Battery Slurry Mixing Control Using Real-Time Viscosity Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for controlling the viscosity of a slurry in secondary battery manufacturing are inefficient, relying on manual sampling and separate viscometer measurements, which can lead to errors and additional resource consumption.
Innovation Solution
An apparatus and method utilizing a processor performing machine learning to predict the viscosity of a slurry in real time during the mixing process, adjusting mixing conditions to meet a target viscosity, by extracting highly correlated mixing process data and generating predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual sampling and separate viscometer measurements are used to control slurry viscosity, then viscosity measurement can be performed, but measurement errors increase and additional resource consumption occurs
Solution Approach 1:
The patent combines the viscosity measurement function with the online mixing process by integrating a viscosity sensor directly into the mixing system. This allows viscosity to be measured continuously during mixing without separate sampling operations, eliminating measurement errors from manual sampling and reducing resource consumption by consolidating measurement and mixing operations into a single integrated system.
Solution Approach 2:
The patent introduces an online viscosity sensor as an intermediary device that directly measures slurry viscosity within the mixing system. This sensor acts as a mediator between the mixing process and control system, providing real-time viscosity data without requiring manual sampling or separate measurement operations, thereby improving measurement accuracy and reducing resource usage.
2Manufacturing precision
If real-time viscosity prediction through machine learning is implemented, then viscosity control precision improves, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical viscosity measurement and control systems with a machine learning-based predictive model. Instead of using sophisticated physical measurement devices and manual adjustment mechanisms, the system uses algorithms that process mixing parameters (speed, time, temperature, material ratios) to predict and control viscosity, thereby improving precision while actually reducing mechanical device complexity.
Solution Approach 2:
The patent changes the approach from direct physical measurement to predictive modeling by transforming viscosity control into a parameter-based system. The machine learning model uses changes in mixing parameters (rotation speed, mixing time, temperature, material composition ratios) to predict viscosity outcomes, allowing precise control through parameter optimization rather than complex measurement and adjustment hardware.
3Productivity
If manual sampling methods are used for viscosity measurement, then device complexity remains low, but measurement errors increase and productivity decreases
Solution Approach 1:
The patent implements continuous viscosity monitoring during the mixing process through online sensing and machine learning prediction. Instead of intermittent manual sampling, the system continuously tracks viscosity changes throughout mixing, ensuring both high measurement precision and improved productivity by eliminating downtime for sampling operations and enabling real-time process optimization.
Data Source
AI summary
An apparatus for controlling viscosity of a slurry includes a mixing module mixing raw materials for a secondary battery, and a processor performing machine learning for prediction of viscosity of the slurry, predicting viscosity of the slurry in real time during a mixing process through machine learning, and adjusting conditions of the mixing process performed by the mixing module such that a predictive viscosity of the slurry meets a target viscosity.