technique is a sensitive technique for estimating the resistance. Therefore, the second technique should be used under conditions that minimize the error in the resistance, namely idle or low current conditions.

[0014] It is known to use both of the above techniques using the first technique as a regular technique and regularly recalibrating the SOC state of charge using the second technique.

[0015] However, in some electrochemical cells, because the variation of open circuit voltage as a function of state of charge SOC has a plateau, the correspondence between open circuit voltage OCV and state of charge SOC may be false.

[0016] Thus, it is known to perform a recalibration by performing a recharge with a SOC state of charge higher than the maximum SOC state of charge corresponding to the end of the plateau.

[0017] Such a technique generally requires interrupting the electrochemical element's mission to perform the recharge. This is particularly the case for frequency regulation missions that involve cycles on the plate. Such interruptions may be incompatible with the mission.

[0018] Methods for estimating the state of charge of the following items are also known: an article by Dong CHAO et al. entitled “Estimation of Power battery SOC based on improved BP neural network” (2014 IEEE International Conference on Mechatronics and Automation), an article by Pu CHI et al. entitled “The ANN models for SOC / BRC estimation of li-ion battery” (Information Acquisition, 2005 IEEE International Conference on Hong Kong and Macao, China), and an article by HOW DICKSON NT et al. entitled “State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Dirven Methods: A Review” (IEEE Access, vol. 7, pages 136116-136136).

[0019] There is therefore a need for a method for estimating the state of charge SOC of an electrochemical element which is more precise and achievable during normal operation of the electrochemical element.

[0020] For this purpose, the description describes a method for estimating the state of charge of at least one electrochemical element of a battery, the method being according to claim 1.

[0021] According to particular embodiments, the estimation method has one or more of the characteristics of claims 2 to 8, taken in isolation or in all technically possible combinations.

[0022] The description also provides a calculator capable of estimating the state of charge of at least one electrochemical element of a battery according to claim 9.

[0023] According to a particular embodiment, the calculator is capable of correcting the calculated accumulated charge quantity used by the second technique so that the calculation of the second technique leads to obtaining the first calculated value of the state of charge when the first technique is determined to be reliable.

[0024] The description also describes a system for managing at least one electrochemical element of a battery, the at least one electrochemical element having terminals, the management system comprising a voltage sensor capable of measuring the voltage at the terminals of said at least one electrochemical element, a current sensor at the terminals of said at least one electrochemical element, a temperature sensor of said at least one electrochemical element, and a computer as described previously.

[0025] The description also provides a battery comprising at least one electrochemical element, and a management system as described previously.

[0026] Characteristics and advantages of the invention will appear on reading the description which follows, given solely by way of non-limiting example, and made with reference to the appended drawings, in which: there figure 1 is a schematic representation of an example of a battery comprising an electrochemical element, the figure 2 is a graph illustrating an example of a state of charge - open circuit voltage characteristic of the electrochemical cell of the figure 1 , there figure 3 is a flowchart of an example of implementation of a method for estimating the state of charge of the electrochemical element, and the figures 4 à 8 illustrating experimental curves that can be obtained following the implementation of certain steps of the estimation process described in figure 3 .

[0027] A 10 battery is shown on the figure 1 .

[0028] As is known per se, a battery is generally an arrangement of a plurality of electrochemical elements but for the sake of simplification of the subject, a case with a single electrochemical element is described in what follows, knowing that the transposition to other arrangements is immediate.

[0029] The battery 10 comprises an electrochemical element 12 and a management system 14 for the electrochemical element 12.

[0030] As explained previously, an electrochemical element 12 is an electricity generating device in which chemical energy is converted into electrical energy.

[0031] The electrochemical element 12 therefore delivers a current and a voltage between two terminals.

[0032] The electrochemical element 12 has a characteristic state of charge SOC - open circuit voltage OCV as seen on the figure 2 This characteristic is referred to as the SOC / OCV characteristic in the following.

[0033] In the figure 2 , the state of charge SOC is expressed as a percentage of a maximum state of charge.

[0034] The SOC / OCV characteristic has four zones, a first zone Z1, a second zone Z2, a third zone Z3 and a fourth zone Z4.

