Monitoring EV Charging at the Grid Edge

I've just published a deep dive into what actually happens when #ISO15118-2 and #OCPP 2.0.1 work together during a #smart #charging session. And some of it might surprise you. For example: did you know that while State of Charge (#SOC) is mandatory, the EV's energy capacity and energy need are optional fields? That means a CPO's backend might know your battery is at 40% — but have no idea whether that's 40% of 60 kWh or 40% of 100 kWh. Good luck optimising that. Here's what the article covers: 👉  The complete #message #flow from #EV to #charger to #CPMS backend and back: every key message explained 👉  The #data gap problem: what EVs share, what they may not, and why it matters for #CPOs 👉  How charging schedules actually get built: layered #profiles, composite #schedules, and the "minimum of all limits" rule 👉  Mid-session #renegotiation: because grid conditions change and drivers change their minds 👉The commercial angle: #fleet optimisation, #demand #response, and the #V2G opportunity If you're building, integrating, or operating #EV #charging infrastructure, these are the insights that separate "it works" from "it works intelligently." Link in the comments. Have you tried implementing ISO 15118-based smart charging? I'd love to hear what worked, what didn't, and what caught you off guard.

⚡ 𝗙𝗿𝗼𝗺 𝗦𝗺𝗮𝗿𝘁 𝗠𝗲𝘁𝗲𝗿𝘀 𝘁𝗼 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲, 𝘁𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲-𝗮𝘄𝗮𝗿𝗲 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆—𝗯𝗲𝗳𝗼𝗿𝗲 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝗵𝗮𝗽𝗽𝗲𝗻. Transformers have 𝗺𝘂𝗹𝘁𝗶-𝘆𝗲𝗮𝗿 𝗹𝗲𝗮𝗱 𝘁𝗶𝗺𝗲𝘀 and are among the 𝗰𝗼𝘀𝘁𝗹𝗶𝗲𝘀𝘁 grid assets. Waiting for a unit to run hot and fail isn’t strategy—it’s 𝗮𝘃𝗼𝗶𝗱𝗮𝗯𝗹𝗲 𝗿𝗶𝘀𝗸. 🔧 𝗧𝗵𝗲 𝗺𝗼𝘃𝗲: Use AMI data + ambient temperature to build a 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗹𝗼𝗮𝗱 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗺𝗼𝗱𝗲𝗹 that shows 𝘂𝘁𝗶𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗱𝘂𝗿𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘁𝗵𝗲𝗿𝗺𝗮𝗹 𝗿𝗶𝘀𝗸 𝗶𝗻 (𝗻𝗲𝗮𝗿) 𝗿𝗲𝗮𝗹 𝘁𝗶𝗺𝗲. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 (𝘀𝗶𝗺𝗽𝗹𝗲 𝘃𝗲𝗿𝘀𝗶𝗼𝗻): 📡 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲: Sum per-meter load to each service transformer (phase-aware). 🌡️ 𝗔𝗱𝗷𝘂𝘀𝘁: Apply a 𝘁𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲-𝗮𝘄𝗮𝗿𝗲 𝗿𝗮𝘁𝗶𝗻𝗴, not just nameplate. 🧮 𝗦𝗰𝗼𝗿𝗲: Track 𝗺𝗮𝗿𝗴𝗶𝗻, 𝗼𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗺𝗶𝗻𝘂𝘁𝗲𝘀, and a thermal 𝗿𝗶𝘀𝗸 𝘀𝗰𝗼𝗿𝗲. 🚨 𝗔𝗰𝘁: Trigger alerts + playbooks (phase balancing, mobile units, targeted upsizing). 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀: • 𝗙𝗲𝘄𝗲𝗿 𝗲𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝘁𝗿𝘂𝗰𝗸 𝗿𝗼𝗹𝗹𝘀 and unplanned outages. • 𝗧𝗮𝗿𝗴𝗲𝘁𝗲𝗱 𝗰𝗮𝗽𝗲𝘅—replace the few units in true thermal distress, 𝗱𝗲𝗳𝗲𝗿 𝘁𝗵𝗲 𝗿𝗲𝘀𝘁. • 𝗘𝗩/𝗣𝗩 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀—spot clustering early, plan upgrades where it matters. • 𝗖𝗹𝗲𝗮𝗿 𝗰𝗼𝗺𝗺𝘀—street-level messaging during heat events. 𝗣𝗶𝗹𝗼𝘁 𝗶𝗻 𝟵𝟬 𝗱𝗮𝘆𝘀 (𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸): 🧭 Pick 𝟮 𝗳𝗲𝗲𝗱𝗲𝗿𝘀 / 𝟯𝟬𝟬–𝟱𝟬𝟬 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 with EV/PV growth 🔗 Validate 𝗺𝗲𝘁𝗲𝗿 ↔ 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 mapping and per-phase balance 🧠 Stand up 𝘁𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲-𝗮𝘄𝗮𝗿𝗲 𝗿𝗮𝘁𝗶𝗻𝗴𝘀 + 𝗿𝗶𝘀𝗸 𝘁𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱𝘀 📊 Run through a peak season; field-check the 𝘁𝗼𝗽 𝟮𝟬 alerts 🎯 Roll out if you see 𝗳𝗲𝘄𝗲𝗿 𝗲𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝗶𝗲𝘀 and 𝗯𝗲𝘁𝘁𝗲𝗿 𝘁𝗮𝗿𝗴𝗲𝘁𝗶𝗻𝗴 of replacements 𝗪𝗵𝘆 𝗻𝗼𝘄: You already have 𝗔𝗠𝗜, 𝘄𝗲𝗮𝘁𝗵𝗲𝗿, 𝗚𝗜𝗦, and ops know-how. This turns data into 𝗽𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻—not just post-mortems. ❓ 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: If you could see transformer risk 𝗮𝘀 𝗶𝘁 𝗳𝗼𝗿𝗺𝘀, what decision would you make 𝘁𝗼𝗱𝗮𝘆 that you usually make 𝗮𝗳𝘁𝗲𝗿 a failure? #SmartGrid #GridModernization #Transformer #UtilityAnalytics #AMI #DistributionGrid #Reliability #DER #EVCharging #Operations #DataEngineering #PowerSystems

