System for quantifying battery cell degradation to predict battery performance metrics

Abstract

The present disclosure provides a method for determining degradation of a battery cell having a lithium-rich manganese (LMR) cathode. The method includes measuring an open circuit voltage (OCV) of the battery cell during the cycle; predicting the OCV offset; determining an OCV hysteresis change of the battery cell; determining battery voltage attenuation using an accurate state of charge (SOC); measuring carbon dioxide (CO2) within the battery cell; measuring a gas composition within the battery cell; estimating a loss of the recyclable active material (LAM); fitting a group of reaction rate constants to the rate-limited kinetic model and the diffusion-limited model; determining a solid electrolyte interface (SEI) and metal Li thickness on the anode and a catholyte interface (CEI) thickness on the cathode; determining battery resistance and voltage drop; performance indicators are predicted using an electrochemical model to obtain remaining available cell life, cell state of health (SOH), cell voltage evolution, and cell resistance and impedance.

Description

Technical Field

[0001] This disclosure relates to a method for analyzing battery cells, and more specifically, to a method for predicting the degradation of LMR battery cells by measuring battery cell performance indicators. Background Technology

[0002] To power the electric motors in electric vehicles, battery packs consisting of numerous battery cells are required. Most of the cells in a battery pack can maintain a charge sufficient to power a vehicle for hundreds of miles. However, after many charge cycles, the cells degrade and may fail to retain enough charge. A common cause of poor cell quality is insufficient solid electrolyte interface (SEI) deposited on the anode of the cell. The SEI is formed by the breakdown of electrolyte solvents, additives, and salts. Other factors can also affect the cell's ability to retain sufficient charge.

[0003] Current practices for analyzing battery cell quality include performing discharge capacity checks (i.e., checking if the battery's capacity (measured in ampere-hours) is within defined specifications) and inventory retention and open-circuit voltage (OCV) monitoring (including holding inventory and checking if OCV decreases over time). While effective, such quality control measures can be time-consuming (potentially leading to significant quality leaks and increased inventory management costs) and data-scarce (i.e., unable to diagnose or predict). Other methods for analyzing battery cell quality include analyzing the SEI on the anode. However, this requires cutting open the battery cell (destroying the cell) to analyze the SEI.

[0004] Therefore, while current battery cell degradation prediction methods have achieved their intended purpose, a new and improved method is still needed to predict battery cell degradation. Summary of the Invention

[0005] According to several aspects of this disclosure, a method for determining the degradation of a battery cell having a lithium-rich manganese (LMR) cathode is provided. The method includes measuring the open-circuit voltage (OCV) of the battery cell during cycling to obtain test data; predicting OCV shifts by integrating the test data into a multi-site multi-reaction (MSMR) framework to obtain MSMR data; determining the OCV hysteresis change of the LMR battery cell at different stages of voltage activation of the battery cell; using the MSMR data and OCV hysteresis change, determining the battery voltage decay using accurate state of charge (SOC); measuring carbon dioxide (CO2) within the battery cell to determine a set of electrolyte consumption data for the battery cell; measuring the gas composition within the battery cell to determine a set of solid electrolyte interface (SEI) / cathode electrolyte interface (CEI) growth and lithium (Li) plating data for the battery cell; and based on the test data, by... The loss of recyclable active material (LAM) is estimated using the stoichiometric shift between the cathode and anode in the battery cell to obtain a set of reaction rate constants; this set of reaction rate constants is fitted to a rate-limited kinetic model and a diffusion-limited model to obtain a set of kinetic model parameters and a set of diffusion model parameters; the kinetic model parameters are used to determine the solid electrolyte interface (SEI) and Li metal thickness on the anode and the cathode electrolyte interface (CEI) thickness on the cathode; the solid electrolyte interface (SEI) and Li metal thickness, along with porosity reduction, are used to determine battery resistance and voltage drop; and electrochemical models are used to predict performance metrics to obtain remaining usable battery cell lifetime, state of health (SOH), battery voltage evolution, and battery resistance and impedance.

[0006] According to another aspect of this disclosure, measuring the open-circuit voltage (OCV) of the battery cell during cycling includes performing an OCV scan every 10 cycles for the first 50 cycles, and then performing an OCV scan every 100 cycles until the end of the battery cell's lifespan to obtain test data.

[0007] According to another aspect of this disclosure, a method for measuring the open-circuit voltage (OCV) of a battery cell during cycling includes three-electrode testing, LMR half-cell OCV, and microscanning cycling.

[0008] According to another aspect of this disclosure, OCV shift is predicted by integrating test data into a multi-site multiple response (MSMR) framework to obtain MSMR data, including smoothing the test data and generating dQ / dV curves for peak identification.

[0009] According to another aspect of this disclosure, the method for predicting OCV offset includes quantifying the OCV offset based on the peak position offset in the dQ / dV curve and determining the OCV hysteresis gap and midpoint.

