Discover the best charge point simulators with an open source license. 

Aldona Krysiak

Let’s be honest, building and testing backend software for EV charging isn’t exactly plug-and-play. You’ve got OCPP versions to juggle, APIs to wrangle, and integration tests that never quite run the same twice.

, a sanity-saving tool that simulates a physical charger so you can confidently get back to building, testing, and shipping.

In this guide, we’ll explain a VCP, compare the top open-source options, and show you why Solidstudio’s 

 is built for folks who care about clean, scalable, test-ready charging infrastructure.

Think of a flight simulator, but for EV chargers. No hardware needed, no cables to trip over. Just pure, emulated OCPP goodness.

With an OCPP simulator from Solidstudio (Virtual Charge Point), you can:

Emulate real-world scenarios like authorization failures, power fluctuations, or surprise disconnects

Run full QA pipelines without waiting on a test station to reboot

Test CSMS scalability before production hits you like a 350kW lightning bolt

During backend development (especially CSMS)

When you want to stress test or break stuff, on purpose

The charge point simulators comparison: Who’s got what?

Let’s break it down. Here’s how Solidstudio’s Virtual Charge Point stacks up against some of the better-known open-source alternatives:

TL;DR? If you’re after fast, flexible, code-driven testing for commercial EV infrastructure, you should look closer at Solidstudio’s VCP.

We built our VCP with one goal: to make life easier for developers, testers, and engineers working with OCPP backends. There is no bloat, no distractions, just a smart, scriptable tool that lets you focus on what matters, validating your charging software.

 Reproduce edge cases, simulate outages, tweak timing

 Spin up dozens (or hundreds) of virtual chargers with ease

 Integrates with your CI/CD flow like a charm

 verifying OCPI integration and roaming logic

 looking to automate the boring bits and focus on what breaks

🐈‍⬛Explore Virtual Charge Point on 

 or researching bidirectional charging (V2G). But what if you focus on backend scale testing? Might be more than you need (and less than you want).

 folks, this one's modular, MQTT-based, and perfect for station-level dev. Just know: it’s heavier to configure and slower to set up for quick test loops.

OpenEVSE is firmware, not a simulator. Super popular in the maker scene. Not helpful if you’re writing backend code or debugging CSMS behavior.

Let’s keep it simple, if you’re testing an OCPP backend, Solidstudio’s Virtual Charge Point is a no-brainer. Josev is great for ISO 15118 and V2G research, EVerest shines if you’re deep into embedded stack dev, and OpenEVSE is perfect if you're the hands-on hardware type.

If you’re serious about building reliable, scalable backend tools in the eMobility space, 

you need a VCP that keeps up with your pace and precision

. One that doesn’t slow you down or force you to hack around limitations.

Solidstudio’s Virtual Charge Point is built for exactly that. It’s fast to spin up, endlessly scriptable, and rock-solid in real-world test environments. Plug it into your workflow, and see how much smoother everything runs.

Discover the best charge point simulators with an open source license. Learn how Solidstudio’s virtual charge point helps you test, scale, and automate EV backend systems.

4 Best Open Source OCPP Charge Point Simulators

Test OCPP-based EV chargers virtually with Solidstudio’s open-source simulator.

Krzysztof is our Head of Products and he leads the technical development behind the suite of our ready-made solutions. Throughout the years in Solidstudio, he gained an invaluable insight into the eMobilty industry’s requirements and is now more than qualified to share his knowledge with others.

Krzysztof Ciombor

Testing OCPP-based EV infrastructure shouldn’t require real hardware.

 Solidstudio’s Virtual Charge Point (VCP) is a powerful, open-source charge point simulator built specifically for backend developers and QA engineers working on CSMS platforms. It enables precise simulation of OCPP 1.6, 2.0.1, and now 2.1 chargers.

Virtual charge point is ideal for integration testing, protocol validation, and CI/CD workflows.

Whether developing a CSMS, preparing for OCPP certification, or automating QA pipelines, this article walks you through what the Virtual Charge Point offers, how it works, and what’s new in the latest release.

What is Solidstudio’s Virtual Charge Point?

 is an open-source software tool that emulates electric vehicle (EV) charging stations using the 

 that connects to your backend system (CSMS), enabling comprehensive tests of your software’s communication, logic, and fault tolerance in controlled environments.

