Frequently asked questions

Chargalytics is a market intelligence platform for the EV charging industry. We collect and process realtime data from charging networks across 29 countries, covering 524,000+ charging sites and 1.58 million charge points.

We turn that data into analytics, benchmarks, forecasts, and tools that help operators, investors, consultants, and anyone building charging infrastructure make better decisions.

Owners & Investors use it to track M&A activity, evaluate operator performance, model returns on new sites, and understand market dynamics before committing capital.

Charge Point Operators use it to benchmark against competitors, monitor network health, plan new locations, and manage the full project lifecycle from site selection to commissioning.

Analysts use it for country-level market analytics, EV adoption tracking, FC usage trends, and demand forecasting across 29 markets.

Consultants use it to deliver data-backed recommendations to their clients, with exportable analytics and financial models they can put into pitch decks.

Regulators use it to assess infrastructure adequacy, monitor compliance, and understand how policy translates into actual deployment and utilisation on the ground.

OEMs use it to understand where charging infrastructure exists (and where it doesn't), which hardware is deployed at scale, and how charging patterns affect vehicle design and sales strategy.

We integrate with national access points (NAPs), government registries, OCPI feeds, DATEX II endpoints, and operator APIs across Europe and beyond. Key sources include NOBIL (Nordics), Mobilithek (Germany), AFIR feeds (Finland, Lithuania), and dozens of country-specific registries.

Realtime status data is ingested continuously from WebSocket, MQTT, and polling sources. We process hundreds of thousands of status events per day across 20 countries with live monitoring.

All data is normalised into a unified schema: same fields, same connector types, same status codes regardless of the source country.

Currently 29 countries with station data: Austria, Australia, Belgium, Canada, Croatia, Czech Republic, Denmark, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, and the United States.

Realtime charging status data is available in 20 of these markets where operator APIs provide live connector information. Coverage is expanding every month.

Our insights articles are freely accessible without an account.

Everything else requires an active subscription: the interactive map, country analytics, CPO benchmark, ChargiPedia, news archive, newsletter, and forum.

The Projects tool (location planning, site design, BOM, financial modelling) requires the Premium tier.

Every new subscription starts with a 7-day free trial, so you can explore the full platform before committing.

Two tiers, both with a 7-day free trial:

For business teams, volume discounts apply: 10% off for 11–30 seats, 15% off for 31+ seats. Annual billing saves one full month on both tiers.

The map displays every charging station in our database, clustered at lower zoom levels and showing individual stations as you zoom in. At a glance you see the density of charging infrastructure across a country or region.

Click on any station to open a detail panel showing the operator, connector types, power levels, address, and historical usage statistics where available.

Layers let you toggle different data overlays on the map. Open the Layers panel in the top toolbar to switch between the charging station layer, POI heatmaps, and other data visualisations.

The charging density legend in the bottom-left corner shows you the colour scale from sparse to dense coverage.

Yes. Click Map type in the toolbar to switch between roadmap, terrain, satellite, and hybrid views. Use Locate me to centre the map on your current position.

Clicking any station opens a detail panel with live data. The Data tab shows:

• Performance snapshot — a composite health score (0–100), average FC load, and sessions per bay per day
• Realtime overview — current status of every connector (available, charging, unknown), updated continuously
• Nearby stations — a comparison chart showing fast-charging minutes per day at this station vs. nearby competitors over the last 7 days
• Usage history — weekly and monthly fast-charging volume and sessions over the full observation window

Stations with realtime data show live connector bars; those without show the latest known static metadata.

The Forecast tab shows how our model evaluates the station:

• Location score — how the site's inherent value compares to the market median (a gauge showing above or below average)
• CPO execution score — how well the operator performs relative to what the location should deliver, with a confidence indicator
• Backtest chart — model predictions (orange) vs. actual observed usage (teal) over the training window, so you can see exactly how well the model fits this station
• 12-month prognosis — forward-looking demand predictions accounting for seasonal patterns, temperature, and EV adoption trends

Country analytics gives you a deep look into EV charging infrastructure and adoption at the national level. Select any country from the dropdown (or click one on the map) and you get KPIs, trend charts, operator breakdowns, pricing data, usage metrics, and market outlook — all on a single, scrollable page.

The page is organised into sections: EV Adoption, Charging Infrastructure, Ad-Hoc Pricing, Fast-Charging Usage, FC Market Outlook, Regulation, and Country News. Each section is covered in detail below.

Use the country selector dropdown at the top right of the page. It shows the country name and station count. After selecting, click Refresh to load the new data. You can also click directly on countries in the adoption map.

The EV Adoption section tracks how battery-electric vehicles are penetrating each market. It includes:

• BEV market share KPI — the current percentage of new car sales that are battery-electric, front and centre
• S-curve forecast — a logistic growth model projecting BEV market share years into the future. Each country is assigned a tier based on where it sits on the adoption curve: Early (below ~5%), Growth (5–25%), Mainstream (25–60%), and Saturation (above 60%). The tier determines the shape and pace of the forecast
• BEV fleet stock chart — cumulative BEVs on the road over time, showing historical actuals and projected growth
• Data table — a year-by-year breakdown of BEV registrations, fleet stock, market share, and growth rate for each country

A snapshot of the country's physical charging network. Six KPI cards show:

• Stations — total number of charging sites
• EVSEs — total charge points (individual plugs/sockets)
• DC Fast Chargers — CCS, CHAdeMO, and NACS bays
• AC Chargers — Type 2 and Type 1 bays
• Operators — number of distinct CPOs active in the country
• Avg FC Power — average rated power of DC fast chargers in kW

Below the KPIs, a top operators table ranks the largest CPOs by network size. Each row shows the operator's station count, bay count, DC share, location score (median site quality from Pulse), and execution score (how well they perform relative to their sites' potential).

