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Smart Charge · 2 September 2025 · 1 hr 12 min

Ep. 24: Kathrin Goldammer & Oliver Arnhold über die Skalierung von Localiser und datengestützte Standortplanung für Ladeinfrastruktur

Transcript

Cleaned up from a machine transcript. Translations are machine-generated and may contain errors.

Welcome to the Smart Charge by RiDERgy Podcast. This is Claudio Geyken, Founder and CEO of RiDERgy. This is your show to learn about all things energy management and smart charging innovation for electric vehicles and fleets.

Welcome to Smart Charge, the podcast for everyone who thinks about charging infrastructure in a data-driven way. Today's guests are the CEO duo of Localizer: Professor Dr. Katrin Goldammer and Oliver Arnhold. Localizer is a Berlin spin-off of the Reiner Lemoine Institute, founded in 2018, and develops a platform that enables municipalities, energy suppliers and CPOs to identify and plan charging locations faster. Katrin is Co-CEO of Localizer and also a professor at HTW Berlin. She brings together research, practice and entrepreneurship. Oliver is Co-CEO and co-founder. Together, both are driving the scaling of data-driven location planning in

Germany and beyond. Localizer now analyzes more than 1.2 million potential sites and is considered the first GDPR-compliant planning software for charging infrastructure. In this episode, we discuss how data can replace gut feeling when choosing locations, how cooperation with cities and utilities accelerates expansion, and what's next on Localizer's roadmap. Put on your headphones, check your charge level and let's go. Great that you're here today. How are you? My first podcast as a trio too. I'm really looking forward to it. Yes, thank you. We're doing well. Thanks so much for the

invitation. Yes, thank you. Before we dive into Localizer and the RLI, maybe you could introduce yourselves briefly. What was your background? How did you come to the RLI? Well, my name is Katrin Goldammer. I'm the managing director of the RLI. The RLI is a research institute. It's called the Reiner Lemoine Institute and is based in Berlin. We do technical research there around the energy transition. And because we want the energy transition in all sectors, we've always worked on mobility topics too. How do you get renewable electricity into cars,

into logistics and so on? I studied electrical engineering and did my doctorate in physics, but then quickly worked in the energy industry. Managed power plants, traded electricity. Then I did energy policy consulting and through various paths I came to the RLI. And from the RLI, Oliver and I founded Localizer in 2018. Our first startup, with the background of this experience from the research institute and with the knowledge of how to do projects, which we wanted to do faster though. The idea of the startup

was to bring scalability into the consulting work we had been doing at the RLI for a while. Interesting. Anything to add? Yes. My name is Oliver Arnhold. I'm managing director of Localizer together with Katrin. I also worked at the RLI before, was even one of the first employees from April 2010. We started with five people and then built up the RLI incredibly. And before my time at the RLI, I studied vehicle engineering and renewable energies. I myself worked in the automotive industry, got to co-develop the first plug-in hybrid of a German car manufacturer

and at the time I wondered why this vehicle with a weight of two and a half tons consumed only two and a half liters of fuel. Of course, it was the electric part. And as a vehicle engineer, I was interested in the electric part. How does this electrical energy factor into the balance? And that was basically the motivation to study renewable energies. That was the entry point into energy transition and transport transition topics. And that's how I came to the RLI. Interesting. And the founding of the RLI was around 2010. How did it come about? How did

it happen? The RLI was founded by the Reiner Lemoine Foundation, which is a non-profit foundation that was founded a few years earlier, shortly after the death of Reiner Lemoine. He was a Berlin engineer who dedicated his life to renewables. Basically, he wanted to use his knowledge for a better world. And then he did that by developing electronics together with his friends for wind power plants and so on. And then – which is also what he's better known for – he founded these two large German solar companies

in the nineties: Solon and Q-Cells, both of them. Yes, he was involved in these two foundings. And he was also involved in the IPOs of these two companies and became wealthy in the process, but knew that unfortunately he was ill and would die. And then he put his money into a foundation, which is the Reiner Lemoine Foundation. He more or less witnessed the founding of this foundation himself. And the foundation started a few years before the institute, first distributed doctoral scholarships, so tried in this way to continue Reiner's life work, and then had the idea that you could also do that well with a research institute. Now it's the case that they founded the institute, but they don't finance it primarily. We finance ourselves through third-party funding. We receive an annual grant from the foundation and have to organize about 95 percent through third-party funding. That means we do a lot of competitive research funding. We also do consulting projects. And that's why I think we've been good at always considering what the

next step should be from a market perspective. We make research that actually creates value. We don't just research for the sake of research. And from that logic, Localizer was created. The question was: what problem do cities and energy suppliers actually have? How can we solve it with data and technology? And that led to the founding of Localizer.

market? So what knowledge do we have that other people could use? And with that mindset, we approached our foundation and said: Could you imagine supporting a spin-off? More like Friends and Family, not as large investors. But could we maybe together turn a good idea from the institute into a company? And that's how Localizer came about. Interesting. And the aspects I also found interesting: Your CV is – I'm a mechanical engineer myself and at KIT

for example, there were also few women interested in electrical engineering or mechanical engineering. What motivated you? And how do you see the topic of women in Germany? Why aren't there that many studying mechanical engineering or electrical engineering? And what can be done to get more people like you? Well, I noticed in school how poor the statistics were. Electrical engineering is one of the subjects where the proportion of women is the lowest. When I started studying,

there were somehow six women out of 120 people, and after the intermediate diploma one woman out of 60. And I was that one. And I also studied it because I thought: Okay, then someone has to increase that proportion. Well, that's got to be me. Who else would do it? Then I'll just do it. And I would have wished that this subject was set up more diversely. And how can that be changed? Well, you can of course

shape the way people interact so that it's pleasant for people. And I can't claim that it was pleasant to study electrical engineering as a woman at TU Berlin at the end of the 90s. It wasn't the case that the environment was diverse, friendly, courteous and equality-oriented; rather, it was discriminatory, racist and sexist. And I would wish that these circumstances change, because that would also make it more attractive for modern people to study such subjects and to stay in such fields. Now I can only

influence that at my level. I can make sure that the organizations I lead have such values. Or I can, as I work at HTW – I'm a professor there – ensure that the environment is courteous for all people who want to study there. And that's how I see my task now. But back in the 90s, you're right, there were very, very few of us. Good. And how did the founding of Localizer come about? How did the idea come about?

