The science behind your projection — in plain words

The blue line is what we expect your battery's State of Health (SOH) to be at each year — what we call the median (P50) projection. It is the most likely outcome given your inputs.

The shaded band around it is the P10–P90 confidence range — 80 % of plausible outcomes. The band gets wider as time passes because the further we project, the less certain we can be.

The red dashed line at 70 % is the typical warranty floor that most EV makers guarantee for 8 years or 160,000 km. When the median crosses this line, we mark the year — useful for resale planning.

The decomposition bar below the chart splits the projected loss into two parts: how much comes from time + climate (calendar) and how much from kilometres + charging style (cycle).

Calendar aging happens just from time passing. Even if you parked the car and never drove it, chemistry inside the cells continues — slow but steady — and heat speeds it up dramatically. A pack at 30 °C ages roughly 35 % faster than one at 20 °C.

Cycle aging happens when energy moves through the battery. Every charge and discharge contributes a tiny amount of wear. The more kilometres driven, the more cycles, the more wear.

Real batteries always show a mix of both. Our simulator computes them separately and adds them together — that is what the decomposition bar shows you.

Heat is the single biggest factor in calendar aging. The reaction inside lithium cells follows a physical law (the Arrhenius equation) where the aging rate climbs steeply with temperature. How steeply depends on the chemistry: LFP has the gentlest temperature response — it ages the slowest in heat — while NMC and NCA climb a little faster.

Two cars from the same factory, identical except one lives in Bogotá and the other in Cartagena, will diverge by 10–15 % SOH after a decade — purely because of climate.

We suggest a zone based on your country — and you pick the one that matches where the car actually lives: if it spends its days on the coast or in the lowlands, choose tropical. The simulator runs the same equation with the temperature of the zone you chose:

One more thing shapes how hot your pack actually runs: its cooling system. Liquid-cooled packs (most EVs since ~2019) actively hold the battery near a safe temperature. Passively-cooled packs — the classic Nissan Leaf, Renault Zoe, VW e-Golf — have no such system: the pack soaks up heat and stays hotter. The simulator accounts for this: a passive pack runs the same physics at an effective temperature about 10 °C higher, which in the field showed up as the air-cooled 2015 Leaf losing capacity almost twice as fast as a liquid-cooled Tesla of the same year (Geotab, 10,000-vehicle study). Same brand-agnostic rule for everyone: it is the cooling system, not the badge.

Not all lithium batteries are the same. The chemistry — the specific recipe inside each cell — controls how fast the pack ages, how it behaves at extreme temperatures, and how many fast-charging cycles it can take.

Most modern EVs use one of these:

Slow charging at home (a normal outlet or Level 2 wallbox) is the gentlest possible scenario for your battery. Energy enters slowly, cells stay cool, and the chemistry barely notices.

DC fast charging (>50 kW at public stations) pushes energy in much faster. Cells heat up, chemistry strains, and a tiny additional amount of wear happens with each session. Used occasionally — like on road trips — the impact is small. Used as your default daily charging method, it adds up.

Real-world evidence is mixed: Geotab (tracking 22,700 EVs in 2026) links heavy fast-charging to roughly +1 %/year of extra degradation, while some fleet analyses find no clear effect at all. The clearest penalty shows up only under the harshest conditions — frequent fast charging in extreme heat (lab work puts that upper bound near +15–20 % per cycle). So the simulator treats fast charging as a mild, upper-bound stressor in the cycle term, not a guaranteed hit.

If we showed only one curve, we would be claiming a precision we do not have. Real batteries vary — manufacturing tolerance is around ±2 %, driving styles vary, climate has microclimates, and we cannot know how much fast charging you will do over 15 years.

Instead, we run 200 simulations in your browser (it takes about 50 milliseconds), each one with slightly different assumptions: a bit warmer, a bit cooler, a bit more DCFC, a bit less. We then take the 10th, 50th, and 90th percentiles to draw the band.

The band tells you something honest: under the inputs you chose, 80 % of plausible outcomes fall inside the shaded area. The other 20 % are unusual but possible — better or worse than the band.

This is a projection from physics-grounded models, not a measurement of your specific battery. Two cars with identical inputs can age differently because of factors no model captures: how often you preconditioned the pack before fast charging, whether your garage has direct sun exposure, manufacturing variance from the cell line.

All the coefficients in our math come from peer-reviewed papers (NREL, Sandia National Laboratory, Naumann 2018, Schmalstieg 2014) plus large fleet observations (Geotab 2026, Recurrent). They are physics-informed defaults, not yet calibrated against our own LATAM scan data.