Unlike basic emulators, Solidstudio’s virtual charge point is designed for 

, reproducing realistic charge point lifecycles including:

 in the electric vehicle infrastructure domain.

Solidstudio’s Virtual Charge Point was recently updated, bringing major improvements aimed at developers working with the latest OCPP standards and certification scenarios.

Essential for testing OCA certification scenarios, this update helps teams validate compliance for production-ready deployments.

Be among the first to simulate charge points using 

, the latest protocol announced by the Open Charge Alliance.

While the interface and usage remain stable, under-the-hood improvements make the simulator more robust. The previous version is still available 

Solidstudio’s Virtual Charge Point is a reliable, actively maintained tool built to support the latest OCPP standards, including 2.1. This makes it a practical long-term solution for EV software teams focused on real-world testing and certification.

Why developers love Solidstudio’s Charge Point Simulator

Simulate everything from standard meter readings to edge-case scenarios like unstable connections or failed authorizations.

 in parallel, script custom behaviors, and integrate seamlessly with CI/CD pipelines.

, including security extensions, ensuring you can test legacy and next-gen setups.

Launch simulations via CLI or Dockerized environments, with straightforward configuration that fits into any workflow.

Solidstudio's charge point simulator, hosted on GitHub under a permissive MIT license, is transparent, adaptable, and actively maintained by a team of EV software specialists.

Who should use Solidstudio’s Virtual Charge Point?

 validating the scalability and fault tolerance of CSMS platforms

 automating regression and edge-case testing

 simulating OCPI roaming sessions with virtual EVSEs

 working with modern OCPP protocols (1.6 to 2.1)

Load test your backend with dozens of virtual charge points

Validate authorization flows under secure TLS connections

Simulate production-like charging lifecycles before rollout

Reproduce rare edge cases for regression debugging

Ensure compatibility with OCA certification criteria

Test your OCPP-based infrastructure safely

If you’re building or testing OCPP-based infrastructure, don’t waste time waiting on hardware or hand-written test scripts. Solidstudio’s 

 gives you a complete, scalable, and up-to-date 

 environment, purpose-built for modern development teams in the EV space.

Learn how to simulate OCPP 1.6–2.1 EV chargers with Solidstudio’s free, open-source Virtual Charge Point, ideal for testing CSMS platforms without hardware.

How To Simulate EV Chargers With a Virtual Charge Point

An introduction to our series uncovering some of the use-cases of AI technology in eMobility.

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 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 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). 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

As electric vehicles become mainstream and the supporting infrastructure grows in complexity, Artificial Intelligence may no longer be just a “nice-to-have” innovation – it's slowly but steadily becoming foundational to scaling, optimizing, and securing the entire eMobility ecosystem.

From intelligent energy management to fraud detection in charging networks, AI is being woven into the very fabric of how we move, charge, and operate in a cleaner transportation future. Let’s explore how AI continues to unlock new possibilities in eMobility.

Smart Battery Management and Health Forecasting

Battery health is at the heart of EV performance and user trust. AI-powered Battery Management Systems (BMS) are shifting from reactive to predictive. Instead of simply monitoring current performance, AI analyzes vast datasets – temperature trends, charge/discharge cycles, driving behavior, and even external conditions – to forecast degradation and adapt usage accordingly.

This means longer battery lifespans, fewer unexpected breakdowns, and more accurate range predictions – key factors in improving customer satisfaction and reducing warranty-related costs for manufacturers.

One of the most impactful applications to emerge from Artificial Intelligence is predictive maintenance. By analyzing charging station logs, energy consumption patterns, and environmental inputs (like temperature or grid status), AI can detect subtle deviations from normal operation.

 they become failures – reducing downtime, maintenance costs, and improving the overall reliability of EV charging infrastructure.

Charging Session Monitoring & Fraud Prevention

In an era where digital fraud is evolving rapidly, AI plays a crucial role in safeguarding charging networks. Advanced anomaly detection models can:

Spot cloned RFID tokens used across stations in real-time

Identify software tampering or unauthorized firmware changes

Detect irregularities in payment patterns or charging behavior

Such capabilities are vital for maintaining user trust, reducing revenue leakage, and ensuring operational integrity.