Well, we had already been working on this topic at the Reiner Lemoine Institute for a very, very long time. Actually, just a year after its foundation, we had our first orders from business and politics to create site assessments for charging infrastructure – from Hesse, from Brandenburg, from Berlin. And back then the federal government even subsidized site assessments or charging infrastructure concepts with 80,000 euros per assessment for a municipality. And then you could take a year: so first the application, the municipality had to make a tender first, then you apply for it,

then a year of implementation. So you already notice what a long period of time that was and how much money was behind it. That's how it was back then. We did that as a research institute. But we realized that you couldn't really accelerate the energy transition that way, because the site assessments we created were, from today's perspective, I think quite good, but they were outdated again after a few months. Back then that wasn't a problem, because the expansion of new charging points wasn't happening at the

speed it is today. Back then – what year are we talking about? It was like that in 2011, 2011, 2012 – those were the beginnings. Back then some CPOs still knew their charging points by number, or even knew some users, because so few people were charging. One of them said: "Hey, that's the Tesla driver, the managing director of that metal construction company, he's always charging there." But that's how it was back then. And we realized:

If we really want to have impact, then we have to automate and scale these site analyses. And let me give you a few numbers. Back then, about a month cost 10,000 to 12,000 euros when we simply put a geographer or an engineer on it to do such site assessments. And today our costs are a factor of 10 lower, and you can't even quantify the speed, because the site analyses today aren't done by us, but by the software. That means our customers themselves do the analyses

day and night. And that's why we're much, much faster. And the founding – that was the actual question – came about like this: We couldn't simply build a software distribution business, Software as a Service, the way we wanted to, in the non-profit research institute. So we approached the foundation and presented them with the idea. So that was the starting point. Okay, and the NOW and the municipalities basically also

demanded a kind of service. If I understood that correctly: The NOW coordinated strongly, and the funds came, I think, mostly from the Federal Transport Ministry. But there were also funding programs at the state level. I think there are even funding programs for hardware in some federal states today. Yes, that's how it was. But out of curiosity: you've managed many different projects, funding programs and others, as you mentioned, Kathrin, that you've supervised. How did you then make the decision? I think not many startups

Did you spin out from the RLI, then? That was one thing. And how did it come about that you did a spin-out, whereas maybe others didn't? From my perspective: I was the managing director of the RLI back then, and I still am today. It was somehow the right combination of a fantastic topic that we believed could scale, and the right people. Oliver wanted to start something new, I wanted to go with him on that journey. And with that combination we did it.

We had some colleagues at the institute back then who said, "Can you take me on with you?" – and we did bring some of them on board initially. By the way, there was also – full disclosure – people at the institute who said, "That's weird, what you're doing. We're a non-profit research institute. Why are you even interested in commercial activity like that?" So we had two different perspectives on it, but I enjoyed doing it. And our experience today would say it was worth it, because we

created a second organization with that, which has its own employees, which today can finance 14 people and which exists in addition to the institute without taking projects away from the institute. The scalable business is something different from the research and consulting work we continue to do from the RLI. I find this tension you just mentioned interesting too. Maybe we could touch on it briefly. I think there is something in the economy – entrepreneurs have found the answer for themselves about why it makes sense to found a company. But there is quite a large part of society that is always a bit more critical about business: "They're just after profit," and so on. You were really somewhere in between in this tension. How did you handle that? What were the arguments that led you to say, "Okay, it definitely makes sense to start a startup"? Well, I can tell you the arguments, and I'll say right away that they

didn't convince everyone at our place either. So you can put forward an argument like: "Look at Reiner Lemoine. The money that went into his foundation came from his entrepreneurial activity. It didn't come from his non-profit work before, but from his entrepreneurial activity. And we're glad it existed, because that enabled the foundation, and that also enabled the institute." Didn't convince everyone, but you could make that as a general argument. And then there's also the

aspect that I found important in my leadership role back then: I wanted to develop the institute further, I wanted to develop the people further, and I thought it was worthwhile to take this experimental risk. And I think when solutions work in the business world, that's also like a kind of seal of approval, because companies want to operate economically of course. And if we really want to pull off the energy transition quickly, then we need this scalability. And that can't depend on individual companies, but rather you have to activate the whole economy, so to speak, or

get the whole economy working as fast as possible. Today we have 20,000 location assessments on our platform per week. 20,000 sites are being surveyed in Europe. And you realize that you simply can't do that with traditional research and consulting work manually or in person. And now to step back for a moment to really illuminate the problem, which is probably very clear to you of course, but for someone who isn't involved: What's the actual problem that makes people need your solution too? Well,

if you're a homeowner and you have an electric vehicle, then it's kind of a no-brainer to screw a charging station or wallbox on your wall. With companies, with fleet vehicles, that's also relatively straightforward. But the uncertainty arises when it comes to public charging needs or charging needs that come up in places that are publicly accessible. And we take that uncertainty away from investors. That's basically the core motivation. I understand. So the uncertainty is about where you should install that charging point. Exactly, and also

what charging power then makes sense, because the charging power has to match the location's purpose of visit. In the early days of electromobility, there was even the term "charging dwell time." That was supposed to describe that you're simply there far too long and have to kill time. I've personally experienced it where I get charging stress because the car charges so fast and you can't shuttle the family through the rest stop bathroom and restaurant quickly enough. And then there's the blocking fee on top of that.

Exactly like that. So the charging power has to match the PUI, and that sounds trivial, but in practice it's not so simple at all. That means we examine the locations, give recommendations for the chargers, so charging powers, and can also estimate the energy quantities that the CPOs can then sell, so in kilowatt-hours per year through 2035. That's of course very exciting for the charge point operator and particularly important for his economic calculation. And for that it applies: location, location, location, right? That is

crucial.

it's always the case with real estate, and it's the same for you. And what makes a good location? What are the top three factors, so to speak, or more if you want to mention more? How much time do we have? Phew, I need to think for a moment. So the factors are different for the different performance classes. I just had a call about truck charging, for example. That's of course quite different from lamp-post charging. I wish I had a lamp-post charger at some locations where I don't have this charging stress. With 3.7 kW you can charge nicely throughout the day. So I think the main factors are: You have to look at the local population – what charging needs arise from the local population. You have to look at points of interest, for example supermarkets, and you have to look at traffic. Those are the three categories. Maybe we should start by thinking in categories. And then within those categories you can of course get as detailed as you want.

And what's also really important is of course taking competition into account. Back when we started, there wasn't much competition. The analysis we printed out on paper held up for a few months. Today we have a completely different frequency. Many charging points are being built. So you have to check the competition daily, check competitors' prices daily. We're slowly moving into operational optimization. Because once the charging points are in place, the business isn't finished – you have to make sure you're competitive in the market, have attractive charging prices and keep an eye on the competition. And that's a completely different field.

