Material fatigue analysis method in pulse charging process of sodium ferric phosphate battery

Abstract

The invention provides a material fatigue analysis method in the pulse charging process of a sodium iron phosphate battery, which comprises the following steps: acquiring a material fatigue accumulation index by collecting high-frequency alternating data and current intensity change in the pulse charging process in real time and combining an electrode material response signal; according to the obtained material fatigue accumulation index, a genetic algorithm is adopted to carry out simulation optimization on a current fluctuation mode, and a structure instability risk assessment value is obtained; processing a matching relation between the optimized current intensity distribution scheme and a material fatigue accumulation index by applying a particle swarm optimization algorithm to obtain a dynamic balance control sequence; according to the obtained dynamic balance control sequence, monitoring the change trend of the cycle life related indexes under the charging efficiency, and judging whether the durability requirement is met or not; if it is judged that the durability requirement is met, the high-frequency alternating data processing model is updated based on the change trend, and an enhanced battery life prediction function is obtained.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for material fatigue analysis during the pulse charging process of a sodium iron phosphate battery. Background Technology

[0002] In the field of new energy battery technology, sodium iron phosphate batteries have become an important direction in energy storage and electric vehicle applications due to their high safety, long lifespan, and low cost. Especially in the research of fast charging technology, improving charging efficiency and extending battery life are key to the industry's development. Research in this area not only concerns energy utilization efficiency but also directly impacts the promotion and application of green energy, possessing significant strategic value.

[0003] However, a common problem in current fast charging processes is the lack of dynamic adaptive adjustments to the internal state of the battery. Many methods often overlook the complex changes in battery materials under high-intensity charging, failing to effectively balance the contradiction between charging speed and material durability. Especially in scenarios like pulse charging with high-frequency alternating loads, simply pursuing rapid energy input can easily lead to irreversible damage to the internal materials, thereby affecting the overall performance and lifespan of the battery.

[0004] Focusing on specific technical challenges, a core issue in pulse charging lies in the difficulty of accurately assessing the electrode material's ability to withstand oscillating loads. Under the influence of high-frequency pulse current, electrode materials experience fatigue due to repeated charge-discharge stress. This fatigue accumulates gradually with increasing cycle count, leading to structural instability. More complexly, this structural change does not occur uniformly but is closely related to fluctuations in current intensity during charging. Frequent switching between current peaks and troughs exacerbates localized damage within the material, thus affecting the battery's cycle life. For example, in real-world charging scenarios, if the pulse current intensity is set too high, microcracks may appear in the electrode material within a short period. These cracks will continue to propagate in subsequent cycles, ultimately causing rapid capacity decay.

[0005] Therefore, dynamically balancing current intensity fluctuations and electrode material durability during pulse charging has become a key issue in improving battery performance and lifespan. Solving this problem requires not only a deep understanding of the material's response characteristics under high-frequency loads, but also finding the optimal balance between the two in practical operation to avoid material damage and ensure charging efficiency. Summary of the Invention

[0006] This invention provides a method for material fatigue analysis during the pulse charging process of a sodium iron phosphate battery, mainly comprising:

[0007] By acquiring high-frequency alternating data and current intensity changes during the pulse charging process in real time, and combining the electrode material response signal, the material fatigue accumulation index is obtained.

[0008] Based on the obtained material fatigue accumulation index, a genetic algorithm is used to simulate and optimize the current fluctuation mode to obtain the structural instability risk assessment value.

[0009] If the structural instability risk assessment value exceeds the preset threshold, the high-frequency alternation parameters are adjusted for the potential local damage area to determine the optimized current intensity distribution scheme.

[0010] By applying the particle swarm optimization algorithm to process the matching relationship between the optimized current intensity distribution scheme and the material fatigue accumulation index, a dynamic equilibrium control sequence is obtained.

[0011] Based on the obtained dynamic balance control sequence, monitor the changing trend of cycle life related indicators under charging efficiency to determine whether the durability requirements are met.

[0012] If the durability requirements are met, the high-frequency alternating data processing model is updated based on the changing trend to obtain an enhanced battery life prediction function.

