A method and system for early warning of overcharge damage risk of a lithium ion battery

Abstract

The present application belongs to the technical field of lithium ion battery safety, and provides a lithium ion battery overcharge damage risk early warning method and system, obtains voltage, current and capacity data and the like of a lithium ion battery sample in abnormal working condition test testing; based on the internal physical and chemical reaction mechanism of the battery, a lithium ion battery electrochemical-thermal coupling damage model is established; key parameters are identified using the same, the change of the key parameters in the battery damage evolution process is solved, a damage degree quantitative evaluation model is constructed, the damage degree of the target battery is calculated according to the quantitative relationship between the electrochemical parameters and the battery damage degree; according to the capacity attenuation, the battery health state and the battery performance deterioration mechanism of the side reaction, the retirement damage threshold value is configured, the damage degree of the target battery is compared with the retirement damage threshold value, the damage risk grade of the target battery is determined, and early warning is carried out. The present application can realize quantitative evaluation of the battery damage degree, and provide a scientific basis for retirement decision.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery safety technology, specifically relating to a method and system for early warning of overcharge damage risk in lithium-ion batteries. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In energy storage systems, lithium-ion batteries have gradually become an important energy storage medium in fields such as electrical energy storage and electric vehicles due to their advantages such as high energy density, long cycle life, and low self-discharge rate. However, lithium-ion batteries have poor thermal stability of electrode materials and flammability of electrolytes. Under extreme conditions, problems such as overcharging, over-discharging, overheating, or short circuits may lead to safety hazards.

[0004] These abnormal operating conditions trigger side reactions, including but not limited to lithium deposition, electrolyte decomposition, and transition metal ion dissolution. The accumulation of these side reactions can lead to increased film resistance, decreased conductivity, and abnormal voltage response within the battery, ultimately resulting in a sharp decline in battery capacity, reduced thermal stability, and even thermal runaway, fire, or explosion, seriously threatening the safety of the battery system. Therefore, researching the failure mechanisms and damage evolution patterns of batteries under abnormal operating conditions has become an important direction for ensuring the safe operation of battery systems.

[0005] Although existing research has explored the performance degradation of lithium-ion batteries under abnormal operating conditions, most of the current research is limited to the analysis of surface phenomena and lacks systematic modeling of internal side reaction mechanisms. In particular, the quantitative relationship between the evolution of side reactions and the degree of battery damage has not been fully studied. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a method and system for early warning of overcharge damage risks in lithium-ion batteries. This invention can predict changes in electrochemical parameters of the battery during the overcharging process, thereby quantitatively assessing the degree of battery damage. Based on the assessed degree of battery damage, the corresponding risk level is determined, and a multi-level early warning response mechanism is constructed, enabling refined control of different damage stages by the battery management system.

[0007] According to some embodiments, the present invention adopts the following technical solution:

[0008] A method for early warning of overcharge damage risk in lithium-ion batteries includes the following steps:

[0009] Obtain voltage, current, and capacity data of lithium-ion battery samples during abnormal operating condition tests;

[0010] Based on the internal physicochemical reaction mechanism of the battery, an electrochemical-thermal coupling damage model for lithium-ion batteries is established.

[0011] Using the aforementioned lithium-ion battery electrochemical-thermal coupled damage model, key parameters are identified, the changes in key parameters during the battery damage evolution process are solved, and a quantitative assessment model for the degree of damage is constructed.

[0012] Using the aforementioned quantitative damage assessment model, the damage level of the target battery is calculated based on the quantitative relationship between electrochemical parameters and battery damage level.

[0013] Based on the degradation mechanism of battery performance caused by capacity decay, battery health status, and side reactions, a retirement damage threshold is configured. By comparing the damage level of the target battery with the retirement damage threshold, the damage risk level of the target battery is determined and an early warning is issued.

[0014] As an alternative implementation method, the process of obtaining voltage, current and capacity data of lithium-ion battery samples in abnormal operating condition tests includes: conducting cyclic tests on lithium-ion battery samples under overcharge, room temperature and temperature below the set value, respectively, and obtaining voltage, current and capacity change curves.

[0015] As an alternative implementation method, the process of establishing an electrochemical-thermal coupled damage model for lithium-ion batteries based on the internal physicochemical reaction mechanism of the battery includes: considering the open-circuit voltage affecting battery voltage change, resistance loss in the conductive path, polarization behavior of interface reaction, and mass transfer limitation effect caused by concentration gradient, to construct an electrochemical model for lithium-ion batteries; based on the principle of energy conservation, incorporating Joule heat, side reaction heat, and heat conduction factors to construct a thermal model to characterize the temperature rise law and heat distribution change of the battery under different operating conditions, and coupling the constructed electrochemical model and thermal model.

[0016] As an alternative implementation, the process of identifying key parameters using the lithium-ion battery electrochemical-thermal coupling damage model includes: identifying and extracting key parameter changes caused by side reactions by establishing a mapping relationship between parameters and reaction pathways, specifically including: increased film resistance caused by lithium deposition, decreased conductivity caused by changes in electrolyte concentration, and the impact of metal ion migration on interface stability.