[0010] According to another aspect of this disclosure, determining the OCV hysteresis change of an LMR cell at different stages of cell voltage activation includes determining the OCV hysteresis change at 3.8 V, 4.0 V, 4.2 V, 4.4 V, and 4.6 V.

[0011] According to another aspect of this disclosure, estimating the loss of recyclable active material (LAM) includes correlating CO2 volume with electrolyte self-decomposition and electrolyte volume reduction to identify dry conditions and loss of utilized electrode area.

[0012] According to another aspect of this disclosure, estimating the loss of recyclable active material (LAM) includes using NMR analysis to correlate the measured gaseous composition with the electrolyte consumed during SEI growth, Li electroplating, and other processes, wherein the gaseous composition includes at least one of ethylene (C2H4), ethane (C2H6), or hydrogen (H2).

[0013] According to another aspect of this disclosure, fitting a set of reaction rate constants to a rate-limited kinetic model and a diffusion-limited model to obtain a set of kinetic model parameters and obtaining a set of diffusion model parameters includes minimizing the squared error between the simulated and tested battery capacities.

[0014] According to another aspect of this disclosure, the method also includes defining lifecycle operations to include charging the battery cell by increasing the battery voltage to about 4.2 volts, and discharging the battery cell to reduce the battery cell voltage from about 4.2 volts to about 2.7 volts.

[0015] According to another aspect of this disclosure, the cathode is formed of a material having the formula xLi2MnO3·(1-x)LiMO2, wherein M represents at least one of nickel (Ni), cobalt (Co) or manganese (Mn), and wherein x is the proportion of lithium manganese oxide components.

[0016] According to another aspect of this disclosure, the anode is formed from at least one of graphite, SiOx, or Si.

[0017] According to several aspects of this disclosure, a method is provided for determining the quality of a battery cell having a lithium-rich manganese (LMR) cathode. The method includes measuring the open-circuit voltage (OCV) of the battery cell to obtain test data; integrating the test data into a multi-site multi-reaction (MSMR) framework to obtain MSMR data and predicting OCV shifts; determining the OCV hysteresis variation of the LMR battery cell at different stages of voltage activation of the battery cell; using the MSMR data and OCV hysteresis variation to determine battery voltage decay using accurate state of charge (SOC); measuring gas composition every 100 battery cell charge cycles to obtain a set of reaction rate constants; based on this set of reaction rate constants, determining a set of electrolyte consumption data and a set of SEI / CEI growth and lithium (Li) plating data for the battery cell; determining the loss of cyclic active material (LAM) by using a set of stoichiometric constants obtained by using the stoichiometric ratio shift between the cathode and anode in the battery cell; and determining battery resistance and voltage drop, remaining usable battery cell lifetime, and state of battery health (SOH) based on the SEI / CEI growth and lithium (Li) plating data and based on the MSMR data and OCV hysteresis variation.

[0018] According to another aspect of this disclosure, a method for measuring the open-circuit voltage (OCV) of a battery cell includes performing an OCV scan every 10 cycles for the first 50 cycles, and then performing an OCV scan every 100 cycles until the end of the battery cell's lifespan to obtain test data.

[0019] According to another aspect of this disclosure, a method for determining OCV hysteresis variations includes smoothing test data and generating dQ / dV curves for peak identification.

[0020] According to another aspect of this disclosure, a method for determining the OCV hysteresis change of an LMR battery cell includes determining the OCV hysteresis change at 3.8 volts, 4.0 volts, 4.2 volts, 4.4 volts, and 4.6 volts.

[0021] According to another aspect of this disclosure, a method for determining the loss of recyclable active material (LAM) includes correlating CO2 volume with electrolyte self-decomposition and electrolyte volume reduction to identify drying conditions and loss of utilized electrode area.

[0022] According to another aspect of this disclosure, the method includes a cathode formed of a material having the formula xLi2MnO3·(1-x)LiMO2, wherein M represents at least one of nickel (Ni), cobalt (Co) or manganese (Mn), and wherein x is the proportion of lithium manganese oxide components.

[0023] According to another aspect of this disclosure, the method includes an anode formed of at least one of graphite, silicon oxide (SiOx), or silicon (Si).

[0024] According to several aspects of this disclosure, a method is provided for determining the quality of a battery cell having a lithium-rich manganese (LMR) cathode. The method includes integrating open-circuit voltage (OCV) measurements of the battery cell into a multi-site multi-reaction (MSMR) framework to obtain MSMR data and predict OCV shift; using the MSMR data and OCV shift, determining battery voltage decay using accurate state of charge (SOC); measuring the gas composition during battery cell charging cycles to obtain a set of reaction rate constants; determining a set of electrolyte consumption data and a set of SEI / CEI growth and lithium (Li) plating data for the battery cell based on this set of reaction rate constants; determining a set of stoichiometric coefficients by finding a set of stoichiometric coefficients using the stoichiometric shift between the cathode and anode in the battery cell to determine the loss of cyclic active material (LAM); and determining battery resistance and voltage drop, remaining usable cell lifetime, and state of cell health (SOH) based on the SEI / CEI growth and lithium (Li) plating data, as well as based on the MSMR data and OCV hysteresis changes.

