India EV Penetration Forecast Model

Business Question — what we set out to answer

India's EV transition is uneven across segments and states. This model projects penetration trajectories using an S-curve (Gompertz) framework calibrated on monthly FAME and VAHAN registrations, asking when two-wheelers and four-wheelers cross 10% and 30% of annual sales — and why states like Maharashtra, Karnataka, and Tamil Nadu lead while others lag.

Data Sources — where the data came from

• VAHAN Dashboard (MoRTH): Monthly segment-wise vehicle registrations by state, FY21–FY26.
• FAME II & PM E-DRIVE: Subsidy disbursal and scheme claims data.
• Society of Indian Automobile Manufacturers (SIAM): Industry sales volumes.

Methodology — how we modelled it

• Segment split: E2W (two-wheelers), E4W (passenger four-wheelers), E3W, and e-buses modelled independently.
• S-curve fit: Gompertz curve fitted to monthly penetration rates using non-linear least squares (scipy.optimize.curve_fit).
• Scenario bands: Base, Accelerated (faster infrastructure + subsidies), and Conservative scenarios.
• State scoring: Weighted index of charging density, state EV policies, and TCO advantage.

Key Insights

• E2W leadership: Two-wheelers are 5–7 years ahead of four-wheelers; they crossed 10% penetration in FY25.
• Price-point inflection: When E2W TCO dropped below ICE on a 5-year basis, adoption bent sharply upward.
• Infrastructure premium: States with higher charging density per capita show markedly steeper S-curves.
• Four-wheeler wait: Passenger EV adoption is gated by fast-charging networks and new platform launches.

Limitations

Model relies on registration data that can lag production; subsidy policy changes can shift the S-curve materially. Scenario bands should be read as ranges, not point forecasts.