[0017] As an optional implementation method, the process of constructing a quantitative damage assessment model includes: using the identified key parameters as core indicators, assigning corresponding influencing factors to each, constructing a quantitative damage assessment model, calculating the damage level of the battery under the current abnormal operating conditions, and mapping its numerical range to the continuous state process of the battery from health to retirement. The quantitative damage assessment model is as follows: ;

[0018] ;

[0019] ;

[0020] Where Dtotal represents the final total damage level of the battery, fT(T) is the temperature response function, and Dchem is the concentration-time dominant damage term of the battery caused by electrochemical side reactions under abnormal operating conditions. τ This is the lithium deposition reaction damage weighting factor, which reflects the weight of the influence of lithium deposition side reactions on the degree of battery damage. ξ The lithium-ion concentration sensitivity coefficient characterizes the sensitivity of the deposited lithium concentration to battery damage, reflecting the sensitivity of the degree of battery damage to changes in lithium concentration. ν This is the transition metal ion dissolution reaction damage weighting factor, which reflects the influence of transition metal ion concentration on battery damage. θ 1 represents the influence coefficient of transition metal ion concentration, reflecting the relative intensity of the influence of changes in transition metal ion concentration on battery damage; ω This is the electrolyte oxidation reaction damage weighting factor, which reflects the degree of influence of electrolyte concentration on battery damage. θ 2 represents the electrolyte concentration influence coefficient, reflecting the relative strength of the impact of changes in electrolyte concentration on battery damage. λ This is the temperature deviation sensitivity coefficient; T opt For optimal operating temperature; T The experimental ambient temperature, c Co This represents the concentration of transition metal ions. c ele Electrolyte concentration; c li This represents the lithium concentration.

[0021] As an alternative implementation method, a process for configuring a retirement damage threshold is carried out based on the battery performance degradation mechanism caused by capacity decay, battery health status, and side reactions. Based on the numerical range of the damage degree of the battery sample, combined with changes in capacity decay, battery health status, and concentration, four progressively increasing critical values ​​for battery damage degree are set. The risk level is determined based on the range of the critical values ​​for battery damage degree that the damage degree of the target battery falls into. Each risk level is equipped with an independent early warning mechanism.

[0022] As an alternative implementation, if the damage level of the target lithium-ion battery is greater than or equal to a preset critical value for battery damage level and less than a second critical value, it is classified as a first risk level; if the damage level of the target lithium-ion battery is greater than or equal to the second critical value and less than a third critical value, it is classified as a second risk level; and if the damage level of the target lithium-ion battery is greater than or equal to the third critical value, it is classified as a third risk level. The higher the risk level, the shorter the determined time to retirement.

[0023] A lithium-ion battery overcharge damage risk warning system includes:

[0024] The data acquisition module is configured to acquire voltage, current and capacity data of lithium-ion battery samples during abnormal operating condition tests.

[0025] The coupling model construction module is configured to establish an electrochemical-thermal coupling damage model for lithium-ion batteries based on the internal physicochemical reaction mechanism of the battery.

[0026] The damage degree quantitative assessment model construction module is configured to use the lithium-ion battery electrochemical-thermal coupled damage model to identify key parameters, solve the changes of key parameters during the battery damage evolution process, and construct a damage degree quantitative assessment model.

[0027] The damage assessment module is configured to use the damage assessment model to calculate the damage level of the target battery based on the quantitative relationship between electrochemical parameters and battery damage level.

[0028] The risk assessment and early warning module is configured to set a retirement damage threshold based on the battery performance degradation mechanism caused by capacity decay, battery health status, and side reactions. By comparing the damage level of the target battery with the retirement damage threshold, the module determines the damage risk level of the target battery and issues an early warning.

[0029] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the steps in the above method.

[0030] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method described above.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] (1) The present invention constructs an electrochemical-thermal coupling model, which systematically considers the key side reaction mechanisms in the abnormal working conditions. It can accurately extract a variety of electrochemical parameters related to damage evolution, establish a quantitative assessment method for battery damage, and make the assessment dimensions more comprehensive and the prediction ability stronger. It effectively breaks through the limitation of traditional methods in slow damage identification.

[0033] (2) Based on the damage model and capacity decay trend, this invention proposes a method for setting and judging the retirement damage threshold, which realizes the assessment of battery usage status and the determination of reuse / retirement. It has good versatility and scalability, and provides a reliable theoretical basis and data support for retirement decision-making.

[0034] (3) The present invention further proposes a risk level system based on the degree of damage, establishes a graded early warning mechanism, realizes early warning control of the battery from normal use to failure, and significantly improves the safety, intelligence and management accuracy of the battery system.

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0036] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0037] Figure 1 The following are the side reaction evolution trends, voltage and current data in the overcharge test of this invention embodiment, wherein (a) is a schematic diagram of the side reaction evolution trend and (b) is the voltage and current data;

[0038] Figure 2 This is a curve showing the change in battery capacity with the number of overcharge cycles during overcharging in an embodiment of the present invention;

[0039] Figure 3 These are the battery charge-discharge cycle test voltage, current and temperature data under normal temperature (25℃) conditions according to embodiments of the present invention, wherein (a) is voltage data, (b) is current data and (c) is temperature data;

[0040] Figure 4 These are the battery charge-discharge cycle test voltage, current and temperature data under low temperature (5°C) conditions according to an embodiment of the present invention, wherein (a) is voltage data, (b) is current data and (c) is temperature data;

[0041] Figure 5 This is a curve showing the change in battery capacity with the number of overcharge cycles under normal temperature (25°C) conditions according to an embodiment of the present invention.