[0025] Further areas of application will become apparent from the description provided herein. It should be understood that these descriptions and specific examples are for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0026] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way.

[0027] Figure 1 The illustration is based on a system comprising a battery cell according to the present disclosure, showing battery components including a cathode, anode, positive current collector, and negative current collector, as well as a degradation determination system communicating with the battery cell.

[0028] Figure 2 As shown in this disclosure Figure 1 The diagram shows a degradation determination system.

[0029] Figure 3 This is a flowchart illustrating a method for determining the lithium-rich manganese (LMR) quality of a battery cell having a lithium-rich manganese (LMR) cathode according to the present disclosure. Detailed Implementation

[0030] The following description is merely exemplary in nature and is not intended to limit this disclosure, its application, or its uses.

[0031] refer to Figure 1A schematic diagram of a system 10 for determining cell quality is shown. System 10 generally comprises a cell 12 and a degradation determination system 14. System 10 is configured to use the degradation determination system 14 to determine and / or predict the degradation of cell 12. In the case of lithium-rich manganese (LMR) cell 12, LMR is susceptible to degradation associated with voltage decay and other side reactions leading to rapid cathode degradation and cell voltage loss. LMR degradation modes are currently not quantified, and it may be difficult to develop accurate cell performance models (e.g., capacity prediction and cell state estimation). Therefore, the system 10 and method disclosed herein are configured to diagnose and predict different degradation mechanisms of LMR-based cell cells under normal operating conditions, including OCV decay leading to capacity loss, gas generation, SEI / CEI growth, and increased cell resistance. Additionally, system 10 provides a physics-based high-fidelity cell model for simulating cell performance metrics.

[0032] Battery cell 12 is used to store electrical energy in the form of chemical energy. The battery cell 12 disclosed herein is typically a lithium-ion battery cell, and more specifically, a lithium-ion battery cell 12 having a manganese-rich (LMR) cathode. In other examples, battery cell 12 is a lithium-ion battery cell having other cathode materials (e.g., lithium cobalt oxide (LiCoO2) battery cell, lithium manganese oxide (LiMn2O4) battery cell, lithium iron phosphate (LiFePO4) battery cell, lithium nickel cobalt aluminum oxide (LiNiCoAlO2 or NCA) battery cell, lithium nickel manganese cobalt oxide (LiNiMnCoO2 or NMC) battery cell, lithium titanate (Li4Ti5O4) battery cell, etc.). 12 (Battery cell, etc.). It should be understood that, without departing from the scope of this disclosure, battery cell 12 may utilize battery chemistry other than lithium-ion. In an exemplary embodiment, battery cell 12 includes a cathode 16, an anode 18, an electrolyte 20 in contact with the cathode 16 and the anode 18, and a gas sensor 22.

[0033] Cathode 16 provides a lithium-ion source and determines the battery's capacity and average voltage. In this example, cathode 16 is made of a mixed metal oxide of lithium, nickel, manganese, and / or cobalt. For example, cathode 16 may be an LMR cathode comprising a material having the formula xLi₂MnO₃·(1-x)LiMO₂, where M represents a transition metal such as nickel (Ni), cobalt (Co), manganese (Mn), etc., and where x is the proportion of the lithium manganese oxide component. It should be understood that cathode 16 may include other materials suitable for forming the cathode.

[0034] When energy is needed, anode 18 stores and releases lithium ions received from cathode. In this example, anode 18 is formed of graphite, silicon oxide (SiOx), silicon (Si), combinations thereof, or any similar material. It should be understood that anode 18 may include other materials suitable for forming an anode.

[0035] Electrolyte 20 provides a medium between the cathode and anode through which lithium ions travel. In this example, electrolyte 20 comprises lithium salts (e.g., lithium hexafluorophosphate (LiPF6), lithium bis(trifluoromethanesulfonyl)imide (LiTFSI), lithium perchlorate (LiClO4), etc.) dissolved in a solvent (e.g., fluoroethylene carbonate (FEC), ethylene carbonate (EC), and dimethyl carbonate (DMC)). It should be understood that electrolyte 20 may include other materials suitable for forming the electrolyte.

[0036] Gas sensor 22 senses and detects gas within battery cell 12. Gas sensor 22 may include, for example, an electrochemical gas sensor that converts the presence of gas into an electrical signal that can be processed and interpreted as an electrical signal. Electrochemical sensors can detect gas through chemical reactions that convert into electric current. Other examples of gas sensor 22 may include semiconductor gas sensors, infrared gas sensors (e.g., carbon dioxide (CO2) sensors, methane (CH4) sensors), photoionization sensors for detecting volatile compounds, and / or catalytic bead sensors. It should be understood that a variety of suitable gas sensors can be used as gas sensor 22 within battery cell 12.

