A method for predicting the open circuit potential curve of a class of cathode materials as they change with lithiation

Abstract

The present invention relates to a method for predicting the open circuit potential curve of a class of cathode materials as it changes with lithiation. The method for generating battery characteristics with a target battery composition includes determining the open circuit potential (OCP) characteristics of two similar battery compositions having different ratios of elements. The OCP characteristics are converted to dQ / dV characteristics and linearly combined to obtain a target dQ / dV characteristic. The target dQ / dV characteristics are integrated to obtain a target OCP characteristic. The battery constructed with the target composition is operated according to the target OCP characteristics.

Description

Technical Field

[0001] The present application generally relates to methods for predicting the open circuit potential profile of a battery pack. Background Art

[0002] Lithium-ion batteries (LIBs) have become the industry standard for both electric drive and portable electronic device applications. LIBs operate based on the movement of lithium ions between a negative electrode (known as the anode) and a positive electrode (known as the cathode). One of the inputs used to predict the charge / discharge behavior of a battery is the corresponding open circuit voltage (OCV) curve, which defines the cell's equilibrium voltage as a function of state of charge. The OCV is unique to the composition of a particular battery because it depends on the thermodynamic properties of the active materials found in the anode and cathode electrodes, with the individual open circuit potential (OCP) curves of each material directly affecting the shape of the overall OCV curve. Summary of the Invention

[0003] A method includes determining a set of predetermined elements for a cathode of a lithium-ion battery, the set comprising a first element and a second element. The method includes determining a first composition and a second composition of the set, the first and second compositions comprising different proportions of the first and second elements. The method includes generating a first open circuit potential (OCP) characteristic corresponding to the first composition and a second OCP characteristic corresponding to the second composition, wherein each OCP characteristic defines voltage (V) as a function of capacity (Q) of the lithium-ion battery. The method includes converting the first OCP characteristic into a first dQ / dV characteristic and converting the second OCP characteristic into a second dQ / dV characteristic. The method includes determining a third composition of the set having a predetermined proportion of the first element. The method includes generating a third dQ / dV characteristic for the third composition by summing a first proportion of the first dQ / dV characteristic and a second proportion of the second dQ / dV characteristic, wherein the first proportion is defined by a predetermined proportion and the first and second proportions sum to 1. The method includes integrating the third dQ / dV characteristic to generate a third OCP characteristic corresponding to the third composition, and charging and discharging the battery having the third composition using the third OCP characteristic.

[0004] The first composition may include the first element in the smallest proportion and the second element in the largest proportion. The second composition may include the second element in the smallest proportion and the first element in the largest proportion. The set of predetermined elements may include at least two different transition metal oxide components, such that within a class of materials including the at least two different transition metal oxide components, the oxidation / reduction coulombs in the lithiation / delithiation characteristics are proportional to the corresponding stoichiometric compositions, and the potentials at which the oxidation / reduction processes occur are approximately equal. A range may be defined by a first difference between the higher proportion of the first element present in the second composition and the lower proportion of the first element present in the first composition, and the first ratio may be defined by dividing the second difference between the predetermined ratio and the lower ratio by the range. The first and second OCP characteristics may be approximated by averaging the lithiation and delithiation characteristics corresponding to the respective first and second compositions. The set of predetermined elements may include Li, Ni, Co, M1, and O, where M1 corresponds to a transition metal, the first element is Ni, and the second element is Co. The set of predetermined elements may include Li, Ni, O, and M1, where M1 is one of Al, Mn, or Mg, the first element is Ni, and the second element is M1. The set of predetermined elements may include Li, O, M1 and M2, wherein M1 and M2 are different transition metal oxide components.

[0005] A battery management system includes a memory for storing a plurality of characteristics corresponding to a plurality of compositions of cathodes for a battery. The battery management system includes a controller programmed to, in response to receiving a parameter of a target composition, (i) determine a class of compositions to which the target composition belongs, (ii) retrieve from the memory a first characteristic corresponding to a first composition and a second characteristic corresponding to a second composition such that the first composition and the second composition belong to the class, (iii) generate an open circuit potential (OCP) characteristic of the target composition by summing a first ratio of the first characteristic and a second ratio of the second characteristic, wherein the first ratio is defined by a target ratio of a first element present in the target composition, and (iv) charge and discharge the battery according to the OCP characteristic.

[0006] The target ratio may be between a first edge ratio defining the ratio of the first element in the first composition and a second edge ratio defining the ratio of the first element in the second composition. The first composition and the second composition may include the first element and the second element, and the first composition may include the first element at the largest ratio and the second element at the smallest ratio. The first characteristic may be an OCP characteristic of the first composition, and the second characteristic may be an OCP characteristic of the second composition. The first characteristic may be a dQ / dV characteristic of the first composition, and the second characteristic may be a dQ / dV characteristic of the second composition. Generating the OCP characteristic of the target composition further includes integrating the dQ / dV characteristic obtained by summing the first ratio of the first characteristic and the second ratio of the second characteristic.

[0007] The method includes determining a lower edge composition of a cathode for a battery pack, the cathode comprising first and second transition metal components, the lower edge composition defining a ratio of the first transition metal below which the 4.2V feature does not exist. The method also includes determining an upper edge composition in which the ratio of the second transition metal is zero. The method includes generating a first open circuit potential (OCP) characteristic for the lower edge composition and a second OCP characteristic for the upper edge composition. The method includes converting the first OCP characteristic into a first dQ / dV characteristic and converting the second OCP characteristic into a second dQ / dV characteristic. The method includes determining a third composition having a predetermined ratio of the first transition metal. The method includes generating a third dQ / dV characteristic for the third composition by summing a first ratio of the first dQ / dV characteristic and a second ratio of the second dQ / dV characteristic, wherein the first ratio is defined by a predetermined ratio and the sum of the first and second ratios is 1. The method includes integrating the third dQ / dV characteristic to generate a third OCP characteristic corresponding to the third composition and outputting the third OCP characteristic to a battery pack management system for charging and discharging a battery pack having the third composition.

