Thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology

Abstract

This invention relates to the field of lithium battery thermal management, specifically to a dynamic early warning and safety control system for thermal runaway based on lithium battery fast charging technology. It dynamically adjusts and corrects the control boundary of the lithium battery by statistically analyzing the battery's usage time and historical real-time parameters. This addresses the problem that fixed thresholds in traditional technologies cannot adapt to battery aging, avoiding frequent false alarms caused by overprotection of aging batteries and preventing the overlooking of real thermal runaway risks due to excessively high thresholds. Furthermore, by forming a battery heat distribution sequence and analyzing heat change trends, it predicts future changes in battery heat distribution. Based on this, it compares the predicted temperature value with the final control boundary, classifies the risk, and allows sufficient reaction time for implementing charging cut-off or cooling measures.

Description

Technical Field

[0001] This invention relates to the field of lithium battery thermal management, specifically a dynamic early warning and safety control system for thermal runaway based on lithium battery fast charging technology. Background Technology

[0002] Currently, lithium battery fast charging technology is widely used in electric vehicles, energy storage devices and consumer electronics due to its short charging time and high efficiency. However, the high current density and concentrated heat generation during fast charging can easily lead to uncontrolled internal battery temperature and pressure buildup, ultimately causing thermal runaway or even fire and explosion accidents. As a result, various lithium battery thermal runaway early warning systems have been developed.

[0003] Currently, most temperature or voltage monitoring systems use fixed thresholds. These systems set uniform upper limits for safe temperature and voltage at the battery factory, triggering an alarm when real-time parameters exceed these thresholds. The disadvantages are:

[0004] 1. The aging phenomenon of lithium batteries with increasing usage time is not taken into account. The safety tolerance of old batteries is significantly different from that of new batteries. Fixed thresholds are very likely to cause false alarms or missed alarms.

[0005] 2. Relying on offline training data, it cannot dynamically adapt to sudden changes in battery heat during actual fast charging. At the same time, this type of system lacks the ability to predict the trend of heat distribution changes and can only alarm after parameters exceed the standard, and cannot identify high-risk moments in advance.

[0006] To address the aforementioned technical problems, this invention proposes a solution. Summary of the Invention

[0007] This invention addresses the problem of fixed thresholds in traditional technologies failing to adapt to battery aging by statistically analyzing lithium battery usage time and historical real-time parameters, dynamically adjusting and revising the control boundary. This avoids frequent false alarms caused by overprotection of aging batteries and prevents the overlooking of true thermal runaway risks due to excessively high boundaries. Furthermore, by forming a battery heat distribution sequence and analyzing heat change trends, future changes in battery heat distribution are predicted. Based on this, the predicted temperature value is compared with the final control boundary to classify and determine risks, allowing sufficient reaction time for implementing charging cut-off or cooling measures. This invention proposes a dynamic early warning and safety management system for thermal runaway based on lithium battery fast charging technology.

[0008] The objective of this invention can be achieved through the following technical solution: a dynamic early warning and safety management system for thermal runaway based on lithium battery fast charging technology, including a real-time parameter acquisition module, wherein the real-time parameter acquisition module is used to acquire the operating parameters of the lithium battery and obtain real-time battery parameters;

[0009] A boundary dynamic generation module is provided, which can count the usage time of lithium batteries, perform conversion analysis on the usage time of lithium batteries, generate boundary dynamic interference duration, and adjust the control boundary of lithium batteries based on the boundary dynamic interference duration.

[0010] The region boundary correction module obtains real-time battery parameters and then performs secondary correction on the lithium battery control boundary based on the real-time battery parameters to generate the final control boundary.

[0011] The risk simulation and extrapolation module acquires real-time battery parameters, obtains battery heat distribution through real-time battery parameters, and predicts future changes in battery heat distribution based on the change sequence of battery heat distribution.

[0012] Then, based on the future battery heat distribution, the predicted high-risk time of the battery is obtained. Based on the difference between the predicted high-risk time and the current time, as well as the danger level of the predicted high-risk time, a graded early warning signal is generated.

[0013] The tiered early warning module outputs tiered early warning signals.

[0014] In a preferred embodiment of the present invention, the method for the real-time parameter acquisition module to acquire parameters is as follows:

[0015] During the fast charging process of lithium batteries, the surface temperature, single cell voltage, charging current and internal pressure of the battery are continuously collected at fixed time intervals, and the collected data are packaged into a real-time battery parameter sequence in chronological order.