Yes, that's exciting. And do you have access to all the data sources you need? Or are there sources where it's impossible? Well, we've been doing this for quite a while now and of course we've gathered a lot of data ourselves and built models. We also validate a lot of things with our customers these days, because when you have such a large customer base, you can essentially do the validation with the customers' real backend data. But we also buy data – specifically when it comes to daily updated price data or utilization data. So: how are charging points that are already on the street actually being used? We buy that from partner companies. Okay. And as far as I know there are certain actors who offer data in a consolidating way. Eco-Movement for example is very well known in the charging industry. So you have various data sources from third parties that aggregate things, I assume.

Actually just two. We try to do as much as possible ourselves. But the two I just mentioned – charging prices and utilization data – those are things we buy from partner companies. And it's also important to always have good cooperation so that it works robustly and reliably. We do everything else ourselves. I think we do so much ourselves because we come from this world. For every research project in the ten years before, we had to gather data, scrape it, make calls, consolidate tables and so on. And as RLI we've always been very good at that – data science, especially the whole world of open data. We're specialists in that. And with a bit of that mindset we probably started. Because we knew it was possible and because we'd gathered a lot of experience in it, we realized we could apply that to our cases too. And we are creative. And I don't mean that in terms of stealing data, but in terms of assembling data into a cool algorithm.

So everything we had in there at the beginning, including sociodemographic data around the probability that someone drives an electric car compared to a combustion engine – for example political affinities, election results and so on. We actually had good exchanges, genuinely interdisciplinary with people from the social sciences, and then tried to represent that through data. And we got something like that, for example, from election commission websites. So I think we put together a good mix. And only now, the way Oliver described it, where densification and these real-time topics become more important, we've worked more with data providers. And do you also – there's the EU Data Act that's apparently coming in September, where data will apparently become more easily accessible –, is that relevant for you? A game changer? Do you see something there? Are you also dealing with that, or are you generally satisfied with the data situation, so to speak.

You also have demands on policy. So maybe not so much with the Localizer glasses on. With the RLI glasses on I have an enormous number of demands, because we could move an incredible amount for better research with better data. And I wish we had access to many more things. I also want them to have open licenses so I'm able to use them. A lot of data is proprietary or even worse: there's simply no license. Nobody has even asked the question of whether research is allowed to use the data or not. That's just incompetence, I think, that exists in many places. So if this Data Act leads to the public sector dealing with that more, we welcome that very much. From the Localizer side we're just very well set up. And because we have regular validation, we also notice that our datasets and our results work well. Yes, and there's actually a third pillar in principle. So we had

I wish I had a lamp-post charger at some locations where I don't have this charging stress. With 3.7 kW you can charge nicely throughout the day. So I think the main factors are: you have to look at the local population – what charging needs arise from the local population. You have to look at points of interest, for example supermarkets, and you have to look at traffic. Those are the three categories. Maybe we should start by thinking in categories. And then within those categories you can of course get as detailed as you want. And what's also really important is of course taking competition into account. Back when we started, there wasn't much competition. The analysis we printed out on paper held up for a few months. Today we have a completely different frequency.

Many charging points are being built. So you have to check the competition daily, check competitors' prices daily. We're slowly moving into operational optimization. Because once the charging points are in place, the business isn't finished – you have to make sure you're competitive in the market, have attractive charging prices and keep an eye on the competition. And that's a completely different field. Yes, that's exciting. And do you have access to all the data sources you need? Or are there sources where it's impossible? Well, we've been doing this for quite a while now and of course we've gathered a lot of data ourselves and built models. We also validate a lot of things with our customers these days, because when you have such a large customer base, you can essentially do the validation with the customers' real backend data.

But we also buy data – specifically when it comes to daily updated price data or utilization data. So: how are charging points that are already on the street actually being used? We buy that from partner companies. Okay. And as far as I know there are certain actors who offer data in a consolidating way. Eco-Movement for example is very well known in the charging industry. So you have various data sources from third parties that aggregate things, I assume. Actually just two. We try to do as much as possible ourselves. But the two I just mentioned – charging prices and utilization data – those are things we buy from partner companies. And it's also important to always have good cooperation so that it works robustly and reliably.

We do everything else ourselves. I think we do so much ourselves because we come from this world. For every research project in the ten years before, we had to gather data, scrape it, make calls, consolidate tables and so on. And as RLI we've always been very good at that – data science, especially the whole world of open data. We're specialists in that. And with a bit of that mindset we probably started. Because we knew it was possible and because we'd gathered a lot of experience in it, we realized we could apply that to our cases too. And we are creative. And I don't mean that in terms of stealing data, but in terms of assembling data into a cool algorithm.

So everything we had in there at the beginning, including sociodemographic data around the probability that someone drives an electric car compared to a combustion engine – for example political affinities, election results and so on. We actually had good exchanges, genuinely interdisciplinary with people from the social sciences, and then tried to represent that through data. And we got something like that, for example, from election commission websites. So I think we put together a good mix. And only now, the way Oliver described it, where densification and these real-time topics become more important, we've worked more with data providers. And do you also – there's the EU Data Act that's apparently coming in September, where data will apparently become more easily accessible –, is that relevant for you? A game changer? Do you see something there?

yes, there are the data we generate ourselves and the data we purchase – so basically just two datasets. And then there's a third pillar of data acquisition, and those are research projects. So we're a Software-as-a-Service provider, but we're still involved in very, very innovative topics in research projects. And preferably of course together with the RLI. And we do that across Europe. So we have projects around the Baltic Sea with all the Baltic Sea littoral states. We just have a project in Austria. And that

is – I'd say – flagship projects, we call them that at our place, where we simply go new ways in research – failure is allowed – test new things, try things out. And those are our projects where we then develop the latest stuff. We can't do nearly as many of those as the RLI can. But of course we benefit from the fact that we have such a long history, a good network and can collaborate really well with the RLI and with other research institutes. And if you mention such an example

where you'd need data – what kinds of data? Is it load profiles, or what could – what comes to mind? Well, if I could wish for something, I'd like to know all distribution grid data georeferenced. Why does that interest me in the context of electric mobility? Well, it interests me because of course grid connection costs scale somehow with the effort and the distance to existing grid connection points, networks and so on. So if I know that somehow a power grid is laid on the right or left side of a

six-lane street, that's important information, right? Because then it makes total sense, when searching for charging points, to go to one side of the street or the other. So that's something that's not only super interesting to know at the RLI, but also in the context of electric mobility. You can generate such datasets yourself if you know your way around power grids well. That happens to be a skill that the RLI has too. But why not more open data in that context? After all, these are things of public infrastructure. Makes total sense. That's a

big one. Exactly. So we even got network plans from grid operators at some point. Then we could actually map the case that Katrin just described. So: the distribution on the left or right side of the street – once under the streetcar or the ICE track – costs a six-figure sum, I think. That's incredibly crucial for our customers, where they place the charging point. But at the end of the day, the collaboration can only work with the grid operator, because we don't even know whether in operating the grid operator makes switchings in his

