Software provider Monolith has partnered with UK-based e-powertrain development and testing services provider CamMotive to provide engineers with the tools to run more efficient, insightful, and scalable battery tests.
Bringing together Monolith’s software platform and CamMotive’s real-world battery data, the collaboration is expected to enhance test data validation, helping engineers detect complex failure characteristics during EV battery development.
The partnership is implementing a hybrid modelling technique for anomaly detection in the battery testing process. This combines physics-based simulations and machine learning methods to identify issues that may not be detected by traditional rule-based detection systems.
Building on Monolith’s deployments in laboratory environments, CamMotive is providing operational test data to evaluate how these models can achieve greater accuracy and insights across real-world scenarios. CamMotive is exploring the integration of an AI toolkit to reduce reliance on physical testing and streamline workflows, using the Monolith platform to support earlier fault detection and smarter testing reviews. Simultaneously, the depth and detail of CamMotive’s battery data set will serve to further enhance Monolith’s battery-model performance.
Monolith’s objective is to cut engineers’ product development cycle in half by 2026. Its platform gives domain experts the power to leverage existing, valuable testing datasets for their product development. The platform analyses and learns from this information, using it to generate accurate, reliable predictions that enable engineering teams to reduce costly, time-intensive prototype testing programs.
“Our partnership with CamMotive has the potential to make EV battery development faster and more efficient. Training machine learning models with robust, real-world data means engineers can find reliable ways to save time, achieve performance gains and reduce costs,” said Richard Ahlfeld, Monolith’s founder and CEO.
Source: Monolith
from Charged EVs https://ift.tt/8LT5P6I
No comments:
Post a Comment