A battery pack protection method and system

Abstract

This invention relates to a battery pack protection method and system, specifically to the field of battery pack structural health management. The method includes: acquiring vibration acceleration signals and local temperature signals of the plastic structure within the battery pack; obtaining a real-time health index based on the vibration acceleration signals; determining a structural temperature value based on the local temperature signals; determining the material aging state based on the real-time health index and the structural temperature value; combining the material aging state and the real-time health index to derive a cracking risk level; when the cracking risk level exceeds a preset risk standard level, identifying the location of the risk anomaly; and performing reinforcement and repair operations based on the risk anomaly location using a preset structural reinforcement method. This invention provides overall protection for the battery pack.

Description

Technical Field

[0001] This invention relates to the field of battery pack structural health management, and in particular to a battery pack protection method and system. Background Technology

[0002] A battery pack is an energy storage unit that integrates multiple battery cells (cells) in series and parallel, and is equipped with a battery management system, a thermal management system, and a structural casing.

[0003] Currently, in battery pack safety management, preventing cracks in the internal plastic support structure due to long-term aging and fatigue is one of the key aspects of ensuring overall safety and lifespan. Existing technologies typically address this risk through regular visual inspections, disassembly and testing, or replacement after obvious structural failures. These methods can, to some extent, detect and address damage that has already occurred.

[0004] However, most of these methods are reactive and post-continuation maintenance, unable to monitor early microscopic damage and aging processes within the plastic structure in real time, let alone implement differentiated, proactive preventative interventions based on damage type, evolution trend, and risk level. This results in structural safety hazards not being detected and eliminated in a timely manner, thus reducing the overall protective effect of the battery pack, and requires improvement. Summary of the Invention

[0005] To improve the overall protection effect of the battery pack, the present invention provides a battery pack protection method and system.

[0006] In a first aspect, the present invention provides a battery pack protection method, which adopts the following technical solution:

[0007] A battery pack protection method, comprising:

[0008] Collect vibration acceleration signals and local temperature signals of the plastic structure in the battery pack;

[0009] Real-time health index is obtained based on vibration acceleration signals;

[0010] Determine the structural temperature value based on local temperature signals;

[0011] The aging status of materials is determined based on real-time health index and structural temperature values.

[0012] The cracking risk level is determined by combining the material aging status and real-time health index.

[0013] When the cracking risk level exceeds the preset risk standard level, the location of the risk anomaly is collected;

[0014] Based on the location of the risk anomaly, reinforcement and repair operations are carried out using a preset structural reinforcement method.

[0015] By adopting the above technical solution, the system first collects vibration and temperature signals of the battery pack's plastic structure. From the vibration signals, it extracts a real-time health index characterizing the structural stiffness, and from the temperature signals, it determines the structural temperature. Combining both, the system accurately assesses the material's aging state. Based on the deteriorating trend of the aging state and the real-time health index, the system further quantifies the cracking risk level. When the risk level exceeds a preset standard, the system no longer merely stays at the warning stage but actively locates the abnormal risk location and ultimately initiates structural reinforcement methods to perform self-repair operations at that location. This proactive intervention at the nascent or early stage of damage effectively curbs the spread of damage and significantly improves the structural safety and overall protection effect of the battery pack.

[0016] Optionally, methods for determining the aging state of materials may also be included:

[0017] Collect real-time charge / discharge status data and heat generation rate data of individual battery cells in the battery pack;

[0018] The heat load input is determined by combining the heat generation rate data with the preset battery pack heat dissipation parameters;

[0019] The rapid temperature change load and the slow temperature change load are determined based on the thermal load input and the structural temperature value.

[0020] The high-rate charge / discharge range is determined based on real-time charge / discharge status data;

[0021] The thermal shock damage factor was determined based on the high-rate charge-discharge range and rapid temperature change load.

[0022] The thermal creep synergistic damage factor was determined based on slow temperature change load and real-time health index.

[0023] The aging state of materials is determined by combining thermal shock damage factor and thermal creep synergistic damage factor.

[0024] Optionally, methods for determining the thermal shock damage factor may also be included:

[0025] Based on the high-rate charge-discharge range, the structural temperature rise segment and the structural temperature drop segment are obtained.

[0026] The rate of temperature change and peak temperature are determined based on the rapid temperature rise and rapid temperature drop segments of the structure.

[0027] The equivalent thermal stress amplitude is obtained by combining the rate of temperature change, peak temperature, and preset thermal expansion coefficient.

[0028] The number of cycles in which the equivalent thermal stress amplitude exceeds the preset material fatigue threshold per unit time is collected;

[0029] The thermal shock damage factor is calculated based on the number of cycles and the equivalent thermal stress amplitude.

[0030] Optionally, a method for calculating the thermal creep synergistic damage factor is also included:

[0031] The time-series correlation was obtained based on slow temperature change load and real-time health index;

[0032] The permanent decay component in the real-time health index that cannot be recovered with temperature load is determined based on the temporal correlation.

[0033] Calculate the cumulative amount of the permanent decay component over the preset continuous loading time, and use it as the creep damage amount;

[0034] Collect the average temperature value of the slow temperature change load;

[0035] The thermal creep synergistic damage factor is calculated by combining the creep damage amount, average temperature value, and continuous loading time.

[0036] Optionally, a method for determining the crack risk level may also be included:

[0037] Collect vehicle operating status signals;

[0038] Instantaneous deformation impact events are derived from vehicle operating status signals, and the impact amplitude of these events is collected.

[0039] The material strength attenuation coefficient and basic risk value are obtained based on the material aging state;

[0040] The impact risk value is determined based on the impact amplitude and the material strength attenuation coefficient.

[0041] The cracking risk level is calculated by combining the basic risk value and the impact risk value.

[0042] Optional methods for collecting data on locations of risk anomalies include:

[0043] Collect samples from detection points where the cracking risk level exceeds the preset risk standard level;

[0044] Based on the real-time health index and the number of points exceeding the detection threshold, calculate the health index gradient value between each point exceeding the detection threshold and its adjacent detection points;

[0045] The point with the largest health index gradient value between each exceeding detection point and its adjacent detection points is selected as the main risk point.

[0046] Collect the structural connection relationships of the battery pack's plastic structure;

[0047] Related risk points are obtained based on structural connections and main risk points;

[0048] The set of main risk points and related risk points is output as the risk anomaly location.

[0049] Optionally, the structural reinforcement method includes:

[0050] The type of repair material is determined based on the location of the risk anomaly and the pre-defined battery pack structure and material information;

[0051] Collect historical crack risk level data and current local temperature data for locations with abnormal risks;

[0052] The damage aggravation trend is assessed based on historical cracking risk level data and current local temperature data to determine the initial damage activity.

[0053] The material injection parameters and repair current parameters are determined based on the initial damage activity and the type of repair material.

[0054] Microelectrode pairs are matched by combining the location of the risk anomaly with the type of repair material;

[0055] The system controls the preset repair material injection unit to inject the repair material corresponding to the type of repair material into the risk and abnormal location according to the material injection parameters, and controls the battery management system to apply directional current through the microelectrode according to the repair current parameters. In this way, the directional current drives the repair material to complete the curing and reinforcement at the risk and abnormal location.