meshed network where maybe feed-in capacities are already allocated for a business park, where maybe today the connection capacity would be available, but it's already reserved because in two years somehow a business park is being built. And I have the idea that we need to go in the direction of simply having standardized data exchange with the grid operators – via open data, models, interfaces, traffic light systems, whatever. But that would be incredibly helpful not only for electric mobility, but for everyone – so also for the producers and for location planning. And if you now

look again in the direction of go-to-market: how did – you also started back then. So then I can imagine: okay, the first projects came in through those funding programs you described at the beginning. But as a company you have to position yourself in the market and make a name for yourself. Can you talk about that, how your – since 2011 was that the beginning, was that at the RLI? Exactly. Yes, that was the entry point, when charging station operators got in touch with us and we developed location concepts for cities and municipalities

we were already super specialized. And then it really came down to: we have to get the scaling to work, so we made the founding and thought: yes, now. We're specialists from the RLI. We have the network, we have the knowledge. And now we really get going. And the big aha moment was for me – how do you put it – the big aha moment was: we couldn't continue supporting a single customer from the RLI. The ones who came to Localizer. They still wanted PDF reports. They weren't interested in paying just a tenth of the costs; they simply wanted that thick PDF document, the report, to go to the decision-making body with it. And at Localizer we really had our hands full with that for the first year and a half, because we were set up and focused on Software as a Service. We didn't want to create PDF reports anymore. We wanted customers to do it with us, with our software, and press the button and then have their kilowatt-hour forecast. But

they didn't want it. And that kept us quite busy at the time. Yes, exciting. And organizationally, how did we do that? We collected some money from friends and family and from ourselves and from our foundation and then had money first to program this software. So we got straight to work and programmed it in the first year. And then a platform was finished where you could have automated everything algorithmically yourself. And then we thought, that's

a great idea. And organizationally, how did we manage that? We raised some money from friends and family and from ourselves and from our foundation and then had funds to program this software. So we got straight to work and programmed it in the first year. And then we had a platform ready where you could have automated everything algorithmically yourself. And then we thought, that's

but now the awakening came. You can determine points at the push of a button, evaluate them and so on. And then what you said happened. So people said, that's all well and good, but a PDF report would be nice too. None of us felt like adding any text to it. That's what happened to us. Then for example at the beginning I still had the idea that with our algorithm we could create rankings really well. So I was always waiting for such a

classic multisite customer who would say: "Well, these are my 10 or 20 or 50 potential locations. Why don't you create a ranking for me." And then I would know: Ah, this one is probably going to be more profitable in the future than the others. That didn't start the way we wanted either. We had a very, very large German customer very early on, who runs a well-known supermarket chain with several hundred branches in Germany – that worked out. And for them we then programmed such an upload for example,

where they could simply upload their Excel spreadsheet with their 500 locations, and then actually got such a ranking back. The locations were automatically read into the software – so we learned from such a customer what they wanted, how they wanted to use the software. So something like that went relatively quickly too. But those were individual projects at the beginning. We didn't yet have the feeling that we now had 10, 12, 15 standard customers all wanting the same thing. That went on for quite a while. And what

happened then? Or do you want to add something? Yes, so we really struggled with that. We also had to make changes in our company. We actually had to end contracts too. We had financial struggles. And then Katrin and I and a small core team basically continued with sales because we were simply convinced of it. I mean, it's a no-brainer to have better analyses for a tenth of the cost, day and night. And then at some point the knot burst. And then we

built the team back up again. Katrin already said, we now have 14 people, with an upward trend. We operate from Norway to Italy, from Poland to England. And that doesn't really surprise us. But what does surprise us is that we can have the same location evaluated by multiple stakeholders. So not only CPOs contact us, but also the location owners, the banks, the investment funds, pension funds. So I've learned a lot in recent years about everything surrounding a charging point – who else

is involved. Exciting. I wanted to add: from my perspective today, I would say we were one and a half or two years very early. And this automated product didn't yet have as strong a market as it does today. But it was the right time for us. We started it in 2018 anyway, but maybe we could have started it in 2019 or 2020 and would have already had this really scalable part with us. And we were very early. On the other

hand, I would say that we've also been on the market for a long time. And we know the development of these customers quite well too. So these customers had different wishes back then than they do today. And we were able to continue our project, or further develop our platform, so that these customer wishes flowed into it. That of course enabled us to become much more customer-friendly and market-oriented too. So we noticed back then what they needed, what they didn't need. We had many conversations and then

said: "Yes, of course, we can do that for you." And that's why we now have a product that we didn't think up from the top down, so to speak, but that emerged together with our customers. And that also turned out to be a very good method for us for further development. Many of the things you just discussed, for example this expansion into other countries, comes from the fact that a customer of ours said: "I'm actually also in other countries. And the software I have there isn't

as good as yours. Can't I sell yours to my colleagues from the other countries too? We said: Well, actually yes, of course we can make it available to them. And that's how we expanded into these countries. So those are customer wishes that we then implemented. And the breakthrough, when did that come – was there a moment or was there a special pivot, as you say in start-up jargon, a change of strategy or product that led to such a breakthrough? Or

I think it was simply the timing, that you waited long enough. Yes, I think there were two things. The first point was that the pressure on our customers kept growing and they realized they couldn't manually review locations with personnel anymore and leaf through all the locations in a PDF. So the first big customer started right away with 4000 locations and with several departments. There was a department that only did location acquisition. They had to

evaluate thousands of locations very, very quickly and even check: is it even worth calling the property owner? They had to calculate very, very quickly. Then they passed the best 100 locations to the next department. They looked a bit more into the detail, all the way to the construction department, which built the things. And when the companies realized that it no longer worked with PDF documents – including the information sharing within the company or with service providers – with us you can simply invite the service providers,

take a photo with your phone, and then the construction meeting is essentially documented on the spot. When this pressure emerged with our customers, that's when the knot burst. Exciting. And you already mentioned the stakeholders. So the most important ones in order: right now those are the CPOs, if I understand correctly. And maybe you could add to that, the others you mentioned as well – how are they relevant to you now? So CPOs, logically, those are the most obvious ones, but there are always other actors. I had already

mentioned the funds. Investment funds that of course manage large portfolios, and they too have to make decisions very, very quickly. Or when it comes to company acquisitions, banks or investor companies contact us and want to know how good is the portfolio of the company I want to buy. We've also accompanied insolvencies – a bit morbid, but there too you have to assess how good the portfolio is that someone has built up. And recently someone said to us:

