Monolith, an artificial intelligence (AI) software provider for engineering applications, has partnered with CamMotive, a UK-based provider of e-powertrain development and testing services, to advance the use of AI in electric vehicle (EV) battery testing.
The collaboration seeks to improve test data validation, enabling engineers to identify complex failure characteristics more effectively during battery development.
The companies are piloting a hybrid modeling approach to anomaly detection, combining physics-based simulations with machine-learning methods. This technique is designed to detect subtle issues that may be missed by traditional rule-based systems, supporting safer and more reliable battery designs.
While Monolith has demonstrated its AI platform in laboratory settings, CamMotive contributes operational test data from its battery testing facility. This integration of real-world datasets with AI models aims to improve accuracy, efficiency, and insight in the EV battery testing process.
“Training machine learning models with robust, real-world data is what makes AI effective for engineering,” said Dr. Richard Ahlfeld, CEO and founder of Monolith. “By applying AI to EV battery testing, engineers can gain earlier insights into potential failure modes, which is critical for improving reliability and accelerating development timelines.”
The collaboration underscores how applied AI can reduce reliance on physical testing, support earlier fault detection, and make EV battery development more scalable. With global automakers, including those in North America, investing heavily in new EV platforms, more efficient battery testing processes are a growing priority.
Monolith continues to develop AI tools for engineering teams, such as its “Next Test Recommender” and AI-powered “Anomaly Detector,” which aim to reduce the cost and time required for product validation.
Filed Under: Batteries, Technology News