[0056] Optionally, a dynamic control method for the repair current may also be included:

[0057] During the application of directional current, the current current value, current voltage value, and loop impedance data flowing through the microelectrode pair are collected;

[0058] The real-time flow front position and curing reaction rate of the repair material are determined based on the current current value, current voltage value, and loop impedance data.

[0059] The repair current adjustment value is determined based on the real-time position of the flow front;

[0060] The current waveform correction parameters are determined based on the curing reaction rate and the preset desired reaction rate threshold.

[0061] The repair process is optimized by continuously applying a directional current based on the repair current adjustment value and the current waveform correction parameter.

[0062] Optionally, methods for verifying the repair effect across cycles are also included:

[0063] After the curing and reinforcement are completed, the final loop impedance value at the time of repair completion is collected.

[0064] At subsequent preset operating cycle nodes, the microelectrode pair is reactivated and its current loop impedance value is collected;

[0065] The rate of change of impedance is obtained based on the final loop impedance value and the current loop impedance value;

[0066] When the rate of change of impedance exceeds the preset degradation threshold, a maintenance prompt message is reported.

[0067] Secondly, this application provides a battery pack protection system, which adopts the following technical solution:

[0068] A battery pack protection system, comprising:

[0069] The acquisition module is used to acquire vibration acceleration signals, local temperature signals, and locations of potential risks and anomalies.

[0070] A memory used to store a program that implements a battery pack protection method;

[0071] The processor is used to load and execute programs stored in memory.

[0072] In summary, this application includes at least one of the following beneficial technical effects:

[0073] 1. The system first collects vibration and temperature signals of the battery pack's plastic structure. From the vibration signals, it extracts a real-time health index characterizing structural stiffness, and from the temperature signals, it determines the structural temperature. Combining both, the system accurately assesses the material's aging state. Based on the deteriorating trend of the aging state and the real-time health index, the system further quantifies the cracking risk level. When the risk level exceeds a preset standard, the system no longer merely provides an early warning but actively locates the abnormal risk location and ultimately initiates structural reinforcement methods to perform self-repair operations at that location. This proactive intervention at the initial or early stage of damage effectively curbs the spread of damage and significantly improves the structural safety and overall protection of the battery pack.

[0074] 2. By identifying instantaneous deformation impact events caused by working conditions such as bumps and collisions and quantifying their impact amplitude, and combining the strength attenuation coefficient obtained from the material aging state with the basic risk value, the impact risk value is calculated. Finally, the dynamic cracking risk level is obtained by integrating the basic risk value and the impact risk value, so that the risk assessment can more accurately reflect the superposition effect of instantaneous overload and cumulative aging, providing a reliable basis for triggering preventive repairs;

[0075] 3. First, the type of repair material is matched based on the risk location and material information. Damage activity is assessed using historical risk data and real-time temperature, and material injection and current parameters are determined accordingly. Then, corresponding microelectrode pairs are matched, and the injection unit precisely delivers the repair material. Simultaneously, a directional current is applied through the battery management system to drive in-situ curing of the material. This solution deeply integrates damage state perception, repair strategy optimization, and execution control, achieving intelligent and adaptive repair processes. This ensures effective targeted reinforcement under complex operating conditions, significantly improving the self-healing capability and long-term reliability of the battery pack structure. Attached Figure Description

[0076] Figure 1 This is a flowchart of a battery pack protection method;

[0077] Figure 2 This is a flowchart of structural reinforcement methods. Detailed Implementation

[0078] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0079] Reference Figure 1 This application discloses a battery pack protection method, including the following steps:

[0080] S10: Collect vibration acceleration signals and local temperature signals of the plastic structure in the battery pack.

[0081] The vibration acceleration signal refers to the time-domain electrical signal acquired by a piezoelectric accelerometer installed at a key location on the battery pack's plastic structure. In this embodiment, the plastic structure is the battery pack module end plate. The specific installation location is predetermined by those skilled in the art and will not be elaborated here.

[0082] Local temperature signal refers to the real-time temperature electrical signal obtained by a thin-film thermistor temperature sensor attached to the surface of the end plate.

[0083] Vibration acceleration signals and local temperature signals are acquired through the signal acquisition module inside the battery pack and transmitted to the analysis unit of the battery management system via the CAN bus.

[0084] S11: Obtain the real-time health index based on vibration acceleration signals.

[0085] The real-time health index is a dimensionless numerical value ranging from 0 to 1, used to quantitatively characterize the current structural integrity status of a plastic structure. The closer the value is to 1, the healthier the structure; the lower the value, the more severe the damage or aging.

[0086] The frequency spectrum of the acquired vibration acceleration signal over a specific period is obtained by performing a Fast Fourier Transform. The first-order resonant frequency of the plastic structure in a healthy state is identified within the spectrum (this frequency value has been experimentally calibrated and stored beforehand). The amplitude of the current signal at this first-order resonant frequency is calculated and compared with a reference amplitude recorded in a healthy state to calculate its attenuation percentage. Finally, the attenuation percentage is converted into a real-time health index within the range of 0 to 1 using a preset mapping curve. The mapping curve is preset by those skilled in the art and will not be elaborated upon here.

[0087] S12: Determine the structural temperature value based on the local temperature signal.

[0088] The structural temperature value refers to a specific temperature reading, with the unit being degrees Celsius, which directly represents the actual temperature of the monitoring point on the plastic structure.

[0089] The structural temperature value can be directly obtained by reading the calibrated digital output value of the local temperature sensor.

[0090] S13: Determine the aging state of materials based on real-time health index and structural temperature values.

[0091] Material aging status refers to a quantitative level, which is divided into four levels: "Level 1 (healthy)," "Level 2 (slight aging)," "Level 3 (moderate aging)," and "Level 4 (severe aging)." It is used to comprehensively describe the degree of performance degradation of plastic materials caused by factors such as heat and force.

[0092] The specific methods for determining the aging state of materials will be explained in detail in subsequent sections S20 to S26, and will not be repeated here.

[0093] S14: Combine material aging status and real-time health index to determine the cracking risk level.

[0094] Cracking risk level refers to a level that characterizes the probability of macroscopic cracks occurring in the next charge-discharge cycle or within the next 100 kilometers of driving distance. It is divided into three levels: "low risk", "medium risk" and "high risk".

[0095] The specific methods for determining the cracking risk level will be explained in detail in subsequent sections S50 to S54, and will not be repeated here.

[0096] S15: When the cracking risk level exceeds the preset risk standard level, collect the location of the risk anomaly.

[0097] The risk standard level is a preset trigger threshold, which is set to "medium risk" in this embodiment. When the cracking risk level reaches or exceeds medium risk, the subsequent location and repair process is triggered. The location of the risk anomaly must be collected first for subsequent steps.

[0098] Risk anomaly locations refer to one or more specific coordinates within the battery pack's internal coordinate system that require priority reinforcement.

