Main goal
Show EV trade-offs clearly
New to EV research? This page shows, in plain language, how people may trade off range, charging time, operating cost, and purchase price when choosing an EV.
If this is your first time on this page, use this order: 1) Try the interactive demo, 2) compare buyer segments, 3) check evidence labels, 4) read market context.
Main goal
Show EV trade-offs clearly
What you control
Range, charge time, operating cost, price
What you observe
Preference direction and relative score
What this is not
A final forecast or policy result
Start from the default preset and move one slider at a time. This makes each effect easier to see.
Read the bars as directional signals: which design change tends to help or hurt preference in this simulation.
Use reported figures for market facts, and treat conceptual or illustrative figures as explanation aids.
No technical background is needed. Keep "Balanced" first, then move one slider at a time to compare how each trade-off changes the score bars.
Higher range is modeled to increase trip confidence and flexibility.
Lower charging time is modeled to improve daily convenience.
Lower running cost is modeled to improve long-term ownership comfort.
Upfront affordability can still anchor adoption for many households.
Profile score
70/100
Adoption tendency
High
Range confidence
64%
Charging convenience
71%
Cost comfort
78%
Different buyers prioritize different things. This board shows how the same four attributes can matter across three common use cases.
Urban Commuter
In dense city traffic, buyers usually care most about compact size, quick charging, and easy monthly costs.
Family Utility
Family buyers often need one car that handles school runs, weekend errands, and occasional out-of-town travel.
Long-Range Premium
For premium EVs, long highway range and ultra-fast charging usually drive purchase confidence.
These ranges are practical design guides for the experiment, not final model estimates.
This page confirms the EV choice-task structure. Full statistical model outputs are not shown here, so treat interpretation as conceptual unless a figure is explicitly labeled reported.
Choice board
3 options
Core Attributes
Range, charge, cost, price
Interpretation level
Conceptual (not full model output)
Policy analysis
Not covered on this page
These are external reference figures from recent public sources. Use them as context for realistic scenario design, not as direct outputs of this page.
Global EV Sales (2024)
17M+
Over 20% of new cars sold worldwide were electric.
Global EV Production (2024)
17.3M
China produced 12.4M EVs, over 70% of global EV output.
Public Charging Network (2024)
5M+ points
1.3M points were added in one year; fast chargers reached about 2M.
Battery Pack Economics (2025)
$99/kWh
BEV pack average in 2025; global all-segment average was $108/kWh.
Range anchor
340 km (global avg)
IEA estimates weighted on-road electric-car range at about 340 km in 2024; average electric SUVs in Europe are near 400 km.
Fast-charge throughput
150 km in 15 min
Ultra-fast charging can add around 150 km in 15 minutes; frontier systems in China cite up to 400 km in 5 minutes.
Chemistry split
LFP ~50% global
LFP represented around half of the global EV battery market in 2024 and over three-quarters in China.
Chemistry trade-off
~35-40% lower cost
In 2025 surveys, average LFP packs were around $81/kWh versus NMC at $128/kWh, with a lower energy-density profile.
Use range levels around 300 / 400 / 500 / 600 km so choice tasks match current market reality. Sub-300 km profiles can be used as economy-edge cases instead of baseline options.
Model charging as km recovered per minute and include power-tier context (regular fast vs ultra-fast). This avoids biased preferences caused by ambiguous "minutes only" labels.
Separate upfront price from battery-system economics. With 2025 BEV packs reported at $99/kWh (all-segment average: $108/kWh), transfer of cost decline to retail pricing should be tested as a scenario, not assumed as immediate.
Do not rely on price alone. Charging experience, battery chemistry, and range confidence can all shift preference. Keep product-choice insights separate from policy acceptance questions.
Before product or policy decisions, validate this module against observed purchase behavior and check three diagnostics: range realism, charging realism, and chemistry-cost realism. Treat non-reported interpretation as conceptual, not empirical.