A battery thermal runaway grading early warning method and system

Abstract

This invention relates to the field of battery monitoring technology, and more particularly to a graded early warning method and system for battery thermal runaway. This invention extracts the correlation coefficient between the imaginary part of the complex impedance and the phase angle as a relational early warning feature, characterizing the structural transformation of impedance evolution from cooperative to decoupled / reverse, avoiding reliance on absolute impedance scales and improving mobility across SOC, across batteries, and across operating conditions. Simultaneously, it performs gating enhancement and weighted second-order smoothing on the phase angle change rate, and uses the earliest effective valley point as the entry marker for an increased risk state of thermal runaway, thus providing an earlier signal before significant anomalies in the correlation coefficient. Furthermore, the proposed conditions for increased thermal runaway risk, accelerated increase in thermal runaway risk, and entry into an imminent thermal runaway state are applicable to both thermal abuse and electrical abuse, with consistent triggering order for risk stages.

Description

Technical Field

[0001] This invention relates to the field of battery monitoring technology, and in particular to a graded early warning method and system for battery thermal runaway. Background Technology

[0002] Lithium-ion batteries are susceptible to thermal runaway under abuse conditions. Thermal runaway in lithium-ion batteries is characterized by its sudden onset, rapid evolution, and short response window, necessitating reliable early warning before critical risk stages. Traditional early warning methods rely heavily on signals such as voltage, temperature, and smoke / gas, but these signals often change significantly only at the critical stage of thermal runaway, resulting in insufficient lead time. In contrast, online electrochemical impedance spectroscopy (EIS) can reflect the internal state evolution of the battery without significant operational disturbances, possessing the potential for earlier early warning. Some existing EIS early warning methods utilize fixed offsets of impedance amplitude / phase angle, rate of change inflection points, or trajectory inflection points under thermal or electrical abuse to trigger warnings based on thresholds. However, these methods are mostly based on characteristic absolute scales / fixed thresholds, typically requiring recalibration of thresholds under new battery consistency and new noise environments. Furthermore, most methods are designed only for a single type of abuse, lacking sufficient cross-abuse transfer validation.

[0003] Existing research on thermal runaway early warning based on electrochemical impedance spectroscopy generally suffers from the following technical problems: Customization for single abuse scenarios is required; early warning features and criteria are often designed separately for thermal and electrical abuse, making direct deployment across different abuse scenarios prone to mismatch and ineffectiveness; thresholds are highly scale-dependent, with existing methods mostly based on impedance characteristic change rates, fixed offsets, or empirical threshold triggers, which are affected by SOC, individual differences, and noise, resulting in insufficient transferability across abuse scenarios; there is a risk of false triggering due to noise, as online measurements inevitably involve disturbances and noise, causing some early warning methods relying on fixed thresholds to produce false alarms or unstable triggering during normal operation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a graded early warning method and system for battery thermal runaway. Under the constraint of fixed-frequency single-frequency online impedance that can be deployed in engineering, a unified graded early warning method for thermal runaway risk is constructed that is applicable to both thermal abuse and electrical abuse. Its criterion is based on relational evolutionary characteristics rather than the absolute scale of the characteristics, thereby reducing the sensitivity to the initial impedance reference and noise, and achieving an early warning timescale from minutes to hours.

[0005] This invention provides a graded early warning method for battery thermal runaway, the method comprising the following steps: S1: Collect single-frequency online impedance data of the battery to be warned and perform basic quantity calculations; S2: Extract relational early warning features based on the aforementioned basic quantities; S3: Identify the effective valley point moment of the phase angle change rate based on the aforementioned basic quantity; S4: Based on the effective valley point time and relational warning characteristics, provide a thermal runaway risk warning for the battery to be warned and output protection actions.

[0006] Preferably, in step S1, a single-frequency sinusoidal excitation with a fixed impedance test frequency is applied to the battery to be predicted, and impedance data is collected online. The collection frequency is 1Hz, and the fixed impedance test frequency is 20Hz.