[0037] During discharge, when a load is applied to battery cell 12, Li + Ions move from the anode 18 to the cathode 16 via the electrolyte 20, and electrons (e) - The lithium moves from cathode 16 to anode 18 to provide energy to the battery load. When charging and when an external voltage is applied, Li... + Ions move from cathode 16 to anode 18 via electrolyte 20 and can be embedded in anode 18. Positive terminal 24 (or cathode current collector) and negative terminal 26 (or anode current collector) allow battery cell 12 to be connected to other systems to measure one or more states of battery cell 12 and / or provide power to external devices.

[0038] While cell 12 with an LMR cathode is generally stable, the LMR cathode is susceptible to degradation associated with voltage decay and other side reactions that lead to rapid cathode degradation and cell voltage loss. LMR cathode degradation modes have not yet been quantified, and accurate cell models for performance, such as those for capacity prediction and cell state estimation, have not been developed.

[0039] Within the scope of this disclosure, the state of charge (SOC) generally refers to the amount or concentration of lithium ions inserted into a lithium-ion intercalating material (i.e., a material capable of intercalating lithium ions) relative to the maximum capacity of lithium ions inserted into the lithium-ion intercalating material. The SOC of battery cell 12 quantifies the current charge level stored in battery cell 12 relative to the maximum charge capacity of battery cell 12. At the molecular level, the SOC of battery cell 12 refers to the distribution of lithium ions between cathode 16 and anode 18. More specifically, the SOC of battery cell 12 quantifies the amount or concentration of lithium ions inserted into anode 18 relative to the maximum capacity of lithium ions inserted into anode 18. In the example, when battery cell 12 is fully charged (i.e., the SOC of battery cell 12 is 100%), anode 18 is fully intercalated with lithium ions. When battery cell 12 discharges, lithium ions move from anode 18 to cathode 16 via electrolyte 20, causing a decrease in the lithium ion concentration in anode 18 and an increase in the lithium ion concentration in cathode 16, thereby reducing the SOC of battery cell 12.

[0040] Overcharging or over-discharging of battery cell 12 may damage components of battery cell 12, such as cathode 16 and / or anode 18, thus shortening the overall lifespan of battery cell 12. Therefore, it is advantageous to determine the state of charge (SOC) of battery cell 12 for battery management purposes. Generally, the SOC of battery cell 12 is not a directly measurable quantity but must be estimated using a mathematical model of the electrochemical processes occurring within battery cell 12. In a non-limiting example, the mathematical model is configured to determine or estimate the SOC of battery cell 12 based at least in part on the open-circuit voltage (OCV) of battery cell 12.

[0041] Open-circuit voltage (OCV) is the voltage of battery cell 12 when it is not connected to a load and has no current. OCV is a key parameter for understanding the battery's state of charge (SOC) and state of health (SOH). A higher OCV generally indicates a higher SOC. Open-circuit voltage (OCV) hysteresis refers to the fact that the battery's OCV varies depending on whether it is charging or discharging. This effect is particularly pronounced in lithium-ion batteries.

[0042] The solid electrolyte interphase (SEI) is a crucial component of lithium-ion batteries. The SEI (or SEI layer) forms on the surface of the anode 18 during initial charge and discharge cycles. The decomposition of the electrolyte 20 occurs at characteristic voltages and is accompanied by gas generation, which must be expelled from the battery cell 12. The gases generated during the battery formation process also provide data that can be used to assess the quality of the battery cell 12. Excessive gas generation may indicate poor quality of the battery cell 12. Excessive gas generation can be caused by a variety of reasons. For example, complete deactivation of electrolyte additives such as ethylene carbonate (VC) and ethylene ethylene carbonate (VEC) can lead to excessive consumption of ethylene carbonate (EC), resulting in gas generation. In this case, the battery cell 12 exhibits very poor charge retention during cycling. Furthermore, poor additive performance due to partial expiration and degradation can also lead to excessive EC consumption and increased gas generation.

[0043] A solid electrolyte interphase (SEI) is a thin film, typically about 100-120 nanometers (nm) thick, composed of various inorganic and organic compounds (e.g., lithium carbonate (Li₂CO₃), lithium fluoride (LiF), and alkyl lithium carbonate (ROCO₂Li)). The SEI plays a crucial role in battery performance and lifespan, allowing lithium ions to pass through while blocking electrons, which helps prevent further reactions that could degrade the battery cell 12. The SEI improves cycle performance and extends battery life by protecting the electrode materials. A common cause of poor battery cell quality is insufficient solid electrolyte interphase (SEI) deposited on the anode 18 of the battery cell 12.

[0044] The cathode electrolyte interphase (CEI) is a critical layer that forms on the surface of the cathode 16 in a lithium-ion battery. Similar to the SEI on the anode 18, the CEI is essential for battery performance and lifespan. The CEI layer is formed during battery operation through reactions between the cathode 16 and the electrolyte 20. This layer helps stabilize the cathode by preventing further unwanted reactions that can degrade battery performance over time.