Infrastructure Planning & Grid Load Balancing

AI excels at pattern recognition and prediction – which is incredibly useful for planning charging infrastructure deployment. By analyzing historical charging behavior, traffic flows, and energy grid data, AI systems can recommend where to place new charging points and predict future load peaks.

In the long term, this contributes to more balanced energy consumption, fewer grid overloads, and a better overall user experience.

Real-Time Route Optimization and Energy Efficiency

AI-driven route planning tools can do more than just avoid traffic – they now consider terrain, current battery state, temperature, and even the availability of fast chargers en route. The result? Efficient paths that reduce range anxiety and optimize energy use, especially for fleet operators looking to cut costs and emissions.

Future Outlook: AI as the Nervous System of eMobility

As AI becomes more deeply embedded in every layer of eMobility – from vehicle systems to backend charging operations – it's transforming from a tool into a decision-making layer. A few additional AI use cases already being piloted or envisioned include:

: Predicting electricity prices and charging behavior to offer cost-efficient, real-time pricing models.

: Managing two-way communication between EVs and the grid for optimized energy flows.

: From voice assistants to route suggestions based on daily habits and calendar events.

Artificial Intelligence in the eMobility digital ecosystem

Watch our exclusive webcast uncovering the details of our biggest CPMS migration project yet.

After founding Solidstudio, Pawel was an originator in terms of pursuing expertise in eMobility. With the build-up of a solid knowledge-base on industry’s needs & wants, he now keeps a close eye on products’ development. He’s also hugely responsible for the business’ side and designs the business models for our products.

Paweł Małkowiak

We’re excited to announce that our insightful webcast uncovering the details of an extremely successful migration project is now available for everyone!

For this special talk, our team traveled to London to visit the headquarters of 

, one of the UK’s leading EV charging providers. We sat down with key members of their team to talk about one of the most important—and technically complex—steps a company can take: 

migrating an entire charging network to a new software backend

All three played essential roles in the migration project from Connected Kerb’s side, and in the webcast they share their firsthand insights into what it really takes to execute a smooth, secure, and future-proof transition:

How Connected Kerb approached the process from technical, strategic, and operational angles

Lessons learned: what worked, what they’d do differently, and what surprised them

The impact of digital flexibility and strong collaboration on long-term success

This was an advanced migration project to our CPO Platform, supported by custom development and long-term maintenance. In total, 

 were successfully migrated—without disruption to users or services. One of the most technically demanding parts of the project was integrating Connected Kerb’s existing eMSP system, including their mobile app user base and already connected OCPI partners.

To align with Connected Kerb’s preferred tech stack, we also migrated the entire backend from 

, conducted comprehensive compatibility testing with OCPI partners, and ensured full OCPP compliance with the specific charging station models in the field.

Behind the Scenes of Connected Kerb’s 9000 Charge Point Migration

Collection of practical use cases of our OCPP Proxy - see if it can benefit your business.

When people think of the digital backbone of EV charging, they usually picture the major players—Charge Point Management Systems and eMobility Service Provider apps. And rightfully so: these core products are essential for handling everything from user authentication to session monitoring and billing.

But beneath this visible layer lies a quieter, more flexible class of tools that make the entire system 

 – especially as the EV charging industry moves from early adoption to large-scale infrastructure. These tools don’t usually make it to product brochures or boardroom demos, but they are 

 of operational success. They ensure that your infrastructure can 

. From behind the scenes, they empower operators to experiment, test, and optimize without putting the core business at risk. Among them is the 

 by Solidstudio: a powerful, standalone component that brings control, observability, and configurability to OCPP communication without disrupting your current CPMS stack.

Here’s why it might just be the missing link in your EV charging infrastructure—and how it helps in situations you may not have even considered yet.

Seamless CPMS Migration: Minimize Risk, Maximize Continuity

Whether you're upgrading to a new CPMS or switching providers, migrating live systems is always risky. The OCPP Proxy acts as a 

 between your Charging Stations and CPMS platforms, allowing you to:

Gradually switch over systems (“incremental switch”)

Execute a full switchover (“big-bang” migration)

 to the previous setup if anything goes wrong

This is particularly valuable for Public CPOs and large-scale operators where 

 is critical. With Solidstudio’s proven migration experience (e.g., ConnectedKerb, Ubitricity), the proxy becomes more than a tool—it’s a safety net.