[0007] Preferably, the impedance data includes complex impedance, and the fundamental quantities include the imaginary part, magnitude, and phase angle of the complex impedance; The impedance data acquired online is expressed in complex impedance form as follows: ; in, This is the complex impedance form of the impedance data acquired online. Let be the real part of the complex impedance. This represents the imaginary part of the complex impedance. The magnitude of the complex impedance is expressed as:

[0008] In the formula, The magnitude of the complex impedance; The phase angle of the complex impedance can be expressed as: ; In the formula, Let be the phase angle of the complex impedance.

[0009] Preferably, the correlation coefficient between the imaginary part of the complex impedance and the phase angle is calculated as a relational early warning feature. The formula for calculating the correlation coefficient between the imaginary part of the complex impedance and the phase angle is as follows: ; In the formula, Let be the correlation coefficient between the imaginary part of the complex impedance and the phase angle of battery sample m, where m is the battery sample index. It is the battery sample index within the window. Let W be the imaginary part of the complex impedance of the battery sample k within the window, and W be the window length. Let k be the phase angle of the battery sample k within the window. Let be the mean of the imaginary part of the complex impedance of the battery samples within the window. It is the average phase angle of the battery samples within the window.

[0010] Preferably, S3 specifically comprises: The formula for calculating the rate of change of phase angle is: ; In the formula, Let be the rate of change of the phase angle at time t. Let be the phase angle at time t. The phase angle at time t-1; The standardized decrease in the rate of change of the phase angle is defined and calculated using the following formula: ; In the formula, This is the standardized decrease in the rate of change of the phase angle. This is the local mean of the rate of change of the phase angle. Let be the rate of change of the phase angle at time t. For robustness; The gating enhancement of the standardized decrease in the rate of change of the phase angle is expressed as follows: ; in, This is the standardized decrease in the rate of change of the phase angle after gating enhancement. The initial threshold, and These are weighting coefficients used to control the steepness and nonlinearity. The enhanced phase angle change rate is constructed based on the standardized decrease in the phase angle change rate after gating enhancement, and its expression is: ; ; In the formula, It is a nonlinear amplification term. This is the standardized decrease in the rate of change of the phase angle. It is the gain coefficient. This is the standardized decrease in the rate of change of the phase angle after gating enhancement. The enhanced rate of change of phase angle, This is the local mean of the rate of change of the phase angle. For robustness; The enhanced phase angle change rate is then subjected to weighted second-order smoothing to obtain the final phase angle change rate, expressed as: ; In the formula, Here, represents the final phase angle change rate, and represents the smoothed sequence corresponding to the optimal solution of the phase angle change rate, used for subsequent valley detection. It is the smoothed trajectory of the enhanced phase angle change rate to be solved, where N is the sample length. These are weighting coefficients. The enhanced rate of change of phase angle, For smooth strength, It is a second-order difference operator; Effective valley moments are identified based on the final phase angle change rate.

[0011] Preferably, the rule for identifying the effective valley point time is as follows: the earliest local minimum of the final phase angle change rate that satisfies bilateral monotonic consistency is defined as the effective valley point time. And in forward The rate of change of the final phase angle within each battery sample decreased. After The final phase angle change rate increases within each sample, and the valley value must meet the following requirements. , This indicates the observation interval to the left of the valley point. This indicates the observation interval to the right of the valley point.

[0012] Preferably, S4 specifically involves: based on the effective valley point time. The correlation coefficient between the imaginary part and the phase angle of the complex impedance The thermal runaway risk evolution process is divided into three risk states: risk state A, risk state B, and risk state C, and their entry time is defined by explicit conditions.

[0013] Preferably, the specific criteria and corresponding protective actions for risk states A, B, and C are as follows: Risk status A indicates an increased risk of thermal runaway. The specific criteria and corresponding protective actions are as follows: when the first valid valley point is detected... When the battery enters risk state A, the upper limit of charging and discharging of the battery under warning should be limited, and the battery under warning should be put into a conservative operating range. At the same time, thermal management should be strengthened, the cooling capacity of the battery under warning should be improved, or the temperature control set point of the battery under warning should be reduced. Risk state B indicates an accelerated increase in the risk of thermal runaway. Its specific criteria and corresponding protective actions are as follows: when the correlation coefficient between the imaginary part of the complex impedance and the phase angle... When the system first crosses zero and enters risk state B, the charging current of the battery under warning should be limited to the minimum or charging should be stopped immediately. The load of the battery under warning should be reduced and the instantaneous power should be limited. The system containing the battery under warning with the module topology should be hierarchically isolated to reduce the spread of risk. The heat dissipation system of the system containing the battery under warning should be upgraded to a high level of operation. Risk state C indicates that thermal runaway is imminent. Its specific criteria and corresponding protective actions are as follows: when the correlation coefficient between the imaginary part of the complex impedance and the phase angle... When the voltage drops below -0.9 for the first time, the battery enters a risk state C. At this point, the energy path of the battery under warning should be cut off immediately, and the highest level of cooling and isolation protection should be implemented.