[0045] The gas produced by battery cell 12 can provide data for evaluating the quality of battery cell 12. Excessive gas production may indicate poor quality of battery cell 12. Excessive gas production can be caused by a variety of reasons. For example, complete deactivation of electrolyte additives such as ethylene carbonate (VC) and ethylene ethylene carbonate (VEC) can lead to excessive consumption of ethylene carbonate (EC), resulting in gas production. In this case, battery cell 12 exhibits poor charge retention during cycling. Poor additive performance due to partial expiration and degradation can also lead to excessive EC consumption and increased gas production.

[0046] Generally, a small gas volume in battery cell 12 results in the highest charging capacity, while an increase in gas volume (e.g., due to EC reduction) is associated with a degradation of charging capacity over time. Excessive ethylene carbonate (EC) reduction consumes lithium salts in the electrolyte, which reduces the total available "lithium stock" in battery cell 12, thereby reducing the final charging capacity. Furthermore, poor electrolyte additive performance causes the SEI layer to break down more quickly. Therefore, additional EC reduction is required to maintain the SEI layer. An SEI layer formed primarily by EC reduction exhibits poor mechanical properties and greater thickness, performing worse than an SEI layer formed in the presence of electrolyte additives.

[0047] Refer again Figure 1 The degradation determination system 14 is used to determine and quantify the degradation of the LMR cathode and battery cell 12. The degradation determination system 14 is in electrical communication with the positive terminal 24 (cathode current collector) and the negative terminal 26 (anode current collector). In one embodiment, the degradation determination system 14 is physically coupled and fixed to the battery cell 12 (or battery pack) so that the degradation determination system 14 can operate even when the battery cell 12 is not installed in the battery pack. Although the degradation determination system 14... Figure 1 The degradation determination system 14 is shown as being fixed to the battery cell 12, but it should be understood that without departing from the spirit and scope of this disclosure, the degradation determination system 14 may be integrated into the housing of the battery cell 12 (or battery pack), disposed within the battery cell 12, and internally connected to the cathode 16 and anode 18, or otherwise integrated with the battery cell 12.

[0048] In another example, the degradation determination system 14 may be a modular component configured to be removable, installable, and replaceable on the battery cell 12. In yet another example, the degradation determination system 14 is located remotely from the battery cell 12 and is in electrical communication with the battery cell 12.

[0049] refer to Figure 2 A schematic diagram of a degradation determination system 14 is shown. In this example, the degradation determination system 14 includes at least a controller 28 and an interface circuit 30.

[0050] Controller 28 is used to implement method 100, such as Figure 3 As shown, the quality of lithium-rich manganese (LMR) battery cells is determined as described below. The controller 28 includes at least one processor 32 and a non-transitory computer-readable storage device or memory 34. The processor 32 may be a custom or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among a plurality of processors associated with the controller 28, a semiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a combination thereof, or generally a device for executing instructions.

[0051] Computer-readable storage device or memory 34 may include volatile and non-volatile storage devices such as read-only memory (ROM), random access memory (RAM), and keep-alive memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operational variables when the processor 32 is powered off. Computer-readable storage device or memory 34 may be implemented using multiple storage devices, such as programmable read-only memory (PROM), electrical PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, or other electrical, magnetic, optical, or combined storage devices capable of storing data, some of which represents executable instructions used by controller 28 to control degradation determination system 14. Controller 28 may also consist of multiple controllers that are electrically in communication with each other. Controller 28 may also include additional elements and / or modules, such as a real-time clock (RTC) module for measuring the elapsed time. In an exemplary embodiment, controller 28 is powered by connection to battery cell 12.

[0052] The controller 28 communicates electrically with the interface circuit 30. In an exemplary embodiment, the electrical communication is established using, for example, general purpose input / output (GPIO) pins, an internal integrated circuit (I2C) bus, a serial peripheral interface (SPI) bus, a parallel communication bus, etc. It should be understood that various additional communication protocols used for communicating with the controller 28 are within the scope of this disclosure.

[0053] Interface circuit 30 is used to connect controller 28 to positive terminal 24 (cathode current collector) and negative terminal 26 (anode current collector). In an exemplary embodiment, interface circuit 30 includes OCV measurement circuit 36 ​​and gas sensing circuit 38.

[0054] OCV measurement circuit 36 ​​is used to measure the voltage and OCV offset of LMR cell 12 during cycling based on the nonlinear voltage decay rate of LMR material. In a non-limiting example, OCV measurement circuit 36 ​​includes, for example, an analog-to-digital converter (ADC). OCV measurement circuit 36 ​​may also include additional components supporting voltage measurement, including, for example, a voltage follower, an input buffer, a multiplexer, etc. OCV measurement circuit 36 ​​also includes components that allow controller 28 to measure the current flowing into / out of positive terminal 24 (cathode current collector) and / or negative terminal 26 (anode current collector), including, for example, a shunt resistor, an electromagnetic current sensor, an ADC, etc.