Pricing analytics for public charging in the selected country. Toggle between DC (fast charging) and AC (destination charging) to compare tariffs.

For each view you see the national average, minimum, and maximum per-kWh price, plus how many CPOs reported pricing data. Bar charts break down pricing by connector type (CCS, CHAdeMO, Type 2, etc.), showing average and range for each.

All prices are in EUR per kWh, excluding VAT, based on ad-hoc (pay-as-you-go) tariffs — no subscription or membership discounts included.

The heart of the analytics page for countries with realtime data. This section shows how heavily the fast-charging network is actually being used. Available only for the 20 countries where we have live status monitoring.

Top-level KPI cards include:

• FC Bays and FC Sites — monitored fast-charging infrastructure
• Minutes per bay per day — average daily usage intensity
• Sessions per bay per day — average daily session count per bay
• Avg session duration — mean charging session length
• Build rate — net new DC bays added per month

Charts below the KPIs include:

• Realtime status — current aggregate status (charging, available, offline) across all monitored bays
• Monthly trend — FC minutes per bay per day over time, showing seasonality and growth
• Month-over-month comparison — this month vs. last month, highlighting the delta
• Volume by CPO — stacked chart showing which operators account for the most FC minutes
• Market share trend — how each CPO's share of total FC volume evolves over time
• Build rate by CPO — which operators are adding the most new bays

An embedded version of our full FC Market Outlook model (see the dedicated FC Market Outlook section below for methodology). Within the country page you see:

• Utilization gauge — current national FC utilization as a dial, showing where the market sits between underserved and overbuilt
• Demand metrics — sessions per BEV per year, FC hours per BEV per year, bays per 1K BEVs, and whether these are measured or estimated
• Model parameters — the economic assumptions driving the projection (CAPEX/bay, operating margin, target IRR, FC price/kWh)
• Four charts — projected FC bays vs. equilibrium, BEV fleet growth, kWh throughput per bay per day, and utilization percentage over the projection horizon

The Regulation section is loaded dynamically from ChargiPedia. It shows all laws, EU directives, national incentive programmes, and standards that apply to EV charging in the selected country. Each entry explains what the regulation requires, who it affects, and when it takes effect.

This connects directly to the full ChargiPedia regulations database, filtered to the country you're viewing.

At the bottom of each country page, a news feed shows recent articles from our news archive filtered to the selected country. Articles are AI-tagged by topic and mentioned companies, so you see exactly what's happening in that market without having to search manually.

Demand pressure measures how intensively a country's fast-charging infrastructure is being used relative to its BEV fleet. It combines realtime charging data with EV adoption statistics to produce a single utilization metric.

We continuously monitor the status of every fast-charging connector (CCS, CHAdeMO, NACS / Tesla) across all countries where we have realtime data. Every status transition (Available → Charging → Available) is recorded, rolled up hourly, then aggregated monthly. From these rollups we derive:

• Sessions per BEV per year — how often, on average, each BEV in the country uses a public fast charger
• FC hours per BEV per year — total fast-charging time divided by the BEV fleet
• Utilization % — what fraction of total available bay-hours are spent charging, calculated only from bays with realtime monitoring
• Bays per 1K BEV — infrastructure provisioning ratio

Countries where we have at least 2 months of realtime connector status data are labeled measured. Utilization is calculated only from the subset of FC bays we actively monitor — the RT coverage percentage tells you how representative the sample is.

For countries without RT data, we assign a demand archetype based on geography and driving patterns (e.g. large highway country, compact dense country, island). The archetype's parameters are calibrated from our measured baselines.

• Below 5% — Low usage. The network has significant spare capacity.
• 5–10% — Comfortable. Healthy balance between supply and demand.
• 10–20% — Moderate pressure. Peak-hour queuing may occur at popular locations.
• Above 20% — High pressure. Systematic capacity shortages likely. New infrastructure urgently needed.

These thresholds are based on 24/7 average utilization. A 10% national average typically means 25–35% during peak hours at busy stations.

The CPO benchmark ranks charge point operators by performance within a country. It answers the question every operator wants answered: how do I compare?

Top-level KPI cards show the number of Active CPOs in the market, Total Active FC Bays, Average minutes per bay per day (the national usage intensity), and Fleet Availability (what percentage of bays are in an available state right now). These give you the market context before diving into individual operators.

The leaderboard is a ranked list of every CPO active in the selected country. Each operator card shows at a glance:

• Sites / Bays — network size
• Sess/bay/day — average charging sessions per bay per day (utilisation proxy)
• Available % — percentage of bays in an available state (uptime)
• Bays (3M) — net bay additions in the last 3 months (growth)
• Bays/site — average bays per site (density)
• Score — composite score combining utilisation, availability, growth, and scale
• Min/bay/day — average minutes of charging per bay per day

The pill buttons above the leaderboard let you re-rank operators by eight different criteria:

• Sessions per Bay — who gets the most sessions per bay per day (pure demand signal)
• Volume — total FC minutes across the entire network (absolute market share)
• Network Size — total active bays (scale)
• Growth — net bays added in the last 3 months (build momentum)
• Availability — percentage of bays available (operational quality)
• Location Score — median Pulse location score across all the operator's sites (portfolio quality)
• Exec Score — median CPO execution score (operational outperformance vs. site potential)
• Price — ad-hoc per-kWh tariff (for operators that publish pricing)

Each sort mode reshuffles the leaderboard so the top-ranked operator for that metric appears first.

