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Global EV industry faces diverging paths: Software, AI, and Policy strategies still maturing

By Ken Horne, Strategic Planning Managers, Software Defined Vehicle Solutions and Cecile Loison, Electric Vehicle & Energy Solutions | Friday, May 16, 2025, 2:14 PM Asia/Singapore

Global EV industry faces diverging paths: Software, AI, and Policy strategies still maturing

This EV industry update covers global adoption trends, SDV integration, AI challenges, and key regional policy shifts, and future technology opportunities.

The deadline for phasing out the sale of combustion engines is now less than a decade away in some regions, and progress toward this milestone has been varied. 

The European Union has been leading the way, while China has decided to focus on mass-producing small, affordable electric vehicles (EV). In the US, EV adoption has been gated by the economics of infrastructure change, not by technology innovation, and adoption of hybrid vehicles is still preferred —until the charging network is fully established, and battery technology is more mature.

In the meantime, integrating elements of the software defined vehicle (SDV) into EVs has helped accelerate the penetration of both technologies in some markets – most notably in China. However, traditional original equipment manufacturers have been struggling to implement SDV capabilities, while the newer entrants have made progress.

Opportunities and hurdles
Despite the initial hype around 5G, the auto industry has more gradually embraced the wireless network, and this is part of the pull towards developing software-defined vehicles. Foreseeably:

  • As 5G and then 6G become more common, over-the-air software updates and the ability for vehicle features to be added post-production is becoming a reality. To support such update capabilities, a telematics control unit will be essential to support these updates and services —— expected to reduce the impact of SDV on the auto industry.
  • A major challenge to overcome, will be consumer acceptance of shifting from the established “pay once for the car” model to a subscription-based model that charges for providing recurring software updates and new features. As these updates are usually critical (for ensuring reliability, safety, and security), the challenge may lay in convincing consumers to expect manageable update costs over the lifetime of the EV.
  • The issue of whether manufacturers develop the all-important Advanced Driver-Assistance System (ASAS) through increments or via granting full vehicle autonomy will increasingly be a factor in how soon EV can attain full autonomy, widely accepted as the long-term industry goal.
  • AI will increasingly be used to tap into the vast volumes of data needed to improve design and performance. However, until concerns around the safety and reliability of AI are addressed, it will limit adoption of EVs. So, car manufacturers will likely be using more AI to verify that the AI algorithms used in autonomous driving software are safe and trustworthy. Which stakeholder will help police the auto industry’s use of AI then?
  • An emerging innovation, Mobility-as-a-Service, which integrates various transportation forms and services into an on-demand service, has been touted as the future of transportation. After several false starts, the strategy has shifted to focus on deploying mobility-as-a-service models in simpler, grid-like cities before expanding to more complex urban areas. Extensive testing —incorporating digital twins — will be used because they can help to improve the efficiency and sustainability of urban mobility with mapping, predictive analysis, real-time network monitoring, predictive maintenance and two-way information flow.
  • In other developments, the EV industry will explore the circular economy, and aim for further progress in battery technology, in terms of recycling and repurposing of lithium-based EV batteries. With their extensive recycling infrastructure, Europe and China will likely lead the charge. In other regions, it will be more challenging to adopt circular economy principles, so progress may stall. The focus will  likely be put in evaluating new battery chemistries — with the goal of improving their power, weight, and/or cost capabilities. Advanced AI algorithms and predictive analytics will likely play an important part in the evaluation processes.

Finally, renewables still have limitations to overcome before they can be widely adopted.

In particular, the inability to store energy for industrial use and the need for a mix of energy sources will limit progress.  This is where Hydrogen’s versatility as an energy carrier — and its ability to decarbonize — will drive research into exploring how it can help support the transition to a clean energy economy.

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