Understanding Fuel Consumption in Sky Ledge
Last updated
Last updated
Fuel is one of the largest operational costs for any fleet. With the fuel consumption reporting module in Sky Ledge, fleet managers now have a data-driven way to measure efficiency and identify opportunities for cost savings.
This article explains what fuel consumption is, why it matters, how it is calculated on the platform, and what to keep in mind when interpreting the results.
What is Fuel Consumption?
Fuel consumption refers to how much fuel a vehicle uses over a given distance. In Sky Ledge, fuel consumption is expressed as litres per 100 kilometres (L/100km)—a standard measurement that allows for consistent comparison across vehicles, regardless of their size or type.
Why is Fuel Consumption Important?
Managing fuel consumption effectively is critical for several reasons:
Cost control: Fuel is often the largest variable expense in fleet operations.
Operational insight: Poor consumption figures can highlight maintenance issues or inefficient driving behaviour.
Performance benchmarking: Fleet managers can compare vehicles and drivers to identify best and worst performers.
Sustainability goals: Reducing fuel use directly contributes to lower emissions and improved environmental performance.
Risk identification: Abnormal consumption patterns may indicate fuel theft or misuse.
Accurate fuel tracking enables more informed decision-making and better long-term planning.
How is Fuel Consumption Calculated?
Sky Ledge has made significant investments in technology and data science to develop its own proprietary algorithm, purpose-built to deliver high-quality fuel consumption insights using real-time telemetry and asset configuration data. This capability is not static; it will continue to evolve as we gather more data, industry feedback, and operational insights across a wide range of vehicles and conditions over the coming weeks, months, and years.
The calculation process involves the following steps:
Collect vehicle data - For each asset, the system gathers odometer readings, speed values, fuel level percentages, and tank capacity.
Identify stationary periods - A vehicle is considered stationary if it maintains a speed of less than 1.5 km/h for longer than two minutes.
Isolate moving segments - All time periods outside stationary segments are classified as moving.
Measure fuel usage during movement - For each moving segment, the system calculates the drop in fuel level (as a percentage).
Estimate litres of fuel consumed - The percentage drop is multiplied by the tank capacity to estimate the volume of fuel used (in litres).
Calculate fuel consumption rate - The final value is calculated using the formula:
(Litres used ÷ Kilometres driven) × 100 = Litres per 100 km
This method provides a normalised, comparable view of consumption across different vehicles and use cases.
Limitations to Be Aware Of
While the reporting engine is designed to deliver reliable insights, some factors may impact accuracy:
Incorrect or missing tank capacity: Tank size must be configured correctly to ensure accurate litre estimates.
Fuel theft or mid-trip refuelling: These can cause unexpected variations in fuel level data.
Sensor source variation: Data quality may vary depending on whether fuel data is collected via digital fuel sensors, CAN bus integration, or external pumps.
Idle time complexity: Calculations currently include idle periods; improvements to idle time granularity and accuracy are in progress.
Special use cases: Vehicles with auxiliary systems (e.g., power take-off units) or non-standard driving patterns may report consumption that doesn’t fully reflect driving efficiency.
Summary
Sky Ledge’s fuel consumption module gives fleet managers a centralised, data-backed view of how fuel is used across their vehicles. It enables comparisons, highlights inefficiencies, and supports proactive operational decisions.
To ensure accurate reporting:
Confirm that each asset has the correct tank capacity set.
Review sensor integrations and check for data gaps.
Use the module to track trends over time, rather than relying on single data points.