The toggle switches the entire benchmark between Fast Charging (DC networks: CCS, CHAdeMO, NACS) and Type 2 (AC destination charging). The two segments have completely different economics, operators, and usage patterns, so they're benchmarked separately.

Most users will focus on FC, but the Type 2 view is useful for understanding destination charging coverage, especially in markets where AC still dominates public infrastructure.

Click an operator in the leaderboard (or the Deep Dive button) to access detailed comparative charts:

• Sessions per Bay ranking — bar chart comparing all operators by utilisation intensity
• Market Share + Fleet Availability — dual-axis chart showing each operator's share of total FC volume alongside their uptime
• Volume Trend — monthly FC minutes for the selected operator over time, with seasonality visible
• kWh Pricing — how the operator's ad-hoc tariff compares to the national average and competitors
• Growth Leaders — which operators are adding the most bays, with trailing 3-month and 12-month net additions

Point at any spot on a map and Pulse will tell you how much fast-charging demand it would see. Not a vague “good” or “bad” — an actual number: estimated kWh per charging bay per day, broken down by month and explainable down to the individual input features that drove the result.

The model works in three stages that multiply together.

Stage 1 — Demand base

A parametric model estimates how much fast-charging demand exists in general for a given country and month. It accounts for three things:

• Temperature — cold weather increases energy consumption per km and makes range anxiety more acute. A Norwegian January produces roughly 70% more fast-charging demand per BEV than a Norwegian July.
• BEV fleet share — more electric cars on the road means more charging sessions. The model uses log-scaled BEV penetration by country and year.
• Country baseline — driving culture, highway density, and charging habits differ structurally between countries. A Norwegian BEV owner uses public fast-charging differently than a Finnish one, even at the same temperature and fleet share.

The demand base is trained on observed station-level usage data. It answers: what does “normal” look like in this country, this month? The output is a number in kWh per EVSE per day — typically between 40 and 120, depending on market and season.

Stage 2 — Location score

The demand base tells you about the market. The location score tells you about this specific spot. It’s a machine learning model (gradient-boosted trees) that evaluates 20 spatial features across several categories:

• Traffic — composite traffic score derived from government AADT (annual average daily traffic) data, weighted by road proximity and type. Also includes highway traffic percentage and max road AADT. This is the single strongest predictor.
• Competition — existing DC fast-chargers within 5 km, 10 km, and 25 km, plus distance to the nearest competitor. More competition means each station captures a smaller share of passing demand.
• Points of interest — fuel stations, food, parking, and other amenities within 1 km and 5 km. Includes retail POI subcategories: malls, brand fast food restaurants, chain stores, and retail density. These are proxies for footfall, dwell time, and the kind of location that attracts drivers.
• Accessibility — distance to the nearest fuel station, food outlet, and any POI. Locations near existing driver infrastructure inherit that traffic.

The model is trained on over 33,934 station-months of observed charging usage across 4,083 stations. It learns what combination of spatial features predicts high or low utilisation — then applies that to any new point.

In areas with dense competitive data, the full 20-feature model runs. In areas with sparse competition data (new markets, rural zones), a separate baseline model using only traffic and POI features takes over. The two are blended using a sigmoid function keyed to local competitive density — so the model degrades gracefully rather than hallucinating in data-sparse regions.

The output is a single number: the location score. It acts as a multiplier on the demand base. A score of 3.0 means this location would attract three times the country/month baseline. A score of 0.5 means half.

Stage 3 — Operator adjustment

The location score is deliberately operator-blind — it measures the land, not who’s standing on it. But when producing a demand forecast for a specific project, a third factor enters: the CPO execution factor. This adjusts for how well a given operator converts location potential into actual sessions (see “What is the CPO execution score?” below). The default is 1.0, meaning plan-case execution.

The multiplication chain

The final demand estimate is simply:

A worked example: a site in Norway, January, with a location score of 1.5 and plan-case execution:

The same location in July (15°C) would have a lower demand base (~52 kWh), producing 78 kWh/EVSE/day — a 38% seasonal drop, which matches what we observe in real data.

Why scores differ across markets

The location score uses a single global scale. A score of 2.5 means the same thing everywhere: 2.5× the local demand base. But because the spatial features that drive the score (traffic density, competition, POI coverage) vary hugely between countries, the distribution of scores differs by market.

Norway’s median station scores around 1.0. Germany’s is around 2.5. The Netherlands sits near 3.0. This isn’t a flaw — it’s the signal. Dense, high-traffic markets with lots of amenities produce higher scores because those features genuinely predict more demand. A 1.5 in Norway is above-average for Norway; the same score in the Netherlands would be below average. Both are correct.

Explainability

Every score comes with a full SHAP (SHapley Additive exPlanations) breakdown showing exactly which features pushed the score up or down, and by how much. You can see that a particular location scores high because it has 18,000 AADT on the nearest road and a fuel station within 200 metres — not just that it scores high. This matters when you’re making investment decisions: you want to know why, not just what.

