See how AI can impact the world of electric vehicles!

Being an eMobility leader in various projects across multiple startups, Piotr was responsible for OCPI and OCPP integration as well as EMP and CPO platforms development. With extensive expertise in the open standards field and eMobility tech solutions, he is leading teams of experienced developers and contributing with invaluable knowledge and experience.

Piotr Majcher

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 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.

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:

 By anticipating component failures and proactively scheduling repairs, downtime can be drastically reduced, leading to greater overall reliability of vehicles

 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.

Smart grids

Learn more about the battery swapping and its benefits and drawbacks.

Experienced content writer with over 4 years of experience in the energy sector and many industry-related publications released.

Gabriela Stawiarska

While the electric vehicles popularity keeps increasing all over the world, range anxiety and charging time still pose a threat to mass EV adoption. It’s not really a surprise, given electric vehicles on a broader scale are still relatively new and those who tend to travel often and on lengthy distances may find them slightly intimidating. In response to the issue, many technologies are being introduced aiming to better the charging experience and shorten the time it takes to charge the battery before setting back on the road.

Some, like rapid chargers, are welcomed with great enthusiasm, others, like battery swapping, not so much.

In this piece, we’ll try to shed some light on what battery swapping is, what the benefits are and why there are so many skeptics.

In a nutshell, battery swapping is a take on omitting the charging session wait time and exchanging the discharged battery for a fully charged one. The main goal behind it is to even out the charging experience with the one everyone knows from fuelling up an ICE car.

The good part of this solution lies in the fact that the process aims for its full automation. With the use of robotic cranes, the battery changing time can in fact be shortened to somewhere between 6 and 10 minutes - approximately as much as one spends at a gas charging station.

Certainly, this solution leaves a major question - what about the batteries? In order for it to work, the car batteries can’t really be owned by the EV owner. In this case, the EV driver must lease them from either the car manufacturer or the battery supplier.

The way this works on paper is rather simple. EV owner drives up to the specialized battery swapping station. The car then goes on a dedicated platform, where the vehicle gets slightly lifted in order to retrieve the battery. The battery is then disconnected from the vehicle and lifted out by a crane or other lifting device (the vision expects this to be an automated process). A new, charged battery is then put into place and connected to the vehicle. The EV is driven off the platform and away from the station. Batteries which have been taken out, stay at the station and are being charged up again. Once they’re fully charged, they're ready to be used for another swapping session.

Up until recently, battery swapping was just a bold idea, not really firmly in practice. However, a Chinese company - Nio managed to put the vision in life on a rather big scale.

Nio is a Chinese super-company producing electric cars. Some call the Shanghai-based EV manufacturer the new Tesla. In April 2021 Nio started its European expansion by launching services in Norway - currently the biggest EV market in this part of the world.

Apart from quite a few car models, which are doing exceptionally well in China, Nio has aroused a lot of interest by introducing the battery swap stations on a rather big scale - earlier this year they announced completion of the 1000th battery swap station. In terms of how this works, the mechanisms are similar to the ones described above.

The swap station looks a bit like an automatic car wash. The car drives in, straight on the platform. Automatic mechanisms unbolt the battery pack and exchange it for a new one. This works for all Nio’s models and is available in three different battery capacities - 75kW, 100kW and 150kW. The exchange takes up to 6 minutes.

Given the amount of swap stations available and the lighting-speed in which one goes for a full battery, it may seem like great technology. There are however quite a few issues to be taken into account before even starting to think about applying this on a larger, international scale.

Nio's battery swapping station, source: https://www.nio.com

Battery swapping - will this ever work on a larger scale?

In order to answer this question, one needs to take into account all the advantages and disadvantages of such a solution. Certainly, at least at the first glance, it may seem like an extremely attractive and efficient way, especially on lengthy distances or when time matters. Such instances occur especially with heavy-duty transport (meaning trucks) or buses in cases when lengthy stop for charging is not possible. However, there’s a lot more to the story.

As it has been mentioned before, “charging” time is the number one attractive feature behind the solution. Given that loading up the EV battery will alway be compared to fuelling up an ICE car, battery swapping may be the closest experience in terms of the length of the procedure. Battery swapping stations (in their more advanced forms) can also pose as an energy storage and a hefty way of incorporating the demand response practices. As batteries are (usually) stored within the charging station, they make up for quite a capable power system. In cases of a high demand, some of the energy could be fed back to the grid - this would’ve happened in cases when there a quite a few idle but charged batteries available. Another positive aspect lies in the fact that with battery swapping stations, the possibilities of installing solid solar panels on the roofs are quite significant. With that, EV drivers could not only enjoy a fast car charge, but also the satisfaction of having it done mostly or entirely via green energy. Both aspects - the battery storage and the chance of using PV are a great way of ensuring the EV charging business remains eco-friendly and contributes to the overall grid’s health. Lastly, battery swapping directly impacts the cost of an electric vehicle. With a solution expecting to lease batteries rather than buying them as a part of a vehicle, EV drivers are exposed to prizes reduced of what often times might be the most expensive part - the battery.