[0014] According to another aspect of the present invention, a battery thermal runaway graded early warning system is provided, the system employing the above-described battery thermal runaway graded early warning method, the system comprising: The online impedance measurement module is used to acquire single-frequency online impedance data of the battery under warning and to perform basic quantity calculations. The feature construction module extracts relational early warning features based on the aforementioned basic quantities; The feature processing module identifies the effective valley point time of the phase angle change rate based on the aforementioned basic quantity. The criterion decision module is used to provide early warning of thermal runaway risk status for the battery to be warned based on the effective valley point time and relational early warning characteristics, and to output protective actions.

[0015] The present invention has the following technical effects: This invention extracts the correlation coefficient between the imaginary part of the complex impedance and the phase angle as a relational early warning feature, characterizing the structural transformation of impedance evolution from synergy to decoupling / reverse, avoiding reliance on the absolute impedance scale, and improving mobility across SOC, battery, and operating conditions. Simultaneously, the phase angle change rate is gated and weighted second-order smoothed, and the earliest effective valley point is used as the entry marker for an increased risk state of thermal runaway, thus providing an earlier signal before significant anomalies in the correlation coefficient. Furthermore, the entry conditions proposed in this invention—increased thermal runaway risk, accelerated thermal runaway risk, and thermal runaway being an imminent state—applicable to both thermal abuse and electrical abuse, with consistent risk stage triggering order. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a battery thermal runaway graded early warning method provided in an embodiment of the present invention; Figure 2 These are the key risk marker identification results of the thermal abuse process under different initial SOC conditions. Among them, (a) represents the correlation coefficient between the imaginary part and phase angle of the complex impedance at 80% SOC; (b) represents the time series of the phase angle change rate after enhancement and smoothing at 80% SOC; (c) represents the phase angle and change rate at 80% SOC; (d) represents the correlation coefficient between the imaginary part and phase angle of the complex impedance corresponding to 40% SOC; (e) represents the time series of the phase angle change rate after enhancement and smoothing at 40% SOC; and (f) represents the phase angle and change rate at 40% SOC.

[0018] Figure 3 The results are the identification of key risk states under the condition of electrical abuse, where (a) represents the correlation coefficient between the imaginary part of the complex impedance and the phase angle at 0.1C; (b) represents the time series of the phase angle change rate after enhancement and smoothing at 0.1C; (c) represents the phase angle and change rate at 0.1C; (d) represents the correlation coefficient between the imaginary part of the complex impedance and the phase angle under the condition of 0.5C; (e) represents the time series of the phase angle change rate after enhancement and smoothing at 0.5C; and (f) represents the phase angle and change rate at 0.5C. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0020] Example 1 like Figure 1 As shown, a graded early warning method for battery thermal runaway includes the following steps: S1: Collect single-frequency online impedance data of the battery to be warned and perform basic quantity calculations; A fixed-frequency single-frequency sinusoidal excitation is applied to the battery to be predicted, and impedance data is collected online. The acquisition frequency is 1Hz, and the impedance test frequency is selected as 20Hz.

[0021] The fundamental quantities include the real part, imaginary part, magnitude, and phase angle of the complex impedance form of the impedance data.

[0022] Specifically, the impedance data acquired online is expressed in complex impedance form as follows: ; in, This is the complex impedance form of the impedance data acquired online. Let be the real part of the complex impedance. is the imaginary part of the complex impedance.