[0042] Figure 6 This is a curve showing the change in battery capacity with the number of overcharge cycles under low-temperature (5°C) conditions according to an embodiment of the present invention.

[0043] Figure 7 This is a battery health status diagram during an overcharge cycle according to an embodiment of the present invention;

[0044] Figure 8 This is a curve showing the change in battery damage according to an embodiment of the present invention;

[0045] Figure 9 These are the capacity decrease rate and capacity change acceleration curves of an embodiment of the present invention;

[0046] Figure 10 This is the voltage change curve of an embodiment of the present invention after experiencing 8 overcharge cycles and then undergoing 20 normal charge-discharge cycles.

[0047] Figure 11 This is a diagram illustrating the decommissioning risk level classification mechanism according to an embodiment of the present invention;

[0048] Figure 12 This is a flowchart of the multi-level early warning management process according to an embodiment of the present invention;

[0049] Figure 13 This is a flowchart of the lithium-ion battery overcharge damage risk warning method according to an embodiment of the present invention. Detailed Implementation

[0050] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0052] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0053] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0054] Example 1

[0055] A method for early warning of overcharge damage risk in lithium-ion batteries, such as Figure 13 As shown, it includes the following steps:

[0056] (I) Construction of Battery Damage Assessment Model

[0057] This invention first constructs a battery damage assessment model based on an electrochemical-thermal coupling mechanism to reveal changes in electrochemical parameters caused by key side reactions inside the battery (such as transition metal ion dissolution, electrolyte oxidation reaction, lithium deposition and lithium dissolution), and establishes a quantitative relationship with the degree of battery damage.

[0058] Specifically, it includes:

[0059] (1) Experimental data response analysis

[0060] To evaluate the battery's performance response and potential risks under different operating conditions, this embodiment conducted cycle tests on the lithium-ion battery under overcharge, room temperature, and low temperature conditions (i.e., temperatures below a set value), and collected change curves of key indicators such as voltage, current, and capacity. Figures 1-7 As shown.

[0061] This embodiment collected data on voltage, current, and capacity changes of the battery during overcharge cycles and calculated its health status indicators. The capacity curve shows that the battery capacity remained relatively stable during the first 15 cycles, with the maximum value consistently above 4.1 Ah, indicating that the battery's apparent performance did not significantly degrade during this stage. Only after the 16th cycle did the capacity show a slight downward trend, but by the 20th cycle, the capacity was still above 3.8 Ah, with an overall decrease of less than 8%, still within an acceptable range. The corresponding battery health status graph shows that the health level remained at 100% in the early cycles, slightly declining after the 16th cycle, with relatively small overall fluctuations. These results indicate that the macroscopic performance of capacity or health status alone is insufficient to accurately reflect the microscopic damage evolution experienced by the battery during overcharge. However, various side reactions may have already occurred inside the battery, such as lithium deposition, electrolyte decomposition, and increased film resistance. These reactions can lead to performance degradation risks before the capacity shows significant decay. Therefore, relying solely on capacity as a health assessment indicator has a significant lag and cannot meet the need for early identification of damage under abnormal operating conditions.

[0062] The results of capacity changes during battery cycling under both normal and low temperature conditions show that temperature has a significant impact on battery damage evolution. For example... Figure 5 As shown, under normal temperature conditions, the battery's capacity remained almost stable after 50 cycles, with only minor fluctuations. This indicates that under normal temperature conditions, the battery structure exhibits good stability, side reactions are effectively suppressed, and the overall damage level is low. However, under low temperature conditions, such as... Figure 6 As shown, the battery capacity exhibits a clear and continuous decay trend, especially after the 30th cycle, with the rate of decline accelerating. This indicates a decrease in the reversibility of internal battery reactions and a significant exacerbation of side reactions (such as lithium deposition and enhanced polarization), leading to reduced utilization of active materials and increased transport resistance. The polarization effect and sluggish ion migration caused by low temperature significantly alter the battery's charge and discharge behavior, thereby accelerating the damage accumulation process. This phenomenon fully verifies that temperature is a crucial factor affecting battery damage pathways, providing an experimental basis and theoretical foundation for multi-condition adaptive modeling.

[0063] Therefore, this invention proposes to extract key internal parameters (such as lithium-ion concentration, electrolyte concentration, and ohmic overpotential) using an electrochemical-thermal coupling modeling method, and introduce a temperature response function to characterize the modulation effect of high and low temperature environments on the activity and transport characteristics of side reactions, thus establishing a quantitative expression model for the degree of damage. This method can accurately capture the gradually accumulating hidden degradation process within the battery before its capacity has significantly decreased, enabling early identification and accurate assessment of battery damage under various abnormal operating conditions, and significantly improving the sensitivity and applicability of the assessment model.

[0064] (2) Electrochemical-thermal coupling modeling

[0065] This invention utilizes an electrochemical-thermal multiphysics coupling modeling method to systematically characterize the multi-source coupling evolution mechanism of lithium-ion batteries under abnormal operating conditions. The model comprehensively considers processes such as charge transfer, mass diffusion, and energy transfer within the battery, accurately describing the dynamic response characteristics of the battery in both spatial and temporal dimensions under abnormal operating conditions.

