Vehicle Owner Battery Renter (VOR) - Rider Perspective¶
Overview¶
This model analyzes the financial decision for delivery riders (vehicle rental operators) considering switching from petrol motorcycles to electric motorcycles with battery circulation subscriptions.
Central Question: "What's in it for me?"
Integrated Workbook¶
📥 Download Full Ecosystem Model
This model is implemented in the VOR tab of the integrated workbook. All parameters are sourced from the Assumptions tab, ensuring consistency across all ecosystem models.
Business Context¶
Design Principle: Bottom-Up Modeling - Start from rider value proposition and derive all upstream requirements.
Delivery riders need to evaluate: - Daily operating costs (fuel vs. swap subscription) - Investment required for electric bike - Time to recover the investment - Long-term savings potential
Critical Constraint: Riders must achieve ≥30% cost savings vs petro to justify switching to electric.
Model Inputs¶
Source: All input parameters are defined in the
Assumptionstab of the Excel workbook and referenced using named ranges throughout the model.
Parameters (Fixed by Context)¶
These values represent physical or market constraints that are generally fixed:
| Parameter | Symbol | Value | Unit | Description | Excel Location |
|---|---|---|---|---|---|
| Petro-bike efficiency | Fuel_Eff |
35 | km/L | Distance per liter of fuel | Assumptions!C5 |
| Electric efficiency | Elec_Eff |
30 | km/kWh | Distance per kWh of electricity | Assumptions!C6 |
| Electric bike cost | Bike_Cost |
900 | USD | Purchase cost of new e-bike | Assumptions!C7 |
| Old bike residual value | Old_Bike_Val |
300 | USD | Value recovered from old fuel bike | Assumptions!C8 |
Scenarios (Changeable for What-If Analysis)¶
These values can be adjusted to explore different scenarios:
| Parameter | Symbol | Value | Unit | Description | Excel Location |
|---|---|---|---|---|---|
| Petro-fuel price | Fuel_Price |
1.40 | USD/L | Cost per liter of gasoline | Assumptions!C13 |
| Electricity cost (rider pays) | Elec_Price_Rider |
0.60 | USD/kWh | Price charged to rider for energy | Assumptions!C14 |
| Daily distance (baseline) | Dist_Day |
150 | km | Average delivery distance per day | Assumptions!C15 |
| Swap cost per day | Swap_Cost_Day |
0.80 | USD/day | Daily subscription fee for circulation service | Assumptions!C16 |
Model Outputs¶
Source: All calculations are performed in the
VORtab using formulas that reference the named ranges from the Assumptions tab.
Bottom-Up Requirement Flow¶
Step 1: Establish Petro Baseline (at 150 km/day) - Fuel consumption: 150 km ÷ 35 km/L = 4.29 L/day - Petro cost: 4.29 L × 1.40 USD/L = 6.00 USD/day - Cost per km: 0.040 USD/km
Step 2: Apply 30% Savings Target - Target electric cost: 6.00 × 0.70 = 4.20 USD/day (0.028 USD/km) - Target savings: 1.80 USD/day (30%) - This is the maximum riders can pay for electric operation
Step 3: Derive Battery Asset Operating Parameters - Energy needed: 150 km ÷ 30 km/kWh = 5.00 kWh/day - Energy price to rider: 0.60 USD/kWh (markup over 0.32 USD/kWh cost) - Energy cost: 5.00 × 0.60 = 3.00 USD/day - Remaining budget for swap subscription: 4.20 - 3.00 = 1.20 USD/day
Step 4: Battery Asset Revenue Constraint - Swap subscription budget: 1.20 USD/day (vs 0.80 at 100 km) - With 5 kWh battery: 0.24 USD/kWh/day swap fee (1.20 ÷ 5) - Energy revenue: 0.60 USD/kWh × 3 swaps × 80% utilization = 1.44 USD/kWh/day - Total revenue potential: 0.24 + 1.44 = 1.68 USD/kWh/day - Still below needed 1.76 USD/kWh/day - but much closer!
Critical Insight: Higher daily distance increases swap budget linearly but spreads battery costs over more kWh, improving unit economics!
Calculated Metrics¶
The following table shows the actual Excel implementation. Formulas are exact copies from the VRS-Vehicle-Rental-Service tab for cross-reference:
| Metric | Excel Formula | Value | Unit |
|---|---|---|---|
| petro_fuel_day | =dist_day/fuel_eff |
4.29 | L/day |
| petro_cost_day | =petro_fuel_day*fuel_price |
6.00 | USD/day |
| elec_energy_day | =dist_day/elec_eff |
5.00 | kWh/day |
| elec_cost_day | =elec_energy_day*elec_price_rider |
3.00 | USD/day |
| total_ev_cost_day | =elec_cost_day+swap_cost_day |
3.80 | USD/day |
| daily_savings | =petro_cost_day-total_ev_cost_day |
2.20 | USD/day |
| petro_cost_per_km | =petro_cost_day/dist_day |
0.040 | USD/km |
| elec_cost_per_km | =total_ev_cost_day/dist_day |
0.025 | USD/km |
| savings_pct | =(daily_savings/petro_cost_day)*100 |
36.7 | % |
| net_investment | =bike_cost-old_bike_val |
600.00 | USD |
| payback_period | =net_investment/daily_savings |
273 | days |
Calculation Logic¶
Daily Operating Costs¶
| Cost Type | Component | Calculation | Value | Unit |
|---|---|---|---|---|
| Petrol | Fuel volume needed | 150 / 35 | 4.29 | L/day |
| Fuel cost | 4.29 × 1.40 | 6.00 | USD/day | |
| Cost per km | 6.00 / 150 | 0.040 | USD/km | |
| Electric | Energy needed | 150 / 30 | 5.00 | kWh/day |
| Energy cost | 5.00 × 0.60 | 3.00 | USD/day | |
| Swap subscription | Base parameter | 0.80 | USD/day | |
| Total EV cost | 3.00 + 0.80 | 3.80 | USD/day | |
| Cost per km | 3.80 / 150 | 0.025 | USD/km |
✅ PRICING SUCCESS: Current pricing shows 36.7% savings vs petro, exceeding the 30% target!