[0023] The magnitude of the complex impedance is expressed as: ; In the formula, The magnitude of the complex impedance; The phase angle of the complex impedance can be expressed as: ; In the formula, Let be the phase angle of the complex impedance.

[0024] S2: Extract relational early warning features based on the aforementioned basic quantities; In this step, to quantify the degree of cooperative or reverse evolution of the imaginary part and phase angle of the complex impedance in local time, the correlation coefficient between the imaginary part and phase angle of the complex impedance is calculated as a relational early warning feature. The formula for calculating the correlation coefficient between the imaginary part and phase angle of the complex impedance is as follows: ; In the formula, Let be the correlation coefficient between the imaginary part of the complex impedance and the phase angle of battery sample m, where m is the battery sample index. It is the battery sample index within the window. Let W be the imaginary part of the complex impedance of the battery sample k within the window, and W be the window length. Let k be the phase angle of the battery sample k within the window. Let be the mean of the imaginary part of the complex impedance of the battery samples within the window. It is the average phase angle of the battery samples within the window.

[0025] In this step, the purpose of setting the window is not to smooth the data, but to estimate the local correlation between impedance characteristics within a finite time range. A window that is too small will lead to unstable correlation coefficient estimation and amplify the impact of noise; a window that is too large will weaken the sensitivity to the transition of thermal runaway stages and increase the warning delay. Therefore, it is necessary to construct local statistics through the window and determine the optimal window length that balances statistical stability and timely warning through parametric experiments.

[0026] S3: Identify the effective valley point moment of the phase angle change rate based on the aforementioned basic quantity; Since the phase angle change rate in online measurement data is small and easily affected by noise, this invention enhances and smooths the phase angle change rate and identifies effective valley moments. As a triggering basis for the risk state of thermal runaway.

[0027] Specifically, S3 is as follows: The formula for calculating the rate of change of phase angle is: ; In the formula, Let be the rate of change of the phase angle at time t. Let be the phase angle at time t. The phase angle at time t-1; The standardized decrease in the rate of change of the phase angle is defined and calculated using the following formula: ; In the formula, This is the standardized decrease in the rate of change of the phase angle. This is the local mean of the rate of change of the phase angle. Let be the rate of change of the phase angle at time t. For robustness; The gating enhancement of the standardized decrease in the rate of change of the phase angle is expressed as follows: ; in, This is the standardized decrease in the rate of change of the phase angle after gating enhancement. The initial threshold, and These are weighting coefficients used to control the steepness and nonlinearity. The enhanced phase angle change rate is constructed based on the standardized decrease in the phase angle change rate after gating enhancement, and its expression is: ; ; In the formula, It is a nonlinear amplification term. This is the standardized decrease in the rate of change of the phase angle. It is the gain coefficient. This is the standardized decrease in the rate of change of the phase angle after gating enhancement. The enhanced rate of change of phase angle, This is the local mean of the rate of change of the phase angle. For robustness; The enhanced phase angle change rate is then subjected to weighted second-order smoothing to obtain the final phase angle change rate, expressed as: ; In the formula, Here, represents the final phase angle change rate, and represents the smoothed sequence corresponding to the optimal solution of the phase angle change rate, used for subsequent valley detection. It is the smoothed trajectory of the enhanced phase angle change rate to be solved, where N is the sample length. These are weighting coefficients. The enhanced rate of change of phase angle, For smooth strength, It is a second-order difference operator; Effective valley moments are identified based on the final phase angle change rate. The rule for identifying the effective valley point time is as follows: the earliest local minimum of the final phase angle change rate that satisfies bilateral monotonic consistency is defined as the effective valley point time. And in forward The rate of change of the final phase angle within each battery sample decreased. After The final phase angle change rate increases within each sample, and the valley value must meet the following requirements. , This indicates the observation interval to the left of the valley point. This indicates the observation interval to the right of the valley point.

[0028] Furthermore, in the rules for identifying effective valley moments, the window length... This determines the time scale used to calculate correlations, thus directly affecting the timing of the entry into a risk state. To examine... To analyze the impact of overcharging, the 0.5C overcharge condition, which exhibits the fastest risk evolution rate, was selected as a representative abuse condition. Table 1 summarizes the entry times of risk states B and C under different window lengths. When the time frame is too short, the correlation estimate is susceptible to local fluctuations. and At that time, the risk state was triggered before the overcharge began at 6201s, indicating that the correlation zero point was crossed prematurely. Over time, strong negative correlation transitions may be over-smoothed. This phenomenon occurs in... This is especially evident at times when the risky state is not identified before thermal runaway begins.