[0008] The range can be defined by the difference between the higher proportion of the first transition metal present in the higher edge composition and the lower proportion of the first transition metal present in the lower edge composition, and the first ratio can be defined by dividing the difference between the preset ratio and the lower ratio by the range. The first and second transition metal components can be such that the oxidation / reduction coulombs in the lithiation / delithiation characteristics are proportional to the corresponding stoichiometric compositions, and the potentials at which the oxidation / reduction processes occur are approximately equal. The 4.2V feature can be defined as a dQ / dV peak that exceeds a preset threshold. The first transition metal can be nickel (Ni) and the second transition metal can be cobalt (Co). BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 shows that LiNi 0.80 Co 0.15 Al 0.05 Example voltage / capacity characteristics of an O2 battery cell.

[0010] Figure 2 shows that LiNi 0.95 Al 0.05 Example voltage / capacity characteristics of an O2 battery pack.

[0011] Figure 3 shows that LiNi 0.80 Co 0.15 Al 0.05 O2 battery pack, LiNi 0.95 Al0.05 Example dQ / dV characteristics of an O2 battery and a first material combination.

[0012] Figure 4 shows that LiNi 0.80 Co 0.15 Al 0.05 O2 battery pack, LiNi 0.95 Al 0.05 Example dQ / dV characteristics of an O2 battery and a second material combination.

[0013] Figure 5 shows that LiNi 0.85 Co 0.15 Al 0.05 Comparison of the reference curve and the estimated curve of O2 composition.

[0014] Figure 6 shows that LiNi 0.90 Co 0.05 Al 0.05 Comparison of reference and predicted characteristics of O2 composition.

[0015] Figure 7 Exemplary voltage / capacity characteristics of an NCM622 battery cell are shown.

[0016] Figure 8 Exemplary voltage / capacity characteristics of an NCM712 battery cell are shown.

[0017] Figure 9 Exemplary voltage / capacity characteristics of an NCM802 battery cell are shown.

[0018] Figure 10 shows that LiNi 0.80 Mn 0.20 O2 battery pack, LiNi 0.60 Co 0.20 Mn 0.20 Example dQ / dV characteristics of O2 battery packs and material combinations.

[0019] Figure 11 shows that LiNi 0.70 Co 0.10 Mn 0.20 Comparison of reference and predicted characteristics of O2 composition.

[0020] Figure 12 A possible configuration of a battery management system is shown.

[0021] Figure 13 An exemplary flow chart for generating battery pack characteristics is shown. DETAILED DESCRIPTION

[0022] Embodiments of the present disclosure are described herein. However, it should be understood that the disclosed embodiments are merely examples, and that other embodiments may take various and alternative forms. The figures are not necessarily to scale; some features may be enlarged or minimized to show details of particular components. Therefore, the specific structural and functional details disclosed herein are not intended to be interpreted as limiting, but merely as a representative basis for teaching those skilled in the art to apply the invention in various ways. As will be understood by those skilled in the art, the various features illustrated and described with reference to any one of the accompanying drawings may be combined with features illustrated in one or more other drawings to produce embodiments that are not explicitly illustrated or described. The combinations of illustrated features provide representative embodiments for typical applications. Various combinations and modifications of features are consistent with the teachings of the present disclosure, but may be desirable for particular applications or implementations.

[0023] Lithium-ion batteries (LIBs) can be characterized by their open circuit voltage (OCV) curves. The OCV curve can be unique to the composition of a particular battery and can provide one of the inputs for predicting the charge / discharge behavior of the battery. The OCV curve depends on the thermodynamic properties of the active materials found in the anode and cathode electrodes, where the individual open circuit potential (OCP) curves of each material affect the shape of the overall OCV curve. Disclosed herein is a method for predicting the OCP curve of a class of cathode active materials. The method can be used as part of a battery management system (BMS) that utilizes OCV curves to provide estimates of battery parameters, or can be used as part of a battery design interface that uses a library of OCP properties of active materials or electrode blends. Battery parameters can include remaining battery capacity, power limits, state of health, and other characteristics.

[0024] Lithium-ion batteries have become the industry standard for both electric drive and portable electronic device applications. LIBs operate based on the movement of lithium ions between a negative electrode (called the anode) and a positive electrode (called the cathode). For example, during battery discharge, the cathode can receive lithium ions from the anode (lithiation), and during battery charging, the cathode can donate lithium ions to the anode (delithiation). Note that the methods described here are also applicable to battery chemistries that use Na, Mg, K, Ca, Al, and Cl ions for charge transfer.

[0025] One input for predicting the charge / discharge behavior of a battery pack is the open-circuit voltage (OCV) curve, which defines the equilibrium voltage of a battery cell as a function of state of charge. OCV is unique to a particular battery pack composition because it depends on the thermodynamic properties of the active materials found in the anode and cathode electrodes, with the individual open-circuit voltage (OCV) curves of each material directly influencing the shape of the overall OCV curve. To interpret the OCV curve with the highest accuracy, it is advantageous to determine the contribution of each active material. However, this process may not always be feasible due to a lack of knowledge of the individual components (e.g., chemical composition) or the long characterization time required to obtain each individual curve. Therefore, a method for predicting the OCP curve of an active material is desirable. Disclosed herein is a method for predicting the OCP curve of a class of cathode active materials.

[0026] Existing techniques use characterization experiments to directly measure a material's OCP or OCV curve. The OCPs of several materials can be combined to form a blended OCP for the cathode or anode, and the cathode and anode OCPs can be combined to form the OCV of a battery pack. However, current methods do not accurately predict (i.e., within + / - 20 mV) the OCP curve of a specific material. The method disclosed herein allows the OCP of a class of cathode materials to be predicted without the need for characterization experiments for each composition. The method disclosed herein can also be used to predict the OCP of "hypothetical" materials that have not yet been synthesized.