[0055] Gas sensing circuit 38 is used to receive, interpret, process, and / or measure electrical signals from gas sensor 22. Gas sensing circuit 38 is in electrical communication with gas sensor 22 within battery cell 12. In an exemplary embodiment, gas sensing circuit 38 includes signal conditioning circuitry (e.g., operational amplifier, low-pass filter, and / or analog-to-digital converter (ADC) and / or microcontroller or microprocessor). In some examples, gas sensing circuit 38 includes, for example, relays, transistors, etc. It should be understood that the OCV measurement circuit 36 ​​of interface circuit 30 and / or gas sensing circuit 38 may also include additional passive or active analog and / or digital electronics, such as resistors, capacitors, inductors, filters, amplifiers, power electronic devices, digital-to-analog converters (DACs), etc. In the example, interface circuit 30 is powered via connections to the positive terminal 24 (cathode current collector) and / or negative terminal 26 (anode current collector) of battery cell 12. Additionally, interface circuit 30 is in electrical communication with controller 28.

[0056] refer to Figure 3 A flowchart of a method 100 for determining the quality of lithium-rich manganese (LMR) battery cells is shown. The method begins at box 102.

[0057] Block 102 depicts the measurement of the open-circuit voltage (OCV) of the battery cell during cycling to obtain test data. Controller 28 enables interface circuitry 30 and OCV measurement circuitry 36 to collect raw LMR test data and measure voltages from the positive terminal 24 (cathode current collector) and negative terminal 26 (anode current collector) to determine the OCV offset of the LMR battery cell 12 during cycling based on the nonlinear decay rate of the LMR material (e.g., the material of cathode 16 and / or anode 18). For example, OCV measurement circuitry 36 can measure or obtain OCV scans every 10 charge and discharge cycles and every 100 cycles during the first 50 cycles of the battery life until the end of the battery life. Defining lifetime cycling operation may include charging the battery cell by increasing the battery cell voltage to about 4.2 volts and discharging the battery cell to reduce the battery cell voltage from about 4.2 volts to about 2.7 volts. In this context, those skilled in the art will understand the term "about". Alternatively, the term "about" is understood to mean + / - 0.1 volts. In the example, a three-electrode test can be used to cycle LMR / graphite coin and pouch cells using a C / 5 current and a C / 100 OCV measurement every 10-100 cycles to identify LMR and full-cell OCV offsets. In another example, OCV measurement circuit 36 ​​can measure and scan LMR half-coin cells with different lower and upper cutoff voltages to identify LMR OCV hysteresis changes, thereby determining a baseline LMR OCV measurement. Furthermore, OCV measurement circuit 36 ​​can perform micro-scan cycles at different C rates to characterize voltage hysteresis transitions.

[0058] Box 104 depicts the prediction of OCV offset to obtain MSMR data by integrating test data into a multi-site multiple response (MSMR) framework. Prediction may include implementing an analysis tool using controller 28 and / or interface circuitry 30, which smooths the test data obtained at box 102 and generates a dQ / dV curve for peak location identification. The peak location offset in the dQ / dV curve can be used to quantify the OCV offset and determine the OCV hysteresis gap and midpoint. For example, the dQ / dV curve and peak location identification can be determined using the following two equations, where the fractional Li occupancy x is described as a function of voltage U, and where the differential dx / dU can be used to determine the peak indicating the individual response / graph. In these equations, f is a scaling factor, and ω j Width parameters for each component.

[0059]

[0060] The OCV hysteresis midpoint and gap can be defined using the following equations.

[0061]

[0062] The following equation can be fitted to the microscan data to calculate the OCV transition parameters, where -1≤ζ≤1 is the OCV transition variable and K is a constant fitted to the microscan test data.

[0063] U(x)=U mid (x)+U gap (x)ζ

[0064]

[0065] Box 106 depicts the determination of the OCV hysteresis change of the LMR cell at different stages of cell voltage activation. For example, the OCV measurement circuit 36 ​​and / or controller 28 can quantify the OCV hysteresis change of the LMR in different phases (e.g., layered → spinel) by measuring the voltage activation at specific cutoff voltages (e.g., at 3.8V, 4.0V, 4.2V, 4.4V, and / or 4.6V).

[0066] Box 108 depicts the use of MSMR data obtained at Box 104 and OCV hysteresis changes obtained at Box 106 to determine battery voltage degradation using accurate state of charge (SOC). Controller 28 can determine battery voltage degradation using accurate state of charge (SOC) measurements over the cycle life of battery cell 12 from the OCV model used in Box 104, where a rate-invariant hysteresis across multiple cycles is determined in Box 106 to obtain a degradation metric reflecting structural changes within battery cell 12.

[0067] Box 110 depicts the measurement of carbon dioxide (CO2) within battery cell 12 to determine a set of electrolyte consumption data for battery cell 12. CO2 is generated as the electrolyte 20 in the battery cell decomposes over time. The measured amount of CO2 can be correlated with electrolyte self-decomposition and electrolyte volume reduction to identify drying conditions and loss of utilized electrode area. In this example, controller 28 and / or interface circuitry 30 can cause gas sensor 22 to detect the amount of CO2 within battery cell 12 every 100 charging cycles. Additionally, controller 28 can correlate CO2 with the degradation of electrolyte 20.