Well, you're bankable. What he meant by that was that our software is based on a standard that we ourselves created with the German Institute for Standardization – not alone, we initiated it, but the National Charging Infrastructure Control Center was also involved, Federal Ministries, State Ministries, but of course also CPOs, from the BDI, a company from the VKU, the Association of Municipal Enterprises was involved. And we created a standard there. And through this standardized approach that we have, our analyses are also accepted by banks and investors. You know

that from the PV industry. When you build some kind of PV park or wind park, you need yield assessments from third parties. And we can do that in the area of charging infrastructure. And that's why we essentially opened up this field of investors for ourselves. So CPOs, investors, property owners – suddenly properties become interesting that weren't interesting before charging infrastructure, simply because they're well-located from a charging infrastructure perspective. For building a residential or office complex, they might not be so attractive. That means property owners too want their portfolios assessed. Housing associations want or

have to build charging infrastructure. Cities and municipalities create plans. That closes the circle a bit – we actually started with that. We've also accompanied cities like Vienna, Hamburg or Berlin on large tenders, where these cities were looking for service providers to expand charging infrastructure. Yes, I see. You mentioned it – that's actually extremely interesting, this analogy to the PV industry or wind, they also have to apply this kind of location assessment tool. Are there many analogies and

where are there big differences compared to these industries? I studied renewable energies, but I have to say I've never created a wind assessment myself. So from the investor companies we know a few KPIs that they bring from the PV and wind industries. We picked up a bit from that. But I can't go into the details. Ultimately it's technically quite different. So I look at annual solar radiation or shading or wind shading or wind park effects. We don't have

that. For us it's these three pillars I mentioned: population, points of interest and traffic, and then competition. And in total there are dozens of factors that detail that further. In the area of population, for example, building structure is important to us. If you put a charging point in a suburb, in a residential area like that, then of course people live there who might be solvent, the electric vehicle share is high. Political voting behavior correlates well with the electric vehicle registration rate. That's what

Katrin hinted at earlier: all factors might be good, but still our software outputs that there's hardly any public charging demand. Why? Because charging at home is of course much more attractive – economically. And our software recognizes that there's no public charging demand there. If you had just as many residents in a building structure where there are more single-family homes and not enough private parking spaces, charging demand comes out again. And so there are, as I said, dozens of factors that we've described in more detail over time. I saw

that around 2,000 CPOs have already used the software. That means I have huge amounts of empirical data. What have you learned? What surprised you, what might also be an interesting anecdote from your data and analyses? So let me add something that I'm still waiting for a bit: we actually didn't write Localizer as

software that spits out these kinds of assessments – that's what it sounded like a moment ago – but we actually built a platform where you can do the entire implementation. You could directly get in touch with the grid operator, they could directly see the data, they could also write in: "This location isn't that favorable, it would be cheaper if you moved it." So we've prepared something that has the potential to bring together this entire sector management task. And that's something that

has not been accepted very well yet. In the background, after evaluating and planning all these locations, essentially the same processes are still running at our company as before. In the context of digitalization, I would have hoped that things would have changed at the network operators over the seven years we have now been on the market. That is something that, for example, has not happened yet, but we are prepared for it. We could easily do all these processes digitally in the platform as well.

do it. Actually, there are these buttons – they're still in the platform: "Invite the network operator here." But you don't see the button anymore. In the original designs, all of that was planned. Ultimately, we're still in the first two stages out of the original seven stages. The others are all prepared. I think at the capital city conference, a CPO once asked me if we couldn't also invite network operators back. That

would be great if that could get started. So to all network operators out there: we are ready. The use case would be that the network operator logs in and then feeds data from the networks – they don't even have to do that. There was a time when network operators received many connection requests because CPOs said: "I need to realize five locations now, so I'll ask the network operator for fifty just to be safe and then pick out the locations that can be realized fastest." That

also hurts the network operator when they process fifty connection requests but only get five orders. Normally, a network operator doesn't know this kind of business at all, because when someone makes a grid connection request for a building or a supermarket, it gets built one hundred percent. There are no options with the network operator. But in the area of e-mobility and batteries, a network operator suddenly has to process apparent power requests because they simply have to process quotes that don't turn into orders. And that would also

take work off the network operator's plate. Katrin mentioned earlier that we would support a process like a non-binding grid connection request. It would be great if you could simply make a non-binding location inquiry with the network operator via a platform like Localizer. The network operator could answer that quickly, doesn't have to make it binding, doesn't have to allocate capacity in their network program. And our customers, the CPOs for example, would be happy if they simply got a thumbs up or thumbs down or some sort of –

I'm not entirely sure, I would need to analyze it more carefully – information. All of that is already planned. But we don't just have twelve network operators in Germany, we have a few more, and of course it's not that easy to set such a process in motion. My utopian vision would be that eventually we would really have all the data from the distribution networks available in real time – not just the capacity of the cables, but also the actual historical data from every fifteen-minute or

real-time slot – and that would be available to everyone and could be used for planning. For the projects, that would create efficiency for everyone. But that would be step ten at some point in your software. You asked about those aha moments. And that is actually an aha moment: I wouldn't have thought it would take so long. We're still not – we still don't

have the solution. And we already had the idea seven years ago. So that really surprised me. Another aha moment was that we receive and process inquiries. Customers then calculate locations. And only by chance did we notice that the same locations had also been processed by other customers. We don't look at, or we can't even look at all the locations that our customers analyze anymore. Each customer project is also confidential. But we noticed that sometimes

the same location was evaluated by different actors. And some really exciting things happen there. I would like to tell you more now, but the suspense is building. Yes. And what role does artificial intelligence play? That's also on everyone's lips. Does that have an impact for you? Do you use AI, or do you want to use it? Yes, well the answer is clear. Yes, we are already using AI and also machine learning. AI has a huge advantage when evaluating large amounts of data, and that is the point where we currently use AI.