The location score is a demand multiplier. It tells you how much fast-charging demand a location would attract relative to a baseline. A score of 2.0 means twice the baseline demand; a score of 0.5 means half. The scale is continuous and has no hard upper limit — prime locations in dense markets can score above 10.

We use a single uniform scale across all markets. This is deliberate. A highway stop in rural Norway and a city-centre site in Amsterdam are not equally attractive — and the score reflects that honestly. Lower-density countries will naturally cluster toward the lower end, while dense, high-competition markets push higher. That's the signal, not a flaw.

A note on market differences. Norway’s median station scores around 1.0, while Germany’s median is around 2.5 and the Netherlands sits near 3.0. This doesn’t mean Norwegian locations are “worse” — it means the absolute demand density is lower, which is exactly what you need to know when building a financial model. A score of 1.5 in Norway is a solid, above-average location for that market. The same score in the Netherlands would be below average.

The percentile column above is based on the global distribution of over 500,000 scored fast-charging stations. Use it as a rough guide, but remember: the score is a demand signal, not a ranking. Two locations scoring 2.5 will have similar demand characteristics regardless of which country they’re in.

The model ingests a wide range of openly available data. No proprietary datasets, no purchased feeds — everything is sourced from government registries, public APIs, and open data portals. Key input categories include:

• Realtime charging data — millions of status events per day from national access points and operator feeds, telling us exactly when and where charging sessions happen
• Road traffic volumes — annual average daily traffic counts from government transport agencies across multiple countries
• Weather and climate — historical temperature and precipitation data, because cold weather changes EV charging patterns more than most people realise
• EV adoption rates — battery-electric vehicle market share by country, tracking how the fleet mix evolves over time
• Points of interest — nearby amenities, fuel stations, parking, retail POIs (malls, brand fast food, chain stores), and other relevant infrastructure from open mapping databases
• Existing charging infrastructure — the competitive landscape of fast-chargers already in the area

All input data is open, publicly available, and legally sourced. We don't scrape, we don't buy secret datasets, and we don't rely on any single vendor's data.

Anyone can download a list of charging station locations. The hard part is having months — eventually years — of continuous, second-by-second observation of what actually happens at those stations.

We've been recording realtime charging events continuously for a long time now — every session start, every status change, every idle hour. That's a time series that cannot be recreated retroactively. You can't go back and observe what happened at a station last January if you weren't already listening.

This historical depth is what separates a model that says "this looks like a good spot" from one that says "here's how much energy a station at this spot would likely deliver in February vs. August, and here's the confidence interval." The longer we collect, the better it gets. There are no shortcuts.

Think of a charging site as two businesses stacked on top of each other. There's the asset — the physical location with its traffic, visibility, and catchment area. And there's the retail operation — the brand, pricing, uptime, customer experience, and everything else the operator brings to the table. We measure them separately, on purpose.

The location score is an asset benchmark. It measures the inherent value of a site based purely on where it is: traffic flow, nearby amenities, competitive density, EV adoption in the area. Operator identity is deliberately excluded. A great piece of real estate scores high regardless of who's running it today.

The CPO execution score is a retail benchmark. It measures how well an operator performs relative to what their locations should deliver. We take the model's predictions for every station in an operator's network, compare them to actual performance, and the gap tells you something real. An operator that consistently outperforms their location scores — better uptime, more sessions, higher utilisation — has strong brand pull, smart pricing, or good customer experience. One that underperforms has the opposite.

This separation is the point. If we mixed operator quality into the location score, a premium motorway site run by a weak operator would look mediocre, and a backstreet location run by Tesla would look brilliant. Neither is useful for site selection. You want to know what the land is worth independent of who's standing on it — and separately, whether the operator is extracting that value or leaving it on the table.

The current model (v8) achieves an overall R² of 0.69, meaning it explains about 69% of the variance in observed charging demand across 4,083 stations and 11 months of data. The demand component alone has an R² of 0.69.

The median predicted-to-actual ratio is 0.99 — meaning the model is well-calibrated at the centre. Half of all predictions fall within a tight band around the actual value, which is a useful range for investment-grade site evaluation.

For context: predicting charging demand at a specific location is harder than it sounds. Every station has a unique combination of traffic patterns, operator brand strength, pricing, local competition, and plain luck. Explaining 69% of that variance from open data alone is a meaningful signal — and it's one that keeps improving with every model version.

We ship model updates regularly. Each version expands the training data, refines the architecture, or incorporates new data sources. Here's the full history:

v8 represents a significant step forward from v6. The R² improved from 0.64 to 0.69 — a meaningful gain when you consider the model is now evaluated against a larger, more diverse dataset. The new retail POI features and improved traffic data give the model a better understanding of what makes a location commercially attractive, beyond just road traffic and competition.

v3 and v4 were internal experiments — never shipped to production, but the lessons (competition signal matters, confidence blending is essential) directly shaped v5 and v6. v7 was skipped during an internal renumbering.

We retrain whenever there's a material reason to: a new data source is integrated, a significant batch of new training months becomes available, or we've validated an architectural improvement. In practice, that's been roughly every 2–4 weeks since launch.

Every new version is validated against the previous one before going live. We keep previous models on standby for instant rollback if anything looks off. No model ships without being tested against real station performance data.

More data, more countries, more time. The model gets better every month because the underlying time series grows. A year of continuous observation is worth more than any clever algorithm, and we're building that clock every day.