[0099] The specific methods for collecting data on risky and abnormal locations will be explained in detail in subsequent sections S60 to S65, and will not be repeated here.

[0100] S16: Perform reinforcement and repair operations based on the risk anomaly location using a preset structural reinforcement method.

[0101] Structural reinforcement methods refer to methods used to reinforce and repair locations with abnormal risks. Specific structural reinforcement methods will be explained in detail in subsequent sections S70 to S75, and will not be repeated here.

[0102] It also includes methods for determining the aging state of materials:

[0103] S20: Collects real-time charge / discharge status data and heat generation rate data of individual battery cells in the battery pack.

[0104] Real-time charge / discharge status data refers to a dataset obtained through the battery management system that characterizes the current operating state of the battery, including real-time charge / discharge current and cell surface temperature.

[0105] Heat generation rate data refers to the amount of heat generated by a single battery cell per unit time due to internal electrochemical reactions and internal resistance. In this embodiment, the heat generation rate is calculated using a pre-defined electrothermal coupling model. This model takes real-time charge / discharge current and battery cell temperature as inputs, and estimates the instantaneous heat generation power of each cell in real time based on the battery's internal resistance-temperature-state of charge lookup table and electrochemical heat generation formula. This lookup table and formula were obtained in advance by those skilled in the art through battery calibration experiments and will not be elaborated upon here.

[0106] S21: Combine the heat generation rate data with the preset battery pack heat dissipation parameters to determine the heat load input.

[0107] Battery pack heat dissipation parameters refer to quantitative parameters describing the overall heat dissipation capacity of the battery pack. In this embodiment, this parameter is an equivalent thermal resistance value, representing the average temperature rise caused by a unit heat power inside the battery pack. This equivalent thermal resistance value is obtained through heat dissipation performance testing of the battery pack under standard conditions and is pre-stored in the system.

[0108] Thermal load input refers to the theoretical equivalent heat flux density acting on the plastic structure of the battery pack, resulting from the combined effects of battery heat generation and the environment.

[0109] The total heat generation power of the battery pack is obtained by summing the heat generation rate data of all collected battery cells. Then, combined with the equivalent thermal resistance, the theoretical average temperature rise trend of the battery pack under adiabatic conditions is calculated; this trend data is the heat load input. The specific calculation method is common knowledge in this field and will not be elaborated here.

[0110] S22: Determine the rapid temperature change load and the slow temperature change load based on the thermal load input and the structural temperature value.

[0111] Rapid temperature load refers to the temperature fluctuation component caused by the instantaneous high-rate charging and discharging of the battery, which is highly synchronized with the change in thermal load input and has a fast rate of change.

[0112] Slow temperature load refers to a temperature trend component that changes gradually due to changes in ambient temperature and overall heat accumulation in the battery pack.

[0113] The time series of structural temperature values ​​are compared and analyzed with the time series of thermal load input. A signal decomposition algorithm (such as Empirical Mode Decomposition, EMD) is used to decompose the structural temperature values ​​into multiple intrinsic mode functions. The mode with the highest correlation coefficient in the time domain and the highest frequency component to the thermal load input curve is defined as the rapid temperature-changing load; the remaining slowly changing trend term after removing the rapid component is defined as the slow temperature-changing load.

[0114] S23: Based on real-time charge and discharge status data, the high-rate charge and discharge range is determined.

[0115] The high-rate charge / discharge range refers to the continuous period of time during which a battery operates at a rate greater than or equal to 1C (i.e., the charging or discharging current value is greater than or equal to the ampere-hours of the battery's rated capacity). For example, for a battery with a rated capacity of 100Ah, the continuous period during which the absolute value of the current is greater than or equal to 100A is the high-rate range.

[0116] The system continuously monitors real-time charge and discharge current data. When the absolute value of the current continuously exceeds the 1C threshold calculated based on the battery's rated capacity, this moment is recorded as the start of the interval; when the absolute value of the current falls back below the threshold and remains below it for a certain period of time (e.g., 10 seconds), the previous moment is recorded as the end of the interval. This marks the time periods of all high-rate charge and discharge intervals.

[0117] S24: Determine the thermal shock damage factor based on the high-rate charge-discharge range and rapid temperature change load.

[0118] The thermal shock damage factor is a dimensionless cumulative value used to quantify the accumulation of fatigue damage caused by rapid temperature fluctuations in plastic materials.

[0119] The specific methods for determining the thermal shock damage factor will be explained in detail in subsequent sections S30 to S34, and will not be repeated here.

[0120] S25: Determine the thermal creep synergistic damage factor based on slow temperature change load and real-time health index.

[0121] The thermal creep synergistic damage factor is a dimensionless cumulative value used to quantify the damage caused by irreversible creep deformation of plastic materials under the combined action of continuous thermal environment and mechanical stress.

[0122] The specific method for determining the thermal creep synergistic damage factor will be explained in detail in subsequent sections S40 to S44, and will not be repeated here.

[0123] S26: Combine thermal shock damage factor with thermal creep synergistic damage factor to determine the aging state of materials.

[0124] The final material aging state (i.e., "Level 1" to "Level 4" in S13) can be determined using a preset aging state fusion formula. The formula is:

[0125] Aging status level = γ × thermal shock damage factor + δ × thermal creep synergistic damage factor.

[0126] Wherein, γ and δ are preset weighting coefficients (specifically set by those skilled in the art, and not elaborated here), representing the relative importance of the two aging mechanisms, thermal shock and thermal creep, respectively. The calculation results are rounded to the nearest integer, and the obtained value can be entered into a preset aging state lookup table to obtain the material's aging state.

[0127] The aging state comparison table records the aging states of different materials corresponding to different calculation results. The comparison content in the aging state comparison table is formed by those skilled in the art by recording the aging states of different materials corresponding to the calculation results in sequence, which will not be elaborated here.

[0128] It also includes methods for determining thermal shock damage factors:

[0129] S30: Based on the high-rate charge and discharge range, the structural temperature rise section and the structural temperature drop section are obtained.

[0130] The structural temperature surge segment refers to the data segment in the high-rate charge and discharge range where the structural temperature rises rapidly and monotonically over time due to the rapid heat generation of the battery.

[0131] The structural temperature drop segment refers to the data segment in which the structural temperature value decreases rapidly and monotonically over time after the high-rate charge / discharge range ends or when the charge / discharge current decreases significantly.

[0132] Within each high-rate charge / discharge interval identified by S23, the collected time-series data of structural temperature values ​​are processed. First, the temperature surge segment is located by finding a continuous positive interval of the first derivative (temperature change rate). Then, after the high-rate interval ends, the temperature drop segment is located by finding a continuous negative interval of the first derivative. The start and end points of each surge and drop segment are recorded.

[0133] S31: Determine the rate of temperature change and peak temperature based on the rapid temperature rise and rapid temperature drop segments of the structure.

[0134] The rate of temperature change refers to the average speed at which the structural temperature changes during a rapid rise or fall, and is measured in °C / s. For a rapid rise, the rate is positive; for a rapid fall, the rate is negative (the absolute value represents the speed of the temperature drop).

