Virtual Process Parameter Modeling for Battery Cell Manufacturing
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Solution Overview
Problem
Current methods for manufacturing battery cells are inefficient, leading to high costs and environmental impact due to complex continuous processes that lack real-time monitoring and control, failing to optimize both economic and ecological objectives effectively.
Innovation Solution
A method that simulates the manufacturing process by converting real devices into virtual ones, analyzing setpoint values, and accounting for deviations to optimize process parameters, allowing for real-time monitoring and adaptive control to achieve desired product properties, minimize costs, and reduce environmental effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If complex continuous manufacturing processes are used for battery cell production, then productivity is improved, but device complexity and difficulty of detecting and measuring process parameters increase
Solution Approach 1:
The continuous manufacturing process is segmented into discrete operational steps (mixing, coating, calendering, drying) with specific process parameters for each step. This segmentation allows complex processes to be managed through multiple simpler, controllable stages while maintaining overall productivity.
Solution Approach 2:
Process parameters are determined and optimized in advance through simulation models before actual manufacturing. The digital twin technology allows preliminary virtual testing and optimization of process parameters, reducing the complexity of real-time process control while maintaining high productivity.
2Manufacturing precision
If manual process development with numerous experiments is used to find optimal parameters, then manufacturing precision is improved, but loss of time and productivity decrease
Solution Approach 1:
Optimal process parameters are determined in advance through simulation models and digital twins before actual manufacturing begins. This preliminary virtual optimization eliminates the need for time-consuming manual experimentation while ensuring manufacturing precision is achieved from the start.
Solution Approach 2:
A digital twin (virtual copy) of the manufacturing process is created to perform experiments and optimizations in the virtual environment. This copying allows numerous experiments to be conducted virtually without consuming real production time, yet achieves the same manufacturing precision goals.
3Adaptability or versatility
If simulation with digital twin is used to determine process parameters, then adaptability and ease of operation are improved, but device complexity increases
Solution Approach 1:
A digital twin (virtual copy) of the manufacturing process is created to perform simulations and optimizations. This virtual copying enables high adaptability and ease of operation for determining process parameters without requiring physical modifications to the actual manufacturing equipment, managing complexity through virtual rather than physical means.
4Reliability
If real-time monitoring and adaptive control are implemented, then manufacturing precision and reliability are improved, but device complexity and measurement requirements increase
Solution Approach 1:
The system implements feedback loops where process parameters are continuously monitored and compared against target values. Adaptive control mechanisms automatically adjust parameters based on deviations detected through measurement systems, ensuring reliability while managing monitoring complexity through automated feedback control.
Solution Approach 2:
Target process parameters and control strategies are determined in advance through digital twin simulations. This preliminary determination of optimal parameters and control rules simplifies real-time monitoring requirements, as the system only needs to compare actual parameters against pre-determined targets rather than performing complex real-time optimization.
Data Source
AI summary
A method for determining process parameters for a manufacturing process of a real product. The manufacturing process includes at least one operation of a real device with at least one process parameter. The real device is provided as a virtual device. A setpoint value of the at least one process parameter is provided. The setpoint value is analyzed and an expected actual value is generated of the process parameter which actually occurs during operation of the real device. The expected actual value is determined taking into account influencing parameters, with the expected actual value deviating from the setpoint value or comprising a set of values with a plurality of values. The virtual device is operated with the at least one process parameter as part of a simulation, wherein at least the actual value to be expected is used.