On the roadmap: incorporating tariff and pricing data (we now track per-kWh prices from multiple markets), grid capacity proximity, and expanding the training set as realtime data comes online in new countries. The architecture is designed so that adding a new country's data improves predictions everywhere — not just in that country.

The FC Market Outlook projects how fast-charging infrastructure will evolve in each country over the next 8 years. Unlike simple trend extrapolation, it's built on an economic equilibrium model that asks: how many fast-charging bays can the market profitably support, given the number of BEVs on the road?

The core idea is that the long-term FC build rate converges toward a ratio of BEVs per FC bay where charging point operators (CPOs) can earn a healthy return on investment. In the short term, land-grab dynamics mean many CPOs are building ahead of demand — accepting losses to secure locations and market share. But over time, economics wins.

The model outputs three scenarios per country: a base case, an optimistic case (aggressive build, slower demand), and a pessimistic case (cautious build, stronger demand). Each produces year-by-year projections of FC bays, BEV fleet size, utilization, and the implied BEVs-per-bay ratio.

The equilibrium is the number of FC bays where a generic bay investment earns an acceptable return. We model a single FC bay as a discounted cash flow problem:

From these inputs, the present value annuity factor at 11% over 10 years is 5.89. That means each bay needs to generate:

€50,000 ÷ 5.89 = €8,490/year in cash flow.

At 60% operating margin, that requires:

€8,490 ÷ 0.60 = €14,150/year in revenue.

Revenue is driven by a single metric: kWh delivered per bay per day. At a given country's FC price per kWh (excl. VAT), the required daily throughput is:

required kWh/bay/day = €14,150 ÷ (price per kWh × 365)

For Norway at €0.51/kWh (the national median), that's 76.0 kWh/bay/day. That is the economic anchor — the throughput where a bay breaks even on an IRR basis. The equilibrium bay count for any year is simply: total national FC kWh demand divided by this number.

All four parameters in the table above are fully adjustable. If you believe CAPEX is heading to €40,000 as hardware prices fall, or that a 15% IRR better reflects your cost of capital, drag the slider and the entire projection recalculates instantly. See “Can I customize the forecast parameters?” below for the full list of adjustable inputs.

Demand is built from two components:

1. BEV stock projections — how many battery-electric vehicles are on the road each year, sourced from national EV adoption data with central/low/high scenarios
2. FC sessions per BEV per year — how often a typical BEV uses public fast-charging. For countries with realtime data, this is measured directly from our charging event database. For countries without, we estimate from an archetype model calibrated against countries with similar characteristics

Total kWh demand = BEV stock × sessions per BEV × kWh per session. The kWh per session is fixed at the base year's observed level (approximately 43 kWh for a 40-minute session at 65 kW average delivery power in 2026). This is a deliberate choice: as battery technology improves and cars accept power faster, the total energy a driver needs doesn't change — driving patterns determine energy consumption, not charging speed.

For Norway, measured data shows 16 FC sessions per BEV per year, each averaging 40 minutes — giving approximately 555 kWh per BEV per year consumed at public fast-chargers.

We don't assume supply jumps to equilibrium instantly. Instead, the projected number of FC bays converges toward equilibrium at a rate governed by two parameters:

• Convergence speed (α) — the fraction of the gap between current bays and equilibrium bays that is closed each year. In the base scenario, α = 0.30 (30% of the gap per year). This reflects real-world inertia: permitting, grid connections, construction timelines, and investment cycles all slow the adjustment.
• Minimum growth floor — even in oversupplied markets, FC deployment doesn't stop. CPOs with permits in hand, grid connections secured, and brand strategies to execute will continue building. The base scenario uses a 2% annual floor.

The result is an exponential smoothing curve: if the market is undersupplied (current bays < equilibrium), build rates accelerate. If oversupplied, they decelerate — but never to zero, because land-grab momentum and strategic considerations keep the industry moving.

Both parameters are adjustable. If you think a post-AFIR regulatory push will accelerate convergence, increase α. If you expect capital markets to tighten and slow the build rate, lower the growth floor. The projection updates in real time.

Average real-world FC charging power is car-limited, not charger-limited — a 350 kW charger still delivers whatever the car's battery management system allows. Today's fleet-wide average is approximately 65 kW. As newer vehicles with faster-accepting batteries enter the fleet, we model this rising linearly to 80 kW by 2030 and continuing toward 100 kW (capped).

Critically, this affects utilization (hours occupied) but not revenue. A bay that delivers 76 kWh at 65 kW is busy for 1.2 hours. The same 76 kWh at 80 kW takes only 57 minutes. The CPO earns the same money either way — the bay is just free sooner.

This means utilization percentages will naturally decline over time even if the market is in perfect equilibrium. A falling utilization number doesn't necessarily signal trouble — it may simply reflect faster cars. The kWh throughput per bay is the better economic indicator.

The scenarios are named from the supply perspective: "high build" means more aggressive infrastructure deployment, which may be good for drivers but compresses CPO margins. "Low build" means tighter infrastructure, which means higher utilization and better unit economics for existing CPOs.

We validate the economic anchor against our own realtime data. For Norway, the model predicts that a bay needs approximately 76 kWh/day (1.2 hours occupied at 65 kW) to break even at 11% IRR. Our actual data from ~12,500 RT-monitored FC bays shows a weighted annual average of 91 kWh/bay/day — about 120% of the breakeven threshold.