[0068] Box 112 depicts the measurement of gas composition within battery cell 12 to determine a set of solid electrolyte interface (SEI) / cathode electrolyte interface (CEI) growth and lithium (Li) plating data for battery cell 12. Certain gas components present within battery cell 12 indicate SEI growth, lithium (Li) plating, and electrolyte consumption and can be correlated with SEI growth, lithium (Li) plating, and electrolyte consumption. Some examples of gases measured by gas sensor 22 may include at least one of ethylene (C2H4), ethane (C2H6), hydrogen (H2), or combinations thereof. In the example, controller 28 and / or gas sensing circuitry 38 may cause gas sensor 22 to measure and / or detect gas components every 100 cycles. In some cases, the identification of gas components may be achieved using, for example, nuclear magnetic resonance (NMR) spectroscopy analysis.

[0069] Box 114 depicts estimating the loss of cyclic active material (LAM) by using the stoichiometric shift between the cathode 16 and anode 18 in cell 12 based on test data to obtain a set of reaction rate constants. For example, controller 28 can use the stoichiometric shift between anode 18 and cathode 16 to identify the range of electrode capacity used and the loss of cyclic active material (LAM) on the electrodes using test data obtained from cycling measurements. LAM is a degradation process in cell 12 in which the active material involved in the electrochemical reaction becomes inactive or unusable for cycling. LAM can occur due to electrode breakage, side reactions, and / or phase transitions. These factors contribute to the overall capacity decay of cell 12.

[0070] Box 116 illustrates fitting this set of reaction rate constants to a rate-limited kinetic model and a diffusion-limited model to obtain a set of kinetic model parameters and a set of diffusion model parameters. For example, controller 28 can use the following equation to determine the rate-limited kinetic model for SEI growth and lithium (Li) plating, where j SEI For the current density associated with SEI formation, k SEI c is the rate constant for the SEI formation reaction. ECThe concentration of the electrolyte component (e.g., ethylene carbonate (EC)), α is the charge transfer coefficient, F is the Faraday constant, R is the universal gas constant, T is the Kelvin temperature, and φ is the stoichiometric coefficient. S φ is the potential of the solid electrode. e Let j be the potential of the electrolyte. tot R is the total current density. film U is the resistance of the SEI film. SEI This is the equilibrium potential of SEI.

[0071]

[0072] Controller 28 can determine electrolyte solvent decomposition using the following equation (diffusion-limited model), where j SEI,diff For the diffusion confinement current density associated with SEI formation, F is the Faraday constant, a n This represents the specific surface area of ​​the electrode. c is the diffusion coefficient of the electrolyte component (e.g., ethylene carbonate (EC)) within the SEI. EC The concentration of electrolyte components, δ SEI The thickness of the SEI layer.

[0073]

[0074] Fitting this set of reaction rate constants to a rate-limited kinetic model and a diffusion-limited model to obtain a set of kinetic model parameters and a set of diffusion model parameters can include minimizing the squared error between the battery capacity from simulation and test measurements.

[0075] Box 118 depicts the determination of the solid electrolyte interface (SEI) and metallic Li thickness on the anode and the cathode electrolyte interface (CEI) thickness on the cathode 16 using the kinetic model parameters obtained at box 116. The reduction in electrolyte (e.g., EC) volume and pore volume due to EC consumption can be determined by controller 28 using the following equation, where... and dV EC The differential volume of electrolyte 20, dn EC M is a trace component of electrolyte 20. EC ρ is the molar mass of electrolyte 20. EC The density of electrolyte 20.

[0076]

[0077] Controller 28 can determine the thickness variation of the SEI layer using the following equation, where Let j be the rate of change of the SEI layer thickness with respect to time. SEI For the current density associated with SEI formation, a nWhere is the specific surface area of ​​the cathode, F is the Faraday constant, and M is the specific surface area of ​​the cathode. SEI Let be the molar mass of the SEI layer, and ρ SEI The density of the SEI material.

[0078]

[0079] Box 120 illustrates the use of SEI and metallic Li thickness, along with a decrease in porosity, to determine cell resistance and voltage drop. Controller 28 can use the following equation to determine the decrease in electrode porosity due to the thickening of the SEI layer, where... a is the rate of change of porosity over time. n Let be the specific surface area of ​​the cathode, and The value represents the rate of change of the SEI layer thickness over time.

[0080]

[0081] Controller 28 can determine the increase in resistance due to SEI growth using the following equation, where R SEI ε is the resistance of the SEI layer. n δ represents the porosity of the SEI layer. SEI Let k be the thickness of the SEI layer, and k SEI The ionic conductivity of the SEI layer is given.

