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Build machine learning models of your battery designs from test data for a simpler, more accurate model than traditional simulation. Self-learning models built using test data can be more accurate than ECM models and less complex than P2D models. With the interactive tools in Monolith, you can quickly gain a deep understanding of your design, make predictions for how your battery will react under different conditions, and optimise parameter values for key use cases.
Understand which design parameters and operating conditions have the greatest impact on battery performance and lifetime. Apply model evaluation tools in Monolith to visually and quantitatively compare the accuracy of different modelling algorithms to find the best model. Build understanding and confidence in your model with sensitivity analysis and explainable AI functions to guide you on where to focus to improve battery performance.
Use machine learning models to predict test outcomes for a wide range of conditions ahead of time. Use prediction tools to exercise your design with different input values and instantaneously see the results. Use interactively sliders to vary inputs and see how scalar and curve outputs are impacted. Make more educated decisions on which parameters require more rigorous testing, or which values can be removed from the test plan altogether.
Apply active learning techniques to create more efficient test plans. Achieve the same test coverage in fewer steps, or test more conditions in the same amount of time. Apply Monolith's proprietary test plan optimization tools to explore your battery design space and get recommendations for the most important values for your test plan. Reorder your test plan or remove unnecessary test steps to find the most efficient test plan without jeopardizing coverage.