Seasonality is dramatic: January peaks at 138 kWh/day (cold weather, longer trips), while April troughs at 54 kWh/day — a 2.6× swing. Winter months easily clear breakeven; summer months don't. This means a Norwegian FC bay is profitable on a full-year basis only if it's large enough to accumulate winter surplus against summer shortfall.

The national average sitting 20% above breakeven might seem healthy, but distribution matters: high-traffic corridor stations can deliver 200+ kWh/day while rural bays sit well below the 76 kWh threshold. The equilibrium model predicts this gap will close as BEV fleet growth delivers more sessions per bay over time.

The model is deliberately simple, and that's a feature. But some limitations are worth understanding:

• FC pricing is static — we use current per-kWh tariffs and don't model future price changes. In practice, pricing may compress as competition intensifies or rise if energy costs increase.
• Uniform economics — the model uses a single CAPEX/margin/IRR assumption per bay. Real-world variation is enormous: a motorway site costs more but earns more; a rural site costs less but sees less traffic. The equilibrium represents a national average.
• No policy modelling — subsidies, AFIR mandates, and grid tariff regulation all affect FC deployment rates. These are reflected indirectly through historical build rates but are not explicitly modelled forward.
• Session kWh is fixed — we assume constant kWh per BEV per year at public FC. If behaviour shifts (e.g., home charging becomes more common, or road trip patterns change), demand per BEV could change.
• Country-level only — the model projects national totals. Within a country, utilization varies enormously: a motorway junction in Sørlandet and a city-centre charger in Tromsø live in different economic realities.

Yes. Every assumption in the model is exposed as an adjustable parameter. If you disagree with our defaults, change them — the forecast recalculates instantly to reflect your market view, not ours.

Adjustable inputs include:

• CAPEX per bay — raise it if you’re modelling premium motorway sites with higher grid costs, lower it if you expect hardware prices to fall
• Operating margin — adjust for your cost structure: energy procurement, site lease terms, maintenance contracts
• Target IRR — set the hurdle rate that matches your own cost of capital or investment committee threshold
• Investment horizon — shorter for conservative underwriting, longer if you plan to operate through a full technology cycle
• Convergence speed (α) — how fast you believe the market will close the gap between current supply and economic equilibrium
• Minimum growth floor — the build rate you expect even in an oversupplied market
• BEV growth multiplier — scale the EV adoption forecast up or down based on your own demand outlook

This matters because the forecast feeds directly into the Projects module. When you plan a new charging site, the financial model — NPV, IRR, payback period — uses the FC Market Outlook to project future demand at that location. If you’ve adjusted the outlook parameters to match your investment thesis, those customized projections carry through into every site’s business case automatically.

In other words: if you believe the market will be tighter than our base case suggests, increase the BEV growth multiplier and lower the convergence speed. Your project financials will then reflect higher utilization and faster payback — consistent with your own belief, not our defaults.

ChargiPedia is our industry encyclopedia. It's a structured, searchable database of every company, product, vehicle, deal, regulation, and person in the EV charging space. Think of it as the Wikipedia of EV charging, except the data is actually current.

Access is free for everyone — no account required.

The CPO directory lists every charge point operator we track. Each card shows the operator's name, logo, country, and network size. Search and filter to find specific operators, or browse by country and size.

Click any operator to open their full profile. A CPO profile includes:

• Network size — stations, bays, and DC share across all countries of operation
• Hardware used — which charger models are deployed, with install base numbers
• CPMS platform — which charging point management software they use
• Key people — executives and their roles, linked to the People directory
• Build rate — a timeline chart showing how fast the network is growing
• Execution scores — how well the operator performs relative to their site portfolio quality
• Pricing — ad-hoc per-kWh tariffs where available
• Stock chart — for publicly listed operators, share price history
• Infrastructure by country — breakdown of the network across all markets where they operate

eMSPs (eMobility Service Providers) are companies that give EV drivers access to charging networks — typically through an app, RFID card, or roaming agreement. They don't own chargers; they provide the payment and access layer.

The directory lists eMSPs with their name, country, and a brief description. Many companies operate as both CPO and eMSP, and cross-references are shown on their profiles.

The Hardware OEM directory catalogues manufacturers of EV charging equipment — from DC fast chargers to AC wallboxes. Each manufacturer profile shows their product line, country, and a description of their market position.

Drill into individual charger models to see detailed specs: rated power, connector types, dimensions, weight, architecture (split/integrated), IP rating, and operating temperature range. Each model page also shows its install base — which CPOs have deployed it and where — so you can see real-world adoption, not just datasheet promises.

CPMS stands for Charge Point Management System — the software platform that CPOs use to manage, monitor, and monetise their charging infrastructure. Think of it as the operating system of a charging network: it handles OCPP communication, session management, billing, and usually the driver-facing app.

The directory lists platforms like has.to.be (now be.ENERGISED), Driivz, Current, Ampeco, GreenFlux, and dozens more. Each profile shows which CPOs use the platform, helping you understand market share and ecosystem relationships.

The services directory lists companies that provide installation, maintenance, consulting, and other services to the EV charging industry. Categories include electrical contractors, turnkey installers, engineering consultancies, and specialist EV charging service providers.

The People directory tracks key individuals in the EV charging industry — executives, founders, and other notable figures. Each profile shows their current role, company, and a career timeline showing how they've moved through the industry. Useful for understanding who's running what, and where industry talent flows.

The EV models section catalogues electric vehicles with their battery capacity, max charging speed (AC and DC), range, plug types, and architecture (400V/800V). Useful for understanding which vehicles can use which chargers and how quickly they charge — relevant context when designing charging stations and selecting equipment.