[0016] In a preferred embodiment of the present invention, the method by which the boundary dynamic generation module obtains the duration of boundary dynamic interference is as follows:

[0017] Record the total number of operating days of the lithium battery from its first use to the current moment, and divide the total number of operating days by the preset aging cycle base to obtain the aging cycle coefficient;

[0018] The initial safety control boundary is multiplied by the aging cycle coefficient and the single-cycle aging degree to generate the lithium battery control boundary, which includes the upper limit of battery temperature and the upper limit of battery pressure.

[0019] In a preferred embodiment of the present invention, the region boundary correction module obtains real-time parameters from historical data and calculates the ratio of the surface temperature in the real-time parameters to the initial threshold of the surface temperature to obtain the temperature ratio.

[0020] The pressure ratio is obtained by calculating the ratio of the internal pressure to the set initial threshold internal pressure.

[0021] The region boundary correction module calculates the average of the temperature ratio and the pressure ratio to obtain the normal operating ratio. The region boundary correction module then multiplies the normal operating ratio by an increased ratio and the product with the initial threshold to obtain the secondary correction control boundary. The secondary correction control boundary is compared with the lithium battery control boundary, and the lower temperature and lower pressure are selected as the final control boundary.

[0022] In a preferred embodiment of the present invention, the specific method for the risk simulation and deduction module to obtain the battery heat distribution is as follows:

[0023] The heat generated per unit time at each sampling moment is calculated based on the surface temperature and charging current in the real-time battery parameters.

[0024] The heat generation per unit time at multiple consecutive sampling moments is arranged in chronological order to form a battery heat distribution sequence;

[0025] Each value in the battery heat distribution sequence corresponds to the heat intensity at a given sampling time.

[0026] In a preferred embodiment of the present invention, the method by which the risk simulation and deduction module predicts future changes in battery heat distribution is as follows:

[0027] Extract the battery heat distribution sequence from multiple consecutive data collection times prior to the current time and calculate the heat difference between adjacent times;

[0028] The average of the three most recent heat differences is used as the next heat change trend to form a future battery heat distribution change sequence.

[0029] In a preferred embodiment of the present invention, the risk simulation and deduction module obtains the heat prediction value in the future battery heat distribution change sequence;

[0030] The predicted temperature value is compared with the final control boundary. If the predicted temperature value reaches more than 90% but less than 100% of the final control boundary, it is judged as a level one danger, and the corresponding time is the predicted high-risk time.

[0031] If the final control boundary is reached or exceeded, it is judged as a level two danger. The time difference between the current time and the predicted high-risk time is calculated as the basis for outputting the graded early warning signal.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. This invention converts and analyzes the total number of operating days and the aging cycle base by statistically analyzing the usage time of lithium batteries. This allows the control boundary to be automatically adjusted according to the usage time, and the control boundary of lithium batteries to be dynamically adjusted. This solves the problem that fixed thresholds cannot adapt to battery aging. It avoids frequent false alarms caused by overprotection of aging batteries, and also prevents the risk of missing the real thermal runaway due to excessively high boundaries.

[0034] 2. This invention also obtains the surface temperature and internal pressure from historical real-time parameters, calculates the normal operating ratio, and then performs a second correction with the initial threshold to select the final control boundary. This achieves dual verification of the dynamic boundary and the current actual operating conditions. It can actively tighten the control boundary according to the real-time temperature and pressure fluctuations of the battery during fast charging, avoiding misjudgments caused by the over-limit of a single parameter.

[0035] 3. The present invention also calculates the heat generation per unit time at each collection moment and forms a battery heat distribution sequence, and analyzes the heat change trend to predict future changes in battery heat distribution. Based on this, the predicted temperature value is compared with the final control boundary to determine level one or level two danger, and the time difference between the current moment and the predicted high-risk moment is calculated as a graded early warning signal output to reserve sufficient reaction time for implementing charging cut-off or cooling measures. Attached Figure Description

[0036] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0037] Figure 1 This is a system block diagram of the present invention;

[0038] Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0039] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1: Please refer to Figure 1 - Figure 2 As shown, the thermal runaway dynamic early warning safety management system based on lithium battery fast charging technology includes a real-time parameter acquisition module, a boundary dynamic generation module, a regional boundary correction module, a risk simulation and deduction module, and a graded early warning module.

[0041] The real-time parameter acquisition module is used to collect the operating parameters of the lithium battery and obtain real-time battery parameters. The specific acquisition method is as follows:

[0042] During the fast charging process of lithium batteries, the surface temperature, single cell voltage, charging current and internal pressure of the battery are continuously collected at fixed time intervals. The collected data are packaged into a real-time battery parameter sequence in chronological order. Each data point in the sequence carries timestamp information and is arranged in the order of collection. The sequence is then sent to the boundary dynamic generation module and the risk simulation and deduction module.

