khadija2027/Forecasting-Electric-Vehicle-EV-Demand-Among-Companies-in-Morocco-2025-2040-

# 📈 Forecasting Electric Vehicle (EV) Demand Among Companies in Morocco (2025–2040) This Python script estimates and visualizes the **projected demand for electric vehicles (EVs)** among companies in **Morocco** over a 15-year horizon, under different adoption scenarios. It helps simulate the impact of public and private initiatives to accelerate EV penetration in the corporate sector. --- ## 🚀 Objective To simulate the adoption of EVs by companies in **Morocco** from **2025 to 2040**, based on eligibility criteria, average fleet sizes, and varying maximum adoption rates across three strategic scenarios. --- ## 🧮 Model Description The simulation is based on three core scenarios: 1. **Scenario 1:** Large Enterprises (GE) adopting EVs under Corporate Social Responsibility (RSE) goals – **5% fleet conversion**. 2. **Scenario 2:** More ambitious target for GEs – **10% fleet conversion**. 3. **Scenario 3:** Combination of: - Large Enterprises converting **20%** of their fleet - **11% of SMEs** (with >100 employees and aided financially) converting **5%** A **logistic growth function** models how adoption increases over time, reflecting gradual market maturity. --- ## 📍 Context: Moroccan Market Assumptions - `N_ent`: **500,000** total companies in Morocco - `V_moy_1`: Average of **200 vehicles** per large company - `V_moy_3`: Average of **10 vehicles** per eligible SME - `P_max`: Maximum adoption levels (5%, 10%, 20%) - `k`: Growth rate (**0.75**) - `t_0`: Inflection year of logistic curve (**2035**) --- ## 📊 Projected Demand Calculation Using the `adoption()` function, EV demand is calculated yearly from **2025 to 2040** for each scenario. ```python def adoption(t, P_max, t_0, k): return P_max / (1 + np.exp(-k * (t - t_0)))