[0029] In comparison, , and All of them can maintain the correct risk status warning sequence after overcharging begins. Among these effective settings, the final choice is... This serves as a reference window because it both suppresses premature triggering of the correlation threshold and preserves a risky state before thermal runaway occurs. Although and This approach could allow some states within a risk state to trigger earlier or at similar times, but considering its relatively small smoothing margin for dealing with local fluctuations, the final choice was... As a more stable and universal setting.

[0030] Subsequently, without making any condition-specific adjustments, the same reference window size was directly applied to the thermal abuse test. Under these conditions, all five initial SOC levels maintained the risk state triggered sequentially. In contrast, a larger window... This resulted in the failure of risk state C in the 40% SOC thermal abuse condition to be triggered. The above results indicate that the selected... The value balances temporal resolution and fluctuation suppression capability in the tested thermal and electrical abuse conditions.

[0031] Table 1. Sensitivity analysis of correlation window length

[0032] S4: Based on the effective valley point time and relational warning characteristics, provide a thermal runaway risk warning for the battery to be warned and output protection actions; Specifically, S4 refers to: based on the effective valley point time. The correlation coefficient between the imaginary part and the phase angle of the complex impedance The thermal runaway risk evolution process is divided into three risk states: risk state A, risk state B, and risk state C, and their entry time is defined by explicit conditions.

[0033] The specific criteria and corresponding protective actions for risk states A, B, and C are as follows: Risk status A indicates an increased risk of thermal runaway. The specific criteria and corresponding protective actions are as follows: when the first valid valley point is detected... When the system enters risk state A, the upper limit of charging and discharging of the battery under warning should be limited, and the battery under warning should be put into a conservative operating range. At the same time, thermal management should be strengthened, the cooling capacity of the battery under warning should be improved, or the temperature control set point of the battery under warning should be lowered.

[0034] Risk state B indicates an accelerated increase in the risk of thermal runaway. Its specific criteria and corresponding protective actions are as follows: when the correlation coefficient between the imaginary part of the complex impedance and the phase angle... When the system first crosses zero and enters risk state B, the charging current of the battery under warning should be limited to the minimum or charging should be stopped immediately. The load on the battery under warning should be reduced and the instantaneous power should be limited. The system containing the battery under warning with the module topology should be subject to hierarchical isolation to reduce the spread of risk. The heat dissipation system of the system containing the battery under warning should be upgraded to a high level of operation.

[0035] Risk state C indicates that thermal runaway is imminent. Its specific criteria and corresponding protective actions are as follows: when the correlation coefficient between the imaginary part of the complex impedance and the phase angle... When the voltage drops below -0.9 for the first time, the battery enters a risk state C. At this point, the energy path of the battery under warning should be cut off immediately, and the highest level of cooling and isolation protection should be implemented.

[0036] This embodiment extracts the correlation coefficient between the imaginary part of the complex impedance and the phase angle as a relational early warning feature to characterize the structural transformation of impedance evolution from cooperative to decoupled / reverse, avoiding reliance on the absolute impedance scale and improving mobility across SOC, battery, and operating conditions. At the same time, the phase angle change rate is gated and weighted second-order smoothed, and the earliest effective valley point is used as the entry mark of the increased risk state of thermal runaway, thus giving an earlier signal before the correlation coefficient becomes significantly abnormal. In addition, the thermal runaway risk increase, thermal runaway risk acceleration, and thermal runaway near-state entry conditions proposed in this embodiment are applicable to both thermal abuse and electrical abuse, and the triggering order of each risk stage is consistent.

[0037] To verify the applicability of the early warning method in this embodiment to thermal runaway caused by thermal abuse under different initial SOCs of the battery, two representative battery operating conditions, 80% SOC and 40% SOC, were selected for visualization. Furthermore, the trigger times of the risk state were statistically summarized under five initial SOC states.