As real owners scan their batteries with SOHpro, we accumulate a calibration log — predicted vs. measured SOH per chemistry × climate cohort. When that log reaches a meaningful sample size, we will publish a calibration report showing how well our predictions held up. Until then, treat the simulator as a thoughtful estimate, not a guarantee.

A real first-scan report goes beyond the projection. When your scanner exposes them, the audit report surfaces five direct indicators measurable from a single scan: round-trip energy efficiency, coulombic efficiency, cell voltage σ, HV insulation margin, and OCV-vs-SOC consistency. Each comes with a healthy band sourced from peer-reviewed literature (NREL, Schuster 2015, Plett 2015, ISO 6469-3). They don't replace the projection — they're independent evidence that complements it.

Your report's own SOH number follows a measurement-first ladder. When the BMS exposes its current vs factory nominal capacity (amp-hours), we use that capacity ratio directly — the textbook definition of State of Health — ranked just below an explicit BMS SOH% and above any age/mileage estimate. Only when no measured capacity is available do we fall back to the physics model this simulator uses. Full ladder in the Report Technical Guide §3.1.

From May 2026 the SOH projection (P10 / P50 / P90) feeds into a 7-tier letter rating on the report. The same composite-Score engine that powers the simulator's central estimate produces a 0–100 SOHpro Score, which maps to a letter A+ → D in the published report (thresholds: A+ ≥97, A 93–96, B+ 88–92, B 82–87, C+ 76–81, C 70–75, D <70). Lower cuts track OEM warranty floors; upper cuts track empirical fleet density per Geotab 2026. Full methodology in the Report Technical Guide §7.

Every certified scan adds a verified entry to your electric's passport — locked to the VIN, co-signed by the partner shop, and timestamped at the moment the BMS was read. The passport travels with the car: the next owner inherits the full history without re-paying for it.

Each visit also strengthens the audit trail. Five direct indicators (round-trip efficiency, coulombic efficiency, σ of cell voltages, HV insulation margin, OCV consistency) and 11 integrity checks (cell activity, balance, thermal, insulation, DTC, SoC↔V consistency, energy / coulombic efficiency, pack authentication, power capability) run on every scan. From the second visit onward, your dashboard shows the trajectory of each: did insulation tighten over the past year? Did cell balance drift? You see the answer in a single chart.

Any past visit can be opened in audit mode. From the multi-visit dashboard, click any visit's date and you get the full forensic snapshot of that scan — same data the partner shop saw on day one, stamped and verifiable. The passport is not a marketing line: it is a stack of co-signed records anyone — buyer, insurer, regulator — can replay.

Only measured values build your trend (since July 2026). The trend line behind your history uses exclusively measured SOH: the BMS's own reading, the BMS's internal capacity ratio, or an energy measurement across two visits. When a report's SOH had to be *estimated* — from age and mileage, or from the same physics model this simulator runs — that estimate appears on the report clearly labeled, but it never becomes a point in your trend. The reason is honesty: if the model's own estimates fed the trend, the "observed" degradation would just be our model echoed back at itself. Estimates inform; only measurements accumulate. Visits whose measured SOH comes from an energy/capacity method (not the BMS's own percentage) show a small "estimated" disclosure badge next to the chart.

Scans persisted before May 1, 2026 are read-only. They keep the values they had when stored, never re-graded by newer math. Their place in the longitudinal arrays is preserved as a gap rather than backfilled — a deliberate choice that protects actuarial replay (the same input must always produce the same output, even years later).

Your SOH says how much capacity is left. Turning that into kilometres needs three things: how much usable energy your pack holds when new (from the catalog), the WLTP range published by the manufacturer, and how warm your climate is. We surface three views side-by-side so you see what each assumption means.

WLTP-equivalent is the simplest view — what would the spec sticker say at your current SOH? It is the apples-to-apples comparison your owner's manual was written for. Math: WLTP × SOH/100. For an Ioniq Electric facelift at 89 % SOH (WLTP 311 km), that is 277 km.

Real-world P50 estimates the typical kilometres your battery delivers in everyday driving. Real-world consumption is consistently ~18 % higher than the WLTP cycle (ADAC EcoTest median across 100+ EVs since 2017), and warmer climates add a small AC penalty. We multiply consumption by the climate factor and the real-world factor, then divide your usable kWh × SOH by it.

P10–P90 band acknowledges that any single number lies. Real driving varies — stop-and-go traffic, payload, AC at maximum, highway versus city. Energy-budget range estimators in the literature cluster at 10–15 % MAPE in real-world validation (Liu et al., *Renewable and Sustainable Energy Reviews* 156, 2021); our band is wider on the adverse side to account for the worst routine conditions.