[0035] The first zone Z1 corresponds to the start of charging and the fourth zone Z4 to the end of charging.

[0036] For the two intermediate zones, since the second zone Z2 and third zone Z3 correspond to a flat portion, the term flat portion (Z23) will be used in the following.

[0037] The flat portion Z23 is a portion in which the open circuit voltage variation OCV is less than 30 mV for a variation of at least 10% of the state of charge SOC.

[0038] Such a type of SOC / OCV characteristic is found in particular when the electrochemical element 12 is an electrochemical element comprising a cathodic active material chosen from the following groups or their mixtures: i) a compound of formula Li x Fe 1-y M y PO 4 (LFMP) where M is selected from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Y, Zr, Nb and Mo; and 0.8≤x≤1.2; 0≤y≤0.6, ii) a compound of formula Li x Mn 1-yz M' y M" z PO 4 (LMP), where M' and M" are different from each other and are selected from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb and Mo, with 0.8≤x≤1.2; 0≤y≤0.6; 0.0≤z≤0.2, iii) a compound of formula Li x Mn 2-yz Ni y M z O 4-dc F c (LMNO) where M represents one or more elements selected from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Co, Cu, Zn, Y, Zr, Nb, Ru, W and Mo;. and 1≤x≤1.4; 0 <y≤0,6 ; 0≤z≤0,2 ; 0≤d≤1 ; 0≤c≤1, iv) un composé de formule Li x Mn 2-y-z M' y M" z O 4 (LMO), où M' et M" sont choisis dans le groupe consistant en B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb et Mo ;.M' and M" being different from each other, and 1≤x≤1.4; 0≤y≤0.6; 0≤z≤0.2, and v) a compound of formula LiVPO 4 F (LVPF). .

[0039] The anode active material is not particularly limited. It is a material capable of inserting lithium into its structure. It can be selected from lithium compounds, carbon materials such as graphite, coke, carbon black and glassy carbon. It can also be based on tin, silicon, carbon and silicon compounds, carbon and tin compounds or carbon, tin and silicon compounds. It can also be a lithiated titanium oxide such as Li 4 Ti 5 O 12 or a niobium titanium oxide such as TiNb 2 O 7 .

[0040] The management system 14 is a system suitable for managing the electrochemical element 12.

[0041] The management system 14 comprises a voltage sensor 16, a current sensor 18, a temperature sensor 20 and a computer 22.

[0042] The voltage sensor 16 is suitable for measuring the voltage across the terminals of the electrochemical element 12.

[0043] The current sensor 18 is suitable for measuring the current at the terminals of the electrochemical element 12.

[0044] The temperature sensor 20 is suitable for measuring the temperature at the terminals of the electrochemical element 12.

[0045] The calculator 22 is capable of implementing a method for estimating the state of charge of the electrochemical element 12.

[0046] The calculator 22 is an electronic circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the calculator and / or memories into other similar data corresponding to physical data in the memories of registers or other types of display devices, transmission devices or storage devices.

[0047] As specific examples, the computer 22 includes a single-core or multi-core processor (such as a central processing unit (CPU), a graphics processing unit (GPU), a microcontroller, and a digital signal processor (DSP)), a programmable logic circuit, such as an application-specific integrated circuit (ASIC), a programmable gate array in situ (FPGA), programmable logic device (PLD) and programmable logic arrays (PLA), state machine, logic gate and discrete hardware components.

[0048] An example of implementation of the SOC state of charge estimation method is now described with reference to the flowchart of the figure 3 .

[0049] The SOC state of charge estimation process consists of two phases, a learning phase P1 and a usage phase P2.

[0050] According to the example described, the learning phase P1 is implemented offline, i.e. the learning phase is not embedded.

[0051] The learning phase P1 includes a learning step E30 of a neural network, a calculation step E32 and an establishment step E34.

[0052] During the learning step E30, the neural network is trained to estimate the state of charge SOC of the electrochemical element 12 from the values of voltage of the electrochemical element 12, current of the electrochemical element 12, temperature of the electrochemical element 12 and capacity C of the electrochemical element 12.