[0038] like Figure 2 As shown, in the initial stage of heating, the correlation coefficient between the imaginary part of the complex impedance and the phase angle... Maintaining a high level for an extended period of time ( Figure 2 (a)(d) in the text indicates that and The changes are still coordinated. However, in both cases, effective valley points were detected at an earlier time. Figure 2 (b)(e); red dots), appearing respectively in =176s (80% SOC) and =177s (40% SOC). Combined Figure 2 As can be seen from (c)(f), the valley point is located After a significant initial decline, it began to recover. This phenomenon illustrates that even... No obvious anomalies were identified, based on It can also determine that the battery has entered thermal runaway risk state A, providing an earlier warning signal of thermal runaway risk.

[0039] As heating continues... It began to decline rapidly and crossed 0 for the first time. Figure 2 (a) in the middle, =847s; Figure 2 (d) =1544s; red dot), at which point the battery enters risk state B. This moment corresponds to a rapid increase in the dominance of side reactions and polarization processes on impedance evolution. Furthermore, when When it first falls below -0.9, ( Figure 2 (a) in the middle, =1159s; Figure 2 (d) in the middle =2267s; Red Star), the battery enters risk state C. At this point, the battery has entered the imminent stage of irreversible thermal runaway. Although there are significant differences in the numerical range of impedance and disturbance level under different SOCs, the three thermal runaway risk stage indicators still show a consistent triggering order, indicating that the proposed warning criterion is transferable across SOC conditions.

[0040] Table 2 is a comparison table of warning times for thermal abuse processes under different initial SOCs. It further summarizes the comparison results of the time when entering the risk state under five initial SOCs, and uses the warning time of the common amplitude trajectory inflection point based on complex impedance as a reference.

[0041] Table 2 Comparison of warning times for thermal abuse processes under different initial SOCs

[0042] As can be observed from Table 2 above, the trigger time of risk state A is significantly earlier than in all SOCs. Inflection point. For example, at 40% SOC. The inflection point occurs at 1352s, while t A The early warning time was 177 seconds, approximately 19.6 minutes earlier than expected. The early warning times for other states of thermal runaway (SOC) also remained in the range of approximately 6.7-13.3 minutes. Furthermore, the proposed risk criterion provided an even earlier warning window compared to the time of thermal runaway occurrence. A The corresponding lead time is approximately 23.0–60.9 min, t B The lead time is approximately 11.5–35.0 min. These results demonstrate that the proposed early warning method can not only stably identify the stages of thermal runaway risk evolution under different initial SOCs, but also, compared to methods relying solely on… The trajectory reversal method has a more stable early triggering capability.

[0043] To verify the effectiveness of the proposed method under conditions of electrical abuse, two sets of significantly different overcharge conditions (0.1C and 0.5C) were selected for visualization. The experimental results are as follows: Figure 3 As shown. Before overcharging, the battery first goes through a normal charging phase, therefore It remained at 1 for an extended period. This indicates that the internal reactions of the battery were dominated by normal kinetic behavior. As the battery continued to charge, The lowest point is the true starting point of overcharging. At 0.1C, the battery begins to overcharge at 9479s, and at 0.5C, it begins to overcharge at 6200s. After the onset of overcharging, the voltage drops rapidly within a short period and first crosses 0 (…). Figure 3 (a)(d); red dot), at which point the risk of battery thermal runaway increases significantly. Furthermore, as overcharging continues, It quickly entered the strong negative correlation zone. Figure 3 (a) and (d); red star indicates that the battery is approaching the thermal runaway stage. Both sets of operating conditions meet the consistency of the warning sequence for different thermal runaway risk stages, indicating that this indicator can be stably triggered not only under thermal abuse but also under electrical abuse scenarios.

[0044] Figure 3 (b)(e) gives y n The timing variations were observed. In the initial stage of normal charging, fluctuations were observed in both cases. Combined with... Figure 3 (c)(f) and The changes are evident from the influence of sampling noise, and The value of y is relatively small, resulting in y n The value did not exceed 0.05 as defined in the thermal runaway risk criterion. Therefore, no valid valley point was identified, thus avoiding false alarms during the normal charging phase.