A database of industry transactions: acquisitions, fundraising rounds, IPOs, joint ventures, and ownership changes. Each entry includes the parties involved, deal value (where disclosed), date, and a summary of what happened.

The section also includes activity charts showing deal volume and value over time, filterable by transaction type, country, and company. Useful for tracking consolidation patterns and capital flows in the industry.

Country-specific regulations, EU directives (AFIR, RED), national incentive programmes, and standards that shape EV charging deployment. Each entry explains what the regulation requires, who it affects, and when it takes effect.

The Energy directory tracks energy companies that have entered the EV charging space — utilities, oil majors, renewable energy developers, and grid operators. Many of the largest CPOs are subsidiaries or divisions of energy companies, and this directory helps you understand those relationships.

Yes. Click the Suggest New Entry button on the ChargiPedia landing page, or use the edit buttons on individual profiles to propose changes. Submissions go into a review queue and are checked before publication. We welcome corrections, updates, and new entries — the more people contribute, the more complete the database becomes.

A curated, searchable feed of EV charging industry news from sources worldwide. Articles are AI-tagged by topic (policy, CPO expansion, hardware, finance, consumer pricing, etc.), country, and mentioned companies. The archive grows daily.

Use the search bar to find specific topics, the sort dropdown to order by date or relevance, and the Filters button to narrow by topic, country, source, or company.

Your morning briefing, your way. The newsletter delivers curated EV charging intelligence to your inbox, translated to your preferred language and delivered on the schedule you choose.

Start with a preset — Nordic Intelligence, DACH Briefing, European Overview, Benelux & France, CPO Competitive Intelligence, Policy & Regulatory Watch, and more — or build a fully custom newsletter by selecting topics, countries, and companies.

Set delivery frequency (daily or weekly), preferred language, and what time you want it. The newsletter is auto-generated from our news archive with AI summarisation.

Long-form analysis and opinion pieces on the EV charging market — economics, consolidation, profitability, and what comes next. Written by our team, backed by data from the platform.

These aren't press-release rewrites. They're original analysis with a point of view. Think first-party research that says something, not content marketing that says nothing. Free to read, no account required.

Your command centre for all charging infrastructure projects. The dashboard shows KPI cards (total projects, locations, scored locations, estimated CAPEX), a filterable project list, and quick-action buttons.

Click New Project to create a project, or Plan Locations to jump straight to the planning map. Projects can be searched and filtered by phase (planning, project, execution, live).

Every project moves through four phases:

1. Plan — Scout and score potential locations using the planning map. Evaluate sites, compare scores, and shortlist candidates.
2. Project — For each location, design the station layout, build a bill of materials (BOM), and run financial projections (CAPEX, revenue, NPV, IRR, payback period).
3. Execute — Manage tasks, collect contractor quotes, track budget, and handle procurement through construction.
4. Live — Station is commissioned. Generate a completion report summarising the project.

Within a project, each location progresses through its own statuses: planning → planned → designed → costed → approved → in_construction → commissioned → live. Different locations in the same project can be at different stages.

The planning map is where you evaluate potential charging locations. It overlays two key datasets:

• FC station density (blue heatmap) — shows where existing fast-charge stations are concentrated, helping you spot gaps in coverage
• AADT traffic volume (coloured road segments) — shows annual average daily traffic from government traffic counts, indicating demand potential

Use the AI search bar to find specific locations naturally — type something like "Burger King near Bergen" and the system finds matching locations, scoring them for charging potential.

You can also click anywhere on the map to score that location. Each scored location gets a composite score based on traffic, proximity to existing stations, nearby amenities, and other factors.

Scored locations appear in a list in the left panel. Use batch operations to select multiple locations and add them to a project in one click.

The project detail page is where you manage a specific project. It includes:

• Project map — all locations in the project plotted on a map, colour-coded by status
• Location cards — each location shows its name, address, score, current status, and key metrics. Click any card to drill into that location's detail
• Phase progression — visual indicator showing the overall project phase and how many locations are in each status
• Project KPIs — aggregate metrics: total locations, average score, estimated CAPEX, projected annual revenue
• Settings — project-level configuration (name, description, team access, financial defaults)

Each project has a built-in email system. Send and receive emails directly within the project context — correspondence with landlords, contractors, utilities, or team members stays attached to the project rather than scattered across personal inboxes.

Features include attachments, location tagging (link an email to a specific location within the project), and a signature editor. All project mail is visible to team members with access to the project.

The score tab is the analytical core of each location. It shows:

• Score gauge — the Pulse location score as a visual dial, with a clear rating (Poor through Excellent)
• SHAP breakdown — a waterfall chart showing exactly which features push the score up or down, and by how much. This is the explainability layer — you see why a location scores the way it does
• Traffic analysis — nearby road AADT values, road types, and the composite traffic score
• Competition analysis — existing DC fast-chargers within 5, 10, and 25 km, with operator names and bay counts
• POI analysis — nearby points of interest: fuel stations, food, retail, parking within 1 and 5 km
• Demand forecast — monthly kWh/bay/day projections from the Pulse model, accounting for seasonality and EV adoption growth

The site designer is a versioned canvas where you plan the physical layout of a charging station. Place chargers from the equipment catalogue, define parking bay positions, and arrange supporting infrastructure.

