Batteries

In a project with industry and academic partners, Alchemite™ machine learning was applied to optimise the formulation and manufacturing of electrodes for lithium-ion batteries. Conventionally, improvements are identified via trial-and-error, with a large matrix of experiments. Use of Alchemite™ to predict electrode performance focuses such experiment and reduces the development time for new battery chemistries. At the end of the project, the proposed electrodes were manufactured and showed excellent lifecycle and capacity, as predicted.

A collaboration between the University of Cambridge, A*STAR and Nanyang Technological University in Singapore, assessed methods for predicting electric vehicle battery states and revealed that a data-driven machine learning model offers the most accurate predictions for State of Charge (SOC) and State of Health (SOH). The project provides a case study for machine learning accurately predicting the health and life of a battery, with the potential for manufacturers to embed these methods into their battery devices, improving in-life service for the consumer.

Intellegens were joined by battery experts Deregallera for this webinar to discuss how machine learning is applied in three key areas of battery development: (1) Battery materials and formulations; (2) Cell and pack design; (3) Battery management systems. View the recording to see how machine learning helps improve charging capacity and energy density, lower development times and costs, and enable more efficient operation.

BEST magazine speaks to Intellegens CTO, Dr Gareth Conduit.