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Electric Dreams: The Role of AI in Shaping the Future of E-Mobility

AI is much more than just a plot device in our favorite sci-fi films, from "Terminator" to "Ex Machina." It's the new normal—a reality we're currently living in and rapidly advancing toward—and the e-mobility industry is no exception.


Electric Dreams: The Role of AI in Shaping the Future of E-Mobility

AI is much more than just a plot device in our favorite sci-fi films, from "Terminator" to "Ex Machina." It's the new normal—a reality we're currently living in and rapidly advancing toward—and the e-mobility industry is no exception.

Lavinia Aparaschivei
| Linkedin
| Content Manager

In Brief

Whether AI is here to stay or destined to take over, one thing is certain: e-mobility is undergoing a massive transformation due to the prominent integration of AI into its infrastructure. Not only is the world taking exponential leaps toward adopting electric vehicles, but because of this demand, anything from EV infrastructure to city power grids has to evolve at the same if not greater pace.

While we can endlessly debate whether Grimes is right to believe in technical determinism, we’re going to talk about how AI is reshaping the e-mobility industry and its competitive landscape, as it’s streamlining processes and optimizing for user needs while maximizing return on investment. Let’s dive in!

Let’s Talk Shop: The Numbers Behind Increased EV Adoption

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First, let’s quickly explore why EV adoption is growing so fast. Sustained policy support is one major factor, backed by the dramatic increase in public spending on subsidies and incentives for electric vehicles. Germany alone allocated around 10 billion euros to EV subsidies through its Umweltbonus (environmental bonus) program—a figure that tripled compared to the previous year’s approximately 3.4 billion euros. Additionally, a growing number of countries have pledged to phase out internal combustion engines or set ambitious vehicle electrification targets for the coming decades.

With ten times more new EV models available in 2024 (over 500!) compared to 2020, the attraction factor for consumers has broadened. This substantial increase is driven by a surge in production from both new entrants and legacy automakers expanding their electric vehicle lineups to meet not only growing consumer demand but also stringent regulatory requirements – with the European Union famously mandating the end of CO2-emitting car sales by 2035. A prime example is one of our partners, BMW Group, which announced it would produce exclusively all-electric models starting at the end of 2027.

Let’s Talk Impact: The Effects of Increased EV Adoption

We have two words for you: future cities. As the EV market and adoption continue to expand, the demand for electricity and supporting infrastructure will have to grow. And it will grow remarkably. In Europe, the number of public charging points is expected to increase from about 375,000 in 2021 to around 2.4 million by 2030. To keep pace, the installation rate of public chargers will need to quadruple, requiring approximately 6,000 new public charging points per week from 2024 to 2030.

The race of finding a needle in a haystack

6,000 new charging points per week should make the decision of their placement easy, right? Just place them… everywhere? It’s a bit more complicated than that. Currently, the identification and qualification of a site is a complex and human-dependent task. Evaluating each individual site can take up to a week. The high capital requirements and long-term investment commitments involved in building and operating charging sites understandably raise concerns about the risk of capital misallocation or an overabundance of high-power chargers in unsuitable locations. With so many factors to consider, it’s not easy to get it right.

Plus, it’s not hard to imagine how this will put tremendous pressure on power generation, distribution, transmission, and trade.

The opportunity of AI

That is, of course, if things remain as is. All these facets of the energy industry are subject to optimization through better data management, which takes us to digital, also takes us to big data, and lands us on AI. All roads lead to Rome. As such, AI can play an instrumental role in converting more and more people to adopt EVs. And if we look at the issue behind the scenes, it can help tremendously with the rapid expansion, efficiency, safety, and reliability of EV charging networks.

According to the World Economic Forum’s white paper titled “Harnessing Artificial Intelligence to Accelerate the Energy Transition,” AI has tremendous potential to support and accelerate a reliable and lowest-cost energy transition, with potential applications ranging from optimizing and efficiently integrating variable renewable energy resources into the power grid, to supporting a proactive and autonomous electricity distribution system, to opening up new revenue streams for demand-side flexibility. Most of us don’t need a nudge to understand the impact and benefits of AI in e-mobility, but the World Economic Forum’s stamp of approval is the cherry on top.

How Exactly is AI the Secret Sauce in E-Mobility?

1. AI can tell you all about EV user habits: Present and future

Now we’re really ripping a page from a dystopian novel. Drivers have unique characteristics, patterns, and preferences in how and when they charge their vehicles. AI can predict these charging patterns and preferences, offering personalized recommendations and incentives as a result.

Going one step further, AI can make accurate predictions about charging behavior and requirements for future users, helping charging station operators optimize their business strategies and resources. Profits can be maximized, energy managed better (which is good for the environment, of course), and drivers are provided with an optimal user experience. It’s a win-win-win situation. A vital aspect here is that the UX matches drivers’ expectations while being seamless and easy to use. Our comprehensive testing can identify any pain points, as it did for some of the largest energy providers in Germany during their process of optimizing their charging apps. This can significantly impact millions by making EV adoption easier.