Designs are versioned — you can create multiple iterations, compare them, and promote the preferred version. The design feeds into the BOM and financial model, so changing the layout automatically updates the cost estimate.

The BOM builder lets you spec out what goes at a location. Add items from two sources:

• ChargiPedia equipment catalogue — charger models from real hardware OEMs, with specs, market prices, and FX rate conversion. Select a model, set the quantity, and pricing populates automatically
• Custom items — anything not in the catalogue: transformers, cables, civil works, signage, grid connection fees. Enter description, quantity, and unit cost

Apply BOM templates to quickly configure common station setups (e.g. "4-bay highway station" or "2-bay urban hub"). Templates can be saved and reused across projects.

The BOM total feeds directly into the financial model as CAPEX. Track procurement status per item: ordered, delivered, installed.

The financial model calculates a full investment case for each location:

• CAPEX — pulled directly from the BOM total, broken down by equipment category
• Revenue projections — based on the Pulse demand forecast for this location, multiplied by your pricing assumptions (price per kWh, energy cost per kWh)
• NPV (Net Present Value) — discounted cash flow over the investment horizon at your chosen discount rate
• IRR (Internal Rate of Return) — the rate at which NPV equals zero
• Payback period — months until cumulative net cash flow turns positive

The model includes a sensitivity analysis that tests 5 parameters across 3 scenarios each (base, optimistic, pessimistic): demand volume, pricing, energy cost, CAPEX, and operating costs. This gives you a range of outcomes rather than a single-point estimate.

Charts show the monthly cashflow (revenue minus costs) and cumulative return (running total of net cashflow vs. initial investment) over the projection horizon.

The bottom section of each location stores deal-level information:

• Site rental terms — lease duration, rent amount, escalation clauses, and other contractual details
• Revenue sharing — if the site owner takes a percentage of charging revenue, configure it here. It feeds into the financial model automatically
• Comments and notes — a threaded discussion for team members to leave notes, updates, and context about the location. Timestamped and attributed to the author

Once a location is approved, it enters the execution phase. This is where the plan becomes reality:

• Tasks — create and assign tasks (e.g. "Submit grid connection application", "Order chargers", "Schedule civil works"). Track progress with status updates and due dates
• Quotes — collect and compare contractor quotes. Attach documents, note terms, and select the winning bid
• Budget tracking — compare actual spend against the BOM estimate. See variances and remaining budget in real time
• Procurement — track equipment orders: what's been ordered, delivered, and installed

When a location goes live, the system generates a commissioning report that summarises the entire journey:

• Completion checklist — all required items verified: equipment installed, grid connected, software configured, signage in place
• Cost summary — final CAPEX vs. original BOM estimate, with variance analysis
• Timeline — actual milestones vs. planned dates, showing where delays occurred
• Sign-off — digital confirmation that the location is ready for public operation

The report can be exported and shared with stakeholders, investors, or board members.

Project settings has four tabs:

Equipment Catalogue — Your organisation's equipment catalog, integrated with ChargiPedia's hardware database. Add charger models from the global catalogue or create custom items. Set your negotiated prices (which may differ from market prices), manage FX rates, and organise equipment into categories.

Financial Defaults — Set the default financial assumptions that populate every new location's financial model. Includes cost of goods sold (energy cost per kWh), revenue assumptions (price per kWh, utilisation ramp), other costs (maintenance, site lease, insurance, back-office), and discount rate. Individual locations can override these defaults.

Contractors — A directory of contractors your organisation works with. Categorise them (electrical, civil, general, specialist), record contact details, and track their project history. When collecting quotes during execution, contractors from this directory can be linked directly.

Approvals — Configure the admin workflow for location phase transitions. Define which team roles can advance a location from one status to the next (e.g. only admins can approve the move from "costed" to "approved"). This ensures proper governance for capital allocation decisions.

A community space for discussing EV charging infrastructure. Categories include:

• Feedback — Bug reports and feature requests for the platform
• Open Discussion — General EV charging industry conversation
• Changelog — Platform updates and release notes from the Chargalytics team

Access requires an active subscription. Create threads, reply to discussions, and upload attachments.

Registration is a 3-step process:

1. Credentials — Click Sign Up in the top navigation. Enter your name, email, and password.
2. Phone verification — Enter your phone number. We send an SMS with a verification code.
3. SMS code — Enter the 6-digit code from the SMS to confirm your phone number. Your account is now created.

After registration, you're redirected to the subscription page to choose a plan and start your 7-day free trial.

We verify your phone number to prevent abuse and ensure one trial per person. Without this, someone could create unlimited accounts to chain free trials indefinitely.

Your phone number is used solely for account verification. It's not shared with third parties and not used for marketing. One phone number = one trial claim.

Yes. Every new subscription starts with a 7-day free trial. You enter your payment details at checkout (processed securely through Stripe), but you won't be charged until day 8. If you cancel during the trial, you pay nothing.

The trial gives you full access to all features in your chosen plan — Analytics or Analytics + Projects — so you can evaluate the platform properly before committing.

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The right sidebar shows your organisation membership and lets you create a company if you haven't joined one yet.

Create an organisation to manage team access. As an owner or admin, you can invite team members by email, assign roles (owner, admin, billing, member), and manage seat assignments. Company subscriptions let you share access across your team with per-seat billing.

Choose your plan on the subscription page. Two tiers are available: Analytics (€24.99/mo) and Analytics + Projects (€99/mo). Toggle between monthly and annual billing — annual saves one full month.

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