[0135] Peak temperature refers to the highest temperature value reached by the structure at the end of the rapid rise phase during a single high-rate charge / discharge event.

[0136] For a identified rapid temperature rise segment, the rate of temperature change is obtained by dividing the temperature difference between the start and end points of the segment by the time difference. The temperature at the end of the rapid temperature rise segment is the peak temperature of this event. The rate of temperature change for a rapid temperature drop segment is calculated in the same way.

[0137] S32: Combine the rate of temperature change, peak temperature and preset thermal expansion coefficient to obtain the equivalent thermal stress amplitude.

[0138] The coefficient of thermal expansion is a material constant that refers to the relative change in length (or volume) of a plastic material when the temperature increases by 1°C. This coefficient is obtained by those skilled in the art from material handbooks based on the specific plastic grade selected and is pre-stored in the system.

[0139] The equivalent thermal stress amplitude is a quantitative value used to characterize the level of alternating thermal stress induced inside plastic materials due to rapid temperature changes and which cannot be freely released due to structural constraints.

[0140] The equivalent thermal stress amplitude can be calculated based on the principles of thermoelasticity. First, due to a sudden temperature change, the material will experience thermal strain: Thermal strain = Coefficient of thermal expansion × Temperature change. Since plastic structures are typically constrained by battery modules, frames, etc., this thermal strain cannot be completely released freely, thus transforming into thermal stress. The equivalent thermal stress amplitude is estimated using the following simplified formula: Equivalent thermal stress amplitude = Material elastic modulus × Coefficient of thermal expansion × Peak temperature change. The peak temperature change can be approximated as (Peak temperature - Initial temperature of the sudden temperature rise). The material elastic modulus is another preset material constant (pre-defined by those skilled in the art, and will not be elaborated here). This calculated value characterizes the maximum stress level that a single temperature shock may induce.

[0141] S33: Number of cycles in which the equivalent thermal stress amplitude exceeds the preset material fatigue threshold per unit time.

[0142] The fatigue threshold of a material refers to the critical stress amplitude below which a plastic material can theoretically withstand an infinite number of cycles without fatigue failure under alternating stress. This threshold is preset by those skilled in the art and pre-stored in the system.

[0143] Cycle count refers to the cumulative number of complete temperature shock events (one surge + one drop) in which the equivalent thermal stress amplitude exceeds the material fatigue threshold within a statistical time window (e.g., the past 24 hours or a complete charge-discharge cycle).

[0144] The system records the calculated equivalent thermal stress amplitude for each event and compares it with a preset material fatigue threshold. Whenever the equivalent thermal stress amplitude caused by a high-rate charge / discharge event exceeds this threshold, the event is counted as a valid fatigue cycle. The system continuously accumulates the number of such valid cycles within a sliding statistical time window to obtain the cycle count.

[0145] S34: The thermal shock damage factor is calculated based on the number of cycles and the equivalent thermal stress amplitude.

[0146] After obtaining the number of cycles and the equivalent thermal stress amplitude, the thermal shock damage factor is calculated based on the preset SN curve, which characterizes the fatigue properties of the plastic material, using the linear cumulative damage criterion (Miner's criterion). This calculation method is common knowledge in the field and will not be elaborated here. The SN curve is obtained by those skilled in the art through standard fatigue tests on plastic materials and stored in the system.

[0147] It also includes a method for calculating the thermal creep synergistic damage factor:

[0148] S40: The time-series correlation is obtained based on slow temperature change load and real-time health index.

[0149] The temporal correlation refers to the corresponding relationship in terms of change between two time series data: slow temperature change load and real-time health index.

[0150] Synchronous time-series data of slow temperature-changing loads and real-time health indices are plotted on the same time axis for cross-correlation analysis. A hysteresis regression model is established between the two (e.g., considering the delayed effect of temperature changes on material stiffness). By analyzing the model residuals, the portion of the health index variation that cannot be linearly explained by current and historical temperature changes is identified. This analytical and interpretive capability constitutes the obtained time-series correlation.

[0151] S41: Determine the permanent decay component in the real-time health index that cannot be recovered by temperature load based on the temporal correlation.

[0152] The permanent decay component refers to the decrease in the real-time health index after a period of slow temperature change load. When the temperature load returns to the initial level or lower, the health index fails to recover and remains at a low level.

[0153] Based on the model established by S40, after a sustained slow temperature change load high-temperature plateau period ends, the temperature begins to decrease and tends to stabilize. The real-time health index is observed at this point. Through model calculation, after deducting the recoverable improvement in the health index that should have been due to the temperature decrease itself, the remaining unrecoverable decline in the health index is extracted and quantified as the permanent decay component for that time period.

[0154] S42: Calculate the cumulative amount of the permanent decay component over the preset continuous loading time as the creep damage amount.

[0155] The sustained loading time refers to the statistical period used for calculating the cumulative creep damage. In this embodiment, this time is a complete battery pack calendar aging assessment cycle, such as 30 days. This period should be significantly longer than a single temperature fluctuation cycle to capture the long-term cumulative effect of creep.

[0156] Creep damage is the sum of all identified permanent degradation components accumulated over a preset continuous loading time. It quantifies the total irreversible structural performance loss caused by the thermal environment during that time period.

[0157] During the continuous loading period, the system continuously records the value of each permanent attenuation component determined by S41. At the end of an evaluation cycle, these independent, discrete attenuation component values ​​are arithmetically summed to obtain the total creep damage during that cycle.

[0158] S43: Collect the average temperature value of slow temperature change load.

[0159] The average temperature value refers to the arithmetic mean of all slow temperature-varying load data within the same continuous loading time used in S42.

[0160] Within the statistical time window corresponding to the calculation of creep damage, the system synchronously accumulates and averages all slow temperature load sampling values ​​within that time period to directly obtain an average temperature value, which is used to characterize the basic thermal environment level acting on the plastic structure within that time period.

[0161] S44: The thermal creep synergistic damage factor is calculated by combining the creep damage amount, average temperature value, and continuous loading time.

[0162] The thermal creep synergistic damage factor was calculated using a creep damage model that considers the accelerating effect of temperature. This model is based on the Arrhenius equation, which assumes that the creep damage rate increases exponentially with temperature. The calculation formula is simplified as follows:

[0163] Thermal creep synergistic damage factor = (creep damage amount / continuous loading time) × exp[activation energy constant / (gas constant × (average temperature value + 273.15))].

[0164] in:

[0165] The activation energy constant is a material property parameter describing the temperature sensitivity of the creep process in plastic materials. It is obtained and stored by those skilled in the art through high-temperature creep tests on materials. The gas constant is a physical constant. (Average temperature value + 273.15) is the conversion of Celsius temperature to absolute temperature (Kelvin).

[0166] The raw value calculated by this formula will be normalized (e.g., divided by a reference damage rate determined through accelerated life testing) to obtain a dimensionless value between 0 and 1, namely the thermal creep synergistic damage factor. The higher the value of this factor, the faster and more severe the creep-dominated aging damage process under a specific thermal environment.

[0167] It also includes methods for determining the crack risk level:

[0168] S50: Collects vehicle operating status signals.