[0045] Table 3 is a comparison of the warning times for the power abuse process under different charging rates, which further verifies the effectiveness of the proposed warning method from a quantitative perspective.

[0046] Table 3 Comparison of warning times for electricity abuse at different charging rates

[0047] As can be observed from Table 3 above, under the 0.1C operating condition, t B and t C The warning times relative to the thermal runaway moment are approximately 224.5 min and 144.0 min, respectively, providing an hour-level time window for system response. Under the more severe 0.5C condition, although the evolution of thermal runaway risk is significantly accelerated, the proposed method still issues warning signals approximately 18.1 min and 16.2 min before the thermal runaway moment, respectively, still possessing sufficient operational space. Therefore, even under conditions of significant differences in cell capacity (280Ah vs 33Ah) and overcharge rate (0.1C vs 0.5C), the proposed thermal runaway risk criterion can still stably provide consistent and timely thermal runaway risk warning signals under electrical abuse conditions, verifying the effectiveness and certain transferability of the unified warning framework in electrical abuse scenarios.

[0048] Example 2 The present invention also provides a battery thermal runaway graded early warning system, wherein the system adopts a battery thermal runaway graded early warning method of Embodiment 1, and the system includes: The online impedance measurement module is used to acquire single-frequency online impedance data of the battery under warning and to perform basic quantity calculations. The feature construction module extracts relational early warning features based on the aforementioned basic quantities; The feature processing module identifies the effective valley point time of the phase angle change rate based on the aforementioned basic quantity. The criterion decision module is used to provide early warning of thermal runaway risk status for the battery to be warned based on the effective valley point time and relational early warning characteristics, and to output protective actions.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A graded early warning method for battery thermal runaway, characterized in that, The method includes the following steps: S1: Collect single-frequency online impedance data of the battery to be warned and perform basic quantity calculations; S2: Extract relational early warning features based on the aforementioned basic quantities; S3: Identify the effective valley point moment of the phase angle change rate based on the aforementioned basic quantity; S4: Based on the effective valley point time and relational warning characteristics, provide a thermal runaway risk warning for the battery to be warned and output protection actions.

2. The battery thermal runaway graded early warning method according to claim 1, characterized in that, In step S1, a single-frequency sinusoidal excitation with a fixed impedance test frequency is applied to the battery to be predicted, and impedance data is collected online. The collection frequency is 1Hz, and the fixed impedance test frequency is 20Hz.

3. The battery thermal runaway graded early warning method according to claim 2, characterized in that, The impedance data includes complex impedance, and the fundamental quantities include the imaginary part, magnitude, and phase angle of the complex impedance. The impedance data acquired online is expressed in complex impedance form as follows: ； in, This is the complex impedance form of the impedance data acquired online. Let be the real part of the complex impedance. This represents the imaginary part of the complex impedance. The magnitude of the complex impedance is expressed as: ； In the formula, The magnitude of the complex impedance; The phase angle of the complex impedance can be expressed as: ； In the formula, Let be the phase angle of the complex impedance.

4. The battery thermal runaway graded early warning method according to claim 3, characterized in that, The correlation coefficient between the imaginary part of the complex impedance and the phase angle is calculated as a relational early warning feature. The formula for calculating the correlation coefficient between the imaginary part of the complex impedance and the phase angle is as follows: ； In the formula, Let be the correlation coefficient between the imaginary part of the complex impedance and the phase angle of battery sample m, where m is the battery sample index. It is the battery sample index within the window. Let W be the imaginary part of the complex impedance of the battery sample k within the window, and W be the window length. Let k be the phase angle of the battery sample k within the window. Let be the mean of the imaginary part of the complex impedance of the battery samples within the window. It is the average phase angle of the battery samples within the window.