For the actual location forecast, which concerns the determination of kilowatt-hours, we are still being cautious, because AI can only learn from the past. But since the changes in user behavior and vehicle technology have developed so strongly, we would essentially be projecting old user behavior and old technology into the future. And I still see limitations there. So, if you think about it: less than ten years ago, many electric vehicles had a range of less than 100 kilometers in winter. You can hardly imagine that today, but that's how it was. And when you had to charge, you were happy if you found a fifty-kilowatt charging point. And today, as I mentioned, you have charging stress because with an eight hundred-volt car you can fully charge the battery again in twenty minutes. Yes, and that's why we basically regularly test the limits of AI. And I think the most important thing is basically to understand these limits

of AI and to communicate them transparently. That's our approach – and that's why we are cautious with location forecasting.

to know and then continuously expand it until you're better with AI than with what you've programmed in the classical way. I would also say we use AI as a tool, just as we also use Python as a tool. But there's still a fundamental analysis and a fundamental algorithm in our model of things that we know, that we can adapt, and that AI hasn't invented. That's how we use it. Yes, that makes sense from two directions. On one hand, we can more easily feed in our experience

that way. We also did large analyses for the Federal Government, for the Transport Ministry and for the national control centre. Some of you may be familiar with these LIST-2030 studies. You can look at the imprint – there's the logo from REI, and also colleagues from Localizer. So feeding this information in is very important. Back then we were allowed to survey 80 percent of the German vehicle market. The Transport Ministry invited the CPOs to the ministry, and under antitrust attorney supervision we were able to survey the car manufacturers

about their production planning: which battery capacities, which quantities, which charging powers the future vehicles would have. And feeding this information into the algorithm is still easier the classical way at the moment. Another aspect is that our customers don't want to look into a black box where just a kilowatt-hour value comes out that you then have to trust, because our customers have very different business models. So if you take a supermarket car park, for example, then you could analyse it and say that there are somehow 3,500 residents within a 500-metre radius of this supermarket. They could theoretically charge at the supermarket car park. But if it's restricted or for insurance reasons you can't charge there at all on weekends or Sundays, that part already falls away. But we don't know at all whether maybe a change will happen at that supermarket in the next six months and then residents will be able to charge there after all. Or are we only looking at the point of interest? We make this very, very

transparent through our algorithm, so that our customers can themselves decide which of these energy forecasts or shares fit their respective business model. They can adjust it, and they can also pick out the elements that fit their business model. And if a municipality or a supermarket doesn't want to lure motorway drivers off the motorway and through the small town to the supermarket – because maybe the mayor doesn't want to – then this column of energy simply isn't taken into account. In the business model,

it won't be considered.

then we wouldn't have done so much advertising either. And I have another anecdote about supermarkets that also clarifies why we still think such a fundamental model is good. We had a large customer in Germany with many branches. And they had already given thought to which branches would be more attractive for people with electric cars. And they had, for example, the hypothesis that branches with a high proportion of organic products – so a different product range – because they know their customer base and their product range very well, that those would have a higher probability of people with electric cars parking there. So supermarkets know extremely well who shops with them and have their own idea about how that correlates with the use of one mode of transport or another. That's incredibly important information for a company like that. An AI that performs the same analyses for everyone won't find that out. And because we keep having this experience and because we also want to enable our customers to incorporate this, the fundamental model is simply the best.

their product range very well, that those would have a higher probability of people with electric cars parking there. So supermarkets know extremely well who shops with them and have their own idea about how that correlates with the use of one mode of transport or another. That's incredibly important information for a company like that. An AI that performs the same analyses for everyone won't find that out. And because we keep having this experience and because we also want to enable our customers to incorporate this, the fundamental model is simply the best. And AI – sure, it's arrived at our place because it's a great tool. Okay, that's interesting. And when it comes to market positioning: I assume there are competitors like in every business model. How do you differentiate yourselves? What are your USPs? Well, when we started, there were already competitors. And in that phase we described earlier, when we were also economically uncertain, we didn't know that we were too early. We

also had the idea that maybe we were too late. It sounds kind of strange somehow. Today it's so clear that we were simply a bit ahead of our time. But back then we didn't know whether we were too early or too late. There were already two or three players back then. All of those players don't exist today. I think one switched to a completely different model. I think they left the charging infrastructure topic and asked us last year whether they could buy our service from us again, because they'd exited the field. So we've seen a lot of players come and go over time. And now there are – I'd estimate fewer than a handful – that are so specialised in Europe. Across all of Europe, yes. That are so specialised. And what sets us apart? We already have a lot of expertise in charging infrastructure planning. We don't just do it because you can do it with data. With data you can do a lot,

also had the idea that maybe we were too late. It sounds kind of strange somehow. Today it's so clear that we were simply a bit ahead of our time. But back then we didn't know whether we were too early or too late. There were already two or three players back then. All of those players don't exist today. I think one switched to a completely different model. I think they left the charging infrastructure topic and asked us last year whether they could buy our service from us again, because they'd exited the field. So we've seen a lot of players come and go over time. And now there are – I'd estimate fewer than a handful – that are so specialised in Europe. Across all of Europe, yes. That are so specialised. And what sets us apart? We already have a lot of expertise in charging infrastructure planning. We don't just do it because you can do it with data. With data you can do a lot,

you can do a lot with data, can you. We have expertise in charging infrastructure planning. We don't just do it because you can do it with data. You can do a lot with data,

sell dog food, or you can also plan charging infrastructure. But we happen to come from that field. We know everything about the customer base, the stakeholders, the technology behind it and so on. We built this with that knowledge. That's what sets us apart. And then we also have a good solution. If you look at the platform – our customers do this comparison with other platforms too – then we regularly get feedback that we've simply built a very user-oriented platform

that meets their wishes well. That's because we developed it very much together with our customer base. We have 14 people on our team. Many of them also drive an electric vehicle themselves. Besides me, there's another automotive engineer. And I don't know if you know automotive engineers – I think the female quota is still a bit lower there than among electric vehicle people. There are a lot of enthusiasts in there. And two of the enthusiasts happen to work with us. We simply enjoy

trying out the electric vehicle topic. We do regular tests with the family too – sometimes to the family's dismay – but also in a professional context. We also borrow vehicles abroad and simply test the charging infrastructure. We've been doing that for years. And all that information naturally flows into it. We can make energy forecasts, but there are many other factors: competitor prices, dynamic pricing is a huge topic now. These are all factors we have to take into account. And we

also support our customers with tips, tricks and consulting advice on this. The topic of dynamic pricing is very, very exciting, also in the sense of the energy transition, that you dynamise traffic flows based on the availability of renewables in the grid. How do you assess that? Are many CPOs looking at this? Will it get rolling soon? Will it change a lot, also based on your experience so far – would your models change quite a bit if you had such prices?