[0169] Vehicle operating status signals refer to data acquired via the vehicle's CAN bus that reflects the mechanical impact on the battery pack during driving. In this embodiment, specifically, it refers to longitudinal acceleration signals and vertical acceleration signals, both measured in m / s². 2 These signals are generated by inertial measurement unit (IMU) sensors located on the vehicle chassis or body.

[0170] S51: Based on the vehicle operating status signal, the instantaneous deformation impact event is obtained, and the impact amplitude of the instantaneous deformation impact event is collected.

[0171] A transient deformation impact event refers to a discrete event in which the vehicle's operating status signal fluctuates significantly within a very short period of time (e.g., less than 0.5 seconds) due to the vehicle driving over potholes, speed bumps, or experiencing a minor collision. This event indicates that the battery pack's plastic structure may have suffered a transient mechanical impact.

[0172] Impact amplitude is a parameter used to quantify the magnitude of a single instantaneous deformation impact event. In this embodiment, impact amplitude is defined as the absolute value of the difference between the peak value of the vertical acceleration signal and the steady-state baseline value before the event, with units of m / s². 2 .

[0173] The system monitors the vertical acceleration signal in real time. When the signal value exceeds a preset impact detection threshold (set in advance by those skilled in the art, and not described in detail here), it is determined that a transient deformation impact event has begun, and the start time of the event is recorded. Monitoring continues until the signal falls below the threshold and remains stable, at which point the event is recorded as ended. Within this time window, the maximum peak value of the acceleration is found, and the absolute value of the difference between this peak value and the steady-state average value before the event is the impact amplitude of this event.

[0174] S52: Obtain the material strength attenuation coefficient and basic risk value based on the material aging state.

[0175] The material strength degradation coefficient is a dimensionless coefficient between 0 and 1, used to characterize the proportion of decrease in mechanical properties of plastics such as tensile strength and impact strength due to material aging. A coefficient of 1 indicates no strength degradation, while a smaller coefficient indicates more severe strength degradation.

[0176] The basic risk value is a dimensionless value between 0 and 1, representing the inherent risk of battery pack structure cracking under stable driving conditions without instantaneous impacts, contributed solely by the aging state of materials.

[0177] It is obtained through a pre-set material performance degradation comparison table and a basic risk mapping table.

[0178] By inputting the material aging status level into the material performance degradation comparison table, the corresponding material strength degradation coefficient can be directly obtained. The material performance degradation comparison table records the different material strength degradation coefficients corresponding to different material aging status levels. The comparison content in the material performance degradation comparison table is formed by those skilled in the art by sequentially recording the different material strength degradation coefficients corresponding to the material aging status levels, which will not be elaborated here.

[0179] By inputting the same material aging status level into the basic risk mapping table, the basic risk value can be directly retrieved. The basic risk mapping table records different basic risk values ​​corresponding to different material aging status levels. The reference content in the basic risk mapping table is formed by those skilled in the art by sequentially recording the different basic risk values ​​corresponding to the material aging status levels, which will not be elaborated here.

[0180] S53: Determine the impact risk value based on the impact amplitude and the material strength attenuation coefficient.

[0181] The impact risk value is a value that characterizes the risk of a single instantaneous deformation impact event inducing cracks or causing the original damage to propagate after taking into account the current material strength decay.

[0182] The impact risk value is calculated using a preset impact risk assessment function. This function is: Impact Risk Value = (Impact Amplitude / Impact Amplitude Reference Threshold) × (1 / Material Strength Attenuation Coefficient). The impact amplitude reference threshold is a preset upper limit of a typical impact amplitude that a virgin material can withstand (set in advance by those skilled in the art, and will not be elaborated here). The calculated raw value is then limited to the range of 0 to 1 by a saturation function (e.g., taking the minimum value of 1). The formula demonstrates that the larger the impact amplitude and the more severe the material strength attenuation, the higher the risk of a single impact.

[0183] S54: The cracking risk level is calculated by combining the basic risk value and the impact risk value.

[0184] The cracking risk level is derived using a risk fusion model. Specifically, within a sliding statistical time window (e.g., the most recent hour or the most recent 100 km travel distance), the impact risk values ​​of all instantaneous deformation impact events within that window are summed to obtain the cumulative impact risk value. Then, a comprehensive risk index is calculated using the following formula:

[0185] Comprehensive risk index = basic risk value + ω × cumulative shock risk value.

[0186] Here, ω is a preset weighting coefficient used to adjust the contribution ratio of dynamic shock risk to static aging risk. It is set in advance by those skilled in the art and will not be elaborated here.

[0187] Finally, the calculated comprehensive risk index is compared with the preset level thresholds to map the final cracking risk level (e.g., "low risk", "medium risk", "high risk"). The specific level thresholds are set in advance by those skilled in the art and will not be elaborated here.

[0188] It also includes methods for collecting data on locations of risk anomalies:

[0189] S60: Collect detection points where the cracking risk level exceeds the preset risk standard level.

[0190] The risk standard level is a preset risk threshold used to trigger precise location of risk. In this embodiment, the level is set to "medium risk". When the cracking risk level of any detection point reaches or exceeds "medium risk", that point is considered to need to enter the abnormal location acquisition process.

[0191] "Exceeding the detection point" refers to a monitoring point in the battery pack whose cracking risk level is assessed to be at or above the aforementioned risk standard level ("medium risk").

[0192] The system iterates through the latest crack risk level assessment results of all monitoring points and marks all monitoring points with a level of "medium risk" or "high risk". The set of location identifiers (such as sensor IDs) of these points is "out of detection point".

[0193] S61: Calculate the health index gradient value between each excess detection point and its adjacent detection points based on the real-time health index and the excess detection points.

[0194] The health index gradient value refers to the drastic change in the real-time health index of a plastic structure from a monitoring point (beyond the detection point) to its directly adjacent monitoring point.

[0195] For each out-of-detection point, its real-time health index is obtained, along with the real-time health indices of all adjacent monitoring points directly connected to it structurally (based on a preset structural connection relationship). The health index difference between the out-of-detection point and each adjacent point is calculated, and the maximum absolute value of these differences is taken as the health index gradient value of the out-of-detection point. The larger this value, the more significant the difference in state between the point and its surrounding area, and the more localized and severe the damage may be.

[0196] S62: Select the detection point with the largest health index gradient value between each detection point and its adjacent detection points as the main risk point.

[0197] The primary risk point refers to the monitoring point with the highest calculated health index gradient value among all the detection points. This point is considered the core location in all current high-risk areas where damage is most concentrated, most severe, and most likely to become a crack initiation point.

[0198] By comparing the health index gradient values ​​of all points exceeding the detection threshold, the points exceeding the detection threshold with the largest gradient value are selected as the main risk points, and their location identifiers are recorded.

[0199] S63: Collect the structural connection relationships of the battery pack's plastic structure.

[0200] Structural connectivity refers to the topological information describing the physical connections and force transmission paths between various plastic structural components (such as end plates, brackets, and crossbeams) within the battery pack, as well as between monitoring points on these components. It defines which points are structurally directly connected.