[0027] The methods disclosed herein can also be used in battery design to generate a library of OCP curves for known materials (whose OCPs can, in principle, be determined through characterization, a lengthier and more resource-intensive process) and "hypothetical" materials that have yet to be synthesized. The methods disclosed herein can also be used in battery design to predict the charge / discharge characteristics of a yet-to-be-assembled battery pack containing at least one "hypothetical" material. The methods disclosed herein can also be used to determine the cathode composition in an unopened battery pack using only electrical testing (without opening the battery pack) and provide a library of OCP curves. The determined OCP of the material(s) can be implemented in a battery management system to improve the accuracy of the system.

[0028] The method is applicable to a class of materials in which the oxidation / reduction peaks of battery components comprising transition metal oxides are proportional to the stoichiometric ratios of the components in the material, wherein lithiation / delithiation of the transition metal oxides occurs at similar potentials (i.e., + / - 20 mV or less).

[0029] An advantage of the disclosed method is that a smaller subset of OCP functionality can be programmed into a BMS or controller. The BMS / controller can then be used to run a wide range of battery packs containing materials with a wide range of intermediate compositions. The composition can be defined in part by an ε parameter that defines the relative percentage of the base composition. ε parameters customized for the intermediate composition can be provided to the BMS for various compositions. Therefore, when used in a BMS, the disclosed method saves programming time when applying the BMS to a new battery pack for a new application. In particular, when slight changes are made to the material composition within the same group during battery pack development, the ε value can be adjusted accordingly without having to reprogram the BMS or perform new measurements on the new material.

[0030] The OCP curve describes the equilibrium potential, thermodynamic properties of lithium content at a given state within an active battery material. The OCP curve can be expressed as potential (V) vs. lithiated state (e.g., Li x AM, where AM represents the active material and x is the lithium content which typically varies from 0 to 1) or expressed as potential (V) vs. capacity (mAh / g), where capacity is defined as x*theoretical capacity of the active material. Theoretical capacity depends on the molecular weight of the active material and the capacity of the active material to accommodate Li + Therefore, each respective active material may define a unique OCP curve.

[0031] Measuring OCP curves precisely and accurately is challenging due to kinetic barriers that can make it difficult to separate thermodynamic properties. The conventional method for measuring OCP curves is to perform intermittent pauses during the lithiation and delithiation cycles of a battery half-cell (active material coated on a current collector vs. lithium metal electrode) and then connect the measurements taken at the pause points. Unfortunately, this process can take weeks to complete and may not exclude all kinetic components that contribute to the desired thermodynamic properties. Therefore, it is desirable to develop OCP curves for predicting materials without having to perform additional characterization methods.

[0032] Disclosed herein is a method for predicting the OCP curve of a class of cathode materials. The method comprises the steps described herein. The validity of the method is established using examples from two cathode groups. However, the method is extendible to all cathode groups where the Coulombic charge characteristic of the lithiation / delithiation of the components is proportional to the stoichiometric ratio of the electrochemically active components, and where lithiation / delithiation of the components occurs at similar potentials (i.e., + / - 20 mV or less).

[0033] In the first step of the method, the edge composition can be determined in the cathode group for which OCP prediction is to be performed. For the following example focusing on a cathode group rich in nickel (Ni), the lithiation / delithiation characteristics in the OCP curve appear to be proportional to the stoichiometric ratio of Ni in the material. One of the features that characterizes Ni-rich cathode materials appears in the relative Li / Li + of approximately 4.2 V (later in the text and figures, “relative to Li / Li + The cathode composition at which the ~4.2 V feature disappears can be predicted to be defined as the lower edge composition. The upper edge composition can be defined by reducing the composition of one of the other transition metals present in the determined cathode group to 0 (disappearance of that component). In an embodiment, cobalt (Co) is a transition metal that varies with Ni, and the composition of cobalt can be reduced to 0. However, in other cathode groups, different transition metal oxides or components (not necessarily Co) can be reduced to 0 or to different values.

[0034] In the second step of the method, the OCP curves of the two edge compositions can be obtained. The OCP curves can be obtained from the literature or directly characterized using known methods. If the OCP curves of the edge compositions are not yet known, the closest known composition within the range of the material in question can be used.

[0035] In the third step of the method, the OCP curves of the two edge compositions (or the OCP curves of the closest available compositions within the material class) are used to predict the OCP curves of the intermediate compositions. Intermediate compositions can be generated by linearly combining the inverse derivatives (dQ / dV vs. V curves) of the OCP curves expressing potential (V) vs. capacity (Q, mAh / g) for the edge compositions (or surrogates) at the appropriate stoichiometric ratios (ε and (1-ε)). The intermediate compositions can be characterized by the following equation:

[0036]

[0037] Alternatively, when a reference potential is provided that defines Q1 and Q2, the integration of this equation can be used to obtain Q 中间 (V). More generally, when there are several "basis function" materials that define a composition range, the following equation can be used:

[0038]

[0039] The resulting dQ / dV vs. V curves for the intermediate compositions can first be integrated, and the results can then be inversely calculated to produce the corresponding OCP curves in terms of potential (V) vs. capacity (mAh / g). The same approach can be used to obtain the OCP of material mixtures and individual materials.

[0040] A first exemplary group that can be used to establish the effectiveness of the method is LiNi 0.95-εx Co εx Al 0.05 O2. In this group, Ni is a component whose stoichiometric contribution also decreases linearly with decreasing charge associated with electrochemical characteristics, while Co is a component whose stoichiometric contribution increases proportionally. The stoichiometric contribution of the third component, aluminum (Al), remains constant at 0.05.