[0066] In terms of electrochemical modeling, this invention considers multiple components affecting battery voltage changes, including open-circuit voltage, resistive losses in the conductive path, polarization behavior of interfacial reactions, and mass transfer limitation effects caused by concentration gradients. The thermal model, based on the principle of energy conservation, incorporates key factors such as Joule heating, side reaction heat, and heat conduction to characterize the temperature rise and heat distribution changes of the battery under different operating conditions.

[0067] This model achieves tight coupling between the thermal and electric fields, enabling it to capture the dynamic feedback relationships between different physical processes. It is particularly suitable for modeling the multi-factor nonlinear evolution within batteries under abnormal operating conditions. As a fundamental support for damage analysis and parameter extraction, this model provides a reliable data input and structural framework for subsequent side reaction modeling and battery health status assessment.

[0068] This embodiment uses a lumped parameter model to describe the battery overcharge process. The voltage in the model consists of four parts: open-circuit voltage, ohmic overpotential, polarization overpotential, and concentration overpotential.

[0069] (1)

[0070] in, Indicates the battery voltage value; This represents the open-circuit voltage of the battery, which is a function of SOC and temperature; This indicates an ohmic overpotential, caused by the internal ohmic resistance of the battery; This represents the polarization overpotential, which is related to the electrochemical reaction kinetics. This indicates a concentration overpotential, caused by the lithium ion concentration gradient in the electrolyte.

[0071] During battery overcharging, the total heat generation rate of the battery includes: reversible heat, ohmic heat, polarization heat, heat generated by the dissolution of transition metals in the positive electrode active material, heat generated by the reaction of deposited lithium with the electrolyte, and heat generated by the oxidation reaction of the electrolyte, as shown in equation (2).

[0072] (2)

[0073] in, Q This indicates the total heat generated by the lithium-ion battery during overcharging. Q rev Indicates reversible heat; Q ohm Indicates ohmic heat; Q act Indicates polarization heat; Q co This indicates the heat generated by the dissolution of the transition metal cobalt in the positive electrode active material; Q Li This indicates that the deposited lithium reacts with the electrolyte to generate heat; Q ele This indicates that the electrolyte oxidation reaction generates heat.

[0074] The constructed electrochemical model and thermal model are coupled.

[0075] (3) Solving for side reaction parameters

[0076] During abnormal operating conditions of lithium-ion batteries, a variety of non-negligible side reactions are gradually activated, including the dissolution of transition metal ions from the cathode material, enhanced electrolyte oxidation, and lithium deposition on the anode surface. These side reactions, through continuous accumulation, significantly affect the physicochemical state inside the battery, thereby altering the overall response characteristics of the system.

[0077] This invention establishes a mapping relationship between parameters and reaction pathways to identify and extract key parameter changes caused by these side reactions. Specifically, these include: increased film resistance due to lithium deposition, decreased conductivity due to changes in electrolyte concentration, and the impact of metal ion migration on interface stability. The trajectory of these key parameter changes will serve as important inputs for subsequent damage quantification models, laying the foundation for accurately identifying the health evolution trend of the battery.

[0078] Specifically, this embodiment describes the effects of side reactions during the overcharging process on electrolyte conductivity, membrane resistance and ohmic overpotential using formulas (3)-(7).

[0079] (3)

[0080] (4)

[0081] (5)

[0082] (6)

[0083] (7)

[0084] in, x for c o and ele ; c Co This represents the concentration of transition metal ions. c ele Electrolyte concentration; c li Lithium concentration; A x Forward exponential factor; E a,x It is the activation energy; R It is the ideal gas constant; σ Electrolyte conductivity; σ 0 represents the initial conductivity value; α The transition metal influence factor reflects the intensity of the effect of transition metal ion concentration on electrolyte conductivity and describes the proportional effect of conductivity on metal ion concentration. B This is a concentration sensitivity index, representing the sensitivity of electrolyte conductivity to changes in transition metal ion concentration; γ The electrolyte influence factor represents the corrective effect of the electrolyte on conductivity. λ The electrolyte oxidation reaction index is an exponential constant that reflects the effect of electrolyte oxidation reaction on ionic conductivity under overcharge conditions. δ Li Indicates the thickness of the surface film formed by lithium deposition; M Indicates molar mass; ρ Indicates density; κ Indicates ionic conductivity; μ is the lithium-ion migration factor, which represents the migration rate of lithium ions inside the battery.

[0085] During the overcharging process of lithium-ion batteries, the activation of side reactions significantly impacts battery performance. This invention extracts three key parameters—lithium-ion concentration, electrolyte concentration, and transition metal ion concentration—to quantify the contribution of side reactions to battery damage. Lithium-ion concentration reflects the lithium deposition and dissolution processes; lithium deposition and reversible dissolution affect the battery's thermal stability and electrochemical performance. Electrolyte concentration characterizes the electrolyte oxidation reaction, which alters conductivity. Transition metal ion concentration reveals the degree of dissolution of the battery's cathode material; the dissolution of transition metals leads to the loss of active materials, reducing its electrochemical reactivity. Changes in these three concentrations reflect the progress of internal side reactions within the battery, providing crucial data support for assessing the degree of battery damage.