Monta's new technical white paper on Load Management, got my inner electrical engineer buzzing! 🤓 We model the entire site as a tree of Load Balancing Groups, each node defined by a per-phase current vector I = (I_L1, I_L2, I_L3). This is a proper graph-based abstraction of the electrical topology. What’s impressive is the dynamic current allocation engine. This isn’t naive load sharing. It’s real-time, phase-aware rebalancing with priority ranking, driven by live MeterValues. When an EV draws less than allocated, the system reclaims and redistributes the excess—amp by amp. And yes, the 6A floor is baked in to maintain IEC 61851-1 compliance and avoid charging session failures on low-capacity branches. It supports AC, DC, and mixed environments with a unified logic layer. Whether it’s single-phase chargers or beefy three-phase DC stations, the system adapts allocation dynamically based on hardware capability, site constraints, and configured priorities. It already integrates with some external meters, but there is a lot more to come. During Q3, we will open APIs and MQTT streams, adding many more options. We will also combine smart charging and load balancing on large sites 🤯 This is the kind of system design that shifts the ROI equation—from upgrading infrastructure to orchestrating it smarter. Seriously worth a read if you’re into grid-constrained EV charging, real-time control systems, or the future of distributed energy logic. Link in comments

How to manage EV Charging Stations with an IoT Solution Electric Vehicles are becoming increasingly popular, driving up the demand for reliable and efficient EV charging infrastructure As the EV population grows, effective management of charging stations becomes crucial We recently worked with an EV Charging company on a EV Charging Solution using our Industrial IOT Gateways Some of the things that they were looking to cover where: 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐑𝐞𝐦𝐨𝐭𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: By integrating IoT technology, Charging stations can be equipped with sensors and connectivity for real-time monitoring of charging activities The Operator was aiming to remotely manage and monitor each station's status, ensuring optimal performance, identifying faults, and minimizing downtime 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: IoT enables predictive maintenance for EV charging stations by collecting and analyzing data on equipment performance This predicts potential issues before they escalate, minimizing the risk of unexpected downtime, reducing maintenance costs, and extending the lifespan of charging infrastructure 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐑𝐞𝐧𝐞𝐰𝐚𝐛𝐥𝐞 𝐄𝐧𝐞𝐫𝐠𝐲 𝐒𝐨𝐮𝐫𝐜𝐞𝐬: IoT seamlessly integrates with renewable energy sources like solar or wind power Charging stations can intelligently harness clean energy when available, promoting sustainability and reducing the environmental impact of EV charging, aligning with the emphasis on eco-friendly transportation solutions 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞: The data generated by IoT-enabled EV charging stations serves as a valuable resource for business intelligence The Operator wanted to get insights into usage patterns, peak charging times, and popular locations This enables informed decision-making, strategic planning, and optimization of charging infrastructure deployment The IOT-GATE-iMX8 - Industrial IoT Gateway that Anders provided ticked all of the boxes and offered the Connectivity and reliability needed to make sure that the project was a real success Want to find out how we can help on your next EV Charging Project? Find out more in the comments