[0053] The voltage, current and temperature values come from the voltage sensor 16, the current sensor 18 and the temperature sensor 20 respectively.

[0054] According to the example described, these values are measured regularly.

[0055] The capacity value C is obtained less frequently and can be obtained by any technique, in particular by determination during a complete charge or discharge.

[0056] The neural network to be learned is a multi-layer perceptron.

[0057] Such a neural network is more often referred to by the abbreviation MLP, which refers to the corresponding English term "multilayer perceptron".

[0058] Thus, a neural network is an ordered succession of layers of neurons, each of which takes its inputs from the outputs of the previous layer.

[0059] More precisely, each layer consists of neurons that take their inputs from the outputs of the neurons in the previous layer.

[0060] In the example, the layers are dense, meaning that a neuron in one layer takes input from all the neurons in the previous layer. The term "fully connected" is sometimes used to refer to this type of layer.

[0061] Each layer is connected by a plurality of synapses. A synaptic weight is associated with each synapse. This is most often a real number that can take both positive and negative values.

[0062] Each neuron is capable of performing a weighted sum of the value(s) received from the neurons of the previous layer, each value then being multiplied by the respective synaptic weight, then applying an activation function, typically a non-linear function, to said weighted sum, and delivering to the neurons of the next layer the value resulting from the application of the activation function. The activation function makes it possible to introduce non-linearity into the processing carried out by each neuron. The sigmoid function, the hyperbolic tangent function, the Heaviside function are examples of activation functions.

[0063] The neural network to be learned is an easily embeddable neural network.

[0064] Also, it is appropriate that the neural network has a limited number of neurons.

[0065] For example, the total number of neural networks is in the order of a hundred neurons, preferably 100 neurons.

[0066] In the example described, the neural network comprises several layers, namely an input layer, a hidden layer and one or more output layer(s).

[0067] The neural network is learned using a supervised learning technique.

[0068] Thus, it is a matter of learning the free parameters of the neural network from a data set obtained by real laboratory experiments, typically for more than 100 different electrochemical elements.

[0069] During these experiments, 12 electrochemical elements undergo successive cycles and the electrical values of these 12 electrochemical elements (current, voltages, state of charge SOC, capacity) and the temperature values are recorded to form the data set.

[0070] The dataset is then split into training set and test set, for example, in a proportion of 80%-20%.

[0071] The training set is used for the neural network to learn its various free parameters through successive iterations until a desired performance criterion is met. This results in a learned neural network.

[0072] The test set is then used to evaluate the performance of the learned neural network.

[0073] The neural network thus constitutes, for the electrochemical element 12, a first model M1 estimating a value of the state of charge SOC from the values of voltage, current, temperature and capacity.

[0074] The first model M1 is rather robust to even strong current biases, due to its structure based on a multilayer perceptron which is without memory effect, that is to say which uses only the current values and not the previous values to obtain the desired estimate.

[0075] Additionally, due to its small size, the first M1 model is easily portable because its memory footprint and processor footprint are reduced.

[0076] However, this means that such a first model M1 has an estimation error, i.e. a gap between the estimated value and the actual value, which is relatively large under certain conditions.

[0077] There figure 4 graphically illustrates the performances obtained experimentally using the first model M1 in the absence of current bias.

[0078] On the figure 4 , the first curve C1 corresponds to the actual value of the state of charge SOC, the second curve C2 corresponds to the value estimated by the first model M1 and the third curve C3 to the estimation error.

[0079] The estimate is imperfect except in the three portions boxed on the figure 4 . These portions correspond to the first zone Z1, to the fourth zone Z4 and to the junction between the second zone Z2 and the third zone Z3. Furthermore, the robustness to current biases appears by comparing the figure 4 to the figure 5 which corresponds to the performance of the first M1 model in the presence of a current bias. The observed error is very little increased.

[0080] In calculation step E32, the statistical estimation error is calculated in the form of a first table TAB1.

[0081] To do this, the difference between the value estimated by the first model M1 and the actual value of the state of charge SOC is calculated for each element of the training dataset.

