AI-Powered EV Intelligence

Predict. Analyse. Electrify.

Advanced ML models predicting Electric Vehicle
CO₂ Savings & Innovation Score with 99%+ accuracy

EV HOLOGRAM

SCROLL

Capabilities

What We Predict

Dual-model intelligence delivering actionable EV insights

CO₂ Savings Predictor

Quantify carbon footprint reduction vs petrol vehicles using XGBoost with 99.57% R² accuracy. Accounts for grid emission factors.

99.57%R² Score

Core Engine

Innovation Score Engine

Composite 0-1 index measuring Tech Edge, Energy Intelligence & User Value via Linear Regression with 99.04% precision.

99.04%R² Score

Market Analytics

Interactive visualizations showing correlations, segment benchmarks, training convergence and feature importance heatmaps.

6+Chart Types

Model Performance

Production-Grade Accuracy

CO₂ Savings Model

XGBoost Regressor

99.57%

Innovation Model

Linear Regression

99.04%

Feature Analysis

Feature Importance

Which vehicle parameters drive our predictions the most

Ready to Evaluate Your EV?

Enter your vehicle specifications and get instant AI-powered predictions

ML Prediction

EV Prediction Dashboard

Enter vehicle specifications to instantly predict CO₂ Savings & Innovation Score

Price Currency

1 EUR = ₹90.91

Vehicle Specifications

Battery Capacity kWh

20 – 130 kWh | Avg: 75 kWh

Efficiency Wh/km

130 – 300 Wh/km | Lower = better

Fast Charge Speed km/h

150 – 1300 km/h range added

Price (EUR €) €

€20,000 – €250,000

Range km

130 – 700 km | Full charge range

Top Speed km/h

120 – 330 km/h

Prediction Results

CO₂ Savings

--

kg CO₂ saved

vs equivalent petrol vehicle

Innovation Score

--

Innovation Index

-- innovation rating

Innovation Performance

--%

Sustainability Score

--%

Innovation Breakdown

Tech Edge

--%

Energy Intelligence

--%

User Value

--%

Environmental Impact

--

Trees equivalent / year

--

Litres petrol saved

--

Charge speed index

--

kWh per 100 km

Market Position Analysis

Market EVs Your Vehicle

EV Performance Rating

-- Calculating...

--

Overall

-- / 5.0

Range Performance

--

Energy Efficiency

--

Charging Speed

--

Value for Money

--

Innovation Level

--

Eco Impact

--

S+ Elite ≥ 90

A Advanced 75–89

B Good 60–74

C Standard 45–59

D Basic < 45

Deep Analytics

Advanced Analytics Dashboard

Model performance, feature correlations, and data-driven EV market insights

99.57%

CO₂ Model R²

99.04%

Innovation Model R²

360

Training Samples

5-Fold

Cross Validation

Model Performance Radar

Comparative performance across 5 evaluation metrics

Feature Importance

Impact on CO₂ vs Innovation predictions

Correlation Heatmap

Feature relationships with target variables

Training Convergence

Model accuracy improvement over training iterations

EV Market Segments

Average scores by vehicle category

Dataset Distribution

Breakdown of 360 EVs by segment

Platform Documentation

About EV_aluate

Architecture, methodology, and use-case documentation for the EV Intelligence Platform

The Platform

EV_aluate is a dual-model machine learning platform engineered to quantify two critical dimensions of electric vehicle performance: environmental sustainability through CO₂ savings prediction, and technological advancement through a composite Innovation Score.

Built on XGBoost and Linear Regression architectures, both models exceed 99% predictive accuracy on a curated dataset of 360 European electric vehicles sourced from the EV Database.

360

EVs in Dataset

99%+

Accuracy

2

ML Models

6

Input Features

Methodology

Data Pipeline

01

Data Collection

360 EV records sourced from ev-database.org with 9 raw features per vehicle

02

Target Engineering

CO₂ savings formula: (range × 0.12) − (efficiency × range / 1000 × 0.4)

03

Innovation Scoring

Composite index = 40% Tech Edge + 40% Energy Intelligence + 20% User Value

04

Outlier Removal

IQR-based outlier detection with 1.5× fence — ensures robust generalization

05

Feature Scaling

Min-Max normalization to [0, 1] range for consistent model behavior

06

Feature Selection

Pearson correlation analysis selecting optimal 5-6 feature subset

07

Train-Test Split

80% training / 20% testing with stratified sampling for balanced evaluation

08

Hyperparameter Tuning

GridSearchCV optimization for XGBoost — n_estimators, max_depth, learning_rate

09

Cross-Validation

5-fold CV delivering R² = 0.9938 (CO₂) and 0.9924 (Innovation)

Applications

Industry Use Cases

Manufacturers

R&D Optimization — Focus resources on high-impact features
Competitive Analysis — Benchmark against market leaders
Product Positioning — Identify market gaps & opportunities
Feature Prioritization — Data-driven design decisions
Cost-Benefit Analysis — Optimize price-performance ratio

Policymakers

Incentive Design — Target subsidies effectively
Emission Targets — Set realistic CO₂ reduction goals
Sustainability Metrics — Track environmental progress
Market Analysis — Understand EV adoption trends
Regulatory Framework — Evidence-based policy decisions

Consumers

Purchase Decisions — Compare EVs objectively
Value Assessment — Evaluate price vs. features
Environmental Impact — Quantify carbon footprint
Total Cost of Ownership — Long-term savings analysis
Performance Comparison — Tech-savvy informed choices

Artifacts

Model Files

Filename	Description	Size	Format
`xgb.pkl`	XGBoost model for CO₂ savings prediction	~2.5 MB	Joblib
`linear.pkl`	Linear Regression model for Innovation Score	~50 KB	Joblib
`columns.pkl`	Feature columns for CO₂ model (5 features)	~1 KB	Joblib
`columns_linear.pkl`	Feature columns for Innovation model (6 features)	~1 KB	Joblib

Stack

Technology Stack

Python 3.8+

FastAPI

XGBoost 2.0+

Scikit-learn

Pandas 2.0+

NumPy

Three.js

Chart.js

GSAP

Joblib 1.3+

Roadmap

Future Enhancements

Real-time Market Data Integration

Live EV pricing and specifications APIs

Q2 2025

Global Currency Expansion

15+ currencies with regional grid emission factors

Q3 2025

Native Mobile Application

iOS and Android with offline prediction capability

Q4 2025

Deep Learning Models

Neural networks for image-based feature extraction

Q1 2026

Charging Network Integration

Real-time charging station availability and routing

Q2 2026