⚡️ Behind the Charge: Charge Smarter not Harder! In my previous post, we explored the basics of charging EVs using OCPP and RFID/Autocharge. Today, we delve into the sophisticated world of Dynamic Load Balancing — a key feature of our CPMS that optimizes the distribution of power across multiple charge points while considering the overall consumption of the connected grid. 💡 Understanding Load Balancing Load balancing in EV charging can be categorized into two main types: Load Balancing and Dynamic Load Balancing. Both play pivotal roles in managing energy distribution efficiently but operate under slightly different principles: 😎 Load Balancing: This involves evenly distributing electrical load across multiple Charge Points in a network. It adjusts dynamically based on how many Charge Points are in use, ensuring an efficient and fair distribution of power. 🦸 Dynamic Load Balancing: Here we take load balancing a step further by adjusting the power allocated to each Charge Point based on real-time energy consumption from a building or other consumers sharing the same grid connection. This method requires an external meter to gauge the building’s consumption, offering a highly responsive and adaptive charging solution. How does it work at Monta? 📉 Monitoring Load: Through an external meter connected directly to our OCPP service or an API connected to our Charge Point Integration Service, we monitor the real-time load. 🤓 Calculating Charge Profiles: Based on this data, our system calculates and applies optimized charge profiles for each connected Charge Point, ensuring efficient power distribution. 🤯 Beyond the Basics: While the process may seem straightforward, in practice, it involves complex calculations. We consider customer preferences such as smart charging based on spot prices, CO2 emissions, availability of renewable energy, and even grid stability. 🚦 Prioritizing Needs: Preferences might also include prioritization based on vehicle use within a fleet. For example, a delivery van needing to leave soon could be charged as a priority. 🔄 Constant Updates: Charge profiles are continually updated with incoming data from the meter, fleet requirements, grid conditions, and spot prices, among others. Dynamic Load Balancing is more than just a feature of our CPMS; it's a testament to our commitment to sustainability and efficiency. By adjusting power distribution dynamically, we not only ensure that EVs are charged according to needs and preferences but also contribute to the stability of the grid and optimize the use of renewable energy resources. Stay tuned for more insights into the technologies and innovations driving the future of EV charging. ➡️ Want to join us to build complex solutions that feel feather light? Check out our Engineering opportunities in Copenhagen, Barcelona, or Berlin: https://lnkd.in/eA47DGD2 #Engineering #EVBetter #OCPP #EVCharging #LoadBalancing

🚗⚡ How do we charge thousands of EVs without overloading the grid or spending millions on upgrades 💸? Our latest work shows how! We developed a simple, scalable control method that manages EV charging in real time, without needing detailed network models or live smart meter data. 🧠📊 Tested on a realistic Aussie network with 3,300+ homes, it keeps voltages stable and assets safe -even with high EV and solar uptake- matching the performance of complex optimisation tools 💪🚀. https://lnkd.in/gGcfBe5B Jing Zhu Arthur Gonçalves Givisiez, PhD Michael Liu William Nacmanson #powerdistribution #electricvehicles #EVs #der #orchestration #smartcharging Melbourne Energy Institute Electrical and Electronic Engineering University of Melbourne

We didn’t start WeaveGrid to build just another EV software or load management company. ⚡ 📉 From day one, we focused on a specific challenge: how to help utilities manage the strain EVs uniquely place on the distribution grid. 📈 It was clear even in the early days that EVs would create a new kind of load—one that doesn’t follow predictable patterns, arrives faster than planners expect, and can silently overwhelm local infrastructure. Transformers weren’t designed for this. Traditional planning tools weren’t either. That’s why we built software like DISCO—to help utilities:  Identify EV-driven overload risk early • Anticipate where clustered EVs create risk • Align charging with local grid capacity • Buy time to make smarter, more targeted infrastructure investments We’re not replacing infrastructure. We’re helping utilities invest in it more strategically—and giving them the digital tools to adapt as EV adoption scales. We break this down in our latest Utility Dive op-ed. If you’re thinking about rate pressures, capital planning, or grid modernization filings, I’d encourage you to take a look. 📖 Link in the comments.