Recently, even limited access to the Internet seems what might be enough to get bombarded with the latest breakthroughs and leaps forward in terms of Artificial Intelligence (AI) and its progression into formerly AI-pristine spheres. While vigorous debates about its potentially hazardous nature ensue all over social media, AI creators and enthusiasts launch more of said tools into the digital world, leaving them there to stay.
As much as it only recently became such a big topic (especially one that made it outside of the purely-tech environment), Artificial Intelligence has been powering a good chunk of the technology known for years. From autonomous vehicles to facial recognition software to voice assistants like Alexa or Siri, AI has morphed from a niche concept into something we get used to using on a daily basis. Even when browsing the web, AI is used to customize the experience and provide everyone with tailored ads. So, while there are plenty of legitimate worries about its potential misuse in the future, it is undeniable that AI has become an everlasting part of our lives.
At this point many people can't help but wonder just how far AI has evolved since its conception, and where it can go from here. As of now, many AI tools are still being developed to help businesses automate mundane tasks or help them achieve new heights in customer service. While this is an incredible use for AI, one could imagine that the possibilities are more far-reaching than what we know today.
What really is Artificial Intelligence? And how does AI work?
Artificial Intelligence (AI) is a branch of computer science that focuses on creating intelligent machines that are capable of completing tasks in the same way as humans (or rather as close to it as it gets). AI technology has been around since the 1950s, and it has grown significantly over time. Today, AI is used in many different industries, from finance to healthcare and, since it’s constantly evolving, researchers find new ways to apply the technology in different fields.
Artificial Intelligence's impact on the world is wide-reaching, from powerful web search engines like Google to user recommendations powered by tech giants such as YouTube and Amazon. AI has even made strides in understanding complex human speech patterns found in devices like Siri and Alexa. Creative applications of this technology can be seen with projects including ChatGPT and AI art.
AI itself can be broken down into two main categories: narrow AI and general AI. Narrow AI focuses on specific tasks related to a certain industry or problem, such as driving a car or playing chess. General AI is focused on more complex tasks, such as understanding natural language and problem-solving.
As AI develops and evolves, its definition is shifting to keep up with the times. What was once considered "intelligent" can no longer be included in this category; for instance, optical character recognition has become an ordinary part of life that falls outside of what we think about when talking about Artificial Intelligence. This shift is known as the AI effect – a concept which highlights how quickly technology advances
In fact, the scale of the entire undertaking reached a level where there are subdomains to Artificial Intelligence, closely related to it, but often classified separately. Machine learning is one of them.
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. The goal of machine learning is to create algorithms that can receive a set of data and use it to learn from the data and make predictions about other data.
The most common method used in machine learning is supervised learning, which requires labeled datasets for training. Labeled datasets are datasets with known input/output relationships, such as images with their corresponding labels. The machine learning algorithm will use these labeled datasets to learn the relationship between input and output data, allowing it to predict outputs for new datasets.
Other methods of machine learning include unsupervised learning, reinforcement learning and semi-supervised learning. Unsupervised learning allows the computer to detect patterns in a dataset without being given labeled data to learn from. Reinforcement learning is based on rewards and punishments, where the algorithm learns by trial and error, gradually improving its accuracy as it receives feedback from its environment. Semi-supervised learning is a combination of supervised and unsupervised learning, which uses both labeled and unlabeled datasets for training.
Artificial intelligence in eMobility - the possibilities
Looking at the technical advancements happening within the field of electric vehicles, there are already quite a few impressive solutions implemented. From smart charging to connected cars, it’s pretty clear that advanced software has become an integral part of the industry.
However, there are still means of taking it all to a new level. There’s a few areas where Artificial Intelligence could find itself a useful spot and potentially aid towards mass adoption. We’ll try to list some of them.
Battery Management Systems Optimization
Battery Management System (BMS) is a system that monitors and manages a rechargeable battery (or group of batteries), such as those found in electric vehicles, UPS systems, and solar energy storage systems. In short, its primary functions can be narrowed down to ensuring the battery remains safe and reliable.
With the use of AI, it potentially would be possible to modernize the way EV battery management systems operate, allowing for more reliable and longer-lasting batteries. By proactively evaluating key factors such as temperature, charge level and driving style - these sophisticated intelligent solutions can predict potential failure scenarios and take measures to extend life expectancy. In such a scenario, both sides could enjoy benefits: EV manufacturers would improve their competitiveness levels on the global transportation market, by significantly enhancing key factors like range, whereas the drivers would be left with more reliable (and thus more enjoyable) vehicles.
Advanced Route Planning & Charging Infrastructure Optimization
Being one of the top things that hold people back from transitioning to electric cars, range anxiety still delays mass EV adoption. With charging infrastructure being often disproportionately distributed and significantly lacking in some areas, the lengthy trips may get at times difficult to plan and execute.
AI-powered route optimization algorithms can help EVs plan out their trips in an optimal manner. By considering factors such as traffic conditions, road grades, charging time and electric vehicle charging stations, AI-powered solutions can help ensure that motorists reach their destinations in the most efficient manner possible, which is by minimizing battery usage and increasing driving range.
Similarly, AI could be used to fill in the infrastructural gaps by analyzing usage patterns of EVs and driving habits. With advanced analytics and predictive models these intelligent solutions are able to determine ideal locations for charging stations while predicting future demand at the same time.
Energy Savings & Capacity Optimization
While the Open Smart Charging Protocol is “already on it”, giving Charge Point Operators ways to maneuver around the intricacies of energy consumption and leveraging it to the best of its capacity, there are ways to further improve it.
With the use of AI algorithms, it may be possible to help optimize the energy consumption of electric vehicles. By monitoring factors like driving conditions, speed, and load levels, AI systems can predict the most efficient way to use the available energy and ensure that EV are being charged at the most efficient times and from the most cost-effective energy sources.
Naturally, there are more ways in which AI can support the eMobility industry. Some other possibilities include:
- Predictive Maintenance: By anticipating component failures and proactively scheduling repairs, downtime can be drastically reduced, leading to greater overall reliability of vehicles
- Predictive pricing: AI can be used to predict the future prices of electricity, allowing EV owners to charge their vehicles at the most cost-effective times.
Finally, it must be noted that nearly the entirety of the case of autonomous vehicles is rooted deeply in artificial intelligence, but that’s for a whole another topic of conversation.