[0082] To limit the storage space allocated to the first table TAB1, the SOC state of charge values are grouped according to a predefined range.

[0083] In this case, the slice is a value of 1%. This means that all SOC states of charge equal to within 1% are considered the same SOC state of charge. This amounts to considering an accuracy for the measurement of the SOC state of charge of the order of 1%.

[0084] This gives a set of several errors for each actual value of the SOC state of charge, one error for each element of the data set presenting the same value of the SOC state of charge.

[0085] The average of the errors associated with a real value of the SOC state of charge is then calculated to obtain the statistical error associated with the real value of the SOC state of charge. The calculated average is, according to the example proposed, an arithmetic average.

[0086] The first table TAB1 then associates with each real value of the state of charge SOC the statistical estimation error of the first model M1.

[0087] The statistical error is then the average deviation from the estimated value.

[0088] When the statistical error is positive, it means that the estimated value is overestimated while for a negative statistical error, the estimated value is underestimated.

[0089] As a remark, it can be noted that the previous tabular formalism can be seen under the formalism of a correction function corresponding to an interpolation of the values of the table.

[0090] This change of formalism makes it possible to obtain exactly the same results as those obtained with the first table TAB1, a formalism which will be used in the following.

[0091] Such a first table TAB1 can be used to obtain a second model M2 obtained by correcting the first model M1 with the first table TAB1.

[0092] Such a model is sometimes called a self-correcting model. This self-correction is made possible by the fact that the first table TAB1 is not very sensitive to the input data provided that the first model M1 is provided to it.

[0093] The performance achieved by such a second M2 model is shown in the figure 6 with the same conventions as for the figure 4 .

[0094] The comparison of this figure 6 with the figure 4 shows that the second portion P2 and the third portion P3 are wider, which allows three portions to be obtained for recalibration as will be described later.

[0095] During the establishment step E34, a second table TAB2 is established relating to the model obtained by correcting the first model M1 with the first table TAB1, i.e. the second model M2.

[0096] From the SOC state of charge values obtained by the self-correcting model, it is possible to establish the second table TAB2 by indicating a “reliable” and “unreliable” mention according to a threshold for the statistical error corresponding to the second model M2.

[0097] Thus, for example, if the statistical error of the second model M2 is greater than the threshold, the state of charge SOC value is not reliable and conversely, if the statistical error of the second model M2 is less than or equal to the threshold, the state of charge SOC value is reliable.

[0098] For example, the threshold is set based on the desired accuracy of the model.

[0099] The neural network and the two tables TAB1 and TAB2 are loaded into a memory of the computer 22.

[0100] The calculator 22 is thus ready to implement the use phase.

[0101] Unlike the learning phase P1 which is carried out only once, the use phase P2 is implemented for a plurality of instants.

[0102] In particular, the usage phase P2 corresponds to a real-time usage phase of the battery 10.

[0103] For example, the instants are equally distributed, for example spaced by a time interval between 100 milliseconds (ms) and 2 seconds (s).

[0104] The steps of the P2 usage phase are implemented for each moment.

[0105] In this sense, it can be considered that the use phase P2 is iterative and that at each moment of implementation, an iteration takes place.

[0106] According to the example described, the usage phase P2 is implemented online, that is to say that the usage phase P2 is embedded and therefore implemented by the computer 22.

[0107] Furthermore, as visible in the figure 3 , the use phase P2 comprises an obtaining step E40, a correction step E42, a calculation step E44, a determination step E46, a selection step E48 or E52 and possibly another correction step E54 and determination step E56.

[0108] During the obtaining step E40, the calculator 22 obtains measurements (values) of the voltage V, the current I, the temperature T and the capacity C of the electrochemical element 12.

[0109] During the correction step E42, the calculator 22 subtracts the current measurement bias from the current value I obtained.

[0110] The current measurement bias comes in particular from the measurement bias intrinsic to the current sensor 18 and its variable precision depending on the amplitude of the current to be measured.

[0111] The calculator 22 thus obtains a corrected current value I.

[0112] When the measurement bias is not known, the bias is considered to be zero.

