Can machine learning clean up the last days of ICE?

Virtualisation and software-orientated design processes could make the transition away from internal combustion engines more efficient. By Christopher Dyer

The automotive industry is steadily moving away from internal combustion engines (ICEs) in the wake of more stringent regulations. Some industry watchers regard electric vehicles (EVs) as the next step in vehicle development, despite high costs and infrastructural limitations in developing markets outside Europe and Asia. However, many markets remain deeply dependent on the conventional ICE vehicle. A 2020 study by Boston Consulting Group found that nearly 28% of ICE vehicles could still be on the road as late as 2035, while EVs may only account for 48% of vehicles registered on the road by this time as well.

For manufacturers, this represents a huge and multi-faceted challenge. There are not only the industry’s looming and ambitious environmental targets to consider but also the drive for CASE (Connected, Autonomous, Shared and Electric) vehicles is increasing design and development complexity. Also, there are the bottom-line pressures where European R&D spend has already increased by 75% between 2011 and 2019. Enter Secondmind, a machine learning company based in the UK. The company works with automotive engineers, helping them to use data-efficient transparent machine learning that combines the subject matter expertise of today's engineers with algorithmic intelligence. Secondmind's Chief Executive Gary Brotman argues that this new breed of machine learning is required to efficiently streamline the vehicle development process, helping automotive companies accelerate the transition away from ICE and ensure sustainable design and development engineering.

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