Enhanced Observability: See What Your CPMS Doesn’t

Most CPMS platforms are black boxes when it comes to real-time traffic. The OCPP Proxy changes that by 

 to external observability systems—without interfering with live operations.

Feed logs into advanced analytics systems

, significantly reducing downtime and enabling better SLA compliance.

Remote Command Execution: Total Control Without CPMS Limitations

Sometimes, your CPMS doesn’t support all the OCPP commands you need. The OCPP Proxy lets you bypass that limitation.

Using the Proxy’s API, you can directly:

Even if your existing backend lacks these features, the proxy opens up 

 that would otherwise require costly development or system changes.

Environment Switching: Simplify Testing & Staging

Managing multiple environments—production, staging, testing—usually requires 

 for each context. It’s time-consuming and error-prone.

dynamically route traffic to different CPMS environments

 without touching the Charging Station config. Perfect for dev teams who want quick environment switches or for companies testing new CPMS solutions.

Troubleshooting Made Easier: Real Logs, Real Time

 that can help isolate charging issues at the network, station, or protocol level. When a session fails or misbehaves, you no longer need to guess what went wrong—you can see it.

And with logs exportable to your existing observability stack, the troubleshooting flow becomes faster, more intuitive, and more effective.

Flexible Transport Layer: Go Beyond Websockets

Not every system is built to manage persistent WebSocket connections. If your architecture prefers 

, routing WebSocket traffic into your preferred transport layer.

This opens up compatibility with legacy systems, third-party middleware, or microservices that weren’t originally built for OCPP, giving you 

The EV charging industry is moving fast—but moving smart is even more important. The

 isn’t just a technical component; it’s a 

 that helps reduce risk, improve observability, and give you real-time control over your charging network.

If your setup involves CPMS transitions, custom integrations, or simply the need for better monitoring and control, this is the tool you didn’t know you needed.

Beyond CPMS - Why the OCPP Proxy Deserves Your Attention

Explore how software-defined vehicles are transforming the automotive market.

Experienced content writer with over 6 years of experience in the energy sector and many industry-related publications released.

Gabriela Stawiarska

Throughout history, there seem to be two types of leaps in technology that captivate us most. The ones that are completely otherworldly and the ones that redefine everyday objects we once took for granted. Anything in the usability-middle oftentimes goes unnoticed. Cars, as a cornerstone of modern life, are no exception to the first group.

Think of it this way: remember when cell phones were basically just fancy walkie-talkies, and then, almost overnight, they turned into pocket-sized supercomputers? That same seismic shift is now coming to the car industry. Software-defined vehicles - cars driven as much by code as by horsepower - are rewriting the rules of the road. With constant over-the-air updates to customizable driving experiences, the traditional metal-and-engine car is under a threat of being replaced by a machine that’s just as much a tech platform as it is a transportation tool. This, however, isn’t probably much of an ‘overnight’ process like it was with smartphones. Chances are, it’ll take a bit longer.

A software-defined vehicle (SDV) represents a significant departure from traditional car designs, where the core functions and features were largely determined by physical hardware. In an SDV, software plays a central role in governing nearly all aspects of the vehicle’s performance, including safety, efficiency, entertainment, and driving dynamics. Software controls everything from basic functions such as braking and steering to advanced systems like autonomous driving, sensor fusion, infotainment interfaces, and driver assistance technologies. This shift is enabled by the increasing complexity of the vehicle’s electronic architecture, which today may include hundreds of electronic control units (ECUs) and millions of lines of code working in unison. The underlying components that make SDVs feasible include three key pillars: software, hardware, and connectivity. Software serves as the brain, providing the logic and decision-making processes that drive vehicle functionality. For the software to operate effectively, it needs to be supported by an appropriate hardware architecture. High-performance computing systems—often built on centralized or zonal computing platforms—are now replacing the numerous independent ECUs found in traditional vehicles to reduce complexity and increase efficiency. These integrations allow for smoother communication between systems, elevated processing power, and better coordination among sensors, cameras, and other electronics. Connectivity is the final piece of the puzzle, enabling SDVs to continuously evolve and adapt over time. As vehicles become part of the larger Internet of Things (IoT), they can exchange data with other vehicles, road infrastructure, and cloud platforms. This constant flow of information empowers features like real-time traffic updates, predictive maintenance, and enhanced energy management. Over-the-air (OTA) updates enable automakers to push new features, improve existing capabilities, and ensure cybersecurity—all without requiring a visit to the dealership. This agile development process means SDVs remain state-of-the-art and responsive to shifting consumer expectations, regulatory policies, and advancements in technology throughout the life of the vehicle.

