While the electric vehicle (EV), artificial intelligence (AI) and machine learning (ML) worlds may seem disparate, in reality, they’re deeply interwoven and rely on each other to survive. The technological conveniences customers expect already heavily incorporate each other without acknowledgment, and they have plenty of opportunities to streamline customer experiences and reduce frustration in drivers’ lives. So how do EVs leverage AI and ML seamlessly?
Why Do EVs Need AI and ML to Be Successful?
AI needs machine learning to assess information for practical applications. EV drivers often overlook their functionality, misconstruing them as built-in. Every hookup and data point creates a more competent industry, making the vehicle more aware of personal driving and charging patterns. ML guides numerous behaviors of EVs without drivers realizing by:
- Optimizing charging based on user driving patterns.
- Adjusting pricing based on electricity demand and grid use.
- Informing installers where to implement more infrastructure.
- Educating engineers on battery optimization and development.
Autonomous vehicles also need AI and ML to stay relevant. Though sensors provide visual awareness to cars, allowing them to keep between road lines and stop at lights, they cannot predict pedestrian behavior or how other drivers react in unpredictable traffic. For self-driving cars to become reliable, ML must educate AI about driving patterns down to geographic specificity for citizens to remain safe.
How Does the Grid Play a Role?
The sustainability narrative relies on technologies to be self-sustaining. While technology could have two separate sectors for energy generators and users, it makes more sense to set a precedent that every environmentally friendly technology operates as both. With the help of AI and ML, EVs can redistribute extra energy stores back to the grid to help during peak times, also known as vehicle-to-grid (V2G) services.
The climate-neutral revolution must advocate for eliminating sole ownership of energy resources. Informing EV drivers how AI and ML increase energy accessibility and ownership is crucial for setting realistic expectations for how practical EV operations will function. Single entities cannot hoard energy, and democratized power-sharing will become the norm.
EV infrastructure has to support its longevity to continue this synergetic relationship. Charging stations can’t actively destroy batteries. Therefore, AI is vital in developing smart charging. Drivers going long distances must trust they’ll arrive in places that won’t harm their vehicle and perpetuate range anxiety. Every hookup matters because it tells manufacturers how to stay relevant to the environment and their customers.
How Will AI and ML Improve EVs?
EVs couldn’t get better with data. Manufacturers wouldn’t know the average commuter distance in New England if it weren’t for driving data informing future battery designs. AI couldn’t determine and draw up digital twins about future EV performance without predictive analysis that only ML can generate from years of data mining. The projected 50% emissions reduction from battery electric vehicles (BEVs) could increase exponentially with a bit of help from ML decision-making.
How fast will the batteries deplete before customers need a fresh one? What’s the end-of-life cycle for these products? These technologies raise endless new questions, such as whether these maintenance behaviors will impact purchasing decisions and if the hardware is resilient against cyberthreats.
The only technologies capable of answering these questions with as little human interference as possible for EV makers are AI and ML. Trials for technological and chemical adjustments to EVs have never been more valuable, because the data informs blueprints logically. The decisions manufacturers make will have so much evidence backing them that customers won’t be able to find faults with these products in the future.
Why EVs Need AI
Nothing is more valuable for tech companies than information. For an industry as necessary as it is politically charged, makers must have intimate consumer awareness to maintain buy-in. Not only will these technologies keep EVs successful in the long term, but they will keep vehicles advancing at a pace that could exceed humanity’s expectations.
A fully-functioning self-driving car could be on doorsteps soon – but only because AI and ML invested in collecting the information manufacturer needed to make productive resource allocations.
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