A New Era of Battery Testing
Richard Ahlfeld, Ph.D.
Founder & CEO of Monolith | Engineering and Intractable Physics solved with Machine Learning
In this newsletter, I share the learnings from Monolith from over 300+ AI projects with engineering leaders and provide:
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How Much Test Data Do I Need To Use Machine Learning? ??
In this blog post, we demystify the enigma surrounding the test data required for effective machine learning. As we dive into the intricacies of data science, we'll explore the factors that influence this pivotal decision and guide you through striking the perfect balance.?
The truth is that even with a small amount of data, AI can be extremely valuable. Methods like Bayesian optimisation and various clever acquisition functions (expected improvement, lower confidence bound, etc.) can tell you what the most insightful tests you should be doing to understand and optimise your product faster.?
From 500 Days to 16 Days - Exciting Breakthrough in Battery Optimization! ??
?? Key Challenge
Optimising design parameters for lithium-ion batteries can be time-consuming, hindering progress in scientific and engineering fields. ??
?? In a study, the authors of "Closed-loop optimization of fast-charging protocols for batteries with machine learning" address this bottleneck and present a game-changing solution!
?? Innovation at its Best
The authors introduce a cutting-edge Machine Learning methodology that optimises the parameter space for current and voltage profiles in fast-charging protocols. This breakthrough not only accelerates the process but also holds the potential to alleviate range anxiety for electric vehicle users.
Read the full paper here??
领英推荐
?? Build AI vs. Buy AI Engineering Dilemma
In the dynamic realm of engineering, Artificial Intelligence (AI) has become an indispensable tool for accelerating testing processes, guiding decision-making, and fostering innovation. As AI's significance transcends novelty to become a crucial asset in staying competitive, engineering leaders grapple with the "build vs. buy" dilemma when seeking the most effective AI solution for their pursuits.
The blog post delves into the considerations surrounding building an in-house AI solution versus purchasing a ready-made tool, emphasizing the critical factors influencing this decision. While the "build" option involves assembling a team of engineers or data scientists to use open-source AI frameworks, it may be suitable for quick, disposable solutions. However, for organizations viewing AI as a transformative force, broader business and technical aspects must be contemplated, such as scope, scalability, and maintainability.
?? AI for Real Battery Test Data | Webinar Teaser
Join us for a new webinar on Artificial Intelligence (AI) applied to battery testing. This session will showcase the potential for test engineers by demonstrating how to effectively explore and how to quickly and easily build an AI-based solution with real battery test data to increase test plan efficiency.
Register for free!??
Feel free to contact the experts at Monolith to discuss your AI Use Case and run a feasibility analysis.
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Test Less. Learn More.
Richard