[0013] By incorporating an enhanced battery life prediction function into the pulse charging control system, iterative adjustments are made to address current fluctuations and localized damage, ultimately determining the path to improve charging efficiency.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0015] To address the structural instability and lifespan degradation issues in batteries caused by high-frequency alternating data, current intensity variations, and material fatigue accumulation during pulse charging, this invention collects key data in real time during the charging process, combines it with electrode material response signals, accurately calculates material fatigue indicators, and uses a genetic algorithm to optimize the current fluctuation pattern and assess structural risks. If the risk exceeds the limit, this invention optimizes the current distribution by adjusting high-frequency parameters and uses a particle swarm optimization algorithm to achieve dynamic balance control, generating an adaptive control sequence, monitoring cycle life and charging efficiency trends to ensure durability meets standards, and finally updating the data processing model to construct an enhanced lifespan prediction function and iteratively adjust the charging path. This invention significantly improves charging efficiency and extends battery life through intelligent control and prediction, providing an innovative solution for battery management systems. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention.

[0017] Figure 2 This is a schematic diagram of the present invention.

[0018] Figure 3This is another schematic diagram of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1-3 This embodiment provides a material fatigue analysis method during the pulse charging process of a sodium iron phosphate battery, which may specifically include:

[0021] S101. By real-time acquisition of high-frequency alternating data and current intensity changes during the pulse charging process, combined with the electrode material response signal, the material fatigue accumulation index is obtained.

[0022] By acquiring high-frequency data during the pulse charging process in real time, initial current change records are obtained. Based on these initial current change records and a pre-established signal analysis model, response signal characteristics of the electrode material are extracted. Using these response signal characteristics, preliminary fatigue accumulation calculations are performed on the electrode material to obtain an intermediate fatigue accumulation value. Further processing of this intermediate fatigue accumulation value, combined with changes in material properties during charging, determines the quantification result of fatigue accumulation. If the quantification result exceeds a preset threshold, an anomaly marker is triggered, and the corresponding charging process data and high-frequency data characteristics are recorded. Secondary signal analysis is performed on the anomaly-marked data, combined with a support vector machine algorithm, to determine the potential risk level of material fatigue accumulation. Based on the potential risk level, corresponding evaluation indicators are generated, completing a comprehensive analysis of electrode material fatigue accumulation.

[0023] For example, in studying the fatigue accumulation of electrode materials, real-time acquisition of pulse charging data is a core step. Recording current changes during the initial charging phase using a high-frequency sensor can capture minute fluctuations. Suppose that in a single charging experiment, the initial current rapidly increases from 0.5 amperes to 2.0 amperes, with multiple small oscillations occurring during this process. These data reflect the instantaneous response characteristics of the electrode material. Combined with a pre-established signal analysis model, frequency domain analysis methods are used to extract response signal features, such as oscillation frequency and amplitude changes, serving as the basis for fatigue accumulation analysis.

[0024] In one possible implementation, preliminary fatigue accumulation calculations are performed based on the extracted response signal features. Assuming that signal feature analysis reveals a peak stress response of 0.8 units for the electrode material during a particular charge cycle, and combining this with historical data accumulation, a median fatigue accumulation value of 1.2 units is calculated. This median value reflects the fatigue state of the material under the current charging cycle, but further adjustments are needed to account for changes in material properties, such as increased internal resistance or capacity decay, to correct the result, ultimately yielding a quantized value of 1.5 units. If the preset threshold is 1.0 unit, this quantization result triggers an anomaly flag, and the system automatically records high-frequency data features during the charging process, such as current fluctuation frequency and peak data, for subsequent analysis.

[0025] For example, when performing secondary signal analysis on data with anomaly markers, a support vector machine (SVM) algorithm can be introduced to classify the potential risk level of fatigue accumulation. Suppose that the algorithm identifies hidden fatigue patterns in the data and categorizes the risk level into low, medium, and high; the current data is classified as medium risk. This process compares historical data with current signal characteristics to determine whether the material is approaching its fatigue limit, thereby improving prediction accuracy. Its beneficial effect lies in providing early warnings and avoiding safety hazards caused by material failure.