[0086] (4) Quantitative assessment of the degree of damage

[0087] To achieve quantitative assessment of battery damage under the influence of side reactions, this invention constructs a damage assessment function based on multi-parameter weighting, expressed as follows:

[0088] (8)

[0089] (9)

[0090] (10)

[0091] in, D chem This is a concentration-time-dominant term for the degree of battery damage caused by electrochemical side reactions under abnormal operating conditions. It mainly reflects the contribution of the concentration changes and reaction duration of key side reaction substances (such as transition metal ions and electrolyte components) to the degradation of battery structure under charge-discharge cycle conditions. It is quantified by a value in the range of 0-1, with a larger value indicating a higher degree of battery damage. τ This is the lithium deposition reaction damage weighting factor, which reflects the weight of the impact of lithium deposition side reactions on the degree of battery damage. ξ The lithium-ion concentration sensitivity coefficient characterizes the sensitivity of the deposited lithium concentration to battery damage, reflecting the sensitivity of the degree of battery damage to changes in lithium concentration. ν This represents the weighting factor for damage caused by the dissolution reaction of transition metal ions, which reflects the influence of the concentration of transition metal ions on battery damage. θ 1 represents the influence coefficient of transition metal ion concentration, reflecting the relative intensity of the influence of changes in transition metal ion concentration on battery damage. ω This is the electrolyte oxidation reaction damage weighting factor, which reflects the degree of influence of electrolyte concentration on battery damage. θ 2 represents the electrolyte concentration influence coefficient, which reflects the relative impact of changes in electrolyte concentration on battery damage. f T ( T The temperature response function (F) quantifies the amplification effect of temperature deviation from optimal operating conditions on damage. This function employs a symmetric quadratic form, reflecting the mechanism by which both high and low temperatures can exacerbate side reactions, thereby accelerating battery damage. λ This is the temperature deviation sensitivity coefficient; T opt The optimal operating temperature (usually 25°C or 298K); T The ambient temperature is the temperature of the experimental environment. D totalThe final total damage level of the battery is composed of the product of the chemical reaction-induced damage term and the thermal stress amplification term, and can be used to characterize the damage evolution behavior under abnormal operating conditions.

[0092] This embodiment assesses the degree of battery damage as follows: Figure 8 As shown, this damage assessment model can accurately reflect the comprehensive impact of various side reactions on battery performance degradation, serving as the quantitative basis for the subsequent decommissioning judgment and risk warning mechanism of this invention.

[0093] The constructed model fully considers the role of different side reactions in the damage formation process, and reflects their impact on the overall battery performance degradation through the combination of weights and sensitivity factors.

[0094] In the model, the degree of lithium deposition, changes in electrolyte composition, the concentration of dissolved transition metal ions, and temperature are considered core indicators, each assigned a corresponding influencing factor. Through the synergistic effect of these parameters, the system can calculate the degree of battery damage under current abnormal operating conditions in real time, and its numerical range can be mapped to the continuous state process of the battery from healthy to retired.

[0095] This model has good scalability and portability, and can be applied to fine evaluation scenarios in the laboratory, as well as embedded in battery management systems for online monitoring and strategy optimization.

[0096] (ii) Setting the damage threshold for battery retirement

[0097] To ensure the safety and reliability of lithium-ion batteries during overcharge, this invention, based on an existing damage assessment model, further sets a scientifically reasonable retirement damage threshold and proposes a retirement judgment logic based on the degree of damage. This threshold judgment mechanism effectively identifies when a battery enters an unacceptable state of degradation, assisting the battery management system in making decisions regarding reuse or retirement.

[0098] Specifically, it includes:

[0099] (1) Basis for retirement determination

[0100] Under long-term operation or abnormal charging conditions, side reactions gradually accumulate inside lithium-ion batteries, causing changes such as lithium deposition, electrolyte oxidation, and dissolution of transition metal ions. These changes lead to increased film resistance, decreased conductivity, and abnormal voltage response. After further analyzing the trend of battery capacity change with cycle number, this invention calculates and compares its first derivative (i.e., the rate of capacity change) and second derivative (i.e., the acceleration of capacity with cycle number), such as... Figure 9As shown in the figure. The results indicate that starting from the 15th cycle, the capacity degradation rate increases significantly, and the second derivative is continuously negative and exhibits local minima in the interval from the 15th to the 17th cycle, reflecting that the battery capacity degradation trend has changed from a linear slow decline stage to a nonlinear accelerated degradation stage, exhibiting typical inflection point characteristics.

[0101] Correspondingly, from Figure 1 As shown in (a), the evolution trend of the side reaction indicates that the reversible dissolution ability of lithium deposition decreases significantly after the 15th cycle, and the residual irreversible deposited lithium gradually increases, indicating that the internal damage has tended to evolve irreversibly.

[0102] Based on the characteristics of abrupt changes in capacity degradation rate and the evolution of side reaction parameters, this invention proposes using the 15th cycle as the threshold for decommissioning damage. This threshold not only possesses clear engineering identifiability and experimental repeatability but also effectively prevents the battery from entering a period of rapid performance degradation, thereby improving the scientific rigor and risk control capabilities of decommissioning decisions and providing an important basis for subsequent early warning mechanisms and tiered utilization decisions.

[0103] Therefore, based on the coupling characteristics of battery performance and damage parameters, this invention defines a retirement damage threshold, which is used as a boundary for judging whether a battery needs to be retired.