A software-defined vehicle] is best understood as a dynamic digital platform on wheels, where functionality is largely determined by software rather than static mechanical components. While legacy vehicles rely on individual electronic control units (ECUs) to manage isolated functions such as engine performance or infotainment, SDVs consolidate much of this intelligence into high-performance computing platforms. In this model, software handles key tasks like coordinating sensor data, executing driver-assistance algorithms, and managing connectivity features - often in real time. To accommodate these capabilities, modern vehicles may contain a network of cameras, radar, lidar, and other sensors that feed data into powerful processors capable of supporting machine learning, advanced analytics, and split-second decision-making.

At the core of an SDV’s architecture is a service-oriented framework that decouples hardware from software. This approach allows manufacturers to design a unified operating system, standardized middleware, and robust cybersecurity controls, independent of specific hardware constraints. As a result, vehicle functionalities - from automated driving routines to infotainment interfaces - can be updated or reconfigured through over-the-air (OTA) software patches. These updates, delivered via secure connectivity channels, ensure that the car’s digital ecosystem remains agile, adapting to new technologies and user preferences long after the original purchase. In practical terms, drivers benefit from both immediate improvements - like bug fixes or fresh infotainment apps - and ongoing enhancements, such as refined autonomous driving algorithms and additional safety features.

Shifting from distributed ECUs to more centralized or zonal architectures also reduces system complexity. In a centralized setup, one or a few powerful controllers manage multiple domains, improving efficiency and minimizing duplication of functions across different ECUs. This design streamlines communication pathways, making it easier to handle data-intensive processes and integrate advanced capabilities. Additionally, the hardware infrastructure is specifically engineered to support high-bandwidth data transfer, robust storage, and fast processing speeds—attributes that are increasingly critical for autonomous driving and connected services.

Ultimately, the software-defined approach aligns well with emerging trends in electrification, automation, and connectivity. Electric vehicles, for example, rely heavily on software to optimize battery usage and motor control, while automated driving systems demand ever-increasing computational power to interpret and act on sensor feedback. Connectivity, meanwhile, transforms each vehicle into a node within the Internet of Things, capable of communicating with other cars, infrastructure, and cloud services. By treating the vehicle as an upgradable digital platform, manufacturers can respond more swiftly to evolving market demands and regulatory landscapes, offering consumers vehicles that continually improve in both functionality and user experience.

According to research from MarketsandMarkets.com, the global market for software-defined vehicles (SDVs) is poised for remarkable growth - rising from an estimated USD 213.5 billion in 2024 to USD 1,237.6 billion by 2030, at a robust CAGR of 34.0%. A number of factors are driving this expansion, including the growing need to enhance the driving experience through advanced software features, reduced recall and manufacturing costs, and increasingly personalized client engagement. Additionally, the rapid adoption of 5G technology and the integration of ADAS (Advanced Driver Assistance Systems) and artificial intelligence (AI) into digital cockpits are creating new opportunities for SDV innovation. These trends are paving the way for advanced FOD (Functions on Demand) and other value-added services, which, in turn, offer fresh revenue streams for OEMs.

Leading innovators such as Tesla (US), Li Auto Inc. (China), NIO (China), Rivian (US), and XPENG Inc. (China) are redefining the competitive landscape in the Software Defined Vehicle market through strategic expansions, partnerships, and product breakthroughs. Tesla has long been at the forefront of software-centric automotive design, offering cutting-edge over-the-air updates and autonomous driving features. Li Auto Inc. and NIO, meanwhile, are gaining global recognition for pioneering electric and intelligent vehicle platforms - ranging from battery-swapping technology to AI-driven infotainment systems. Rivian, with its focus on electric adventure vehicles, has forged partnerships to scale production and push the boundaries of sustainable mobility. XPENG is building its market share on the strength of advanced driver-assistance systems, agile product development, and a growing international footprint. Collectively, these players exemplify how software-driven innovation can reshape not only the driving experience but also the entire automotive value chain, from manufacturing processes to long-term customer engagement.

