The partnership will provide technical support for existing HORIBA MIRA and Monolith customers, as well as OEM vehicle manufacturers and Tier 1 battery suppliers looking to enhance their development process.
The agreement gives Monolith access to HORIBA MIRA’s in-house R&D battery test data to train and enhance its Anomaly Detector (AD) and Next Test Recommender (NTR) algorithms and demonstrate how the technology works at scale.
In addition, HORIBA MIRA’s advanced cloud-based data driven solutions will be integrated with Monolith’s tools to advance its current product offerings. Its data driven digital twins, calibration optimisers and global real-world scenario generators have been designed to provide an eco-system for efficient virtual development and testing, reducing the need for expensive prototype vehicles and time required to develop optimised calibrations for batteries and other powertrains.
According to the two companies, the combined offer will provide a complete system level optimisation toolchain, accelerating the development of sustainable transport solutions through data-driven insights.
Commenting Dr. Richard Ahlfeld, CEO and Founder of Monolith, said, “This partnership combines leaders in physical and AI-driven test and validation processes. The complementary tools and expertise of Horiba Mira and Monolith will accelerate battery development and cut testing in half for our joint clients using data-driven techniques.”
“As global automotive companies move towards a stronger focus on virtual development, partnerships which enhance our physical and virtual engineering capability are key to supporting customers with the robust tools they need,” added Guy Foulger, Engineering & Technology Director, HORIBA MIRA. He continued, “Working with Monolith, HORIBA MIRA will bring almost 80 years of automotive engineering and test experience to enhance the development of sustainable transport solutions, leveraging the power of machine learning, AI and high-fidelity data-driven insights.”
Monolith is already enabling AI for engineering with its bespoke SaaS platform that uses no-code, machine-learning software, giving domain experts the ability to leverage existing, valuable testing datasets for their product development.
The platform can analyse and learn from this information, using it to generate accurate, reliable predictions that enable engineering teams to reduce costly, time- intensive prototype testing programmes.
By integrating innovations such as a ‘Next Test Recommender’ tool and the industry’s first AI-powered ‘Anomaly Detector’ functionality, Monolith has enabled engineers to develop higher-quality products in half the time.