[0113] During the calculation step E44, the calculator 22 calculates the value of the state of charge SOC according to two distinct techniques, as illustrated schematically in the figure 3 by two arrows to the determination step E46.

[0114] According to a first technique, the calculator 22 applies the first model M1 to the voltage value V obtained, the corrected current value I, the temperature value T and the capacity value C obtained during the obtaining step E40.

[0115] This amounts to performing inference using the neural network learned offline to obtain a value of the SOC state of charge without self-correction.

[0116] The calculator 22 thus obtains an estimated SOC state of charge value.

[0117] The calculator 22 then applies the correction of the first table TAB1 to the estimated state of charge SOC value.

[0118] To do this, the calculator 22 reads the error value corresponding to the range to which the estimated SOC state of charge value belongs and subtracts this read value from the estimated value.

[0119] This gives a corrected SOC state of charge value which corresponds to the SOC state of charge value obtained using the first technique noted SOC TECH1.

[0120] Due to the simultaneous use of the first model M1 and the first table TAB1, the first technique corresponds to the use of a self-correcting neural network.

[0121] According to a second technique, the calculator 22 carries out the so-called coulometric technique presented previously. Such a technique corresponds to a second model M2.

[0122] This technique is based on the fact that the state of charge SOC of an electrochemical element 12 depends directly on the ratio between the quantity of charge accumulated (or counting of Ampere-hours in reference to the unit often used for this quantity) and the capacity of the electrochemical element 12.

[0123] Thus, the calculator 22 then uses the current values and the capacity value according to the following formula: SOC = SOC 0 + ∫ 0 t I × dt C

[0124] More precisely, the calculator 22 holds in memory a value of the quantity of charge accumulated at the previous iteration Ah previous, adds to this value the result of the time integration of the corrected value of the current over the time interval elapsed since the previous iteration. The value thus obtained is the value of the quantity of charge accumulated at the current iteration Ah current, a value which is stored by the calculator 22.

[0125] The calculator 22 then performs the division by the value of the capacity and thus obtains a value of the state of charge SOC according to the second technique SOC TECH2.

[0126] At the end of the calculation step, the calculator 22 thus has two distinct values of the state of charge SOC of the electrochemical element 12, namely SOC TECH1 and SOC TECH2.

[0127] According to the example described, during the determination step E46, the computer 22 determines whether the value of the state of charge according to the first technique SOC TECH1 is a reliable value.

[0128] According to the example described, the calculator 22 uses the second table TAB2.

[0129] The second table TAB2 is thus used to determine whether the value of the state of charge according to the first technique SOC TECH1 is within a confidence interval.

[0130] At the end of the determination step E46, the computer 22 has determined that the value of the state of charge according to the first technique SOC TECH1 is either unreliable or reliable.

[0131] In case the state of charge value according to the first technique SOC TECH1 is unreliable, this means that the state of charge value according to the second technique SOC TECH2 is more reliable than the state of charge value according to the first technique SOC TECH1.

[0132] The calculator 22 then considers that the estimated value of the state of charge SOC is equal to the value of the state of charge according to the second technique SOC TECH2 and selects, during the selection step E48, the value of the state of charge according to the second technique SOC TECH2 as the final estimated value of the state of charge SOC, i.e. the output value of the estimation method.

[0133] This output of the final estimated value of the state of charge SOC is schematically illustrated by element 50 on the figure 1 The final estimated value is, for example, displayed on a display device.

[0134] On the contrary, in the case where the value of the state of charge according to the first technique SOC TECH1 is reliable, this means that the value of the state of charge according to the first technique SOC TECH1 is more reliable than the value of the state of charge according to the second technique SOC TECH2.

[0135] The calculator 22 then considers that the measured value of the state of charge SOC is equal to the value of the state of charge according to the first technique SOC TECH1 and selects during the selection step E52, the value of the state of charge according to the first technique SOC TECH1 as the final estimated value of the state of charge SOC, i.e. the output value of the estimation method.

[0136] The calculator 22 then implements the correction step E54.

[0137] The calculator 22 then calculates a value of the accumulated charge quantity corresponding to the value of the state of charge according to the first SOC TECH1 technique using the value of the capacity C.