[0043] The boundary dynamic generation module is used to statistically analyze the usage time of lithium batteries, generate boundary dynamic disturbance duration, and adjust the control boundary of lithium batteries based on the duration. The control boundary refers to the upper limit of temperature and pressure that lithium batteries can withstand under safe operating conditions.

[0044] The specific method for obtaining the boundary dynamic interference duration is as follows: record the total number of operating days of the lithium battery from its first use to the current moment, divide the total number of operating days by the preset aging cycle base to obtain the aging cycle coefficient. The aging cycle base is a reference duration preset according to the nominal cycle life of the lithium battery, used to measure the aging progress of the battery. Multiply the initial safety control boundary by the aging cycle coefficient and the single-cycle aging degree to generate the current lithium battery control boundary. The initial safety control boundary refers to the upper limit of safe temperature and the upper limit of safe pressure calibrated when the lithium battery leaves the factory, representing the battery's ability to withstand new conditions.

[0045] Single-cycle aging refers to the percentage decrease in the safety tolerance of a lithium battery after each complete aging cycle, reflecting the performance degradation of the battery as the usage time increases.

[0046] After the above calculations, the obtained upper temperature and upper pressure limits are the lithium battery control boundaries after aging correction, and are output to the region boundary correction module.

[0047] After obtaining real-time battery parameters, the region boundary correction module performs a secondary correction on the lithium battery control boundary based on these parameters to generate the final control boundary. The specific correction method is as follows:

[0048] S1: The region boundary correction module obtains the surface temperature and internal pressure from the real-time parameters, calculates the ratio of the current surface temperature to the initial surface temperature threshold to obtain the temperature ratio. The initial surface temperature threshold is the maximum safe operating temperature recommended by the battery manufacturer. Similarly, the module calculates the ratio of the current internal pressure to the initial internal pressure threshold to obtain the pressure ratio.

[0049] S2: The area boundary correction module calculates the average of the temperature ratio and pressure ratio obtained above, that is, it takes the average of the two to obtain the normal operation ratio.

[0050] S3: The area boundary correction module expands the normal operating ratio and multiplies it with the corresponding initial threshold to obtain the secondary correction control boundary. Since the normal operating ratio is usually less than 1, if it is used directly for correction, it will lead to an overly tight boundary and frequent false alarms. Therefore, it needs to be multiplied by a preset expansion coefficient to make it more in line with the actual safety margin. The expansion coefficient is a value greater than 1 that is pre-calibrated based on engineering experience.

[0051] S4: Compare the secondary corrected control boundary calculated above with the lithium battery control boundary output by the boundary dynamic generation module item by item, and select the lower temperature upper limit and the lower pressure upper limit as the final control boundary.

[0052] The risk simulation and deduction module acquires real-time battery parameters, obtains battery heat distribution through real-time parameters, and predicts future changes in battery heat distribution based on the change sequence of heat distribution; then, based on the future heat distribution, it obtains the predicted high-risk time of the battery, and combines the difference between the predicted high-risk time and the current time with the hazard level to generate a graded early warning signal.

[0053] The risk simulation module calculates the heat generated per unit time at each data collection moment based on the surface temperature and charging current in the real-time battery parameters.

[0054] The heat generation per unit time at multiple consecutive sampling times is arranged in chronological order to form a battery heat distribution sequence. Each value in the sequence corresponds to the heat intensity at a sampling time, and the chronological order of the sequence reflects the trajectory of heat change over time.

[0055] Extract the battery heat distribution sequence from multiple consecutive acquisition times before the current time, calculate the heat difference between two adjacent times, that is, the heat of the later time minus the heat of the previous time, to obtain the heat change in each time interval;

[0056] Then, the average of the three most recent heat differences is taken as the next heat change trend. This average reflects the typical magnitude and direction of heat change in a recent period. According to this trend, the heat prediction values ​​for multiple consecutive collection times in the future are deduced in turn, thus forming a future battery heat distribution change sequence.

[0057] After obtaining the estimated heat value from the future battery heat distribution variation sequence, the risk simulation and extrapolation module first converts the estimated heat value into the corresponding predicted temperature value. Then, it compares the predicted temperature value with the upper temperature limit in the final control boundary output by the region boundary correction module.

[0058] If the predicted temperature value reaches between 90% and 100% of the final control boundary, it is judged as Level 1 danger. The time corresponding to the predicted temperature value is marked as the predicted high-risk time. Level 1 danger means that the battery is about to approach the safety boundary and there is a risk of thermal runaway, but it is still controllable.

[0059] If the predicted temperature value reaches or exceeds the final control boundary, it is classified as a Level 2 hazard, indicating a high risk of thermal runaway.

