Reasoning & math
AuraScore 83/100

Commercial Fleet Electrification Transition and Range Feasibility Plan

Evaluate route feasibility, battery capacity degradation, and total cost of ownership for commercial EV fleets.

Use this template when planning the conversion of medium-to-heavy commercial vehicle routes from internal combustion to battery electric powertrains. It calculates energy demand, charging infrastructure requirements, and phased operational milestones.

Template

Role: Senior Fleet Optimization Analyst specializing in commercial vehicle telematics and powertrain economics.

Context

  • Fleet size under transition evaluation: {{fleet_size}}
  • Average daily distance per vehicle: {{daily_route_distance_km}}
  • Nominal vehicle payload requirement: {{payload_capacity_kg}}
  • Commercial electricity tariff: {{energy_tariff_rate}}
  • Baseline diesel operating cost: {{diesel_baseline_cost_per_km}}
  • Available depot charging infrastructure capacity: {{depot_charging_power_kw}}

Task

Synthesize fleet telematics parameters and operational cost assumptions into a structured mathematical feasibility and deployment plan that calculates battery consumption limits, depot charging schedules, and financial parity milestones for the target fleet.

Method

  1. Calculate the baseline daily kilowatt-hour demand per vehicle based on {{daily_route_distance_km}} adjusted for {{payload_capacity_kg}} weight penalties and auxiliary HVAC loads.
  2. Apply state-of-charge reserve buffers (minimum 20% floor) to determine effective battery pack sizing requirements.
  3. Compute concurrent depot charging throughput against {{depot_charging_power_kw}} to establish staggered charging time windows per vehicle batch.
  4. Calculate daily operational expenditure per vehicle using {{energy_tariff_rate}} and compare against {{diesel_baseline_cost_per_km}}.
  5. Model net operating savings across the {{fleet_size}} cohort across 12-month, 36-month, and 60-month operating horizons.
  6. Identify critical operating boundary conditions where extreme temperatures or payload variations break single-charge route viability.
  7. Structure a sequenced deployment plan outlining pilot vehicle selection, charger commissioning, and driver training milestones.

Constraints

  • All consumption modeling MUST assume a minimum 20% battery reserve margin at destination arrival.
  • Calculations MUST explicitly reflect auxiliary load draw during peak weather conditions.
  • Do NOT assume midday opportunist charging unless depot capacity is exceeded.
  • Energy cost calculations MUST include peak demand charges where applicable.

Output format

Present the complete plan using the following markdown sections:

  1. Mathematical Fleet Energy Baseline (formulas, kilowatt-hour consumption, charging duration)
  2. TCO Comparison Matrix (energy vs. diesel baseline across 1, 3, and 5 years)
  3. Depot Charging Schedule (hour-by-hour power allocation table)
  4. Phased Deployment Plan (three implementation phases with timelines) Keep total output between 450 and 700 words.

Self-review

  • Confirm that power draw never exceeds {{depot_charging_power_kw}} in any single charging block.
  • Verify all mathematical calculations match the inputs provided.
  • Check that every variable is referenced logically in the underlying formulas.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

research-analysis
research-reasoning-math
transport-logistics
fleet
electrification
logistics