[0138] The calculator 22 then corrects the second model M2 by modifying the value of the quantity of charge accumulated at the current iteration Ah which becomes the value deduced previously using the first technique.

[0139] This can be interpreted that in this case the ampere-hour counter is reset to the value that can be deduced using the first self-correcting model.

[0140] The correction step E54 which has just been described corresponds to a recalibration step of the second model M2.

[0141] This recalibration is schematically illustrated by arrow 55 on the figure 3 .

[0142] There figure 7 is an experimental figure clearly showing the interest of this recalibration for the case where the current bias is -2A.

[0143] On the figure 7 , the first curve C1 corresponds to the actual state of charge value of the SOC state of charge, the second curve C2 corresponds to the final estimated value of the state of charge, the third curve C3 to the estimation error.

[0144] When the final estimated value comes from the first technique, a brace delimiting the portion associated with the mention TECH1 is present in the figure 7 and conversely, when the final estimated value comes from the second technique, a brace delimiting the portion associated with the mention TECH2 is present in the figure 7 .

[0145] The portions for which the final estimated value comes from the first technique are the same as previously (first zone Z1, the fourth zone Z4 and junction between the second zone Z2 and the third zone Z3).

[0146] The portions for which the final estimated value comes from the second technique correspond respectively to the remainder of the second Z2 and to the remainder of the third Z3.

[0147] In these portions, the final estimated value gradually moves away from the actual value of the state of charge SOC until it reaches a deviation such that the value obtained by the first technique becomes more reliable. It is then possible to correct the deviation ΔSOC which is represented on the figure 7 This corresponds to recalibration and helps limit the error.

[0148] When in the previous iteration the value according to the first SOC TECH1 technique was not reliable, the calculator 22 determines, during the determination step E56, the current measurement bias.

[0149] The current measurement bias is deduced by using the difference in state of charge between the two techniques since the last instant for which the first calculated state of charge value was determined to be unreliable.

[0150] It thus comes that the current measurement bias can be calculated by the following formula: I biais = C ∗ SOC T 2 t − SOC T 1 t Δ t

[0151] In the previous formula, I biais denotes the measurement bias and Δ t the time elapsed since the start of the time interval during which the second SOC TECH2 technique is used.

[0152] This measurement bias value thus determined is stored by the calculator 22 to be used during the implementation of the correction step of the following iteration, as illustrated by arrow 58 on the figure 3 .

[0153] The interest in correcting the bias current appears clearly with the figure 8 which corresponds to the same experimental conditions as those of the figure 7 except that the measurement bias is corrected

[0154] The reduction in error achieved is evident.

[0155] The output of the estimated state of charge SOC value is schematically illustrated by element 60 on the figure 1 The estimated value is, for example, displayed on a display device.

[0156] In each case, the estimated value is therefore the most reliable value.

[0157] Such a method therefore makes it possible to estimate the value of the state of charge SOC of the electrochemical element 12 with greater precision.

[0158] In fact, the process corresponds to the use of a hybrid model using two distinct techniques: a first technique from the world of machine learning based on the application of a self-correcting neural network and a second technique from the world of measurement and based on coulometric counting.

[0159] This hybrid model is able to select the best of the two values.

[0160] It should be noted that this gain in precision is obtained with a relatively low computational overhead compared to using the second technique alone because the neural network used has a very small memory footprint (few neurons). This means that the gain in precision remains compatible with an embedded application.

[0161] Furthermore, the method also makes it possible to carry out a recalibration of the second technique while the electrochemical element 12 is in normal use, even in the flat portion Z23. This thus allows the electrochemical element 12 to continue its mission while having a good estimate of the state of charge SOC. This results in better availability of the electrochemical element 12 and / or avoids oversizing the battery 10.

[0162] Finally, thanks in particular to the choice of a neural network that estimates solely on the basis of current values without memory effect, the first technique is relatively robust to current bias. This makes it possible to compensate for the deviation caused by a current bias, resulting in increased accuracy.

