Multi-Channel Battery Cycling for Charging Profile Optimization
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Solution Overview
Problem
Existing battery charging and discharging methods are resource-intensive and often result in suboptimal performance due to insufficient testing of variable combinations, leading to issues like dendrite formation, slow charging speeds, and reduced battery life, without providing a thorough understanding of performance tradeoffs.
Innovation Solution
A method and system for developing optimized battery charging profiles using automated, remote-accessible battery cyclers that perform experiments, parameterization, and model-based simulations to generate detailed performance reports, reducing the need for extensive testing and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If extensive battery cycling experiments are performed to test multiple variable combinations, then charging profile optimization is improved, but resource consumption and testing time increase
Solution Approach 1:
The system performs preliminary battery characterization and parameter estimation during initial cycling experiments. These preliminary results are used to create initial charging profiles and identify key variables, allowing subsequent testing to focus only on critical parameter combinations rather than exhaustively testing all possibilities.
Solution Approach 2:
The system creates virtual copies of physical battery testing through computer simulations and models. These digital twins replicate battery behavior under various charging conditions, allowing multiple variable combinations to be tested in silico before physical experiments, significantly reducing the number of required physical tests.
2Loss of information
If multiple variable combinations are tested to understand performance tradeoffs, then battery performance understanding is improved, but device complexity and resource requirements increase
Solution Approach 1:
The testing system is segmented into modular components: a battery cycler for physical testing, a simulation environment for virtual testing, and a data processing system for analysis. Each component handles specific tasks independently, making the overall complex testing process manageable and scalable without requiring all components to be present simultaneously.
Solution Approach 2:
The system introduces data processing and analysis tools as intermediaries between physical/battery testing and performance understanding. These intermediaries automatically process raw cycling data, identify performance tradeoffs, and generate optimized charging profiles, reducing the complexity burden on the testing infrastructure itself.
3Manufacturing precision
If traditional battery testing methods are used, then basic charging characteristics are obtained, but detailed performance tradeoffs and optimized charging profiles cannot be achieved
Solution Approach 1:
The system merges physical battery cycling experiments with computer simulations and data processing in an integrated platform. This combination allows physical testing to provide real battery behavior data while simulations extrapolate results to various conditions and generate optimized charging profiles, achieving detailed performance understanding without proportionally increasing testing time.
Solution Approach 2:
The system implements feedback loops where cycling data is continuously processed to refine charging profile recommendations, which are then tested through additional cycling experiments. This iterative feedback process progressively improves charging profile optimization while efficiently using testing resources by focusing subsequent experiments on areas needing improvement.
Data Source
AI summary
Aspects of the present disclosure relate to a method of developing a battery charging profile. The method includes performing an experimentation process on a battery, performing a parameterization process on the battery, developing a battery model for the battery based on experimentation data and parameterization data, generating simulated battery data using the battery model, and generating a battery performance report based on the simulated battery data. Other aspects relate to a multi-channel, multi-tenant system for developing a battery charging profile. The system includes a first battery cycler and a second battery cycler, wherein the first and second battery cyclers are configured to deliver a charge signal to a battery disposed therein. The system also includes a data storage infrastructure in communication with the first and second cyclers and a multi-tenant data processing application stored on the data storage infrastructure.