Software-Defined Vehicles: Redefining the Road Through Code and Connectivity

A look into the new OCPI 3.0: key updates, new features, and benefits for EV charging

The Open Charge Point Interface (OCPI) is a leading protocol enabling scalable and automated electric vehicle roaming. Managed by the EVRoaming Foundation, OCPI fosters seamless interactions between Charge Point Operators and eMobility Service Providers. It empowers EV drivers with effortless access to charging infrastructure across various networks worldwide, while reducing the complexity of traditional roaming solutions like centralized hubs.

OCPI facilitates real-time data exchange about EV charging locations, availability, and tariffs.

By providing a standardized framework, OCPI simplifies roaming arrangements, enabling EV drivers to access any connected charge point without pre-registration. This improves transparency, enhances market development, and ensures operators can execute their business models effectively.

OCPI was first conceptualized in 2012 by ElaadNL, a Dutch innovation hub specializing in Smart Charging and charging infrastructure. Collaborating with prominent grid operators in the Netherlands, ElaadNL sought to streamline communication between EV charging operators and service providers by introducing a standardized protocol. This initiative aimed to simplify connections across various charging networks, fostering greater interoperability and ease of use for the growing EV ecosystem.

Initially overseen by the Netherlands Knowledge Platform for Public Charging Infrastructure (NKL), OCPI’s governance transitioned to the EVRoaming Foundation in May 2020. The first official version, OCPI 2.0, was launched in 2015, laying the groundwork for what has become a globally embraced standard. Since then, the protocol has seen continuous refinement, with version 2.2.1 currently in widespread use worldwide. This global footprint spans Europe, the Americas, Asia, and Oceania, supporting seamless EV roaming and data exchange among diverse stakeholders​​.

The upcoming OCPI 3.0 addresses limitations in earlier versions and introduces enhancements. Notable features include support for Plug & Charge and Autocharge, robust communication between CPOs and drivers, and strengthened security measures. By adopting industry-standard frameworks and simplifying interoperability, OCPI 3.0 aligns with emerging trends, such as autonomous vehicle charging and GDPR-compliant data sharing.

With OCPI, the EV ecosystem takes a significant step toward a more interconnected and efficient future, driving the global transition to sustainable mobility​​.

Enhanced Communication Between CPOs, eMSPs, and Drivers

OCPI 3.0 resolves previous communication gaps between Charge Point Operators (CPOs) and e-Mobility Service Providers (eMSPs). Earlier versions did not allow direct messaging to inform EV drivers about issues such as billing or charging disruptions. In OCPI 3.0:

CPOs can send real-time push notifications to EV drivers through the eMSP's communication channels, such as mobile apps.

eMSPs can display essential updates like pricing changes or error messages directly on the charge point screen, significantly improving transparency and reducing user frustration.

To prevent charging sessions from exceeding reserved credit limits, OCPI 3.0 introduces better control mechanisms. While earlier versions relied on frequent monitoring and manual intervention, this version provides automated functionality to ensure sessions remain within the predefined credit scope, enhancing reliability for both providers and users.

OCPI 3.0 enhances security by advocating the use of industry-standard protocols, ensuring data integrity during transmission. The focus on securing direct and hub-based connections minimizes risks of unauthorized data alteration, ensuring higher trust among roaming partners.

OCPI 3.0 introduces support for Plug & Charge and Autocharge, enabling vehicle authentication directly through the charging cable connection. This eliminates the need for RFID cards or mobile apps, simplifying the process for EV drivers. This functionality is particularly valuable for:

Autonomous vehicle charging scenarios, where no human interaction is available.

Enhanced convenience, as drivers can start charging by simply connecting their vehicle to the station.

Let’s read our posts

4 Best Open Source OCPP Charge Point Simulators

Recommended

eMobility Fundamentals - the eBook

Why does software matter in eMobility?

The Ultimate Guide to EV Charging Software Migration - the ebook

How To Simulate EV Chargers With a Virtual Charge Point

Artificial Intelligence in the eMobility digital ecosystem

Behind the Scenes of Connected Kerb’s 9000 Charge Point Migration

Beyond CPMS - Why the OCPP Proxy Deserves Your Attention

Software-Defined Vehicles: Redefining the Road Through Code and Connectivity

OCPI 3.0: key updates in the newest version