[0026] In one possible implementation, an assessment index is generated based on the potential risk level. For example, a medium-risk level could be assigned an assessment index of 75 points, with a maximum score of 100. A score below 80 points would require maintenance measures. This index not only quantifies the degree of fatigue accumulation but also provides a basis for optimizing subsequent charging strategies, such as reducing the charging rate or shortening the charging time, thereby extending the lifespan of the electrode materials.

[0027] For example, when comprehensively analyzing the fatigue accumulation of electrode materials, combining the aforementioned quantitative results and evaluation indicators allows for verification from two perspectives: changes in the material's microstructure and the degradation of its macroscopic performance. Assuming that microstructure analysis shows an increase in lattice defects, while macroscopic performance is characterized by a 10% decrease in capacity, these two aspects mutually support each other, jointly demonstrating that fatigue accumulation has reached a state requiring attention. This multi-dimensional analysis method improves the reliability of the assessment, helps in developing more scientific material management strategies, and significantly reduces operational risks.

[0028] S102. Based on the obtained material fatigue accumulation index, a genetic algorithm is used to simulate and optimize the current fluctuation mode to obtain the structural instability risk assessment value.

[0029] By acquiring material fatigue accumulation indices, preliminary data on material conditions is processed to obtain an initial assessment basis for structural risk. Based on this initial assessment, a genetic algorithm is used to simulate current fluctuation patterns, determining the correlation between fluctuation patterns and structural risk. Key fluctuation patterns are extracted from the correlation data, and an in-depth analysis of the correspondence between material fatigue and instability values ​​is conducted to derive intermediate results for risk derivation. Using these intermediate results, combined with the changing trends of material conditions, multi-dimensional data comparisons are performed on structural risk to obtain a quantitative basis for instability values. Based on this quantitative basis, if the instability value exceeds a preset threshold, the risk derivation results are marked, identifying high-risk materials. Using these identification tags, combined with data records from the analysis process, the assessment results are categorized and stored to obtain the final structural risk classification data.

[0030] For example, when studying the acquisition of fatigue accumulation indices and structural risk assessment of electrode materials, one can start with data processing to initially organize relevant information about the material's state. Suppose that in an experiment, stress data of the electrode material was collected after multiple charging cycles, and some data points showed abnormal fluctuations, such as the current value dropping from 1.2 amperes to 0.8 amperes in a short period. This may indicate instability in the material's internal structure. By organizing this data, an initial assessment basis can be formed, laying the foundation for subsequent analysis.

[0031] For example, when using a genetic algorithm to simulate current fluctuation patterns based on the initial assessment criteria, fluctuation data can be input into the algorithm model to simulate fluctuation trends under different charging conditions. Suppose that the simulation reveals that when the charging rate is high, the current fluctuation frequency increases to 5 times per second, and this gradually shows a potential correlation with structural risk. Through repeated iterative optimization, correlation data between fluctuation patterns and risk can be extracted to support further analysis.

[0032] For example, when extracting key fluctuation patterns and analyzing the correlation between material fatigue and instability values, attention can be paid to data points with large fluctuation amplitudes. Suppose that in a certain experiment, the fluctuation amplitude reaches 0.3 amperes and lasts for a long time, which may correspond to the accumulation of microscopic defects within the material. By combining historical data, intermediate results of the risk can be derived, clarifying the specific impact of fluctuation patterns on material fatigue.

[0033] For example, when comparing intermediate results with material state change trends across multiple dimensions, analysis can be performed from various perspectives, such as current fluctuations and internal resistance changes. Suppose the internal resistance increases from an initial 0.5 ohms to 0.9 ohms in the experiment, while the fluctuation frequency also increases, indicating that the material instability is intensifying. By comparing these values, quantitative evidence of instability can be obtained, providing a more comprehensive perspective for risk assessment.

[0034] For example, if the instability value exceeds a preset threshold (e.g., a threshold of 0.7 units, but the actual value is 0.85 units), the risk derivation result is flagged. The system can automatically generate identification labels for high-risk materials and record relevant data, such as fluctuation frequency and internal resistance, for subsequent tracking. This flagging method helps to quickly identify potential problems.