We've already anticipated that. With some customers, we're already supplying price data that flows into their dynamic pricing systems. You can imagine it very well: if you know what the current daily prices of competitors are, you can think of methods for how you want to structure your own pricing. And we also have API connections. That means our customers can retrieve the data not only in our platform in the browser, but also via the API and feed it into their

backends or into their algorithms, where the data then reaches the charging station and the price can be set at direct payment. That means we're already getting into operational optimization. Dynamic pricing will be a huge topic from a demand perspective. But we're still looking through the renewable lens a bit. From a generation perspective, it will also be a topic, ultimately also via the procurement price of energy. There are already a few charging stations that advertise heavily with this

– when the sun shines, it's a few cents cheaper than when there's no sun. Others look at it only from the demand side: if there's just not much going on, then the price is reduced to attract customers. And we're currently preparing these pricing mechanisms. But there are very big differences among customers. Some look at demand from the user side, others look at energy, others at marketing. A lot will happen there. And that's also one of those topics where I think: well, if I don't come from the field, then I can throw the data into an AI. And then it generates forecasts from it. But we happen to come from the field. I used to trade electricity myself. I know how the exchanges work. With that knowledge, we'd approach such a task rather than just shoving it into a data model. That's what you get with us. We try to stay out of consulting. But I think we

would also still be extremely good consultants for our entire customer base, because of course we can also understand these management decisions – this make or buy – because we can see through all of it. But our company is supposed to be a software-as-a-service company. So we have to constantly regulate ourselves a bit and remind ourselves that we actually don't want to do projects and actually don't want to do consulting, but rather we'd like to make the software so good that it solves our customers' problems. A vision that drives us at RiDERgy

is to enable intelligent charging, right through to bidirectional charging, at as many charging points as possible. Now, in public charging, it's especially more difficult in the fast-charging segment than in other use cases or applications. Nevertheless, there are also AC chargers in public charging – which is your core focus at Localizer – now also at street lamps for example, but also in Holland, I think, very, very much, even more than in Germany, lots and lots of AC chargers. And with DC charging, you could also steer the flows a bit via these

dynamic prices. How do you see this topic, and do you already have experience values? And do you think it will also change a lot when you're then able to charge when there are many renewables, and when bidirectional charging is possible and becomes common practice? Well, the potential is huge. In my second degree, I wrote my master's thesis on exactly this topic. That's quite a few years ago now – on bidirectional charging. And it is

noch längst nicht Realität. Aber ich sehe da ganz großes Potenzial. Also, einerseits kann man Puffer schaffen, um die Flüsse zu smoothen. Man kann aber auch die Fahrzeuge als dezentrale Energiespeicher nutzen. Und das ist aus meiner Perspektive auch der Schlüssel zu einem neuen Strommarkt. Wir werden die erneuerbaren Energien ausbauen – alle sind sich da einig. Aber wir brauchen auch Flexibilität im Netz. Und diese Flexibilität können wir durch intelligentes, bidirektionales Laden schaffen. Das ist ja auch immer noch nicht so verbreitet, weil es auch noch technische Hürden gibt und es auch noch nicht so standardisiert ist. Aber es wird kommen.

So it's a bit like the network topic we mentioned earlier. You really can't imagine why it's taking so long to actually implement this in practice. But it will – I'd use the word "game changer" for this. So if vehicles can really feed energy back into the grid, that's a game changer. Maybe still at RiDERgy: we've already carried out projects with the transition to Katrin. One project, for example, with Deutsche Post and the grid operator Edis. I believe,

the fifth largest grid operator in Germany, part of the E.ON group, Edis, in Brandenburg. Exactly – we managed to control Deutsche Post's charging processes from the control center of the grid operator as a test. The electricity didn't flow back, but the grid operator could essentially control the charging processes through us. Technically all of that is possible. But I think the point is the regulatory framework.

Okay, so one more question: have you discovered geographical differences in what you do? So I assume your focus is on Germany, but as you mentioned, you also work in geographically very different regions. Are there interesting findings or data? Yes, for example, when we activate a customer project in Sweden or Norway for a German customer who wants to enter that market and then asks us: "Hey, you guys made some kind of mistake up there at that parking garage

You've got 500 charging points in it." We say: "No, it's not a mistake. They're really there." And every country has different growth curves for us, multiple ones – a conservative and a progressive scenario. And what's totally important, which actually sounds totally banal, but it's sometimes not done correctly even by large consulting firms: of course you always have to factor in current registration numbers when looking at growth curves. We've already seen forecasts where more electric vehicles are registered in the future than the total number of vehicles registered per month today. That

would mean vehicle purchases are rising like this – and we don't see it that way. And that's very different in the different countries in Europe. Holland, for example, is ahead of us in registration rates. But Germany will overtake Holland because the throughput of new vehicles is higher. And then electric trucks – I think that was already touched on. That would be another interesting topic that I think is very trendy right now. How do you view the electric truck topic? What

changes for you there, or how is it relevant for you? Well, you go into the map in our system, right-click and set a truck location. And then you click on it again and say: "Start calculation." And then in the truck sector megawatt-hours per year are output, also until 2035, for various charging power levels – so for overnight charging and for fast charging.

After four and a half hours of driving time you have to take a break. That's where you could charge with MCS, with megawatt charging. Today it's often still CCS charging. We have the product. It's already being sold. Many customers use it and are planning truck charging infrastructure across Europe. My suspicion is that it might even happen a bit faster than with cars. Cars have taken a very long time to ramp up. The vehicles are there and with trucks it's less about emotions. So the car market

is very much driven by emotions. In the past, people maybe didn't want to drive anything else but a Tesla, which somehow suggested sexiness because there are tons of acceleration videos on the internet. It made the topic trendy somehow. With trucks it's simply a business calculation. And if it's better, then freight companies just buy electric trucks. And I think we're there now. So you're saying that the goal of, I think, 30 percent newly sold electric trucks by 2030 is realistic in your view. Yes,

I think we're on a good path in Germany. Good. And the Electrotrucker shows it in his videos, that the CCS charging stations, which were originally more intended for cars, also fit in terms of parking space sizes. Do you see it the same way, that ultimately there's no need or not many MCS chargers are needed? And just for the listeners maybe who don't know: Megawatt Charging means you can charge above 1,000 kilowatts,

compared to CCS with typically 350 to 400 kilowatts. Yes. The advantage of MCS is this high charging peak. But the high charging peak doesn't run from zero to 100 percent SOC. That means the MCS charger has its advantage when you want to charge briefly at a low SOC. If you're standing there anyway for longer, it evens out again. For example, I have an 800-volt vehicle, a Kia. When I charge at a charging station that maybe

only has 150 kilowatts of power, but the vehicle can charge at 240 kilowatts, then the charging power at a fast charger is throttled after 50 percent SOC because the battery simply gets too hot, and then drops down to around 100 kilowatts. But if I'm at a charger that can only do 150 kilowatts, then the battery doesn't get that hot in the initial phase. And the 150 kilowatts continue to be delivered into the high SOC range. That means in terms of actual time, if I'm standing there for a while, the 150

kilowatt charger isn't much slower than the charger running at 300 kilowatts with many vehicles. It's really only an advantage if I'm actually only charging for 10 minutes. And it's probably the same with trucks. You have to observe it a bit. The MCS technology is significantly more expensive than the other technology. You also have a different connector, which is triangular. And you have to see whether that really brings this advantage in operation if truckers only charge very briefly. And I'm