[0163] It can also be noted that the statistical error profile is repeatable regardless of the types of stress used, which was unexpected. The statistical error profile is certainly linked to the chemistry of the electrochemical element 12 but invariant according to the stress which makes the method more suitable for use in a wide variety of possible applications.

[0164] Other embodiments benefiting from the aforementioned advantages are also conceivable.

[0165] In particular, it is possible to calculate the value of the state of charge SOC of the electrochemical element 12 using a single technique to reduce the computational load.

[0166] In such a case, the most reliable technique is determined from among the first technique and the second technique based on a reliability criterion depending on the value of the correction function for the calculated value of the state of charge SOC and only when the technique determined as reliable is not the one that was the subject of the calculation, it is calculated.

[0167] This avoids calculating two values at every moment, knowing that one of the two will not be used.

[0168] A switchover system can even be considered, i.e. the technique by which the value of the state of charge SOC of the electrochemical element 12 is calculated at time t is the technique which was determined to be reliable at the previous time.

[0169] This also reduces the number of times a SOC state of charge value calculation is performed after determination step E46 because the determined technique is different from the technique chosen to perform the calculation during calculation step E44.

Claims

1. Method for estimating the state of charge (SOC) of at least one electrochemical element (12) of a battery (10), the method being implemented by a calculator (22), the calculator (22) storing a first model (M1) giving from a value of voltage, a value of current, a value of temperature and a value of capacitance of the at least one electrochemical element (12) the value of the state of charge (SOC) of the at least one electrochemical element (12), the first model (M1) being a trained neural network, the calculator (22) storing a correction function giving, for each value of the state of charge (SOC), the statistical error of estimation by the first model (M1), the method comprising, for a plurality of instants, the steps of: - obtaining values of the voltage, the current, the temperature and the capacitance of at least one electrochemical element (12), - computation of the value of the state of charge (SOC) of at least one electrochemical element (12) according to a first technique and a second technique, the first technique including the following operations: - application of the first model on the value of the voltage, the current, the temperature and the capacitance of the at least one electrochemical element (12) at the same instant, to obtain an estimated value of the state of charge (SOC), and - application of the correction function to the estimated value of the state of charge (SOC), to obtain a first computed value of the state of charge (SOC), the second technique including the following operations: - computation of the value of the amount of charge accumulated by using the obtained values of current, - deduction of a second computed value of the state of charge (SOC) by computing the ratio of the value of the accumulated charge quantity and the capacitance of the at least one electrochemical element (12), - determination of the most reliable technique among the first technique and the second technique according to a reliability criterion depending on the value of the statistical error of estimation of the second model (M2) corresponding to the first model (M1) corrected by the correction function for the first computed value of the state of charge (SOC), - computation of the computed value of the state of charge (SOC) according to the technique determined as being reliable if the value has not been computed previously, and - selection of the computed value of the state of charge (SOC) according to the technique determined as the estimated value of the state of charge (SOC).

2. Method for estimating according to claim 1, wherein the method further includes a step of correcting the computed accumulated charge quantity used by the second technique so that the computation of the second technique leads to obtaining the first computed value of state of charge (SOC) when the first technique is determined as being reliable.

3. Method for estimating according to claim 1 or 2, wherein the method further includes a step of correcting the value of current obtained by subtracting the measurement bias on the current, in order to obtain a corrected value of the current, the steps of computation being applied to the corrected value of the current.

4. Method for estimating according to claim 3, wherein the method further includes, when the first technique is determined as being reliable at an instant after a time interval during which the second technique has been determined as being reliable at each instant in the time interval, a step of determining the measurement bias on the current by using the difference of state of charge between the two techniques since the beginning of the time interval.

5. Method for estimating according to any one of claims 1 to 4, wherein the neural network of the first model (M1) is a multilayer perceptron.

6. Method for estimating according to any one of claims 1 to 5, wherein the neural network of the first model (M1) includes a number of neurons less than or equal to 100.

7. Method for estimating according to any one of claims 1 to 6, wherein the at least one electrochemical element (12) having a state of charge (SOC) - open circuit voltage (OCV) characteristic with a flat portion, a flat portion being a portion wherein the variation of the open circuit voltage (OCV) is less than 30 mV for a variation of at least 10% of the state of charge (SOC).