[0082]

[0083] Box 122 depicts the use of electrochemical models to predict performance metrics. Performance metrics may include the remaining usable lifetime of the battery cell, the state of health (SOH) of the battery cell, the voltage evolution of the battery cell, and the resistance and impedance of the battery cell. In the example, the electrochemical model may include a pseudo-two-dimensional (P2D) model, also known as a Newman model. The P2D model simplifies the three-dimensional structure of the battery to a two-dimensional framework, thereby determining performance metrics in terms of computational efficiency while still capturing the fundamental electrochemical processes. The P2D model can use determined and acquired reaction models with hysteresis OCV, SEI and CEI growth, and Li electroplating, stoichiometric shifts due to loss of cyclic active material (LAM), and electrolyte volume reduction to predict the remaining usable lifetime of the battery cell, the state of health (SOH), the voltage evolution of the battery cell, and the resistance and impedance of the battery cell.

[0084] The system 10 and method 100 disclosed herein for predicting and determining LMR cell degradation offer several advantages. LMR cells are highly susceptible to degradation associated with voltage decay and other side reactions leading to rapid cathode degradation and cell voltage loss. System 10 and method 100 are configured to diagnose and predict various degradation mechanisms of LMR-based cells under normal operating conditions, including OCV decay leading to capacity loss, gas generation, SEI / CEI growth, and increased cell resistance. Additionally, system 10 provides a physics-based high-fidelity cell model of LMR-based cells that can be used to simulate cell performance metrics.

[0085] The descriptions in this disclosure are merely exemplary in nature, and changes that do not depart from the spirit and scope of this disclosure are intended to fall within its scope. Such changes should not be considered as departing from the spirit and scope of this disclosure.

Claims

1. A method for determining the degradation of a battery cell having a lithium-rich manganese LMR cathode, comprising: The open-circuit voltage (OCV) of the battery cell is measured during cycling to obtain test data; MSMR data are obtained by predicting OCV shifts by integrating the test data into a multi-site multi-response MSMR framework; Determine the OCV hysteresis change of the battery cell at different stages of voltage activation of the battery cell; Using the MSMR data and the OCV hysteresis change, the battery voltage decay is determined with accurate State of Charge (SOC). Measure the carbon dioxide (CO2) inside the battery cell to determine a set of electrolyte consumption data for the battery cell; The gas composition within the battery cell was measured to determine a set of solid electrolyte interphase (SEI) / cathode electrolyte interphase (CEI) growth and lithium Li plating data for the battery cell. Based on the test data, the loss of the cyclic active material LAM is estimated by using the stoichiometric ratio shift between the cathode and anode in the battery cell to obtain a set of reaction rate constants; The set of reaction rate constants is fitted to the rate-limited kinetic model and the diffusion-limited model to obtain a set of kinetic model parameters and a set of diffusion model parameters; The kinetic model parameters are used to determine the solid electrolyte interface (SEI) and metallic Li thickness on the anode and the cathode electrolyte interface (CEI) thickness on the cathode. The battery resistance and voltage drop were determined using the solid electrolyte interface (SEI) and the reduction in Li thickness and porosity. as well as Electrochemical models are used to predict performance metrics to obtain remaining usable cell lifetime, state of health (SOH), cell voltage evolution, and cell resistance and impedance.

2. The method according to claim 1, wherein, Measuring the open-circuit voltage (OCV) of the battery cell during cycling involves performing an OCV scan every 10 cycles for the first 50 cycles, and then every 100 cycles until the end of the battery cell's lifespan to obtain test data.

3. The method according to claim 1, wherein, Measuring the open-circuit voltage (OCV) of the battery cell during cycling includes three-electrode testing, LMR half-cell OCV, and microscan cycling.

4. The method according to claim 1, wherein, OCV shift is predicted by integrating the test data into a multi-site multi-response MSMR framework to obtain MSMR data, including smoothed test data and dQ / dV curves for peak identification.

5. The method according to claim 4, wherein, Predicting OCV offset involves quantifying the OCV offset based on the peak position offset in the dQ / dV curve and determining the OCV hysteresis gap and midpoint.

6. The method according to claim 1, wherein, Determining the OCV hysteresis variation of the LMR battery cell at different stages of voltage activation of the battery cell includes determining the OCV hysteresis variation at 3.8 V, 4.0 V, 4.2 V, 4.4 V, and 4.6 V.

7. The method according to claim 1, wherein, Estimate the loss of recyclable active material (LAM) by correlating CO2 volume with electrolyte self-decomposition and electrolyte volume reduction to identify drying conditions and loss of utilized electrode area.

8. The method according to claim 1, wherein, Estimating the loss of recyclable active material LAM involves using NMR analysis to correlate the measured gas composition with the electrolyte consumed during SEI growth, Li electroplating, and other processes, wherein the gas composition includes at least one of ethylene (C2H4), ethane (C2H6), or hydrogen (H2).

9. The method according to claim 1, wherein, The set of reaction rate constants is fitted to the rate-limited kinetic model and the diffusion-limited model to obtain a set of kinetic model parameters and a set of diffusion model parameters, including minimizing the squared error between the simulated and tested battery capacities.

10. The method according to claim 1, further comprising: Define lifecycle operations to include: The battery cell is charged by increasing the battery voltage to approximately 4.2 volts; and The battery cell is discharged to reduce the battery voltage from about 4.2 volts to about 2.7 volts.

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