[0201] The structural connection relationships are established by those skilled in the art during the system design phase based on the CAD model and finite element analysis results of the battery pack, and then retrieved after being stored in the system.

[0202] S64: Obtain associated risk points based on structural connection relationships and main risk points.

[0203] Associated risk points refer to monitoring points that, although their own cracking risk level may not exceed the standard, are directly connected to the main risk point in structure. When the main risk point is damaged, the force flow will be redistributed, making them highly susceptible to becoming secondary high-stress areas and thus facing higher risks.

[0204] Based on the structural connectivity, all monitoring points directly connected to the main risk point are identified. From these adjacent points, those whose real-time health indices are lower than a preset associated risk threshold (set in advance by those skilled in the art, and not elaborated here) are selected. These points are then identified as associated risk points. They represent potential paths through which damage may spread or trigger cascading failures.

[0205] S65: Output the set of main risk points and related risk points as the risk anomaly location.

[0206] The system packages the location identifier of the main risk point with the location identifiers of all related risk points to form a location set, which is then output as the final risk anomaly location information.

[0207] Reference Figure 2 Structural reinforcement methods include:

[0208] S70: Determine the type of repair material based on the location of the risk anomaly and the preset battery pack structure and material information.

[0209] Battery pack structural material information refers to data describing the specific material grades and key physical properties (such as coefficient of thermal expansion, glass transition temperature, and surface energy) of each plastic component within the battery pack. This information is determined during the battery pack design phase and pre-stored in the system as a static database.

[0210] Repair material type refers to a category of adhesives or composite materials suitable for different plastic materials, with specific curing methods and performance matching. For example, "Type A - Epoxy Conductive Adhesive" is suitable for PA66 material, and "Type B - Polyurethane-based Conductive Adhesive" is suitable for PP material.

[0211] The system queries the battery pack structure and material information database to obtain the material grade of the component where the risk anomaly location (especially the main risk point) is located. Then, according to a preset substrate repair material matching table, it selects the type of repair material with the best compatibility and bonding strength with the substrate grade.

[0212] The battery pack structure and material information database records the component numbers, material grades, and preset physical property parameters of all key plastic components within the battery pack. The content of this database is entered by those skilled in the art during the battery pack design phase based on the bill of materials (BOM) and material data sheets, and will not be elaborated upon here.

[0213] The substrate repair material matching table records the recommended optimal repair material type for different plastic material grades. The contents of this matching table are pre-formulated and entered by those skilled in the art based on material compatibility test results, and will not be elaborated upon here.

[0214] S71: Collect historical crack risk level data and current local temperature data at locations with abnormal risks.

[0215] Historical crack risk level data refers to all crack risk levels ("low", "medium", "high") and their corresponding timestamps recorded by monitoring points corresponding to the location of the risk anomaly within a preset historical time period (such as the past 24 hours).

[0216] The current local temperature data refers to the latest structural temperature value at the risk anomaly location (main risk point) collected through the S12 method.

[0217] Historical cracking risk level data can be obtained by extracting the historical risk level sequence of the location from the risk assessment history log stored in the system. The risk assessment history log records the cracking risk level obtained from each assessment of all monitoring points and its corresponding assessment timestamp. This log is automatically generated and continuously stored by the system after executing the risk assessment process (S14 or S54). Its data format and storage method are common knowledge in the field and will not be elaborated here.

[0218] Meanwhile, the current local temperature data can be collected using the S12 method.

[0219] S72: Assess the damage aggravation trend based on historical crack risk level data and current local temperature data to determine the initial damage activity.

[0220] Initial damage activity is a dimensionless index used to comprehensively characterize the recent rate of deterioration of damage sites and the degree of adverse environmental conditions for repair. A higher value indicates that the damage is expanding rapidly or that the ambient temperature is unfavorable for repair, requiring more aggressive intervention.

[0221] First, historical cracking risk level data is analyzed. If the risk level shows a continuous upward trend or high-frequency fluctuation in the near future, a higher trend deterioration score is obtained. Second, the current local temperature data is compared with the preset optimal construction temperature range for the repair material, and a corresponding environmental adverse score is obtained based on the degree of temperature deviation. Finally, the trend deterioration score and the environmental adverse score are weighted and fused, and normalized to obtain a value between 0 and 1, which is the initial damage activity. This assessment process is automatically completed by a preset damage activity assessment model. The scoring rules, weight coefficients, and normalization methods in the model are preset and configured by those skilled in the art based on actual engineering experience and experimental data. The specific implementation details are conventional technical means that can be reasonably inferred by those skilled in the art after mastering the inventive concept of this application, and will not be elaborated here. The initial damage activity determined through this step provides a key quantitative basis for the subsequent accurate and dynamic formulation of repair strategies.

[0222] S73: Determine the material injection parameters and repair current parameters based on the initial damage activity and the type of repair material.

[0223] Material injection parameters refer to the specific instructions that control the delivery process of repair materials, mainly including injection volume and injection flow rate.

[0224] Repair current parameters refer to the specific instructions for controlling the current to heat and cure the repair material, mainly including the current magnitude, the duration of energization, and the current waveform.

[0225] The preset repair process parameter table provides the material injection parameters and repair current parameters. This table records recommended material injection parameters (such as injection volume and injection flow rate) and repair current parameters (such as current magnitude, energizing duration, and current waveform) for the same repair material type at different initial damage activity levels. The repair process parameter table was developed and pre-stored in the system after experts obtained the optimal parameters through preliminary repair process experiments (such as designing different damage scenarios, ambient temperatures, and combinations of repair parameters). Specific parameter values ​​in the table are not detailed here.

[0226] S74: Match microelectrode pairs by combining the location of the risk anomaly and the type of repair material.

[0227] Microelectrode pairs are miniature conductive elements (such as platinum wires or corrosion-resistant alloy sheets) embedded in plastic structures, appearing in pairs and integrated with microfluidic networks. Their function is to generate Joule heating when energized, and to ohmically heat the repair material.

[0228] The system finds the microelectrode pair number covering the coordinates of the risk anomaly location in a preset electrode location mapping map, thereby obtaining the microelectrode pair.

[0229] The electrode location mapping diagram records the unique numbers of all microelectrode pairs embedded within the battery pack's plastic structure and their corresponding three-dimensional spatial coordinate ranges. This mapping diagram is determined during the battery pack structure design and microchannel / electrode network manufacturing stages and is entered into the system by those skilled in the art based on the design drawings; the specific coordinate data is not detailed here. By querying this mapping diagram, the system can map risky and abnormal locations in physical space to corresponding controllable and executable hardware units (microelectrode pairs).

[0230] S75: Control the preset repair material injection unit to inject the repair material corresponding to the repair material type into the risk and abnormal location according to the material injection parameters, and control the battery management system to apply directional current through the microelectrode according to the repair current parameters, so as to use the directional current to drive the repair material to complete the curing and reinforcement at the risk and abnormal location.

[0231] The repair material injection unit refers to an automated fluid delivery system consisting of a miniature precision metering pump, a repair material storage tank, a multi-way solenoid valve, and an interface connecting to the microchannel.