[0104] This embodiment performs overcharge cycle tests on lithium-ion battery samples. The voltage and current changes during the overcharge experiment are as follows: Figure 1 As shown; the change in battery capacity during overcharging with the number of overcharge cycles is as follows: Figure 2 As shown; the battery health status during overcharge cycles is as follows Figure 8 As shown in the figure, observing the capacity change, voltage response, and side reaction intensity revealed that when the deposited lithium could still be completely reversibly dissolved, the battery capacity did not decay, the battery health remained at 99%, and the safety was good, with the damage level assessed at around 0.47. The performance degradation at this stage is within a reasonable range and will not significantly affect normal operation; therefore, it can continue to be used.

[0105] However, when the damage level increases further to greater than 0.47, capacity begins to decay. The deposited lithium can only partially dissolve, leading to uneven lithium-ion distribution within the battery. Undissolved deposited lithium can also cause localized hot spots, increasing the risk of overheating. Furthermore, the deposited lithium forms a surface film, increasing internal film resistance, leading to an increase in ohmic overpotential, increased internal resistance, decreased efficiency, and accelerated temperature rise, thus accelerating the damage process. Simultaneously, side reactions such as transition metal ion dissolution and electrolyte oxidation are also exacerbated, further accelerating battery degradation. These results indicate that this point is a critical turning point in the battery's transition from a usable to an unusable state.

[0106] Therefore, to ensure accurate timing of battery retirement and minimize the risks caused by excessive damage, this embodiment sets 0.47 as the damage threshold for battery retirement.

[0107] To verify the rationality of the battery damage threshold set in this invention in engineering applications, a supplementary experiment was designed: the lithium-ion battery was subjected to 8 overcharge cycles (below the threshold of 15 cycles), followed by 20 normal charge-discharge cycles. The experimental results are as follows: Figure 10 As shown, the voltage plateau is stable, with no significant capacity decay or cycle performance degradation, indicating that the battery still possesses good reusability before reaching the damage threshold. This result indirectly supports the rationality and conservatism of setting 15 cycles as the safety threshold in this invention, and helps improve the fault tolerance and reliability of the actual retirement strategy.

[0108] (2) Decision-making process for setting damage threshold

[0109] The retirement damage threshold set in this embodiment is 0.47. The decision-making process for determining whether a battery is suitable for continued use is as follows:

[0110] Calculate the degree of damage to the battery;

[0111] The degree of battery damage is compared with the set retirement damage threshold;

[0112] If the damage level is less than 0.47, continue use and perform regular health checks;

[0113] If the damage level is greater than or equal to 0.47, the battery is deemed to be decommissioned and requires further management based on the risk level.

[0114] (III) Construction of a multi-level risk early warning mechanism

[0115] To achieve graded management of retired batteries, this embodiment designs a multi-level risk warning mechanism, which allows batteries to be flexibly managed according to risk level even if they have reached the retirement standard.

[0116] (1) Risk level classification mechanism

[0117] This invention further subdivides the battery's retirement status into multiple risk levels based on the numerical range of battery damage severity indicators, combined with multi-dimensional information such as capacity decay and abnormal voltage behavior. The boundaries between each level are set according to the typical characteristic changes exhibited by the battery at different damage stages, possessing clear hierarchical logic and physical meaning.

[0118] Low-level risk corresponds to slight degradation and initial performance decline; medium-level risk reflects significant performance degradation and reduced operational capacity; high-level risk indicates that the battery is on the verge of serious failure and poses a significant safety hazard.

[0119] This embodiment further subdivides the battery retirement status into three risk levels based on the numerical range of battery damage severity, combined with capacity changes, battery health status, and concentration changes. Figure 11 As shown:

[0120] Risk Level I [0.47, 0.53]: The battery begins to enter a higher damage range, capacity begins to decline, and performance degradation becomes obvious. At this point, some of the deposited lithium in the battery dissolves, reducing its dissolution capacity, increasing internal resistance, and decreasing battery safety, which may lead to temperature rise and localized overheating. The battery should be retired at this stage; continued use poses a safety hazard.

[0121] Risk Level II [0.53, 0.88]: Battery damage worsens, internal resistance increases significantly, and undissolved lithium deposits form film resistance, leading to further deterioration of thermal stability and electrochemical performance. At this point, the risk of overheating increases, side reactions become more severe, and the battery is no longer suitable for continued use and must be retired.

[0122] Risk Level III [0.88, 1]: The battery has completely lost its normal function. The internal side reactions are violent, thermal stability is lost, and there are major safety risks such as fire and explosion. At this point, the battery is completely unusable and must be retired immediately.

[0123] (2) Design of a multi-level risk early warning mechanism

[0124] This invention designs a graded risk warning system for different risk levels, aiming to ensure the safe operation of the battery system by calculating the degree of battery damage and taking dynamic response measures. The system encompasses various forms, including but not limited to the following: early warning alerts, operational strategy interventions, and fault isolation.

[0125] Early warning mechanism: When the battery's damage level approaches a certain risk threshold, the system will issue a corresponding warning message to remind the user that the battery is about to enter a risk zone. Users can then perform necessary checks or record information based on the warning, providing a reference for decisions regarding further battery use or retirement.