Investment Recovery¶
| Metric | Calculation | Value | Unit |
|---|---|---|---|
| Daily Savings | 6.00 - 3.80 | 2.20 | USD/day |
| Savings Percentage | (2.20 / 6.00) × 100 | 36.7 | % |
| Net Investment | 900 - 300 | 600 | USD |
| Payback Period | 600 / 2.20 | 273 | days |
| Payback Period | 273 / 30 | ~9 | months |
Key Performance Indicators¶
| Metric | Value | Interpretation |
|---|---|---|
| Petro cost per km | 0.040 USD/km | Baseline fuel cost |
| Electric cost per km | 0.025 USD/km | 37.5% cheaper than petro |
| Daily cost savings | 2.20 USD/day | Strong incentive for adoption |
| Savings percentage | 36.7% | Exceeds 30% target |
| Payback period | 273 days (~9 months) | Fast ROI |
| Net investment | 600 USD | Manageable upfront cost |
Decision Criteria¶
✅ Favorable if: - Daily savings ≥ 30% vs petro (1.80 USD/day at 150 km baseline) - Swap subscription + energy cost ≤ 4.20 USD/day - Payback period < 18 months - Service reliability > 95%
✅ Current Status (150 km/day baseline): - Savings: 36.7% (✅ Exceeds 30% target) - Daily cost: 3.80 USD (✅ Well below 4.20 USD limit) - Payback: 273 days (✅ < 18 months) - Competitive advantage established
Sensitivity Analysis¶
Impact of Daily Distance on Ecosystem Economics¶
Critical Driver: Daily km traveled determines energy consumption, which drives the entire value chain.
| Daily Distance | Petro Cost | Electric Target (70%) | Energy Need | Energy Cost | Swap Budget | Swap Fee/kWh | Total Revenue/kWh |
|---|---|---|---|---|---|---|---|
| 100 km | 4.00 USD | 2.80 USD | 3.33 kWh | 2.00 USD | 0.80 USD | 0.24 USD/kWh | 1.20 USD/kWh |
| 150 km | 6.00 USD | 4.20 USD | 5.00 kWh | 3.00 USD | 1.20 USD | 0.24 USD/kWh | 1.68 USD/kWh |
| 180 km | 7.20 USD | 5.04 USD | 6.00 kWh | 3.60 USD | 1.44 USD | 0.24 USD/kWh | 1.92 USD/kWh |
| 200 km | 8.00 USD | 5.60 USD | 6.67 kWh | 4.00 USD | 1.60 USD | 0.24 USD/kWh | 2.08 USD/kWh |
Key Findings:
- Swap fee per kWh stays constant at 0.24 USD/kWh across all distances (30% savings constraint)
- Total revenue per kWh increases with distance due to more swaps/higher utilization
- At 150 km/day:
- Rider saves 1.80 USD/day (30%)
- Battery asset revenue: 1.68 USD/kWh/day
- Gap to profitability: only 0.08 USD/kWh/day (vs 0.32 at 100 km)
- At 180+ km/day: Model becomes profitable without further optimization!
Implication: Target riders doing 150-180 km/day to make economics work.
Interdependencies with Other Models¶
Dependencies FROM Other Models¶
| Source Model | Data Required | Impact |
|---|---|---|
| bss.md | Battery lifecycle cost | Affects swap subscription pricing |
| sns.md | Swap station density | Affects service availability |
| eps.md | Electricity cost structure | Affects operating costs |
Dependencies TO Other Models¶
| Target Model | Data Provided | Impact |
|---|---|---|
| sns.md | Rider demand patterns | Station placement & capacity planning |
| bss.md | Daily swap frequency | Battery fleet sizing |
| eps.md | Charging demand profile | Power capacity requirements |
Excel Implementation¶
Workbook Structure¶
Workbook: models/dirac-abs-ecosystem-model.xlsx
Tabs Used:
1. Assumptions - All input parameters (both fixed and scenario-based)
2. VRS-Vehicle-Rental-Service - All calculations and outputs
Excel Features¶
- Named Ranges: All parameters from the Assumptions tab are defined as named ranges for easy reference
- Cell Formatting:
- Green cells (FFE2EFDA): Calculated results
- Blue header (FFD9E1F2): Column headers
- White cells: Metric names and formulas (for reference)
- Formula-Based: All calculations use Excel formulas referencing named ranges, avoiding circular references
- No Hardcoded Values: All inputs come from the Assumptions tab, ensuring consistency across scenarios
Version History¶
| Version | Date | Changes | Author |
|---|---|---|---|
| 1.0 | 2025-11-02 | Initial model with baseline parameters | OVES Team |
| 1.1 | 2025-11-02 | Excel implementation completed with Assumptions tab integration | OVES Team |
| 1.2 | 2025-11-03 | Refactored to multi-step formulas; Calculated Metrics now use exact Excel formulas for cross-reference | OVES Team |
Next Steps: 1. ✅ ~~Build Excel implementation~~ (Completed) 2. Validate parameters with real rider data from market research 3. Develop sensitivity analysis for key variables (fuel price, swap cost, daily distance) 4. Create integration with bss.md for swap pricing validation 5. Add scenario comparison charts/visualizations