As much as the benefits look rather promising, too bad, it seems like the disadavtages outweigh the whole solution. First and foremost the costs of building a battery swap station are inordinate. It is estimated that one such station may cost anything between 450 000 - 600 000£, which means that with the current prizes, building up a whole network must cost millions of dollars. Naturally, like with any other tech advanced solution, the prizes are likely to go down in the future, yet still, they’ll probably be higher than the majority of CPOs could invest. On top of that, it must be remembered that the cost does not include the land they’re supposed to be built on. Here’s where another issue emerges. While “classic” EV chargers does not require much space and can be installed in existing infrastructure, the battery swap stations are a lot more spacious and require a construction to be set in order for it to run.

The biggest obstacle though may be the interoperability of such stations. As much as Nio is settling strong in the Chinese market, it must be remembered that their stations only work with their own car models. Given, more and more brands are introducing their own electric vehicles, the diversification of available models gets bigger and bigger, making it extremely difficult to match. This more or less leaves three options for the battery swapping;

each car manufacturer starts developing their own swap stations, which would involve a huge investment and most probably the lack of space

battery swapping stations are developed in such a way as to serve various models, which in practise would cost even more, if at all possible

car manufacturers agree on universal battery instalment, which in many cases would require significant changes in the whole production process

All of the above are beyond complicated in terms of their broad-range adoption. Things complicate even more with the battery ownership in the above scenario. Simultaneously, the concept of universal batteries emerges, making it a whole another case to be discussed.

Some battery-swapping ventures have taken place already in the past. For example Tesla has put forward the idea as they were looking for investors. However, the plan got cancelled probably faster than it has been introduced. Renault also tried. They even launched a sub-brand called Better Place in order to facilitate the managing and setting up the swap stations. After almost 5 years, the company filed for bankruptcy.

As much as we might have brought some negativity into the narration, it must be underlined, that although extremely difficult for a wide-range adoption, battery swapping may still be a decent option in some cases. Looking at China’s example, there are definitely instances where the solution can work. For European standards it may not necessarily be cars, but there’s a whole plethora of other ways to facilitate it, i.e. with the scooter businesses.

What is battery swapping?

Get our informative PDF on ISO 15118 and learn more about the standard.

Interoperability and standardization have become a big part of the EV transformation. As the industry evolves, more and more players come to the game, bringing new developments and ideas. With that, the entire ecosystem undergoes massive changes and it may get difficult to keep track of what’s happening.

That way, interoperability is critical for eMobility. 

Open standards are necessary to ensure that different types of electric vehicles can interoperate with each other and with the infrastructure that supports them, which is run by multiple CPOs and EMPs.

ISO 15118 is an international standard that defines the communication protocols and data structures used in electric vehicle charging. It specifies the two-way communication between Electric Vehicles (EV), including Battery Electric Vehicles and Plug-In Hybrid Electric Vehicles, and the Electric Vehicle Supply Equipment (EVSE).

Communication protocols and data structures

ISO 15118 is designed to be interoperable, meaning that EVs and EVSEs from different manufacturers should be able to communicate with each other. The standard is also designed to be scalable, so that it can be used for both residential and public charging stations.

It enables interoperability between electric vehicles and charging stations, and allows for sophisticated features such as billing based on time of use or energy consumption. ISO 15118 also provides a framework for charging station operators to manage access control and load balancing.

ISO 15118 is built upon Public Key Infrastructure (PKI)

, which means  electric vehicles can be identified and authenticated. This authentication can take place before, during or after a transaction.

The standard is built out of 8 parts, each defining a different aspect covered by ISO 15118 as a whole.

To learn more about ISO 15118, its specifications and features as well as how it works and what are the documents defining each functionality, download the paper from the box on the right side of the page.

ISO 15118 - what is it?

Learn about demand-responsive transport and see how it impacts the passengers and commute.

 has been gaining popularity amongst energy-related terms for quite a while now and it doesn’t seem to be slowing down in its popularity. It’s to no surprise as new technologies used to ensure energy efficiency are a hot topic in the rather unstable times and the ability to adequately address the demand in real-time is a strong game changer.