[0126] Operational strategy intervention: When the battery damage reaches the medium-risk range, the system will initiate operational strategy adjustments, proactively limiting the battery's charge and discharge range, reducing power output, or shortening the battery's operating time to prevent further damage and reduce safety risks. This intervention can effectively slow down battery degradation, extend its lifespan, and allow for continued use while ensuring safety.

[0127] Fault isolation mechanism: When battery damage reaches a high-risk level, the system will immediately take emergency measures, such as initiating emergency power cut-off and module isolation, to disconnect the battery system from other parts and prevent safety incidents. This measure ensures that the battery does not continue to operate under severe damage, thereby effectively preventing potential dangerous accidents such as fires and explosions.

[0128] Through the aforementioned graded risk warning and response mechanism, this invention can effectively identify potential risks of batteries, help the battery management system take corresponding preventive and response measures at different stages of damage, and ensure the stability and safety of the battery system.

[0129] For different risk levels, this embodiment designs the following multiple early warning mechanisms, such as... Figure 12 As shown, by predicting the damage process of batteries in advance, a scientific basis can be provided for battery retirement, reducing potential safety risks.

[0130] First Warning: The system will issue its first warning when the battery damage enters Risk Level I. During this stage, battery performance can still be maintained at a certain level, but as damage accumulates, the risk of battery use gradually increases. At this point, the system reminds the user to conduct regular checks and recommends early retirement and regular monitoring to prevent further damage. This warning is issued approximately 165,630 seconds (about 45 hours) before battery failure, indicating that the battery is about to enter a high-risk zone, and recommends taking appropriate preventative measures.

[0131] Second Warning: When the battery damage reaches Risk Level II, the system will issue a second warning. At this time, the system triggers the warning and limits the charging and discharging range, strongly recommending retirement to avoid safety hazards from continued use. This warning is issued approximately 103,290 seconds (about 28 hours) before battery failure, indicating that the battery's safety has significantly decreased and continued use poses a high risk.

[0132] Third Warning: When the battery damage reaches Risk Level III, the system will issue a third warning. At this point, the battery's safety is significantly reduced, side reactions are severe, and the system will automatically stop using the battery and initiate the retirement procedure to ensure its safe disposal. This warning is issued approximately 44,000 seconds (about 12 hours) before battery failure, indicating that the battery's safety has been greatly reduced and its use must be stopped immediately.

[0133] Through these early warning mechanisms, this invention can issue timely warnings when battery damage is still manageable, preventing continued use of batteries that have reached retirement standards and ensuring battery safety and reliability. Each warning level predicts the battery failure time in advance and takes corresponding management measures within a specified time before failure to prevent excessive battery damage and ensure the accuracy and robustness of the battery management system.

[0134] Especially in the third warning stage, the system can issue an alert 12 hours before the battery completely fails, providing users with sufficient time to take emergency measures and avoid safety hazards caused by sudden battery failure. Through this mechanism, battery safety is significantly improved, ensuring battery stability during use and effectively reducing the risk of accidents due to excessive battery damage. Early warning not only enhances system safety but also provides a reliable basis for the scientific management and retirement of batteries.

[0135] Example 2

[0136] A lithium-ion battery overcharge damage risk warning system includes:

[0137] The data acquisition module is configured to acquire voltage, current and capacity data of lithium-ion battery samples during abnormal operating condition tests.

[0138] The coupling model construction module is configured to establish an electrochemical-thermal coupling damage model for lithium-ion batteries based on the internal physicochemical reaction mechanism of the battery.

[0139] The damage degree quantitative assessment model construction module is configured to use the lithium-ion battery electrochemical-thermal coupled damage model to identify key parameters, solve the changes of key parameters during the battery damage evolution process, and construct a damage degree quantitative assessment model.

[0140] The damage assessment module is configured to use the damage assessment model to calculate the damage level of the target battery based on the quantitative relationship between electrochemical parameters and battery damage level.

[0141] The risk assessment and early warning module is configured to set a retirement damage threshold based on the battery performance degradation mechanism caused by capacity decay, battery health status, and side reactions. By comparing the damage level of the target battery with the retirement damage threshold, the module determines the damage risk level of the target battery and issues an early warning.

[0142] Example 3

[0143] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the steps in the method provided in Embodiment 1.

[0144] Example 4

[0145] An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the steps in the method provided in Embodiment 1.