[0232] The system first sends a command to the repair material injection unit, controlling the metering pump to extract material from the corresponding repair material type storage tank according to the material injection parameters. The material is then routed via a solenoid valve and precisely pumped to the high-risk, abnormal location through a microchannel. Subsequently, a command is sent to the battery management system, instructing it to supply power from the low-voltage auxiliary power supply to the designated microelectrode pair according to the repair current parameters. The current flows through the electrodes and the injected conductive repair material, generating Joule heating, causing the material to heat up and undergo a thermosetting reaction, thereby forming a reinforcing structure at the damaged site.

[0233] It also includes a method for dynamically controlling the repair current:

[0234] S80: During the application of directional current, the current current value, current voltage value, and loop impedance data flowing through the microelectrode pair are collected.

[0235] The current value refers to the instantaneous magnitude of the current flowing through the microelectrode pair, which is measured in real time by a current sensor.

[0236] The current voltage value refers to the instantaneous magnitude of the voltage applied across the microelectrode pair, measured in real time by a voltage sensor.

[0237] Loop impedance data refers to the real-time electrical impedance of the loop formed by the microelectrode pair and the repair material between them, calculated using Ohm's law based on the real-time acquired current and voltage values.

[0238] During the application of directional current, it is necessary to collect the current value, current voltage value, and loop impedance data flowing through the microelectrode pair for subsequent steps.

[0239] S81: Determine the real-time flow front position and curing reaction rate of the repair material based on the current current value, current voltage value, and loop impedance data.

[0240] The real-time flow front position refers to the farthest spatial position reached by the liquid portion of the repair material within the repair area defined by the microelectrode pair, used to determine whether the material has sufficiently covered the predetermined repair area.

[0241] The curing reaction rate refers to the degree of progress of the chemical reaction in which the repair material changes from a liquid to a solid state per unit time, and is usually measured by the increase in the degree of curing per unit time.

[0242] First, a correlation model is established between the electrical impedance of the repair material and its physical state. When the repair material is in a liquid state, the impedance is low; as the material flows forward and fills the voids, the impedance decreases slowly and steadily; when the material begins to solidify, its molecular cross-linking leads to a decrease in conductivity, and the impedance shows an upward trend. By analyzing the real-time change curve of the loop impedance data, the stage from the initial decrease to the gradual leveling off is mapped to the material flow process, thereby determining the frontier position; the stage from the lowest point to the increase in impedance is mapped to the start of solidification, and the solidification reaction rate is quantified based on its upward slope. The above method of inverting the material state based on impedance signals is a common technique in this field for monitoring invisible processes, and its specific algorithm implementation can be adapted and optimized by those skilled in the art according to the characteristics of the selected repair material.

[0243] S82: Determine the repair current adjustment value based on the real-time flow front position.

[0244] The repair current adjustment value refers to the incremental or decremental value used to correct the current amplitude in the initially set repair current parameters in order to optimize the repair process.

[0245] By comparing the real-time flow front position with a preset repair area model, if the flow front position lags behind the model's expectation, a positive repair current adjustment value is generated. This moderately increases the current to enhance Joule heating, thereby reducing the viscosity of the repair material and accelerating its flow and filling. If the flow front position is ahead or there is a risk of overflow, a negative repair current adjustment value is generated to slow down the flow process. The specific magnitude of the adjustment value can be calculated using a preset proportional-integral control algorithm based on the deviation between the flow front position and the expected position. The purpose is to enable the actual flow process of the material to dynamically track and match the ideal repair path. The specific parameters of this control strategy (such as the proportional coefficient and integral time constant) are tuned by those skilled in the art based on the system response characteristics and will not be elaborated here.

[0246] S83: Determine the current waveform correction parameters based on the curing reaction rate and the preset desired reaction rate threshold.

[0247] The desired reaction rate threshold refers to a preset, optimal range of curing reaction rates. Curing that is too fast can easily generate internal stress, while curing that is too slow results in low efficiency. The desired reaction rate threshold is set in advance by those skilled in the art and will not be elaborated upon here.

[0248] Current waveform correction parameters refer to specific instructions for adjusting the current waveform (such as duty cycle and frequency) in the current parameters to be corrected.

[0249] The current waveform correction parameters can be found in the preset curing current correction table. This table records the recommended current waveform correction parameters (such as duty cycle adjustment, frequency adjustment, or waveform mode switching instructions) corresponding to different curing reaction rates (usually quantified as "slower," "normal," "faster," etc.) compared with the desired reaction rate threshold. This table is pre-formulated and stored by those skilled in the art based on the thermosetting kinetics of the repair material and process test data (exploring the influence of different waveforms on curing rate and curing quality). Specific parameter values ​​are not elaborated here.

[0250] S84: Continuously apply directional current based on the repair current adjustment value and current waveform correction parameters to optimize the repair process.

[0251] The system superimposes the repair current adjustment value with the current amplitude in the original repair current parameters to obtain a new current amplitude command. Simultaneously, it applies the current waveform correction parameters to the original current waveform command, generating a new waveform command. Then, the battery management system continues to apply directional current through the microelectrodes based on the updated current amplitude and waveform. This process cycles in real time (S80 to S84) until repair is complete, forming a closed-loop, adaptive intelligent control system for the repair process.

[0252] It also includes methods for cross-cycle verification of repair effectiveness:

[0253] S90: After the curing and reinforcement are completed, collect the final loop impedance value when the repair is completed.

[0254] The final loop impedance value refers to the loop impedance value between the microelectrode pairs immediately after the restoration curing process is completed, the material has completely cooled and reached a stable state. This value characterizes the electrical properties of the restoration in its initial healthy state.

[0255] After the repair is completed, the system performs a precise measurement, records the stable impedance value at this time, and archives it as the final loop impedance value of the repair point.

[0256] S91: At subsequent preset operating cycle nodes, reactivate the microelectrode pair and collect its current loop impedance value.

[0257] Operating cycle nodes refer to pre-set time points used to periodically review the repair effect. These nodes can be fixed time intervals (such as every 30 days) or event points strongly related to battery use (such as every 100 complete charge-discharge cycles or 5,000 kilometers of cumulative driving).

[0258] The current loop impedance value refers to the loop impedance value measured in real time when the system applies a weak probe current, insufficient to trigger heating, to the microelectrode pair corresponding to the repair point at a certain operating cycle node.

[0259] When a preset operating cycle node is reached, the system controls the battery management system to apply a constant, low-power probe current (e.g., 1mA, DC) to the microelectrode pair at that repair point. Simultaneously, the voltage across the electrodes is measured, and the current loop impedance value is calculated using Ohm's law.

[0260] S92: Obtain the rate of change of impedance based on the final loop impedance value and the current loop impedance value.

[0261] Impedance change rate refers to the percentage change in current loop impedance relative to final loop impedance, used to quantify the degree of degradation in restoration performance. It is a dimensionless percentage value.

[0262] Calculation method: Impedance change rate = (|current loop impedance value - final loop impedance value|) / final loop impedance value × 100%. The formula calculates the absolute percentage of impedance change, which is used to characterize the degree of deviation from the initial state.