2. AI can fill in the gaps in charging station availability

Building on the point above, by anticipating drivers’ charging needs, operators can ensure their stations are available when and where they are needed the most. With advanced analytics, predictive models, and the ability to select from different solvers, these intelligent solutions can optimize the design and distribution of charging networks by focusing on parameters like budget, charger density, population density, and POI coverage. This effectively turns a process that could take up to a week into a matter of minutes, transforming a potential financial waste into an energy gold mine.

e-Mobility made easy use case

The future is now: e-mobility made easy

Ensure that every component of the charging ecosystem works together smoothly for your users. Here’s how we did it for thousands of people across Germany.

Read full use case 

3. AI can assess damage better in case of an accident

Accidents happen to the best of us, but most drivers have experienced the frustration of filing an insurance claim after a minor accident. Yet technology applications in insurance, also known as insurtech, are based on the deep learning capabilities of AI which can allow drivers to file their own damage assessments with the help of a simple app; in turn, the risk assessments are being analyzed based on risk factors filtered out of big data. Et voilà!

The benefits of AI in the BFSI industry are something we discussed in depth in our recent FinTech Frontiers article series, so if you’re curious to learn more, you can start here.

4. AI can optimize route planning. No more range anxiety

Range anxiety is as real as road rage. Nobody wants to be stuck in the middle of nowhere without charge. Being one of the top things that hold people back from transitioning to electric cars, it’s safe to say that range anxiety is still delaying mass EV adoption.

In comes AI. AI algorithms can optimize the power consumption of vehicles by adjusting the speed, acceleration, and deceleration to maximize range for the most efficient power usage. By effectively analyzing traffic conditions, road grades, charging time, and electric vehicle charging stations, these futuristic solutions minimize battery usage and increase driving range, thus ensuring that each driver reaches their destination.

5. AI can make vehicle-to-grid a perfect marriage

What’s more, AI extends its utility to managing energy consumption and interaction with power grids. By adding AI to Vehicle-to-Grid (V2G) technology, vehicles can act as energy storage units, contributing excess power back to the grid during peak hours.

These AI algorithms optimize charging schedules, ensuring grid stability and efficiency, transforming EVs from mere means of transport into active players in a smarter, cleaner energy ecosystem. Mind blown?

This beautiful marriage could represent a significant leap towards more sustainable energy practices, reducing energy costs, enhancing the use of renewable resources, and promoting eco-friendly transportation. This showcases the transformative potential of AI in fostering a more interconnected and sustainable future where EVs serve a dual role, revolutionizing both the automotive and energy industries, and ultimately transforming urban living.

Future Cities

It’s funny how we started talking about cars and now we’re segueing into how the livelihoods of millions of people are being affected by EVs. It’s not such a big leap as you might imagine.

Stepping away from simply focusing on charging networks, let’s envision self-driving EVs integrated into smart city infrastructure, offering personalized commutes with optimized temperature, lighting, and music. These AI-driven EVs promise not only to enhance our travel experiences but also to improve traffic management across entire cities, reduce accidents, decrease urban pollution, and significantly cut down emissions. This vision of a not-so-distant future extends to a more harmonious relationship between technology and the environment, where EVs initiate a de-urbanization trend, as autonomous vehicles offer a cheaper, faster, and safer way to commute.

We’re witnessing the rise of Mobility-as-a-Service (MaaS), enabling users to quickly plan trips using different means of transportation, by booking, managing, and paying for rides using personal devices. The integration of AI-based algorithms could optimize, monitor, and coordinate autonomous car fleets while offering hyper-personalized options to individual users.

usability ux testing

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MaaS interfaces will become the window to these new commuter habits that are reshaping the future of cities. They must interact with users in a way that makes them as accessible and adaptable as possible. In this context, testing these software solutions becomes critical because their adoption could herald a new dawn of accessible transportation.

Future Outlook

We’ve seen leading companies like Apple aggressively pursuing the development of self-driving cars with AI at their core. While their EV project, internally nicknamed Project Titan, was very recently canceled, this does not suggest that EVs are not the future. It’s merely a testament to the fact that Apple’s ambitions were simply beyond their capabilities. “When you looked at Apple’s future initiatives, the car project was always the most far-fetched for Apple. This just isn’t in their wheelhouse,” says Dan Morgan, a senior portfolio manager at Apple.

Apple’s initial investment in this technology still reflects the broader industry trend towards integrating cutting-edge AI for enhanced vehicle safety, energy efficiency, and—what Apple excels at—user experience. These efforts were not just about creating another car; they were about redefining the entire concept of mobility and transport by creating vehicles that are not only self-sufficient in navigation but also capable of learning and adapting to the driver’s preferences and needs, providing a personalized driving experience. This can become one of the biggest market differentiators for e-mobility companies. Putting users first means providing them with exactly what they need in the way they need it; and given that there are no second first impressions, making sure your UX checks all the boxes is more important than ever.

As these technologies continue to mature, we can expect a significant shift in the e-mobility landscape, leading to a future where cars are as intelligent, personal, and responsive as the devices we use in our daily lives.

From enhanced safety and efficiency to personalized experiences and a sustainable future, AI in electric vehicles represents a leap toward a new era of intelligent, eco-friendly, and interconnected mobility. EVs have the potential to become integral to our daily lives, reshaping not just how we travel, but also how we live, work, and interact with our urban landscapes, ushering in an era of smarter, cleaner, and more sustainable cities. The question now is—are you ready?


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