curious. Maybe you'll have the colleague on board at some point. Then I'll listen to his podcast again. Yes, soon we'll have a podcast where we can talk about it in more detail. But I was interested whether you have a strong opinion on that. There are these two use cases that you mentioned: the short charging after 4.5 hours of operation during the break, and then the electric trucks stand overnight

so to speak. And even very slow charging is sufficient for that. And it's funny that you just said "only 150 kW", because five years ago we would have said: "Wow, 150 kW, amazing!" And then the first even higher ones were built, and we thought: "What kind of crazy power levels are these?" Now you say "only 150 kW". So, of course, when you're on the side of electrical engineering and electricity infrastructure, you get to huge topics

to talk about. It's not what we do at Localizer, but it's what we'd do at the RLI then. So the question: What does this mean for the power grids? The RLI, for example, commissioned by the BVG here in Berlin, together with the Öko-Institut, conducted a study on the ramp-up of electric mobility within the trucks operating in Berlin. So: What use cases do they have for charging? Where will this take place? And you end up talking about quite substantial amounts of electricity

that then need to be made available, not just on the kilowatt side, but also on the kilowatt-hour side. Do you happen to have that in mind? Something like over 20,000 electric trucks that we expect in the next five or ten years. I don't want to say anything wrong. And then it's a lot about: Where are they actually located? There are lots of companies that have depot facilities. So we're strongly assuming that depot charging will develop there. Then the next question is: Are maybe

others allowed to charge there too? Because you have to think about it: Many companies park their vehicles on the street. But it's not intended that each of these vehicles can then charge there. So could they perhaps share something? It's also about preparing this depot-sharing early on, which will then be necessary to relieve the strain on public spaces. We're coming to the conclusion. What are topics that still concern you or that you'd like to give an outlook on for the future?

pleased if you already want to give an outlook.

Yes, we've actually already touched on them. We now have really great operational data in there. So we can see the utilisation of existing charging points in terms of quantity, in percentage time utilisation and also in kilowatt-hours. You can simply see how a charging point performs out on the street, and alongside it you can compare it to the forecast. You couldn't imagine a better tool than that. Plus the charging prices, updated daily. That's the current status. And when we look to the future, I still haven't

given up hope that with the integration of grid operators we'll make real progress. Because the original goal – and if you trace it back to Reiner Lemoine – we want to make the energy transition scalable. Renewable energy has to be included. The grid is basically the backbone of the energy transition, and integrating it, that would be my dream. But I think I'll have to be patient for another 12 months. And what's fun beyond that is expanding into other countries.

More customers who also want to go into countries where we're already present, or somehow start a new country again, learn something new again, also about the differences we've mentioned. That kind of thing is fun for me. Cool. Yes, so at the conclusion you have one more sentence, or how people can get in touch with you, what you want to give to the listeners. Getting in touch is very simple. You just go to our website and request a demo. Then you can try

for free for two weeks. You'll have friendly colleagues with us. We take a lot of time for onboarding, calculate a few locations together with you. And then you can simply try out and book the right tool for you.

That's exactly how it works on the Localizer side. And at the RLI it's like this: We always look forward to joint project development or commissioning. So if someone says, I don't need this automatic tool at all, but I want to conduct a project, I want to learn something about the power grids – how are they developing, what are possible scenarios for the energy transition in my operations on site and so on – these are things we can address with our engineering scientific methods. We can write a study for that, we can

build a tool for it, develop software. We're happy to be a point of contact for that at the RLI. You can also find that on the web at rli-institut.de. Then thank you very much for the conversation. I wish you little charging stress and see you soon. Thank you. See you soon. Bye. And that was another episode with Katrin and Oliver from Localizer. I found it particularly interesting how they decided to make this spin-off. That's not a trivial matter at all. And the Reiner Lemoine Institut hasn't made that many other spin-offs. That means they also had to get a lot of internal approval, and these spin-off debates, whether it would be the right thing for such an institution to start a startup – I think the arguments there are definitely on target, in my opinion. And besides, I found it interesting how many CPOs are already using Localizer and that they're almost the only ones on the market who have grown so large, and how much data they're processing – starting with the data from the charging points, but so much more – and what they also

can do with it.

still have a lot on their agenda, especially to optimize the operations of charging point operators, which is absolutely essential for them, and in particular to accelerate grid connections and bring network operators on board – a cool vision that they've had from the beginning and are now consistently pursuing. And something that hasn't been mentioned, by the way, is that they also do a lot of work with hydrogen. So if you want to learn more, please feel free to get in touch with them. And if you enjoyed the episode and think someone else might like it too, please

share it, feel free to comment, and see you soon.

Episode notes

In dieser Folge von Smart Charge sprechen wir mit Prof. Dr. Kathrin Goldammer und Oliver Arnhold, den Co-CEOs von Localiser, einem Berliner Spin-off des Rainer-Lemoine-Instituts. Seit der Gründung 2018 hat sich Localiser zu einer führenden Plattform für datengestützte Standortplanung entwickelt - mit Analysen für mehr als 1,2 Millionen potenzielle Standorte und als erste DIN-konforme Planungssoftware für Ladeinfrastruktur in Deutschland anerkannt. Kathrin und Oliver schildern den Weg von der Forschung in die unternehmerische Praxis - inklusive der Hürden beim Spin-off aus einer gemeinnützigen Forschungseinrichtung und der Aufgabe, Stakeholder vom Mehrwert einer skalierbaren Software zu überzeugen. Wir beleuchten, wie Localiser Kommunen, CPOs, Versorgern und Investoren hilft, Bauchgefühl durch belastbare Daten bei der Planung von Ladeinfrastruktur zu ersetzen. Zentrale Themen: - Wie Localiser die Kosten im Vergleich zu klassischen Gutachten um den Faktor 10 senkte und Standortanalysen massiv beschleunigte - Warum Standardisierung und „bankable“ Methoden für Investoren und große CPOs entscheidend sind - Die Entwicklung der Datenquellen: von soziodemografischen Merkmalen und Verkehrsströmen bis hin zu tagesaktuellen Preisen und Auslastungsdaten - Potenziale in dynamischer Preisgestaltung, Depot-Sharing und der Integration von Netzbetreibern in den Planungsprozess - Learnings aus der frühen Nachfrage nach PDF-Berichten, Anpassungen im Onboarding und den Auslösern für Wachstum Außerdem sprechen wir über den EU Data Act, geografische Unterschiede bei der EV-Adoption und die kommende Welle der E-LKW-Infrastruktur. Kathrin und Oliver skizzieren ihre Vision, Netzbetreiber und offene Daten vollständig einzubinden - und Localiser damit von einem Planungstool zu einem Rückgrat der skalierten Energie- und Mobilitätswende weiterzuentwickeln.