[0146] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0147] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0148] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0149] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0150] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for early warning of overcharge damage risk in lithium-ion batteries, characterized in that, Includes the following steps: Obtain voltage, current, and capacity data of lithium-ion battery samples during abnormal operating condition tests; Based on the internal physicochemical reaction mechanism of the battery, an electrochemical-thermal coupling damage model for lithium-ion batteries is established. Using the aforementioned lithium-ion battery electrochemical-thermal coupled damage model, key parameters are identified, the changes in key parameters during the battery damage evolution process are solved, and a quantitative assessment model for the degree of damage is constructed. Using the aforementioned quantitative damage assessment model, the damage level of the target battery is calculated based on the quantitative relationship between electrochemical parameters and battery damage level. Based on the degradation mechanism of battery performance caused by capacity decay, battery health status and side reactions, a retirement damage threshold is configured. By comparing the damage level of the target battery with the retirement damage threshold, the damage risk level of the target battery is determined and an early warning is issued. The process of establishing an electrochemical-thermal coupled damage model for lithium-ion batteries based on the internal physicochemical reaction mechanism includes: considering the open-circuit voltage affecting battery voltage change, resistance loss in the conductive path, polarization behavior of interfacial reactions, and mass transfer limitation effect caused by concentration gradient, to construct an electrochemical model for lithium-ion batteries; based on the principle of energy conservation, incorporating Joule heat, side reaction heat, and heat conduction factors to construct a thermal model to characterize the temperature rise and heat distribution changes of the battery under different operating conditions, and coupling the constructed electrochemical model and thermal model. The process of constructing a quantitative damage assessment model includes: using identified key parameters as core indicators, assigning corresponding influencing factors to each, constructing a quantitative damage assessment model, calculating the damage level of the battery under the current abnormal operating conditions, and mapping its numerical range to the continuous state process of the battery from health to retirement. The quantitative damage assessment model is as follows: ； ； ； Where Dtotal represents the final total damage level of the battery. Let be the temperature response function, and Dchem be the concentration-time dominant term representing the degree of battery damage caused by electrochemical side reactions under abnormal operating conditions. This is the lithium deposition reaction damage weighting factor, which reflects the weight of the influence of lithium deposition side reactions on the degree of battery damage. The lithium-ion concentration sensitivity coefficient characterizes the sensitivity of the deposited lithium concentration to battery damage, reflecting the sensitivity of the degree of battery damage to changes in lithium concentration. ν This is the transition metal ion dissolution reaction damage weighting factor, which reflects the influence of transition metal ion concentration on battery damage. θ 1 represents the influence coefficient of transition metal ion concentration, reflecting the relative intensity of the influence of changes in transition metal ion concentration on battery damage; ω This is the electrolyte oxidation reaction damage weighting factor, which reflects the degree of influence of electrolyte concentration on battery damage. θ 2 represents the electrolyte concentration influence coefficient, reflecting the relative strength of the impact of changes in electrolyte concentration on battery damage. λ This is the temperature deviation sensitivity coefficient; T opt For optimal operating temperature; T The experimental ambient temperature, c Co This represents the concentration of transition metal ions. c ele Electrolyte concentration; c li This represents the lithium concentration.

2. The method for early warning of overcharge damage risk in lithium-ion batteries as described in claim 1, characterized in that, The process of obtaining voltage, current and capacity data of lithium-ion battery samples in abnormal operating condition tests includes: conducting cyclic tests on lithium-ion battery samples under overcharge, room temperature and temperature below the set value, and obtaining the change curves of voltage, current and capacity.

3. The method for early warning of overcharge damage risk in lithium-ion batteries as described in claim 1, characterized in that, The process of identifying key parameters using the aforementioned lithium-ion battery electrochemical-thermal coupling damage model includes: identifying and extracting key parameter changes caused by side reactions by establishing a mapping relationship between parameters and reaction pathways. Specifically, this includes: increased film resistance caused by lithium deposition, decreased conductivity caused by changes in electrolyte concentration, and the impact of metal ion migration on interface stability.

4. The method for early warning of overcharge damage risk in lithium-ion batteries as described in claim 1, characterized in that, Based on the mechanism of battery performance degradation caused by capacity decay, battery health status, and side reactions, the process of configuring retirement damage thresholds is carried out. According to the numerical range of damage degree of battery samples, combined with changes in capacity decay, battery health status, and concentration, four progressively increasing critical values ​​for battery damage degree are set. The risk level is determined according to the range of the critical value of battery damage degree that the damage degree of the target battery falls into. Each risk level is equipped with an independent early warning mechanism.

5. The method for early warning of overcharge damage risk in lithium-ion batteries as described in claim 1, characterized in that, If the damage level of the target lithium-ion battery is greater than or equal to the preset critical value of battery damage level, but less than the second critical value, it is classified as the first risk level. If the damage level of the target lithium-ion battery is greater than or equal to the second critical value, but less than the third critical value, it is classified as the second risk level. If the damage level of the target lithium-ion battery is greater than or equal to the third critical value, it is classified as the third risk level. The higher the risk level, the shorter the determined time to retirement.

6. A lithium-ion battery overcharge damage risk warning system, employing the lithium-ion battery overcharge damage risk warning method according to any one of claims 1-5, characterized in that, include: The data acquisition module is configured to acquire voltage, current and capacity data of lithium-ion battery samples during abnormal operating condition tests. The coupling model construction module is configured to establish an electrochemical-thermal coupling damage model for lithium-ion batteries based on the internal physicochemical reaction mechanism of the battery. The damage degree quantitative assessment model construction module is configured to use the lithium-ion battery electrochemical-thermal coupled damage model to identify key parameters, solve the changes of key parameters during the battery damage evolution process, and construct a damage degree quantitative assessment model. The damage assessment module is configured to use the damage assessment model to calculate the damage level of the target battery based on the quantitative relationship between electrochemical parameters and battery damage level. The risk assessment and early warning module is configured to set a retirement damage threshold based on the battery performance degradation mechanism caused by capacity decay, battery health status, and side reactions. By comparing the damage level of the target battery with the retirement damage threshold, the module determines the damage risk level of the target battery and issues an early warning.

7. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, complete the steps of the method according to any one of claims 1-5.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the steps of the method according to any one of claims 1-5.

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