[0041] Two possible edge compositions that can be used to demonstrate the effectiveness of the proposed method are LiNi 0.95 Al 0.05 O2 (where the Co component is reduced to 0) and LiNi 0.80 Co 0.15 Al 0.05 O2, the standard nickel cobalt aluminum oxide (NCA) cathode. Although LiNi 0.80 Co 0.15 Al 0.05 O2 is a material from the Ni-rich NCA group (it has the smallest reported charge associated with the feature at ~4.2V (when present), but it is not considered a true edge composition because there should be compositions with lower Ni content as part of this class of materials. This composition lacks the feature at ~4.2V and has the highest possible Ni content of all NCA materials lacking it. Based on the comparison with LiNi 0.80 Co 0.15 Al 0.05 O2 and LiNi 0.95 Al 0.05 The charge of O2 at ~4.2 V is related to the characteristic, and the lower Ni content is extrapolated to predict that the lower edge composition should be LiNi 0.78 Co 0.17 Al 0.05 O2. However, as mentioned above, LiNi 0.80 Co 0.15 Al 0.05 O2 can be used as an alternative composition for lower edge OCP, as LiNi 0.78 Co 0.17 Al 0.05 The OCP data for O2 materials are unknown.

[0042] In order to verify the effectiveness of this method, LiNi 0.80 Co 0.15 Al 0.05 O2 and LiNi 0.95 Al 0.05 OCP curve of O2 material. LiNi 0.80 Co 0.15 Al 0.05 The OCP of O2 can be characterized by using a Li metal foil as both the counter electrode and the reference electrode in a coin cell with a charge of 4.7 mA / g 活性材料 The slow cycling rate of the current is used to lithiate and delithiate the material. From the data, the OCP curve can be approximated by averaging the lithiation and delithiation branches, as shown in Figure 1 shown. Figure 1 An OCP graph 100 is shown showing that LiNi 0.80 Co 0.15 Al 0.05 Potential (V) vs. capacity (Ah / g) characteristics of an O2 battery cell. Lithiation curve 102 shows the performance of the battery cell during lithiation cycles. Delithiation curve 104 shows the performance during delithiation cycles. Average curve 106 shows the average of lithiation curve 102 and delithiation curve 104.

[0043] LiNi 0.95 Al 0.05 The OCP curve of O2 is based on data available in the literature. This data includes the OCP curve of O2 at 10 mA / g 活性材料 The lithiation and delithiation curves were obtained at a slow cycling rate of . In order to convert the published data into OCP curves, the average values of the lithiation and delithiation curves were calculated. Both the literature data and the calculated average values are shown in Figure 2 middle. Figure 2 An OCP graph 200 is shown showing that LiNi 0.95 Al 0.05 Potential (V) vs. capacity (Ah / g) characteristics of an O2 battery. Lithiation curve 202 shows the battery performance during lithiation cycles. Delithiation curve 204 shows the performance during delithiation cycles. Average curve 206 shows the average of lithiation curve 202 and delithiation curve 204.

[0044] To apply the formula in equation (1), the derivatives of the two OCP curves can be calculated. The inverse of the derivative yields Figure 3 and Figure 4 The dQ / dV vs. V curve is shown in Figure 2. Figure 3 A graph 300 showing dQ / dV vs. V characteristics of a first material combination is shown. Figure 4A graph 400 is shown showing the dQ / dV vs. V characteristics of the second material combination. The first composition curve 302 shows that LiNi 0.80 Co 0.15 Al 0.05 The dQ / dV vs. V characteristics of the O2 composition. The second composition curve 304 shows the LiNi 0.95 Al 0.05 dQ / dV vs. V characteristics of O2 composition. Note that Figure 3 and Figure 4 The 4.2V characteristic is shown as a peak near 4.2V in the dQ / dV curve. When the dQ / dV value at the peak exceeds a preset threshold, the 4.2V characteristic is considered present. When there is no distinct peak near 4.2V in the dQ / dV curve, the 4.2V characteristic may not exist. That is, the dQ / dV curve is flat near 4.2V. Figure 3 A first performance curve 306 is shown for a composition generated using an ε value of 0.667. Figure 4 A second performance curve 406 is shown for a composition generated using an ε value of 0.333.

[0045] The first composition curve 302 and the second composition curve 304 may be used to generate LiNi according to the following equation 0.85 Co 0.15 Al 0.05 O2 (ε value is 0.667) and LiNi 0.90 Co 0.05 Al 0.05 The dQ / dV vs. V of O2 (ε value is 0.333) material is expressed as:

[0046]

[0047] To verify the accuracy of the method, it is beneficial if the target composition has been previously characterized. 0.85 Co 0.15 Al 0.05 O2 and LiNi 0.90 Co 0.05 Al 0.05 The dQ / dV vs. V representation of O2 materials has been previously synthesized and characterized in the literature. 0.95 Al 0.05 O2 material, lithiation and delithiation curves are at 10 mA / g 活性材料 In this way, an average curve can be formed and the corresponding dQ / dV vs. V curve can be obtained as described in this article.

[0048] To convert the published data into an OCP curve, the average of the lithiation and delithiation curves was calculated. The resulting OCP curve was plotted on Figure 5 and Figure 6 middle. Figure 5 A graph 500 is shown which compares LiNi 0.85 Co 0.15 Al 0.05 A reference curve 502 composed of O2 (ε value of 0.667) is compared to an estimated curve 504. Reference curve 502 can be obtained from the literature as described, and estimated curve 504 can be calculated using equation (3) with an ε value of 0.667 as described. Reference curve 502 can represent the average of the corresponding lithiation and delithiation curves. Figure 6 A graph 600 is shown which compares LiNi 0.90 Co 0.05 Al 0.05 A reference curve 602 composed of O2 (ε value of 0.333) is compared to an estimated curve 604. Reference curve 602 can be obtained from the literature as described, and estimated curve 604 can be calculated using equation (3) with an ε value of 0.333 as described. Reference curve 602 can represent the average of the corresponding lithiation and delithiation curves.

[0049] Figure 5 and Figure 6 The predicted LiNi 0.85 Co 0.15 Al 0.05 O2 (ε value is 0.667) and LiNi 0.90 Co 0.05 Al 0.05 The curves generated by the dQ / dV vs. V curve for O2 (ε value of 0.333) are compared. This is achieved by integrating the dQ / dV and then taking the inverse of the result to generate an OCP curve in the form of potential (V) vs. capacity (mAh / g) characteristics. The good agreement between the measured and predicted OCP (with deviations varying between 0.1-15 mV) provides a first example of validation of the methods disclosed herein. Note that although shown as curves, the OCP and dQ / dV characteristics can also be stored as tabulated values in a memory device.

