The story that started it all
I bought a DS3 E-Tense.
The dealer told me 350 km.
What I was told vs what I got
"You'll easily do 350 km on a charge. Perfect for your commute."
— The salesperson. Confident. Wrong.
150–180 km
My real-world reality
~1 year
Before I had to sell it
I owned the DS3 E-Tense for about a year. In that time I went through the full cycle: initial excitement, growing frustration, then the slow realisation that the car simply couldn't do what I needed it to do — not because it was a bad car, but because the number I had based my entire buying decision on was built on optimistic laboratory conditions that had nothing to do with real driving.
The dealer wasn't lying exactly. They were quoting the WLTP figure — Europe's official test standard. What they didn't mention, and what I didn't know to ask, is that WLTP testing happens in a climate-controlled lab, at moderate speeds, with no heating or air conditioning running. It is a compliance number. Not a promise.
I sold the car at a loss. That stings. But what stings more is knowing that thousands of other people are making the same decision every month — in dealerships, in online listings, in used car markets — with no tool to tell them what a car actually does on a real road.
Context that shaped the thinking
Range anxiety isn't a myth.
I watched it happen daily.
Before building this, I spent time working in the rental car industry. What I saw confirmed everything: range anxiety is real, it is common, and it is almost entirely caused by information failure — not by the cars themselves.
Customers would return electric rentals early because they didn't understand that 35% battery at 120 km/h in January is genuinely different from 35% battery at 90 km/h in July. Some would refuse the electric option entirely after one bad experience. The cars were fine. The information around them was broken.
I also have a background in economics, which gave me a framework for thinking about what was happening: information asymmetry. Dealers know things buyers don't. Official figures obscure more than they reveal. And the people paying the price — literally — are ordinary buyers making $25,000 to $60,000 decisions with incomplete data.
The dealership problem hasn't gone away
I recently visited several dealerships to see what had changed. Sales staff still routinely quote WLTP or EPA figures as if they are guarantees. When I asked one salesperson what the real winter range was, they said: "That depends on the driver." Not wrong. But also not useful to someone deciding whether to buy. The information gap that existed when I bought my DS3 is still very much there in 2025.
Why EPA isn't the full answer either
Even the better standard
doesn't tell the whole story.
The American EPA figure is genuinely better than WLTP — it's tested at higher average speeds and includes a highway cycle that more closely reflects actual use. For most buyers in the US, EPA is a reasonable starting point. But it still has limits.
EPA testing doesn't account for cold weather. It doesn't reflect 75 mph sustained motorway driving. It doesn't adjust for your specific city's stop-start traffic patterns, your roof-mounted ski box, or the 3°C winter morning you drive in every day. It's a standardised figure, which means by definition it can't be personalised.
And in Europe, where WLTP figures can overstate real range by 30–50% depending on conditions, the gap is even more severe. A 400 km WLTP claim translating to 220 km on a cold motorway isn't a surprise to anyone who works in the industry. But it is a complete surprise to the person who just signed the finance agreement.
What we bring to this
Qualified to build this.
Motivated to get it right.
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Economics Background
Bachelor's degree in economics. Trained to analyse information asymmetry, pricing failures, and how markets behave when buyers and sellers don't share the same data.
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Industry Experience
Direct experience in the rental car sector, observing real EV behaviour across hundreds of trips, climates, and driving patterns. Range anxiety up close, daily.
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First-Hand Ownership
Owned and operated an EV with a significant range gap between claim and reality. Not a theoretical problem — a real financial and practical one that cost real money.
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Physics-Based Approach
Built a custom V5.7 physics engine using aerodynamic drag, rolling resistance, and thermal battery models rather than flat multipliers. Hitting ~95% accuracy on most models.
What we'll always be honest about
We use a physics model, not real-world testing. Our engine is calibrated against published data and real ownership reports, but we have not driven every car on this list. We are transparent about our confidence tiers and accuracy estimates.
We are independent. No manufacturer relationships, no affiliate deals with dealers, no sponsored placements. The rankings reflect the maths, not who paid us.
We charge $1.99 for personalised reports. This is how we keep the free tools free. No advertising, no selling your data, no pressure to buy.
Our accuracy varies by model. Cars with unusual aerodynamics, adaptive suspension, or complex power management may have wider confidence intervals. We flag these clearly.
The founder remains anonymous. For privacy reasons we don't publish a personal name. We believe the methodology speaks for itself — and we welcome scrutiny of the engine, the data, and the outputs.
What we're actually trying to do
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Give people one honest number before they spend tens of thousands
Not a promise. Not a spec sheet. A real-world estimate with explicit uncertainty ranges, computed from physics and calibrated on real data.
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Level the information playing field between buyers and dealers
A buyer who arrives at a dealership knowing that the "400 km WLTP" car actually does 230 km in winter is a buyer who can't be misled on range.
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Make EV adoption better — not slower
Range anxiety slows EV adoption. Most of it is caused by unrealistic expectations, not bad cars. Accurate information before purchase reduces buyer's remorse and builds long-term confidence in the technology.
Try the calculator we wish had existed.
Free to use. Physics-based. No manufacturer affiliations. Built by someone who learned the hard way what it costs when the numbers lie.