For the teams that price EV risk
Know how every EV's battery is aging, and why.
Price EV risk on evidence instead of worst-case assumptions. Battery health, degradation forecasts and real-world range for every EV you cover, predicted from data you already hold. One integration.
- Score a whole portfolio from make, model, year and mileage
- Set reserves and buybacks on battery evidence
- Flag at-risk vehicles before lease return or renewal
1,200+
EV models covered
±4 points
battery health, for 9 in 10 cars under 7 years old
11-year
degradation forecasts
The blind spot
Generic curves don't see batteries.
Depreciation models built for combustion treat two cars with the same age and mileage as equal. In an EV the pack decides, and packs age differently with every fast charge and winter motorway.
So renewals get priced on worst-case assumptions, reserves drift away from reality, and the difference surfaces as a write-down at lease return.
Two EVs, identical on paper
Same model, four years old, 92,000 km
In line with age and mileage
Below the expected band, flag for review
A generic depreciation curve prices these two identically. The packs disagree.
One EV data layer for teams responsible for EV risk.
Insurers
Assess battery condition, real-world range and repair-relevant exposure before underwriting, renewal or claim decisions.
Price the renewal of a four-year-old EV on its predicted battery health instead of a worst-case assumption.
Leasing companies
Plan lease returns, buybacks and remarketing with battery and range evidence in the loop.
Check a returning vehicle's battery against similar cars before it goes to remarketing.
Fleet managers
Track vehicle fitness, replacement timing and operating cost signals across mixed electric fleets.
Decide which vans still handle winter routes after five years, and which to rotate onto shorter duties.
EV risk changes when the battery story changes.
Age and mileage are only the start. SuperElectric adds EV-specific signals that help teams explain why two similar vehicles can carry different risk and timing.
1 · Battery health
For electric vehicles, battery state influences risk, value, replacement timing and customer satisfaction. SuperElectric turns technical battery signals into a clear business view for non-specialist teams.
You get
- Predicted state of health from age and mileage, no workshop visit
- A calibrated 90% confidence band behind every figure
- Degradation forecasts up to 11 years ahead
2 · Portfolio risk
Scored across a whole book, battery health separates cohorts aging as expected from pockets where value is at risk. Problem vehicles surface before renewals, lease returns and remarketing windows, not after they cost money.
You get
- Bulk scoring from make, model, year and mileage alone
- At-risk vehicles flagged before renewals and lease returns
- No telematics hardware, no workshop inspections
Portfolio view
Predicted battery health across a 480-vehicle lease portfolio: 86 fall below 90%
Illustrative portfolio. Each vehicle is scored from make, model, year and mileage, no telematics required.
3 · Fleet suitability
Realistic range across temperature and speed helps teams identify vehicles that fit seasonal routes, driver needs and replacement plans before operational issues appear.
You get
- Real-world range across 100+ speed and temperature combinations
- Winter and summer figures for the routes vehicles actually drive
- Combined with battery health, a range figure for the exact vehicle
How the data gets used
From technical EV data to decisions teams can defend.
Take the data the way your team already works: a REST API for internal systems and dashboards, bulk scoring for portfolio files, or recurring reports your analysts can open directly.
Every figure is reproducible, so pricing, reserve setting, remarketing and renewal decisions come with evidence that stands up to auditors, internal teams and customers.
-
Set EV-specific battery assumptions per model
-
Benchmark batteries against similar vehicles
-
Flag vehicles before lease return or renewal
-
Support claims, total-loss and remarketing decisions
-
Explain battery-driven decisions to internal teams and customers
What insurers and leasing teams ask before getting started.
Make, model, year and mileage per vehicle. Predictions are model-based, so there is no telematics hardware to fit and no workshop visit. If you can export a vehicle list, you can score a portfolio.
Battery health estimates are within ±4 percentage points of the measured value for 9 in 10 cars under 7 years old, and within ±10 points for older ones. Range predictions are within 3% of real-world results. Every range figure is adjusted for the battery degradation of that specific car, unlike WLTP, which assumes a brand-new battery.
We cover the European market with 1,200+ EV models across all major manufacturers, updated at least quarterly as new models launch. Mixed fleets are normal: score the EVs with SuperElectric and keep your existing process for the rest.
Predictions are reproducible: the same vehicle inputs always return the same figures, so every number can be traced back to the data behind it. All infrastructure is EU-hosted on a dedicated instance per client, with a 99.5% uptime SLA.