[0050] A second exemplary group that can be used to establish the effectiveness of this method is LiNi 0.80-εx Co εx Mn 0.20 O2. In this group, Ni is again the component whose stoichiometric contribution also decreases linearly with decreasing charge associated with electrochemical characteristics, while Co is the component whose stoichiometric contribution increases proportionally. The stoichiometric contribution of the third component, manganese (Mn), remains constant at 0.20.

[0051] Two edge compositions that can be used to demonstrate the effectiveness of the proposed method are LiNi 0.80 Mn 0.20 O2 (where the Co component is reduced to 0) and LiNi 0.60 Co 0.20 Mn 0.20 O2.LiNi 0.80 Mn 0.20 O2 and LiNi 0.60 Co 0.20 Mn 0.20 The OCP curves for both O2 are based on data available in the literature. The data include lithiation and delithiation curves at 10 mA / g 活性材料 In order to convert the published data into OCP curves, the average of the lithiation and delithiation curves can be calculated. Both the literature data and the calculated average (representing the OCP curve) are shown in Figure 7 、 Figure 8 and Figure 9 middle. Figure 7 A first OCP graph 700 is shown including lithiation characteristics 702 , delithiation characteristics 704 , and average characteristics 706 of an NCM622 battery cell having a composition ratio of Ni:Co:Mn of 6:2:2. Figure 8 A second OCP graph 800 is shown including lithiation characteristics 802 , delithiation characteristics 804 , and average characteristics 806 of an NCM712 battery cell having a composition ratio of Ni:Co:Mn of 7:1:2. Figure 9 A third OCP graph 900 is shown including lithiation characteristics 902 , delithiation characteristics 904 , and average characteristics 906 of an NCM802 battery cell having a composition ratio of Ni:Co:Mn of 8:0:2.

[0052] As in the first embodiment, the derivative of the two OCP characteristics can be taken, and the inverse of the derivative produces a graph 1000 of the dQ / dV vs. V curve, as shown in FIG. Figure 10 shown. Figure 10 The NCM622 dQ / dV characteristics 1002 of the NCM622 composition and the NCM802 dQ / dV characteristics 1004 of the NCM802 composition are shown. The NCM622 characteristics 1002 and the NCM802 characteristics 1004 and equation (1) can be used to construct LiNi 0.70 Co 0.10 Mn 0.20 NCM712 dQ / dV characteristics 1006 of O2 (NCM712) material, which has LiNi 0.80 Mn 0.20 O2 and LiNi 0.60 Co 0.20 Mn0.20 O2 (ε = 1 - ε = 0.5, as shown in Equation (4)). Then the LiNi 0.70 Co 0.10 Mn 0.20 The predicted dQ / dV vs. V characteristics 1006 of the O2 material are integrated, and the inverse of the result can be used to generate an OCP curve in the form of potential (V) vs. capacity (mAh / g).

[0053]

[0054] LiNi 0.70 Co 0.10 Mn 0.20 O2 materials have been previously characterized in the literature, e.g. Figure 8 The data are included in the 10 mA / g 活性材料 The lithiation and delithiation curves were obtained at a slow cycling rate of 1.5 Å. The data can be converted to an OCP curve by taking the average of the lithiation and delithiation curves. Figure 11 A comparison graph 1100 is shown that directly compares OCP characteristics 1102 obtained from the literature with predicted OCP characteristics 1104 predicted using the disclosed method. The good agreement between the measured characteristics 1102 and the predicted OCP characteristics 1104 (with deviations varying between 0.1-15 mV) provides a second example of validating the method disclosed in this invention.

[0055] Although it has the general formula LiNi y-εx Co εx M z O2 (where z is a constant and M corresponds to Al or Mn) provides two examples, but LiNi 0.95-εx M 0.05+εx The electrochemical properties of O2 (where M = Al, Mn, and Mg, and M ≠ Co) are similar to those of LiNi 0.95-x Co x Al 0.05 The electrochemical properties of the O2 group make it possible to extend this method to LiNi 0.95-εx M 0.05+ε x O2 group. This approach can be further extended to all classes of cathode materials that contain at least two different transition metal oxide components LiM1 x M2 y O2 and have the following two general properties: within this class of materials, (a) the oxidation / reduction coulombs in the lithiation / delithiation curves are proportional to their cathode stoichiometric composition and (b) the potentials at which the oxidation / reduction processes occur are roughly equal (i.e., + / - 20 mV or less).

[0056] Using LiNi 0.95-0.15ε Co 0.15ε Al 0.05 The above method was verified using a Ni-rich NCA cathode group with O2 and 0<ε<1. 0.80-0.20ε Co 0.20ε Mn 0.20 The proposed method is further validated by a group of Ni-rich NMC cathodes with O2 and 0 < ε < 1. The results show that the proposed method can accurately predict the OCP properties of different compositions without the need for extensive characterization testing.

[0057] This method can also be extended to LiNi x-yε Co yε M z O2 (where z is a constant and M can correspond to various transition metals, including but not limited to Mg, Fe, Ti, Cr and others). This approach can also be extended to cathodes with similar x-yε Co yε M z O2 electrochemical properties of the general category of cathode groups, such as LiNi 0.95-x M 0.05+x O2 (where M = Al, Mn and Mg, but M ≠ Co).

[0058] Furthermore, this approach can be extended to all classes of cathode materials containing at least two different transition metal oxide components, i.e., in the case of LiM Ix M IIy The M in O2 I and M II , and satisfy the following two general properties: within this class of materials, (a) the oxidation / reduction coulombs in the lithiation / delithiation curves are proportional to their cathode stoichiometric composition and (b) the potentials at which the oxidation / reduction processes occur are roughly equal (i.e., + / - 20 mV or less).

