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EV choice module · beginner-friendly walkthrough

EV Product-Choice Experiment

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.

CIEMB context: 2024 Survey format: choose 1 of 3 EV options Trade-offs compared: range, charging, cost, price Context data: 2024-2025 EV benchmarks
Start Here

First-Time Visitor Guide (60 Seconds)

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

Step 1: Keep "Balanced" first

Start from the default preset and move one slider at a time. This makes each effect easier to see.

Step 2: Compare direction, not exact truth

Read the bars as directional signals: which design change tends to help or hurt preference in this simulation.

Step 3: Check the evidence label

Use reported figures for market facts, and treat conceptual or illustrative figures as explanation aids.

Interactive Figure

Build an EV Profile and See Preference Changes

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.

540 km

Higher range is modeled to increase trip confidence and flexibility.

30 min

Lower charging time is modeled to improve daily convenience.

$11 /100 km

Lower running cost is modeled to improve long-term ownership comfort.

$37k

Upfront affordability can still anchor adoption for many households.

Profile score

70/100

Adoption tendency

High

Demo only: this animation shows trade-off direction, not final coefficient estimates.

Range confidence

64%

Charging convenience

71%

Cost comfort

78%

Vehicle Lens

Three Common EV Buyer Profiles

Different buyers prioritize different things. This board shows how the same four attributes can matter across three common use cases.

Urban Commuter

City Core Hatchback

In dense city traffic, buyers usually care most about compact size, quick charging, and easy monthly costs.

Target range
320-420 km
Charge rhythm
15-25 min quick stops
Price window
$28k-$40k

Family Utility

Household Crossover

Family buyers often need one car that handles school runs, weekend errands, and occasional out-of-town travel.

Target range
450-560 km
Charge rhythm
20-30 min corridor stops
Price window
$36k-$52k

Long-Range Premium

Grand Touring EV

For premium EVs, long highway range and ultra-fast charging usually drive purchase confidence.

Target range
580-720 km
Charge rhythm
Ultra-fast 10-18 min
Price window
$55k-$72k

These ranges are practical design guides for the experiment, not final model estimates.

Scope and Evidence

What Is Measured vs. Demo Content

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

Industry Intelligence · Updated February 2026

EV Market Context: Scale, Charging, and Battery Benchmarks

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.

2024 EV Car Sales by Major Market (million units)

Emerging EV markets also accelerated in 2024. This supports segment-specific product design instead of one-size-fits-all assumptions.

Technical Benchmarks That Influence EV Product Design

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.

Design implication: represent charging in delivered range terms (km gained), not minutes alone, and treat battery chemistry as a separate product decision.

Attribute Calibration

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.

Charging Variable Design

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.

Cost Architecture

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.

Evidence Pair

Real-World Context and Example Survey Screen

Electric cars charging in an urban mobility context
Real-world context: charger visibility and trip confidence can strongly influence EV adoption.
EV choice stimulus set used for profile evaluation
Example layout of how EV options are shown in a choice task.
Design Translation

How to Use This Page Responsibly

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.