[0060] After completing the hazard classification, the time difference between the current time and the predicted high-risk time is calculated. The risk simulation and deduction module uses the hazard classification result and the time difference together as the basis for outputting the classification warning signal and sends it to the classification warning module.

[0061] Thresholds, preset values, or preset ranges are set for result comparison and analysis to determine whether they are good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be adjusted appropriately based on seasonal or common-sense influence conditions. Similarly, the weighting ratio coefficients and influence factors are set based on the magnitude of each parameter's influence on the results, and the specific values ​​are allocated to ultimately reflect the impact on the results. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be adjusted appropriately based on seasonal or common-sense influence conditions.

[0062] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A dynamic early warning and safety management system for thermal runaway based on lithium battery fast charging technology, characterized in that, It includes a real-time parameter acquisition module, which is used to acquire the operating parameters of the lithium battery and obtain real-time battery parameters; A boundary dynamic generation module is provided, which can count the usage time of lithium batteries, perform conversion analysis on the usage time of lithium batteries, generate boundary dynamic interference duration, and adjust the control boundary of lithium batteries based on the boundary dynamic interference duration. The region boundary correction module obtains real-time battery parameters and then performs secondary correction on the lithium battery control boundary based on the real-time battery parameters to generate the final control boundary. The risk simulation and extrapolation module acquires real-time battery parameters, obtains battery heat distribution through real-time battery parameters, and predicts future changes in battery heat distribution based on the change sequence of battery heat distribution. Then, based on the future battery heat distribution, the predicted high-risk time of the battery is obtained. Based on the difference between the predicted high-risk time and the current time, as well as the danger level of the predicted high-risk time, a graded early warning signal is generated. A graded early warning module, which outputs graded early warning signals.

2. The thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology according to claim 1, characterized in that, The method by which the real-time parameter acquisition module acquires parameters is as follows: During the fast charging process of lithium batteries, the surface temperature, single cell voltage, charging current and internal pressure of the battery are continuously collected at fixed time intervals, and the collected data are packaged into a real-time battery parameter sequence in chronological order.

3. The thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology according to claim 1, characterized in that, The method by which the boundary dynamic generation module obtains the duration of boundary dynamic interference is as follows: Record the total number of operating days of the lithium battery from its first use to the current moment, and divide the total number of operating days by the preset aging cycle base to obtain the aging cycle coefficient; The initial safety control boundary is multiplied by the aging cycle coefficient and the single-cycle aging degree to generate the lithium battery control boundary, which includes the upper limit of battery temperature and the upper limit of battery pressure.

4. The thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology according to claim 1, characterized in that, The region boundary correction module obtains real-time parameters from historical data and calculates the ratio of the surface temperature in the real-time parameters to the initial threshold of the surface temperature to obtain the temperature ratio. The pressure ratio is obtained by calculating the ratio of the internal pressure to the set initial threshold internal pressure. The region boundary correction module calculates the average of the temperature ratio and the pressure ratio to obtain the normal operating ratio. The region boundary correction module then multiplies the normal operating ratio by an increased ratio and multiplies it by the initial threshold to obtain the secondary correction control boundary. The secondary correction control boundary is compared with the lithium battery control boundary, and the lower temperature and lower pressure are selected as the final control boundary.

5. The thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology according to claim 1, characterized in that, The specific method by which the risk simulation and deduction module obtains the battery heat distribution is as follows: The heat generated per unit time at each sampling moment is calculated based on the surface temperature and charging current in the real-time battery parameters. The heat generation per unit time at multiple consecutive sampling moments is arranged in chronological order to form a battery heat distribution sequence; Each value in the battery heat distribution sequence corresponds to the heat intensity at a given sampling time.

6. The thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology according to claim 1, characterized in that, The method used by the risk simulation and deduction module to predict future changes in battery heat distribution is as follows: Extract the battery heat distribution sequence from multiple consecutive data collection times prior to the current time and calculate the heat difference between adjacent times; The average of the three most recent heat differences is used as the next heat change trend to form a sequence of future battery heat distribution changes.

7. The thermal runaway dynamic early warning and safety management system based on lithium battery fast charging technology according to claim 1, characterized in that, The risk simulation and deduction module obtains the heat prediction value in the future battery heat distribution change sequence; The predicted temperature value is compared with the final control boundary. If the predicted temperature value reaches more than 90% but less than 100% of the final control boundary, it is judged as a level one danger, and the corresponding time is the predicted high-risk time. If the final control boundary is reached or exceeded, it is judged as a level two danger. The time difference between the current time and the predicted high-risk time is calculated as the basis for outputting the graded early warning signal.

Images