[0035] For example, when combining identification tags with data records for categorized storage, assessment results can be divided into high, medium, and low risk levels. If a material is marked as high-risk, its data will be preferentially stored in the high-risk database, along with relevant fluctuation patterns and instability values. This categorized storage method facilitates subsequent analysis and management. Through the above multi-dimensional analysis and examples, a complete assessment logic is formed, from data processing to risk classification. Each step is closely linked, and the data and results support each other, ensuring the comprehensiveness and reliability of the assessment. This method helps to promptly identify potential problems in the material's condition, providing important references for optimizing charging strategies and extending material lifespan.

[0036] S103. If the structural instability risk assessment value exceeds the preset threshold, the high-frequency alternation parameters are adjusted for the potential local damage area to determine the optimized current intensity distribution scheme.

[0037] If the structural instability risk assessment value exceeds a preset threshold, raw signal data related to local damage is acquired through a data acquisition system to determine the preliminary distribution range of the potential area. Based on the preliminary distribution range, a pre-established damage identification model is used to perform a refined scan of the potential area to obtain the specific location information of local damage. Using this specific location information, high-frequency alternating parameters are initially configured based on the distribution characteristics of the local damage to determine the initial parameter adjustment scheme. If the parameter adjustment results of the initial scheme do not meet the preset standard, the high-frequency alternating parameters are repeatedly corrected using an iterative calculation tool to obtain an optimized parameter combination. Based on the optimized parameter combination, a corresponding current intensity distribution scheme is generated to determine the coverage uniformity of the current intensity within the potential area. If the coverage uniformity meets the preset conditions, the current intensity distribution scheme is recorded through a data storage module to obtain the final area protection configuration.

[0038] S104. By applying the particle swarm optimization algorithm to process the matching relationship between the optimized current intensity distribution scheme and the material fatigue accumulation index, a dynamic equilibrium control sequence is obtained.

[0039] The particle swarm optimization algorithm is used to process the data correlation between current intensity and distribution scheme, generating a preliminary intensity adjustment configuration. Based on this configuration, relevant cumulative index data of material fatigue are acquired to determine the initial results of the fatigue assessment. If the initial results exceed a preset threshold range, data correction is performed on the cumulative indices to obtain adjusted assessment data. Based on the adjusted data, a dynamic equilibrium control sequence is generated, and its applicability in intensity adjustment is assessed. If the suitability of the control sequence meets preset conditions, the final dynamic equilibrium configuration is recorded using a data storage tool, obtaining the equilibrium state record information. Using this record information, a long-term monitoring scheme for current intensity is generated, determining the execution basis for the scheme. Based on this basis, a pre-established monitoring model is used to continuously track the cumulative indices of material fatigue, obtaining real-time updated state data.

[0040] S105. Based on the obtained dynamic balance control sequence, monitor the changing trend of cycle life related indicators under charging efficiency, and determine whether the durability requirements are met.

[0041] By using a dynamically balanced control sequence, data on the changes in cycle life-related indicators under charging efficiency are obtained to determine preliminary efficiency analysis results. Based on these preliminary results, a pre-established evaluation model is used to further process the changes in cycle life, yielding detailed lifespan assessment data. For this detailed lifespan assessment data, if certain indicators deviate from durability standards, data correction tools are used to adjust these indicators, determining whether the adjusted data meets preset threshold ranges. Based on the adjusted data, the correlation between charging efficiency and cycle life is obtained, determining the degree of matching between the two in a balanced state. Using this matching information, a particle swarm optimization algorithm is used to fine-tune the control sequence, obtaining an optimized sequence configuration. For this optimized sequence configuration, changes in cycle life-related indicators are continuously tracked to determine whether they are stably maintained within durability standards. Based on the continuous tracking results, a dynamic adjustment strategy for charging efficiency is generated, determining the final balanced state data.

[0042] S106. If the durability requirements are met, the high-frequency alternating data processing model is updated based on the changing trend to obtain an enhanced battery life prediction function.

