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AI-Based Study Shows How Tech Is Essential for Grid Adoption of EVs

Research is heading into the hands of artificial intelligence (AI) in a University of Michigan Transportation Research Institute (UMTRI) study exploring how electric vehicle (EV) charging and grid modernization relate. Its findings could reveal more overlaps between these technologies to expedite grid adoption.

What it demonstrates can guide governments of all levels toward the most productive use of time and resources when shifting toward an EV-based and sustainable future for mobility.

How Did AI Perform a Study About EVs and the Grid?

Utilidata partnered with UMTRI to install smart grid chips in local EV chargers to get more data about how it impacts the grid. The never-before-seen study incorporated artificial intelligence to determine voltage and current patterns as people use it regularly. It could determine everything from how drive-time impacts charging to brand-specific trends.

They needed to incorporate AI in this groundbreaking study to have two million EVs on Michigan roads by 2030 to follow their environmental objectives.

Almost every state and nation in the world has these metrics to meet to collaborate in reducing the adverse effects of climate change, which is why studies like these that accumulate easy-to-parse data that are easily shareable and accessible are paramount in progress.

What Is the Impact of AI-Based Studies Like These?

There is an immediate need for researchers to use AI in projects because it can expedite stalling sectors. AI mediation eliminates the time between investments and government intervention when it can provide real-time data. The data reveals where budgets can allocate funds and where to install EV chargers for the most value. However, the grid has to keep up, and that’s the most complicated factor.

First, it will perpetuate the value of AI in data-driven studies. They are a priceless supplement to manual data collection, especially in already-smart technology where machinery integrates smoothly. The return on investment is incomparable, as companies make the upfront investment for the technology, and they save countless down the line in wasted hours of humans poring over data that human error is more likely to taint. With AI, human oversight can verify the validity, getting the best of both strengths.

Additionally, it will inspire everyone worldwide to see AI as a resource in accelerating climate-friendly advocacy and research and development previously seen as too expensive, complex or inaccessible.

For example, lithium-ion batteries are costly to the environment and for manufacturers’ pockets – how can these develop or shift to combine with other renewable technologies to make them more sustainable? They have a 10-to-15-year lifespan, but what if cars or chargers combined with solar power or additional renewable energy?

For sectors like the grid, which require a near-complete overhaul to meet projected EV demand, it helps everyone from engineers to city planners to electricians collaborate with clear-cut data on the next steps.

What Will Happen Because of the Study?

What has the data revealed to researchers, and how will they apply these findings? The results will not become public until late 2023. Still, they continue their hard work by collaborating with the U-M Electric Vehicle Center for more research – $130 million funded by the state. They will announce a roadmap soon. In the meantime, leadership in the project claims the extension of the original study will elaborate on how the findings will influence consumer behavior and policy.

The funds will also spread education about the sector for more skilled workers and focus on honing in on battery engineering and manufacturing to make the process more streamlined and efficient. It’s particularly relevant as EV batteries haven’t garnered the cleanest reputation for their lack of recycling infrastructure and environmental abuse from raw material extraction.

Related studies are happening simultaneously that validate and expand the potential of what AI has expounded. A recent MIT study – that didn’t employ AI – postulates that the foundation for EV innovation is the strategic placement of charging stations. EV stations could go anywhere there’s room, but that isn’t how humanity should install them. Home charging provides more opportunities than it seems, giving policymakers ideas for government-funded incentives for contributing to EV charger and grid development.

AI Will Drive the Future of EVs and the Grid

Michgian’s AI-driven study concerning EVs and the grid will change renewable mobility infrastructure worldwide. It will normalize the usage of AI in industry-shifting research and development while catalyzing necessary pushes toward productive eco-friendly progress. Setbacks in EV development, from supply chain disruptions to inadequate recycling, have misconstrued the sector’s potential to eliminate the transportation sector’s greenhouse gas emissions.

Studies like these will be the starting point for efficient and sustainable grid analysis based on empirical data from regular EV users.

The post AI-Based Study Shows How Tech Is Essential for Grid Adoption of EVs appeared first on Datafloq.



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