[0263] S93: When the impedance change rate exceeds the preset degradation threshold, a maintenance prompt message is reported.

[0264] The degradation threshold is a preset percentage change in impedance that indicates a significant degradation in the restoration's performance, requiring maintenance attention. This threshold is pre-set by those skilled in the art and will not be elaborated upon here. When the rate of change in impedance reaches or exceeds this value, it indicates that the restoration may be showing signs of failure such as cracking, debonding, or material aging.

[0265] The system compares the calculated impedance change rate with a preset degradation threshold. If the impedance change rate is greater than or equal to the degradation threshold, a maintenance reminder is generated and sent to the cloud monitoring platform or vehicle service center via the vehicle communication network. This message should include at least: vehicle VIN code, repair point location, current impedance change rate, and detection time, to prompt further inspection or preventative maintenance.

[0266] Based on the same inventive concept, embodiments of the present invention provide a battery pack protection system, including:

[0267] The data acquisition module is used to acquire vibration acceleration signals, local temperature signals, risk and abnormal locations, real-time charging and discharging status data, heat generation rate data, number of cycles, average temperature value, vehicle operating status signals, impact amplitude, exceeding detection points, structural connection relationships, historical cracking risk level data, current local temperature data, current current value, current voltage value, loop impedance data, final loop impedance value, and current loop impedance value.

[0268] A memory used to store a program that implements a battery pack protection method;

[0269] The processor is used to load and execute programs stored in memory.

[0270] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0271] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A battery pack protection method, characterized in that, include: Collect vibration acceleration signals and local temperature signals of the plastic structure in the battery pack; Real-time health index is obtained based on vibration acceleration signals; Determine the structural temperature value based on local temperature signals; The aging status of materials is determined based on real-time health index and structural temperature values. The cracking risk level is determined by combining the material aging status and real-time health index. When the cracking risk level exceeds the preset risk standard level, the location of the risk anomaly is collected; Based on the location of the risk anomaly, reinforcement and repair operations are carried out using a preset structural reinforcement method; The structural reinforcement method includes: The type of repair material is determined based on the location of the risk anomaly and the pre-defined battery pack structure and material information; Collect historical crack risk level data and current local temperature data for locations with abnormal risks; The damage aggravation trend is assessed based on historical cracking risk level data and current local temperature data to determine the initial damage activity. The material injection parameters and repair current parameters are determined based on the initial damage activity and the type of repair material. Microelectrode pairs are matched by combining the location of the risk anomaly with the type of repair material; The preset repair material injection unit is controlled to inject the repair material corresponding to the repair material type into the risk and abnormal location according to the material injection parameters, and the battery management system is controlled to apply directional current through the microelectrode according to the repair current parameters, so as to use the directional current to drive the repair material to complete the curing and reinforcement at the risk and abnormal location. It also includes methods for determining the crack risk level: Collect vehicle operating status signals; Instantaneous deformation impact events are derived from vehicle operating status signals, and the impact amplitude of these events is collected. The material strength attenuation coefficient and basic risk value are obtained based on the material aging state; The impact risk value is determined based on the impact amplitude and the material strength attenuation coefficient. The cracking risk level is calculated by combining the basic risk value and the impact risk value. It also includes methods for collecting data on locations of risk anomalies: Collect samples from detection points where the cracking risk level exceeds the preset risk standard level; Based on the real-time health index and the number of points exceeding the detection threshold, calculate the health index gradient value between each point exceeding the detection threshold and its adjacent detection points; The point with the largest health index gradient value between each exceeding detection point and its adjacent detection points is selected as the main risk point. Collect the structural connection relationships of the battery pack's plastic structure; Related risk points are obtained based on structural connections and main risk points; The set of main risk points and related risk points is output as the risk anomaly location.

2. The battery pack protection method according to claim 1, characterized in that, It also includes methods for determining the aging state of materials: Collect real-time charge / discharge status data and heat generation rate data of individual battery cells in the battery pack; The heat load input is determined by combining the heat generation rate data with the preset battery pack heat dissipation parameters; The rapid temperature change load and the slow temperature change load are determined based on the thermal load input and the structural temperature value. The high-rate charge / discharge range is determined based on real-time charge / discharge status data; The thermal shock damage factor was determined based on the high-rate charge-discharge range and rapid temperature change load. The thermal creep synergistic damage factor was determined based on slow temperature change load and real-time health index. The aging state of materials is determined by combining thermal shock damage factor and thermal creep synergistic damage factor.

3. The battery pack protection method according to claim 2, characterized in that, It also includes methods for determining thermal shock damage factors: Based on the high-rate charge-discharge range, the structural temperature rise segment and the structural temperature drop segment are obtained. The rate of temperature change and peak temperature are determined based on the rapid temperature rise and rapid temperature drop segments of the structure. The equivalent thermal stress amplitude is obtained by combining the rate of temperature change, peak temperature, and preset thermal expansion coefficient. The number of cycles in which the equivalent thermal stress amplitude exceeds the preset material fatigue threshold per unit time is collected; The thermal shock damage factor is calculated based on the number of cycles and the equivalent thermal stress amplitude.

4. A battery pack protection method according to claim 2, characterized in that, It also includes a method for calculating the thermal creep synergistic damage factor: The time-series correlation was obtained based on slow temperature change load and real-time health index; The permanent decay component in the real-time health index that cannot be recovered with temperature load is determined based on the temporal correlation. Calculate the cumulative amount of the permanent decay component over the preset continuous loading time as the creep damage amount; Collect the average temperature value of the slow temperature change load; The thermal creep synergistic damage factor is calculated by combining the creep damage amount, average temperature value, and continuous loading time.

5. A battery pack protection method according to claim 1, characterized in that, It also includes a method for dynamically controlling the repair current: During the application of directional current, the current current value, current voltage value, and loop impedance data flowing through the microelectrode pair are collected; The real-time flow front position and curing reaction rate of the repair material are determined based on the current current value, current voltage value, and loop impedance data. The repair current adjustment value is determined based on the real-time position of the flow front; The current waveform correction parameters are determined based on the curing reaction rate and the preset desired reaction rate threshold. The repair process is optimized by continuously applying a directional current based on the repair current adjustment value and the current waveform correction parameter.

6. A battery pack protection method according to claim 1, characterized in that, It also includes methods for verifying the repair effect across cycles: After the curing and reinforcement are completed, the final loop impedance value at the time of repair completion is collected. At subsequent preset operating cycle nodes, the microelectrode pair is reactivated and its current loop impedance value is collected; The rate of change of impedance is obtained based on the final loop impedance value and the current loop impedance value; When the rate of change of impedance exceeds the preset degradation threshold, a maintenance prompt message is reported.

7. A battery pack protection system, characterized in that, include: The acquisition module is used to acquire vibration acceleration signals, local temperature signals, and locations of potential risks and anomalies. A memory for storing a program that implements a battery pack protection method as described in any one of claims 1 to 6; The processor is used to load and execute programs stored in memory.

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