Data drives vital elements of our society, and the ability to capture, interpret, and leverage critical data is one of Altair's core differentiators. While Altair's data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation, and securities trading, they're also useful in a variety of more common business intelligence applications, too.

Explore how machine learning drives EV adoption insights - click here.

An Altair team undertook a project utilizing Altair® Knowledge Studio® machine learning (ML) software and Altair® Panopticon™ data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level.

This guide explains the team's findings and the process they used to arrive at their conclusions.

Today, an e-motor cannot be developed just by looking at the motor as an isolated unit; tight requirements concerning the integration into both the complete electric or hybrid drivetrain system and perceived quality must be met. Multi-disciplinary and multiphysics optimization methodologies make it possible to design an e-motor for multiple, completely different design requirements simultaneously, thus avoiding a serial development strategy, where a larger number of design iterations are necessary to fulfill all requirements and unfavorable design compromises need to be accepted.

The project described in this paper is focused on multiphysics design of an e-motor for Porsche AG. Altair’s simulation-driven approach supports the development of e-motors using a series of optimization intensive phases building on each other. This technical paper offers insights on how the advanced drivetrain development team at Porsche AG, together with Altair, has approached the challenge of improving the total design balance in e-motor development.

Electric vehicles (EV) offer the exciting possibility to meet the world’s transportation demands in an environmentally sustainable way. Mass adoption could help reduce our reliance on fossil fuels, but the lithium-ion (Li-on) batteries that power them still present unique challenges to designers and engineers, primary among them to ensuring safety against battery fire. To achieve vehicle manufacturer’s ambitious adoption goals, it is necessary to improve the safety of Li-on batteries by better understanding all of the complex, interconnected aspects of their behavior across both normal and extreme duty cycles. Altair is focused on developing a comprehensive understanding of automotive battery safety issues which it has named the Altair Battery Designer project. It combines innovative design methods and tools to model and predict mechanical damage phenomena as well as thermal and electro-chemical runaway. Altair has developed an efficient way to calculate mechanical and short-term thermal response to mechanical abuses, providing accurate computational models and engineer-friendly methods to design a better battery.

A conversation with Selcuk Sever, Principal Engineer at Switch Mobility, discussing its collaboration with Altair to accurately predict the range of its electric buses. With accurate range prediction, Switch Mobility can give its public transport authority customer confidence that electric buses can meet the requirements of their bus routes.