[0059] This method can be extended to the general formula Li 1+x M 1-x Li-rich cathode materials for O2, where more than one Li unit per transition metal exists to allow anionic redox processes to occur (O 2- to O 1- ). Some examples include but are not limited to: Li 1.05 (Ni a Co b Mn c ) 0.95 O2、 Li 1.1 (Ni a Co bMn c ) 0.9 O2、Li 1.15 (Ni a Co b Mn c ) 0.85 O2、 Li 1.2 (Ni a Co b Mn c ) 0.8 O2、Li 1.25 (Ni a Co b Mn c ) 0.75 O2、Li 1.3 (Ni a Co b Mn c ) 0.7 O2 and Li{Li 1 / 3 (Ni a Co b Mn c ) 2 / 3}O2, where the sum of a, b, and c is 1. In some compositions, another dopant may be present that acts as a transition metal, for example, M' = Ti, V, Cr, Mn, Fe, Mo, Ru, or Ir, instead of Ni and Co. Other types of stabilizing elements such as M'' = Al, Si, Sc, Zn, Ga, Ge, Y, Zr, Nb, Sn, and W may also replace Mn.

[0060] The method can also be extended to Li-rich cathode materials with the general formula Li2MO3, where M = Si, Ti, V, Cr, Mn, Fe, Co, Ni, Ge, Zr, Mo, Ru, Rh, Pd, Sn, Hf, W, Os, Ir, Pt or Pb. Other compositions may include Li2M I M II O3, where M I is a redox metal that can exceed an oxidation state of 4+, such as V, Cr, Ru, Rh, Pd, W, Os, Ir, or Pt, and M II It is difficult to achieve stabilization agents beyond the 4+ oxidation state, such as Ti, Mn, Fe, Co, Ni, Ge, Zr, Sn or Pb (such as Li2Ru 0.75 Sn 0.25 O3、Li2Ru 0.75 Ti 0.25 O3, etc.). In some compositions, oxygen can be doped / substituted by anions such as F or Cl (e.g. Li2Mn 2 / 3 Nb 1 / 3 O2F).

[0061] This method can be extended to other Li-excess materials, such as Li 1.211 Mo 0.467 Cr 0.3 O2、Li 1.17 Ni 0.33 Ti 0.42 Mo 0.08 O2 or Li 1.25 Mn 0.5 Nb 0.25 O2. This method can be extended to olivine LiMPO4 (where M = Fe, Ni, Co and Mn) and spinel LiMn2O4 and LiNi 0.5 Mn 1.5 O4, and including Li4Ti5O 12 and Li4Mn5O 12 mixed spinel compounds.

[0062] The present method can be applied to lithium-ion secondary batteries, as well as to sodium, magnesium, potassium, calcium, aluminum, and chloride-ion secondary batteries. The method can be applied to any arbitrary hypothetical set of "base" materials, which defines a specific composition range for which there is sufficient experimental or theoretical evidence to show that the intermediate material OCP function follows equation (1) or a variant thereof (e.g., an integral form).

[0063] The battery management system can be programmed to include multiple open circuit potential functions Q for defining the edge composition of a group of materials i (U), where these functions have been measured experimentally and / or predicted computationally. BMS can define the factor (ε i value), which defines the capacity fraction of each edge composition comprising the material of the intermediate composition used in the battery.

[0064] The BMS may include i Value-weighted Q i (V) functions (or their derivatives dQ i / dV) to obtain the logic of the relationship Q(V) of the intermediate components. Q can be predefined for each edge component of a specific reference equilibrium voltage V0 i0 (V0) capacity value. The BMS may include a function that inverts the relationship Q(V) to obtain V(Q) (e.g., by linear interpolation between QA (VA) and QB (VB)).

[0065] The BMS may include logic to estimate the open circuit potential V of a target material of an intermediate composition using an estimate or measurement of the cell equilibrium potential. The BMS may be programmed to estimate the capacity or state of charge of the target material from the derived equation Q(V) for that material. The BMS may include logic to estimate the target active material in the cell, the amount of remaining cyclable external capacity of the additional active material, and the amount of cyclable lithium. For example, the BMS may implement strategies as disclosed in U.S. Patent No. 8,188,715 and U.S. Publication No. 2017 / 0194669, the entire contents of which are incorporated herein by reference.

[0066] Figure 12 A diagram of a battery management system 1200 is shown. BMS 1200 may include a battery pack 1208 electrically connected to an electrical load 1210 and a charging device 1212. Charging device 1212 may be an external device (e.g., an external charger) and / or an internal device (e.g., a generator). Battery management system 1200 may include a controller 1202. Controller 1202 may include a processing unit for executing instructions. Controller 1202 may include volatile and nonvolatile memory for storing programs and data. The memory may include any non-transitory memory (e.g., a non-transitory computer-readable medium), volatile memory, non-volatile memory, magnetic memory, optical memory, random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital or analog medium. BMS 1200 may include one or more voltage sensing units 1204 configured to measure voltages within and / or across terminals of battery pack 1208. The BMS 1200 and / or controller 1202 may include circuitry for isolating and scaling voltages. The BMS 1200 may include a current sensor 1206 configured to measure the current flowing through the battery pack 1208. The BMS 1200 may be coupled to a user interface 1214 configured to allow a user to input various parameters. For example, the user may enter the battery pack type via the user interface 1214. The battery pack type may include information about the battery pack chemistry. The battery type may allow the controller 1202 to select corresponding battery pack characteristics. If the battery pack type has corresponding characteristics stored in memory, the controller 1202 may retrieve and use the stored characteristics. If the battery pack type indicates a new composition, the controller 1202 may retrieve the corresponding edge composition and perform the above-described method to generate new battery pack characteristics. The new battery pack characteristics may be stored in memory for later use. In some configurations, the battery pack 1208 may be encoded with the battery pack type and communicated to the controller 1202. The battery pack type may indicate the general chemistry and epsilon value of the battery pack 1208.