5. The battery thermal runaway graded early warning method according to claim 4, characterized in that, Specifically, S3 is: The formula for calculating the rate of change of phase angle is: ； In the formula, Let be the rate of change of the phase angle at time t. Let be the phase angle at time t. The phase angle at time t-1; The standardized decrease in the rate of change of the phase angle is defined and calculated using the following formula: ； In the formula, This is the standardized decrease in the rate of change of the phase angle. This is the local mean of the rate of change of the phase angle. Let be the rate of change of the phase angle at time t. For robustness; The gating enhancement of the standardized decrease in the rate of change of the phase angle is expressed as follows: ； in, This is the standardized decrease in the rate of change of the phase angle after gating enhancement. The initial threshold, and These are weighting coefficients used to control the steepness and nonlinearity. The enhanced phase angle change rate is constructed based on the standardized decrease in the phase angle change rate after gating enhancement, and its expression is: ； ； In the formula, It is a nonlinear amplification term. This is the standardized decrease in the rate of change of the phase angle. It is the gain coefficient. This is the standardized decrease in the rate of change of the phase angle after gating enhancement. The enhanced rate of change of phase angle, This is the local mean of the rate of change of the phase angle. For robustness; The enhanced phase angle change rate is then subjected to weighted second-order smoothing to obtain the final phase angle change rate, expressed as: ； In the formula, Here, represents the final phase angle change rate, and represents the smoothed sequence corresponding to the optimal solution of the phase angle change rate, used for subsequent valley detection. It is the smoothed trajectory of the enhanced phase angle change rate to be solved, where N is the sample length. These are weighting coefficients. The enhanced rate of change of phase angle, For smooth strength, It is a second-order difference operator; Effective valley moments are identified based on the final phase angle change rate.

6. The battery thermal runaway graded early warning method according to claim 5, characterized in that, The rule for identifying the effective valley point is as follows: the earliest local minimum of the final phase angle change rate that satisfies bilateral monotonic consistency is defined as the effective valley point. And in forward The rate of change of the final phase angle within each battery sample decreased. After The final phase angle change rate increases within each sample, and the valley value must meet the following requirements: , This indicates the observation interval to the left of the valley point. This indicates the observation interval to the right of the valley point.

7. The battery thermal runaway graded early warning method according to claim 6, characterized in that, Specifically, S4 refers to: based on the effective valley point time. The correlation coefficient between the imaginary part and the phase angle of the complex impedance The thermal runaway risk evolution process is divided into three risk states: risk state A, risk state B, and risk state C, and their entry time is defined by explicit conditions.

8. A battery thermal runaway graded early warning method according to claim 7, characterized in that, The specific criteria and corresponding protective actions for risk states A, B, and C are as follows: Risk status A indicates an increased risk of thermal runaway. The specific criteria and corresponding protective actions are as follows: when the first valid valley point is detected... When the battery enters risk state A, the upper limit of charging and discharging of the battery under warning should be limited, and the battery under warning should be put into a conservative operating range. At the same time, thermal management should be strengthened, the cooling capacity of the battery under warning should be improved, or the temperature control set point of the battery under warning should be reduced. Risk state B indicates an accelerated increase in the risk of thermal runaway. Its specific criteria and corresponding protective actions are as follows: when the correlation coefficient between the imaginary part of the complex impedance and the phase angle... When the system first crosses zero and enters risk state B, the charging current of the battery under warning should be limited to the minimum or charging should be stopped immediately. The load of the battery under warning should be reduced and the instantaneous power should be limited. The system containing the battery under warning with the module topology should be hierarchically isolated to reduce the spread of risk. The heat dissipation system of the system containing the battery under warning should be upgraded to a high level of operation. Risk state C indicates that thermal runaway is imminent. Its specific criteria and corresponding protective actions are as follows: when the correlation coefficient between the imaginary part of the complex impedance and the phase angle... When the voltage drops below -0.9 for the first time, the battery enters a risk state C. At this point, the energy path of the battery under warning should be cut off immediately, and the highest level of cooling and isolation protection should be implemented.

9. A graded early warning system for battery thermal runaway, characterized in that, The system employs a battery thermal runaway graded early warning method according to any one of claims 1-8, and the system comprises: The online impedance measurement module is used to acquire single-frequency online impedance data of the battery under warning and to perform basic quantity calculations. The feature construction module extracts relational early warning features based on the aforementioned basic quantities; The feature processing module identifies the effective valley point time of the phase angle change rate based on the aforementioned basic quantity. The criterion decision module is used to provide early warning of thermal runaway risk status for the battery to be warned based on the effective valley point time and relational early warning characteristics, and to output protective actions.

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