Industry Voices | Maximizing AI Innovation in EV ManufacturingIndustry Voices | Maximizing AI Innovation in EV Manufacturing

Electric vehicles are reshaping the automotive industry, offering environmental and economic advantages such as lower emissions and reduced maintenance costs. The global transition toward EVs is accelerating, with nearly 14 million new electric cars registered in 2023 alone. This rapid growth and the increasing demand for sophisticated features drive the need for AI-powered solutions across the entire EV value chain. 

AI is poised to drive the next phase of innovation within the sector, providing solutions to the unique challenges faced by EV manufacturers.

Earlier this month, the U.K. government highlighted the importance of this technology by unveiling the AI Opportunities Action Plan, a strategy integrating AI to stimulate economic growth. From improving battery-management systems to enhancing range estimation, it’s reshaping how EVs are designed, built and experienced.

The impact is significant: According to the IBM Institute for Business Value, AI is projected to increase the perceived value of EVs by over 20%.

Despite its potential, AI innovation requires significant investment. With 80% of AI projects failing, it’s vital to understand why and how businesses can mitigate these risks.

The Role of AI in EV Manufacturing
New research from the University of Arizona shows AI can improve battery safety and chemistry, preventing "thermal runaway" in lithium-ion batteries, reducing risks of fires and explosions. Inspired by weather forecasting models, AI can predict and mitigate temperature spikes before they escalate, preventing catastrophic events. Additionally, IBM Research is using AI to optimize battery chemistry, which, according to Mercedes-Benz. Other researchers are thinking beyond the internal chemistry of cars and discovering how AI can optimize energy consumption in EVs. By integrating real-time traffic data, weather conditions and even road-gradient information, algorithms can calculate the most efficient travel routes, minimizing energy consumption as a result. A GPS system developed by researchers at the Arab Academy for Science in Giza has demonstrated AI’s ability to save up to 46% in energy usage by avoiding steep inclines.
By processing data from cameras, radar and lidar, AI can assist drivers by helping EVs to navigate complex environments and make real-time decisions for safe driving. Volkswagen, for example, has integrated Generative AI to allow drivers to interact with vehicles naturally, such as requesting rerouting to nearby charging stations.

Equally, companies like Nauto are developing AI-powered systems to address safety concerns such as distracted driving. Utilizing dual-facing cameras and sensors, the technology monitors driver behavior, identifies potential hazards such as pedestrians and cyclists, and analyzes road conditions. 

The Consequences of AI Failures in EV Manufacturing

Despite its benefits, AI projects in EV manufacturing fail at twice the rate of traditional development.
Tesla’s 2024 autopilot failures highlight the regulatory risks AI malfunctions pose. After NHTSA linked 13 fatal crashes to Tesla’s autopilot, the automaker recalled 362,000 vehicles. Similar incidents can result in regulatory investigations, costly recalls and legal action.
News of AI failures, especially those involving safety concerns, also can generate significant negative press, potentially damaging the company's image and eroding consumer trust.
Financial losses may be suffered when AI fails, including recall costs, legal fines, lawsuits and the costs of re-engineering and testing to fix the AI failures.

How EV Manufacturers Can Mitigate AI Risks

In the initial stages, manufacturers should be realistic when identifying where to apply AI. In some cases, projects fail because the technology is applied to problems that are overly ambitious, or infeasible. 

Once the project is underway, rigorous testing and validation procedures are essential. AI models should be thoroughly tested in both simulated and real-world environments to identify potential failures before they impact production operation.

Additionally, AI models are only as good as the data they are trained on. Many projects fail because of a lack of adequate data to train an effective AI model; manufacturers should prioritize collecting high-quality, unbiased data to ensure reliability.

The Value of Failed AI Trials 

AI failures, when managed correctly, can still provide valuable insights and contribute to future innovation. Treat each project as a pilot and focus on the way failed attempts can help you refine subsequent efforts:

In Summary

While the potential for AI in EV manufacturing is undeniable, the high failure rate of projects can understandably make businesses cautious. However, by approaching AI strategically, rigorously testing models and leveraging failed projects for future success, AI can become a cost-effective driver of innovation.We predict that, in the coming decades, AI will play a crucial role in optimizing EV performance. To remain competitive and contribute to a more sustainable future, EV manufacturers should seriously consider investing in AI solutions to address the unique challenges within this industry.