Similarly to the energy industry, some sort of demand-responsive programmes are being adapted in other sectors, including the area that interests us the most - transportation.

To begin with, it’s worth noting that it may come under many names: demand-responsive transit, Dial-a-Ride transit (DART) or flexible transport services. Meaning the same, all refer to quasi-public form of (usually group) transportation, where the route and time-schedule depends on demand rather than fixed patterns.

Demand-responsive transport (further named as DTR) may refer to all kinds of vehicles including taxis, buses or even trucks. The way it works with this system is that passengers can easily arrange for one of these services with the help of a special mobile app (in some cases it may as well be a phone call - especially used by the elderly). With this system, passenger requests are collected and strategically grouped together so vehicles can best serve those locations. Then a complex routing and scheduling process begins to calculate pick up and drop off times.

Passengers are provided with custom pick-up points and time frames, enabling them to traverse the zone in an efficient manner. Certain DRT systems may also feature designated termini which can act as hubs of connectivity at either end of their routes - these could include popular urban locations such as city centres or airports/transport interchanges, providing users with onward access beyond the boundaries of the operating zone.

The communication behind the system is usually done via Fleet Telematics Systems (FTS), which streamline the communication between a commercial vehicle fleet and their central dispatching office. By installing FTS, companies gain access to mobile Vehicle System components as well as stationary Fleet Communication Systems which can be either stand-alone software maintained by the motor carrier or an internet service executed by system suppliers. The associated database stores all collected data such as vehicle positions and messages for future use.

Some may say that this is nothing else but a well-known taxi (or now Uber), in fact when taxi cabs were first introduced, they revolutionized personal transportation because their flexibility allowed passenger needs to be met from point-to-point. This has led them to become recognized as DRT and some jurisdictions still acknowledge them as such.

Currently it’s more common for demand-responsive transport to refer to a group or shared transportation rather than ‘serve-one’ taxis.

The mentioned-before taxis should probably open up the list as they’re what might be deemed the precursors of DTR. Now, one can add Uber/Lyft and other similar services to the equation.

School buses are another great example of demand-responsive transport. While in some European countries these are only known from American movies, in other parts of the world school buses make for a convenient way for both children and their parents to ensure safe and efficient daily school commute.

Similarly to school buses, their equivalents for adults come under arranged transportation for workers. The perfect example of that would be the below scenario:

15km away from the regional city center is a factory employing over 200 hundred people from the city. Everyday, around the time it takes to commute from the city to the factory, several buses are being dispatched to ‘strategic’ spots across the city, from where they take the workers directly to the factory.

Next DTR example is paratransit or social transport. Paratransit is an accessible public service designed to ensure individuals with disabilities have a reliable mode of transportation. This shared ride system serves as a lifeline, providing essential travel independence and flexibility for those who can't utilize traditional buses. This includes vehicles that can accommodate wheelchairs.

Finally a great example of demand-responsive transport is the ‘first mile - last mile’ solution especially well-fitted for the rural areas. The solution is designed to ensure the smooth ride on the beginning and end of longer journeys, i.e. the portions of the routes it takes to get to the bus stations/airports. This is very convenient for those who travel to unknown locations and find it hard to get to a hotel for instance. Dedicated transportation enabling picking up passengers and taking them directly to the destination spot is also a great marketing point for hotels and resorts. (Similar case is with the shopping malls dispatching free-of-charge buses across the city, by which the customers can easily get to the shopping centres).

There are quite a few benefits to this form of transportation. Those include ecological, financial, social and efficiency-based advantages.

DRT has its ways in revolutionizing transportation for those living in underserved communities. By bridging the gap between limited public transit access, individuals can now more easily take advantage of opportunities such as employment and essential services that have been out of reach due to inadequate transport options previously available.

For children and people with disabilities, special transportation arranged to suit their needs increases safety by limiting the need to go for public transit, which in this case may not be designed well to match the requirements. For people with disabilities it may be too hard and too dangerous to go on a public bus. Similarly, with the children, who by the use of a school bus are limiting any hazardous events like getting lost.

Demand-responsive transport is also a great way to reduce the amount of vehicles on the road. When planned with enough care, a well-designed local DRT may encourage people to leave their cars and use this form of transport instead. By addressing the demand directly, this way also reduces the so-called empty runs. An empty run is referred to any route that a vehicle makes without the passengers or load, i.e. simply because it was planned in a strict-pattern time schedule. With DRT, no such things occur, as vehicles are only dispatched when there’s real demand for it. This saves time (and money) as well as reduces the emissions.

Artificial Intelligence in the eMobility ecosystem

10 MARCH 2023 • 7 MIN READ