[0067] Controller 1202 can be programmed to operate battery pack 1208 within a preferred operating range. Controller 1202 can operate load 1210 and charging device 1212 to operate battery pack 1208 within the preferred operating range. Controller 1202 can be programmed to determine the state of charge (SOC) of battery pack 1208. For example, based on voltage and current measurements, controller 1202 can implement an algorithm to calculate the SOC. The SOC calculation can utilize stored voltage / capacity characteristics of battery pack 1208. As an example, after a sufficient rest period, the terminal voltage measurement of battery pack 1208 can be correlated with capacity and SOC. The open-circuit voltage can be used to estimate the SOC by determining the capacity value corresponding to the measured open-circuit voltage. Controller 1202 can be programmed to charge and discharge battery pack 1208 based on the SOC. For example, if the SOC falls below a threshold, controller 1202 can request charging device 1212 to charge battery pack 1208.

[0068] Figure 13 A flowchart 1300 is shown of a possible order of operations for implementing the method. These operations may be implemented in one or more controllers programmed to perform these operations. In operation 1302, a battery pack chemistry may be determined or selected. In this step, a predetermined set of elements for forming the cathode may be selected or determined. The set of elements may be as described above. In operation 1304, a first composition may be determined as having a first combination of elements. The first composition may be a lower edge composition, which defines a proportion of the first element below which the 4.2V feature disappears. The first composition may include a minimum proportion of the first element and a maximum proportion of the second element.

[0069] In operation 1306, a first OCP characteristic may be generated corresponding to the first composition. The first OCP characteristic may define voltage (V) as a function of capacity (Q) of a battery pack constructed using the first composition. The first OCP characteristic may be derived from testing or published results. In operation 1308, the first OCP characteristic may be converted into a first dQ / dV characteristic by taking a derivative of the first OCP characteristic.

[0070] In operation 1310, a second composition may be determined as a second combination of elements. The second composition may be a higher edge composition defined such that a ratio of the second element is 0. The second composition may include the first element with the largest ratio and the second element with the smallest ratio.

[0071] In operation 1312, a second OCP characteristic may be generated corresponding to the second composition. The second OCP characteristic may define voltage (V) as a function of capacity (Q) of a battery pack constructed using the second composition. The second OCP characteristic may be derived from testing or published results. In operation 1314, the second OCP characteristic may be converted into a second dQ / dV characteristic by taking the derivative of the second OCP characteristic. Note that operations 1304-1308 may be performed in parallel with operations 1310-1314.

[0072] In operation 1316, the first and second dQ / dV characteristics may be combined to obtain a third dQ / dV characteristic corresponding to the target composition. The third dQ / dV characteristic may be generated by taking a first ratio of the first dQ / dV characteristic and a second ratio of the second dQ / dV characteristic. The first ratio may be defined by a target ratio of the first element present in the target composition. The sum of the first ratio and the second ratio is 1. A range may be defined by a first difference between a higher ratio of the first element present in the second composition and a lower ratio of the first element present in the first composition, and the first ratio may be defined by dividing the second difference between the target ratio and the lower ratio by the range.

[0073] In operation 1318, the third dQ / dV characteristic may be integrated to obtain a third OCP characteristic corresponding to the target composition. The third OCP characteristic may be output to a battery pack management system.

[0074] In operation 1320, a battery pack constructed using the target composition may be operated according to the third OCP characteristic. Operating the battery pack may include charging and discharging the battery pack. Operating the battery pack may also include generating battery pack parameters related to the battery pack, such as state of charge, capacity, charge power limit, discharge power limit, and battery life estimate.

[0075] The method offers the advantage of predicting battery characteristics before the battery pack is constructed. The method can use previously generated battery characteristics to derive characteristics for batteries with similar chemistries. When incorporated into a battery management system, the system can autonomously generate battery characteristics based on known characteristics. This provides significant flexibility, as the system can enable new battery pack types to operate without requiring extensive reprogramming or battery pack testing and characterization.

[0076] The processing disclosed herein, method or algorithm can be delivered to / implemented by a processing device, a controller or a computer, and the processing device, the controller or the computer may include any existing programmable electronic control unit or a dedicated electronic control unit. Similarly, the processing, method or algorithm can be stored as data and instructions that can be executed by a controller or a computer in many forms, and the many forms include but are not limited to information permanently stored on a non-writable storage medium such as a ROM device and information rewritably stored on a writable storage medium such as a floppy disk, a magnetic tape, a CD, a RAM device and other magnetic and optical media. The processing, method or algorithm can also be implemented in a software executable object. Alternatively, appropriate hardware components, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), state machines, controllers or other hardware components or devices or a combination of hardware, software and firmware components can be used to implement the processing, method or algorithm in whole or in part.

[0077] Although exemplary embodiments have been described above, it is not intended that these embodiments describe all possible forms encompassed by the claims. The words used in the specification are descriptive rather than restrictive, and it should be understood that various changes can be made without departing from the spirit and scope of the present disclosure. As previously mentioned, the features of the various embodiments can be combined to form other embodiments of the present invention that may not be explicitly described or illustrated. Although the various embodiments may have been described as providing advantages or being preferred in terms of one or more desired characteristics relative to other embodiments or prior art embodiments, those of ordinary skill in the art recognize that one or more features or characteristics can be compromised to achieve the desired overall system properties, depending on the specific application and implementation. These properties may include, but are not limited to, cost, strength, durability, life cycle cost, marketability, appearance, packaging, size, usability, weight, manufacturability, ease of assembly, etc. Therefore, with respect to one or more characteristics, embodiments described as being less desirable than other embodiments or prior art embodiments are not outside the scope of the present disclosure and may be desirable for specific applications.

