Energy Storage Generative Design for Virtual Battery Optimization
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Solution Overview
Problem
Battery manufacturing is a complex, iterative process that requires optimization of various parameters such as electrode production, cell assembly, and module integration, posing challenges in achieving efficient, safe, and sustainable energy storage devices.
Innovation Solution
A computer-implemented method employing machine learning and generative design to automatically build models of energy storage devices, simulate their performance, and evolve design parameters to achieve product and model objectives, incorporating advanced data science, cheminformatics, and structure-based modeling to optimize chemical, configuration, and process spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional iterative manufacturing processes are used for battery production, then manufacturing experience and physical testing can identify performance issues, but the process is time-consuming, costly, and requires extensive physical prototyping
Solution Approach 1:
The patent applies preliminary action by performing generative design and simulation before physical manufacturing. Multiple design iterations are explored virtually using AI models and simulations to predict battery performance, safety, and manufacturing feasibility. This allows design issues to be identified and resolved before committing to physical production, eliminating the need for extensive physical prototyping and testing cycles.
Solution Approach 2:
The patent creates virtual copies of battery designs through AI-generated 3D models and digital twins. These digital representations allow for comprehensive simulation and evaluation of multiple design variants without physical manufacturing. The virtual models capture geometric, material, and process parameters, enabling performance prediction and optimization before physical production.
2Manufacturing precision
If multiple design parameters are manually optimized through iteration, then performance goals can be achieved, but the complexity of coordinating chemistry, geometry, and manufacturing parameters increases
Solution Approach 1:
The patent implements a multi-functional AI system that simultaneously handles chemistry optimization, geometric design, manufacturing parameter coordination, and performance prediction. The generative design platform integrates multiple functions into a unified system that explores the design space across all parameters concurrently, rather than requiring separate manual optimization processes for each parameter.
Solution Approach 2:
The system employs parameter changes by using AI models to explore variations in chemical composition, geometric dimensions, and manufacturing parameters simultaneously. The generative design process systematically varies multiple parameters across their feasible ranges, using simulations to evaluate performance outcomes and identify optimal parameter combinations that satisfy multiple objectives.
3Reliability
If physical testing and experimentation are performed to validate battery designs, then performance and safety can be confirmed, but the cost and resource requirements increase significantly
Solution Approach 1:
The patent replaces physical testing with virtual testing using high-fidelity simulations and digital twins. The simulation models replicate battery behavior under various operating conditions, enabling validation of performance and safety without consuming physical materials. Multiple test scenarios can be evaluated computationally, reducing the need for physical prototypes and extensive material consumption.
Solution Approach 2:
The patent substitutes mechanical and physical testing systems with computational simulation systems. Instead of building and testing physical battery prototypes, the system uses AI-driven simulations to predict performance outcomes. This substitution eliminates material consumption associated with physical testing while maintaining validation capability through virtual experimentation.
4Adaptability or versatility
If extensive design exploration is performed to find optimal battery configurations, then innovative solutions can be discovered, but the computational and time resources required increase
Solution Approach 1:
The patent performs preliminary filtering and pre-screening of design options using AI models before conducting full simulations. The generative design process quickly evaluates numerous design variants using lightweight models to identify promising candidates, then applies more computationally intensive simulations only to the most promising designs. This hierarchical approach enables extensive design exploration while managing computational energy consumption.
Data Source
AI summary
A computer-implemented method and corresponding system perform generative design of an energy storage device. The method automatically builds at least one model of the energy storage device. The building is based on a design parameter space and employs a machine learning process. The method automatically performs a simulation of the energy storage device using the design parameter space, a design evaluation space, and the at least one model built. The performing produces at least one prediction. The method automatically evolves at least one of (i) the design parameter space and (ii) the design evaluation space. In an event the at least one prediction indicates that a product design objective or model design objective has been achieved, the method automatically converges on the design parameter space evolved, thereby completing a generative design of the energy storage device and, otherwise, repeats the building, performing, and evolving.