[0043] S107. By integrating the enhanced battery life prediction function into the pulse charging control system, iterative adjustments are made to address current fluctuations and localized damage, thus determining the final path for improving charging efficiency.

[0044] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A method for material fatigue analysis during the pulse charging process of a sodium iron phosphate battery, characterized in that, include: By acquiring high-frequency alternating data and current intensity change data during the pulse charging process using acquisition equipment, and combining this with the response signal of the electrode material, a quantitative index of material fatigue accumulation is generated. The high-frequency alternating data and the current intensity change data are input into a pre-built signal analysis model to extract the response signal characteristics of the electrode material; Based on the characteristics of the response signal, a preliminary calculation of fatigue accumulation is performed on the electrode material to generate an intermediate fatigue accumulation value; The fatigue accumulation intermediate value is processed and combined with the material property change data during the charging process to generate a quantitative result of the material fatigue accumulation. When the quantization result exceeds the preset range, an anomaly flag is triggered, and the corresponding charging process data and high-frequency data characteristics are recorded. Secondary signal analysis is performed on the data corresponding to the anomaly markers, and combined with a pre-trained classification model, the potential risk level of material fatigue accumulation is determined. Based on the potential risk level, corresponding evaluation indicators are generated to complete the analysis process of fatigue accumulation of the electrode material.

2. The method according to claim 1, characterized in that, The process involves acquiring high-frequency alternating data and current intensity variation data during pulse charging using a data acquisition device, and combining this data with the response signal of the electrode material to generate a quantitative index of material fatigue accumulation, including: The high-frequency alternating data during the pulse charging process is recorded in real time by the acquisition device to generate an initial current change record; Based on the initial current change record, and combined with a pre-built signal processing module, the response signal characteristics of the electrode material are extracted; The response signal characteristics are analyzed in multiple dimensions, and combined with the high-frequency alternating data, preliminary quantitative data of material fatigue accumulation is generated. The preliminary quantitative data is corrected, and combined with the current intensity change data and material property changes, a final quantitative index of material fatigue accumulation is generated.

3. The method according to claim 1, characterized in that, The step of performing secondary signal analysis on the data corresponding to the anomaly markers, combined with a pre-trained classification model, to determine the potential risk level of material fatigue accumulation includes: Signal decomposition is performed on the charging process data and high-frequency data features corresponding to the anomaly markers to extract key signal fluctuation features; The key signal fluctuation characteristics are input into the pre-trained classification model to generate a preliminary classification result of the risk level; The preliminary classification results are compared with historical charging data to generate the potential risk level of material fatigue accumulation. Based on the potential risk level, corresponding risk assessment data is generated to complete further analysis of the material fatigue accumulation.

4. The method according to claim 2, characterized in that, The process of performing multi-dimensional analysis on the response signal characteristics, combined with the high-frequency alternating data, to generate preliminary quantitative data on material fatigue accumulation includes: The response signal features are subjected to signal layering processing to extract multi-dimensional feature data; By combining the high-frequency alternating data, the multi-dimensional feature data is quantified to generate preliminary quantitative data on the material fatigue accumulation.

5. The method according to claim 3, characterized in that, The process of comparing the preliminary classification results with historical charging data to generate the potential risk level of material fatigue accumulation includes: The preliminary classification results are corrected, and combined with the historical charging data, the final risk level classification data is generated. Based on the final classification data, a potential risk level for material fatigue accumulation is generated.

6. The method according to claim 1, characterized in that, The process of generating corresponding evaluation indicators based on the potential risk level to complete the analysis of fatigue accumulation of the electrode material includes: Based on the potential risk level, generate corresponding assessment indicator data; The evaluation index data are categorized and stored to complete the analysis process of fatigue accumulation of the electrode material.

7. The method according to claim 6, characterized in that, The process of classifying and storing the evaluation index data to complete the analysis of fatigue accumulation of the electrode material includes: The evaluation index data is processed in a hierarchical manner to generate a data structure for classified storage; Based on the data structure, the analysis process for fatigue accumulation of the electrode material is completed.

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