Battery degradation testing: saving months and millions 

Discover how AI-guided testing is revolutionising battery degradation testing, saving both time and money. This white paper provides in-depth insights into the latest advancements and practical applications of AI in battery testing labs. Here’s why you should dive into this comprehensive guide: 

• Enhanced accuracy: Learn how machine learning models can make precise predictions about battery degradation, optimising your testing processes. 

• Efficiency boost: Understand how AI can identify the most crucial tests, helping you allocate resources effectively and avoid unnecessary testing. 

• Data insights: Discover the power of deep learning AI models in automating data inspection, improving error detection, and ensuring data integrity. 

• Future proofing: Gain insights into the evolving field of AI in battery performance modelling, with cutting-edge methods developed by industry leaders. 

• Speed to market: Explore how AI-guided methods can accelerate battery validation and degradation studies, bringing products to market faster. 

Who should read this white paper 

This white paper is essential reading for professionals and stakeholders in the battery development and testing industry, particularly those in the following roles: 

• Battery engineers: Enhance your understanding of how AI can streamline and improve testing workflows. 

• Data scientists: Gain insights into advanced machine learning applications specific to battery testing data. 

• R&D managers: Discover strategies to optimize testing processes and reduce time to market. 

• Quality assurance professionals: Learn how AI can improve the accuracy and reliability of battery degradation testing. 

• Lab technicians: Understand the practical applications of AI in daily testing routines to improve efficiency. 

By reading this white paper, you'll gain valuable knowledge that can transform your approach to battery testing and help you stay ahead in the competitive landscape.