A method for optimizing high-efficiency lithium-ion battery electrolyte

Abstract

This invention relates to the field of lithium-ion battery electrolyte technology and discloses a method for efficient optimization of lithium-ion battery electrolytes. The method includes: acquiring initial electrolyte formulation data and real-time performance monitoring data; analyzing the initial formulation data based on electrolyte component correlation maps, identifying key component nodes and weak component nodes, and calculating formulation stability coefficients; evaluating performance defect values ​​based on real-time performance monitoring data, and generating a formulation optimization priority sequence based on the stability coefficients; generating an initial optimization strategy set based on the priority sequence, including component ratio adjustments, lithium salt concentration gradient settings, and additive compounding schemes; dynamically correcting the initial strategy set upon receiving user instructions; executing the corrected strategies for experimental testing, collecting feedback data, and updating the component correlation maps. This method achieves comprehensive analysis of electrolyte formulation and performance, improves the targeting and efficiency of optimization, forms a closed-loop optimization mechanism, and adapts to electrolyte optimization needs in different scenarios.

Claims

1. A method for optimizing lithium-ion battery electrolyte for high efficiency, characterized in that, Includes the following steps: Acquire initial formulation data and real-time performance monitoring data of lithium-ion battery electrolyte. The initial formulation data includes solvent component ratio, lithium salt concentration and additive type. The real-time performance monitoring data includes conductivity change value, interface stability index and cycle life decay rate. Based on the analysis of the electrolyte component correlation map, the initial formulation data was identified, key component nodes and weak component nodes were identified, and the electrolyte formulation stability coefficient was calculated. Based on the real-time performance monitoring data, the performance defect value of the current electrolyte system is evaluated, and a priority sequence for formulation optimization is generated by combining the electrolyte formulation stability coefficient. An initial set of optimization strategies is generated based on the formula optimization priority sequence. The initial set of optimization strategies includes component ratio adjustment schemes, lithium salt concentration gradient setting schemes, and additive compounding schemes. Receive optimization operation instructions input by the user, and dynamically modify the initial set of optimization strategies according to the optimization operation instructions; The electrolyte experimental test was conducted using the dynamically corrected initial optimization strategy set, and the test feedback data was collected and the electrolyte component correlation spectrum was updated. The process of constructing the electrolyte component correlation map includes: Collect historical electrolyte formulation data and corresponding performance test data, and extract multi-dimensional characteristics of solvent components, lithium salts and additives; The component node topology is constructed based on a graph database, and the interaction relationship between components is represented by directed edges connecting the nodes. Configure attribute information for each component node, including component activity value, compatibility index, and environmental response coefficient; Calculate the weight values ​​of associated edges based on historical interaction frequency and intensity, and mark key interaction paths and weak interaction paths; The calculation process for the stability coefficient of the electrolyte formulation includes: Extract the dynamic weight parameters of all component nodes in the electrolyte component correlation map; The arithmetic mean of the weights of critical interaction paths and the geometric mean of the weights of weak interaction paths are calculated. The arithmetic mean and geometric mean are integrated by a weighted fusion algorithm to generate the electrolyte formulation stability coefficient.

2. The method for high-efficiency optimization of lithium-ion battery electrolyte according to claim 1, characterized in that, The evaluation process for the performance defect value includes: Real-time monitoring of electrolyte conductivity changes and comparison with the ideal conductivity curve to generate conductivity deviation. The interface deposition morphology data of the electrode electrolyte interface are obtained by interface imaging technology, and the interface stability deviation index is calculated. Analyze the capacity decay curves of cyclic charge-discharge tests and derive the cycle life decay rate; The conductivity deviation, interface stability deviation index, and cycle life decay rate are normalized and substituted into the defect assessment model to generate performance defect values.

3. The method for optimizing lithium-ion battery electrolyte according to claim 2, characterized in that, The process of generating the formula optimization priority sequence includes: The performance defect values ​​and electrolyte formulation stability coefficients were standardized. The urgency coefficient for formula optimization is calculated using a logistic regression algorithm. The electrolyte component nodes are sorted according to the urgency coefficient of formulation optimization to generate a formulation optimization priority sequence.

4. The method for optimizing lithium-ion battery electrolyte according to claim 3, characterized in that, The process of generating the initial set of optimization strategies includes: Nodes of components to be optimized are selected according to the aforementioned formulation optimization priority sequence; For the solvent component node, the adjustment range of the generated component ratio is positively correlated with the urgency coefficient of the formulation optimization. For the concentration gradient setting scheme of lithium salt nodes, the gradient difference is correlated with the interface stability deviation index; For additive nodes, compound formulations are generated, and the types of compound formulations are matched with historical compatibility indices.

5. The method for optimizing lithium-ion battery electrolyte according to claim 4, characterized in that, The dynamic correction process includes: Parse the target adjustment parameters in the user-input optimization operation instructions; When the target adjustment parameters involve component proportions, the dynamic weights of the affected components are recalculated and the component proportion adjustment scheme is updated. When the target adjustment parameters involve correlation, the weight values ​​of the correlation edges are adjusted and the additive compounding scheme is regenerated; When the target adjustment parameters directly specify the weights, the original dynamic weight parameters are overridden and all dependent schemes are corrected synchronously.

6. The method for optimizing lithium-ion battery electrolyte according to claim 5, characterized in that, The execution process of the electrolyte experimental test includes: Prepare experimental electrolyte samples according to the revised component ratio adjustment scheme; Test groups with different concentration gradients were prepared according to the lithium salt concentration gradient setting scheme; A series of compound electrolytes were prepared using an additive compounding scheme; Electrochemical performance testing, interface stability testing, and cycle life testing were conducted.

7. The method for optimizing lithium-ion battery electrolyte according to claim 6, characterized in that, The process of collecting the test feedback data includes: Record the change curve of the conductivity of the electrolyte sample over time; Data on the thickness and uniformity of the interface layer on the electrode surface were obtained using scanning electron microscopy. Collect capacity retention data and decay inflection point parameters during cyclic testing; Calculate the deviation of each group of test results from the target performance index.

8. The method for high-efficiency optimization of lithium-ion battery electrolyte according to claim 7, characterized in that, The updating process of the electrolyte component correlation map includes: The activity values ​​and compatibility indices of the component nodes were recalculated based on the test feedback data. Adjust the weight values ​​of associated edges based on performance deviation; Update the marker status of critical and weak interaction paths; Store the updated topology in the graph database.

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