8. Method for estimating according to any one of claims 1 to 7, wherein the at least one electrochemical element (12) comprises an active cathode material chosen from the following groups or mixtures thereof: i) a compound with the formula LixFe1-yMyPO4 (LFMP) where M is selected from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, Y, Zr, Nb, and Mo; and 0.8 ≤ x ≤ 1.2; 0 ≤ y ≤ 0.6, ii) a compound with the formula LixMn1-y-zM'yM"zPO4 (LMP), where M' and M" are different from each other and are selected from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb, and Mo, with 0.8 ≤ x ≤ 1.2; 0 ≤ y ≤ 0.6; 0 ≤ z ≤ 0.2; iii) a compound with the formula LixMn2-y-zNiyMzO4-d-cFc (LMNO), where M represents one or a plurality of elements chosen from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb, and Mo; and 1 ≤ x ≤ 1.4; 0 < y ≤ 0.6; 0 ≤ z ≤ 0.2; 0 ≤ d ≤ 1; 0 ≤ c ≤ 1, iv) a compound with the formula LixMn2-y-zM'yM"zO4 (LMO), where M' and M" are chosen from the group consisting of B, Mg, Al, Si, Ca, Ti, V, Cr, Fe, Co, Ni, Cu, Zn, Y, Zr, Nb, and Mo; M' and M" being different from each other, and 1 ≤ x ≤ 1.4; 0 ≤ y ≤ 0.6; 0 ≤ z ≤ 0.2; and v) a compound with the formula LiVPO4F (LVPF).

9. Calculator (22) adapted to estimate the state of charge (SOC) of at least one electrochemical element (12) of a battery (10), the calculator (22) storing a first model (M1) giving from a value of voltage, a value of current, a value of temperature and a value of capacitance of the at least one electrochemical element (12) the value of the state of charge (SOC) of the at least one electrochemical element (12), the first model (M1) being a trained neural network, the calculator (22) storing a correction function giving, for each value of the state of charge (SOC), the statistical error of estimation by the first model (M1), the calculator (22) being, for a plurality of instants, adapted to: - obtain values of the voltage, the current, the temperature and the capacitance of at least one electrochemical element (12), - compute the value of the state of charge (SOC) of a first technique and a second technique, the first technique including the following operations: - application of the first model on the value of the voltage, the current, the temperature and the capacitance of the at least one electrochemical element (12) at the same instant, to obtain an estimated value of the state of charge (SOC), and - application of the correction function to the estimated value of the state of charge (SOC), to obtain a first computed value of the state of charge (SOC), the second technique including the following operations: - computation of the value of the amount of charge accumulated by using the obtained values of current, - deduction of a second computed value of the state of charge (SOC) by computing the ratio of the value of the accumulated charge quantity and the capacitance of the at least one electrochemical element (12), - determine the most reliable technique among the first technique and the second technique according to a reliability criterion depending on the value of the statistical error of estimation of the second model (M2) corresponding to the first model (M1) corrected by the correction function for the first computed value of the state of charge (SOC) - compute the computed value of the state of charge (SOC) according to the technique determined to be reliable if the value has not been computed previously, and - select the computed value of the state of charge (SOC) according to the technique determined as the estimated value of the state of charge (SOC).

10. Calculator according to claim 9, wherein the calculator (22) is also adapted to correct the computed accumulated charge quantity used by the second technique so that the computation of the second technique results in obtaining the first computed state of charge (SOC) value when the first technique is determined as being reliable.

11. System for managing (14) at least one electrochemical element (12) of a battery (10), the at least one electrochemical element (12) having terminals, the system for managing (14) comprising: - a voltage sensor (16) adapted to measure the voltage at the terminals of at least one electrochemical element (12), - a current voltage sensor (18) adapted to measure the current at the terminals of said at least one electrochemical element (12), - a temperature sensor (20) adapted to measure the temperature of said at least one electrochemical element (12), and - a calculator (22) according to claim 9 or 10.

12. Battery (10) comprising: - at least one electrochemical element (12), and - a system for managing (14) according to claim 11.