Claims

1. A method comprising: determining a predetermined set of elements for a cathode of a lithium-ion battery, the predetermined set of elements comprising a first element and a second element, and determining a first composition and a second composition of the set, the first composition and the second composition comprising different proportions of the first element and the second element; generating a first open circuit potential (OCP) characteristic corresponding to the first composition and a second open circuit potential (OCP) characteristic corresponding to the second composition, wherein each open circuit potential (OCP) characteristic defines a voltage V as a function of a capacity Q of the lithium ion battery pack; converting a first open circuit potential (OCP) characteristic into a first dQ / dV characteristic, and converting a second open circuit potential (OCP) characteristic into a second dQ / dV characteristic; determining a third composition of the group having a predetermined ratio of the first element; generating a third dQ / dV characteristic of the third composition by taking a sum of a first ratio of the first dQ / dV characteristic and a second ratio of the second dQ / dV characteristic, wherein the first ratio is defined by a predetermined ratio and the sum of the first ratio and the second ratio is 1; integrating the third dQ / dV characteristic to generate a third open circuit potential (OCP) characteristic corresponding to the third composition; and charging and discharging the battery pack composed of the third composition using the third open circuit potential (OCP) characteristic, The set of predetermined elements includes at least two different transition metal oxide components, such that within a class of materials including the at least two different transition metal oxide components, the oxidation / reduction coulombs in the lithiation / delithiation characteristics are proportional to the corresponding stoichiometric compositions, and the potentials at which the oxidation / reduction processes occur are approximately equal. 2 . The method of claim 1 , wherein the first composition comprises a first element in a smallest proportion and a second element in a largest proportion.

3. The method of claim 1, wherein the second composition comprises a smallest proportion of the second element and a largest proportion of the first element.

4. The method of claim 1 , wherein a range is defined by a first difference between a higher ratio of the first element present in the second composition and a lower ratio of the first element present in the first composition, and the first ratio is defined by dividing a second difference between the preset ratio and the lower ratio by the range.

5. The method of claim 1, wherein the first open circuit potential (OCP) characteristic and the second open circuit potential (OCP) characteristic are approximated by averaging lithiation characteristics and delithiation characteristics corresponding to respective first and second compositions. The method according to claim 1 , wherein the set of predetermined elements includes Li, Ni, Co, M1, and O, wherein M1 corresponds to a transition metal, the first element is Ni, and the second element is Co. 7 . The method according to claim 1 , wherein the set of predetermined elements includes Li, Ni, O, and M1, wherein M1 is one of Al, Mn, or Mg, and the first element is Ni and the second element is M1. 8 . The method according to claim 1 , wherein the set of predetermined elements comprises Li, O, M1, and M2, wherein M1 and M2 are different transition metal oxide components.

9. A battery pack management system comprising: a memory for storing a plurality of characteristics corresponding to a plurality of compositions of cathodes for use in the battery; and A controller is programmed to, in response to receiving a parameter of a target composition, (i) determine a class of compositions to which the target composition belongs, (ii) retrieve from a memory a first characteristic corresponding to a first composition and a second characteristic corresponding to a second composition such that the first composition and the second composition belong to the class, (iii) generate an open circuit potential (OCP) characteristic of the target composition by taking a sum of a first ratio of the first characteristic and a second ratio of the second characteristic, wherein the first ratio is defined by a target ratio of a first element present in the target composition, and (iv) charge and discharge the battery pack according to the open circuit potential (OCP) characteristic, wherein the first characteristic is the open circuit potential (OCP) characteristic of the first composition and the second characteristic is the open circuit potential (OCP) characteristic of the second composition. 10 . The battery pack management system according to claim 9 , wherein the target ratio is between a first marginal ratio defining a ratio of the first element in the first composition and a second marginal ratio defining a ratio of the first element in the second composition. 11 . The battery management system according to claim 9 , wherein the first composition and the second composition include a first element and a second element, and wherein the first composition includes a largest proportion of the first element and a smallest proportion of the second element.

12. The battery management system according to claim 9, wherein the first characteristic is a dQ / dV characteristic of the first component, and the second characteristic is a dQ / dV characteristic of the second component, where Q represents the capacity of the lithium-ion battery pack and V represents the voltage of the lithium-ion battery pack.

13. The battery management system of claim 12 , wherein generating the open circuit potential (OCP) characteristic of the target composition further comprises integrating a dQ / dV characteristic obtained by taking a first ratio of the first characteristic and a second ratio of the second characteristic, wherein Q represents the capacity of the lithium-ion battery pack and V represents the voltage of the lithium-ion battery pack.

14. A method comprising determining a lower edge composition of a cathode for a battery, the composition comprising first and second transition metal components, the lower edge composition defining a proportion of the first transition metal below which the 4.2 V feature is absent; determining a higher edge composition in which the proportion of the second transition metal is 0; generating a first open circuit potential (OCP) characteristic of a lower edge component and a second open circuit potential (OCP) characteristic of an upper edge component; converting the first open circuit potential (OCP) characteristic into a first dQ / dV characteristic, and converting the second open circuit potential (OCP) characteristic into a second dQ / dV characteristic, where Q represents the capacity of the battery pack and V represents the voltage of the battery pack; determining a third composition having a predetermined ratio of the first transition metal; generating a third dQ / dV characteristic of the third component by taking a sum of a first ratio of the first dQ / dV characteristic and a second ratio of the second dQ / dV characteristic, wherein the first ratio is defined by the predetermined ratio and the sum of the first ratio and the second ratio is 1; integrating the third dQ / dV characteristic to generate a third open circuit potential (OCP) characteristic corresponding to a third composition; and Outputting the third open circuit potential (OCP) characteristic to a battery pack management system to charge and discharge the battery pack composed of the third component, The first and second transition metal components are such that the oxidation / reduction coulombs in the lithiation / delithiation characteristics are proportional to the corresponding stoichiometric compositions, and the potentials at which the oxidation / reduction processes occur are approximately equal.

15. The method of claim 14, wherein a range is defined by a difference between a higher proportion of the first transition metal present in a higher edge composition and a lower proportion of the first transition metal present in a lower edge composition, and the first proportion is defined by dividing the difference between the predetermined proportion and the lower proportion by the range. The method of claim 14 , wherein the 4.2V feature is defined as a dQ / dV peak exceeding a preset threshold.

17. The method of claim 14, wherein the first transition metal is nickel (Ni) and the second transition metal is cobalt (Co).

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