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Exploiting AI to accelerate the deployment of electric vehicle charging stations.

The electric vehicle (EV) industry is taking form worldwide, driven by a growing emphasis on sustainability and the implementation of strict emissions regulations. If we take the European road, their 2035 directive mandates the cease of new internal combustion engine (ICE) car sales, with an exemption for vehicles that run on e-fuels. This highlights the need for a robust network of EV charging stations. Building such a network presents challenges: a significant skill gap in the workforce, quality assurance concerns, and supply shortages pressuring manufacturers. As a result, the EV infrastructure deployment needs to catch up in many countries. France, for example, experienced a significant delay of over one year and a half before reaching the symbolic number of 100,000 deployed charging stations, and this target was only reached last month. In addition, it is not only regular chargers but actually fast public EV chargers that will help drive the adoption of EVs, thanks to their quick charging capability.

How can AI help overcome the current operational challenges and enable to speed up the rollout of EV charging infrastructures?

Alleviating the Talent Shortages in a Rapidly Electrifying World

Today, while there is a rising need for skilled workers, the workforce is decreasing. If we take the UK case, we can see that there has been a 30% decline in technicians since 2006, with a projected shortfall of 25,000 EV-trained technicians by 2030

This talent scarcity is partly due to the following: 

  • Experienced professionals are retiring, leaving companies with many vacancies
  • The difficulty of hiring and retaining new technicians

By leveraging First Time Right Automation in electric car charger installations, charging point operators can undertake an efficiency revolution. This technology, which analyzes photos captured by field workers, allows real-time feedback during the installation, enhancing its accuracy and success rate, especially for inexperienced technicians. Indeed, AI-powered field apps can become the new companions of EV station field workers by serving as on-the-job training tools, improving workers’ skills on a day-to-day basis. 

The image recognition system is also crucial in quality assurance during EV charging station installation. It automates the labor-intensive quality control process by systematically analyzing photographs captured during the installation. Thanks to Computer Vision, it is possible to identify and examine potential issues, enhancing the reliability of EV charging infrastructure and boosting customer satisfaction.

First-Time Right Installations to Avoid Supply Shortages 

We are currently facing a global material shortage emerging from supply chain disruptions due to COVID-19. This latter underlines the importance of successfully achieving EV charger installations on the first visits, meaning getting them "Right the First-Time." Indeed, incorrect installations could cause stations to break down too rapidly, which would require lengthy component replacement and, therefore, hinder the EV market's growth. By providing real-time feedback during EV charging station installation, Computer Vision reduces the risk of rapid breakdowns and promotes efficient resource utilization.

In conclusion, Just like many other field services industries, the eMobility sector needs digital solutions to be even more efficient despite a limited field workforce.

Adopting First Time Right Automation in electric car charger installations presents a promising solution to accelerate the deployment of EV charging infrastructure since it 

  • Addresses the skill gap by improving workers' skills as they go
  • Ensures quality control, thanks to real-time feedback
  • Optimizes resource utilization, thanks to fewer repeat visits and more careful handling of components that enable EV charging stations to work.

To know more about AI and, more specifically, computer vision for field workers, download our Field Force Empowerment white paper.


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