One of the most common questions from eBike users is: “How far can I go on a single charge?” At ENVO, we use both theoretical and simulation-based methodologies to provide realistic and scientifically grounded answers. This dual-approach allows us to offer both a standardized, upper-bound estimate and a dynamic, scenario-specific evaluation of eBike range.

Method 1: Theoretical Range Estimation  

This method is based on controlled parameters to calculate the maximum possible range of an eBike under ideal conditions. It is useful for comparison across models and configurations.

Step 1: Calculating Battery Capacity  

Each ENVO eBike is equipped with a lithium-ion battery, whose energy content is measured in watt-hours (Wh):

Battery Capacity (Wh)=Voltage (V)×Ampere-hours (Ah)

Example — ENVO D50:

• Voltage: 48V

• Capacity: 15Ah

• Battery Capacity: 48V×15Ah=720Wh

This means the battery can theoretically deliver 720 watts for one hour.

Step 2: Power Consumption via PAS (Pedal Assist System) Levels  

ENVO eBikes use PAS levels from 1 (least assist) to 5 (maximum assist). The motor draws power as a percentage of its rated maximum.

D50 Motor Specs:

• Maximum power: 816W (48V × 17A)

At PAS Level 1 (10% of max power):

Power Draw=816W×0.10=81.6W

Step 3: Estimating Run Time  

Run Time (hrs)=Power Draw (W)Battery Capacity (Wh)Run Time=81.6W720Wh≈8.82 hours

Step 4: Estimating Range  

Assuming an average speed of 17 km/h at PAS 1:

Range=8.82 hrs×17 km/h=149.94 km

This figure represents the maximum theoretical range under flat terrain, optimal weather, and light assistance.

Advantages of Method 1  

• Consistent, apples-to-apples comparison across models

• Clear understanding of how battery capacity and PAS levels affect range

• Useful for estimating upper performance limits

However, real-world performance is often lower due to terrain, wind resistance, rider weight, tire pressure, and start-stop frequency.

Method 2 (Archived): Real-World Simulation Using Grin’s Trip Simulator  

To address the limitations of theoretical estimates, ENVO also uses Grin Technologies’ Trip Simulator—a dynamic modeling tool that simulates eBike range and performance based on real-world variables.

About the Trip Simulator  

Grin’s Trip Simulator is a web-based application that models eBike behavior over specific routes using inputs such as:

• Elevation and terrain gradients

• Headwinds, tailwinds, and temperature

• Load (rider and cargo weight)

• Assist level and pedal contribution

• Component configuration: motor, controller, and battery

Unlike static models, it adjusts power output in response to terrain and environmental demand. Output includes:

• Total energy consumed (Wh)

• Efficiency (Wh/km)

• Estimated range

• Battery depletion profile

ENVO’s Implementation Strategy  

To test performance in real-world conditions, we selected four Canadian cities and ran simulations based on commonly traveled commuter or recreational routes.

Simulator Configuration:  

• Motor: 500W nominal, 816W peak rear hub motor

• Controller: 48V, 17A max output

• Battery: 48V, 15Ah (720Wh)

• Load: 80 kg rider + 10 kg cargo

• Assist Mode: Moderate PAS with 60W rider input (typical for a leisure ride)

Simulated City Routes and Results  

1. Vancouver, BC  

• Route: Chilliwack to Downtown Vancouver

• Distance: 114 km

• Result: Completed on a single charge with moderate assist.

2. Toronto, ON  

• Route: CN Tower to St. Catharines

• Distance: 113 km

• Result: Achievable on one full charge with conservative PAS settings.

3. Calgary, AB  

• Route: Downtown Calgary to Drumheller

• Distance: 130 km

• Result: Reachable within one charge, despite elevation changes.

4. Montreal, QC  

• Route: Old Montreal to Hawkesbury

• Distance: 103 km

• Result: Completed with battery capacity to spare.

5: Extreme Hill Climbing

The scenarios discussed above reflect typical real-world commuting and recreational routes, but to validate the robustness of our approach, let’s examine an extreme case: a continuous 3.5 km steep climb, such as heading toward a mountain destination. In this scenario, due to the sustained high power demand and limited opportunity for regenerative or low-consumption phases, the motor must work near its peak capacity. As a result, up to 70% of the battery capacity can be consumed just during this climb.

Benefits of Method 2  

• Accounts for real elevation, weather, and rider conditions

• Offers location-specific range predictions

• Helps validate hardware choices and PAS settings

• Improves reliability and planning for daily or long-distance trips

Method 3 (Current): ENVO's Proprietary eBike Trip Simulator

Since this article was originally published, ENVO has developed and released its own proprietary eBike Trip Simulator, purpose-built around the components, motor profiles, controller behavior, and battery telemetry of the actual ENVO lineup — rather than a generic e-bike modeling framework. It is the tool we now recommend for any real-route range planning.

Why we built our own tool

The previous simulator was an excellent generic-purpose tool, but it was not aware of the specific firmware behavior, PAS curves, controller cutoffs, or battery telemetry of ENVO bikes. Our in-house simulator models the bike you are actually buying — not an approximation of it.

What it accepts as inputs

• Elevation gradients and terrain along your specific route

• Headwinds, tailwinds, and ambient temperature

• Rider weight and cargo load

• Assist level and pedal-power contribution

• ENVO model selection (each model's actual motor, controller, and battery profile is preloaded)

What it returns

• Total energy consumed (Wh)

• Efficiency along the route (Wh/km)

• Estimated range and remaining battery percentage at destination

• Battery-depletion profile across the trip

Key advantages over the older approach

• Bike-specific accuracy: uses actual ENVO motor curves and battery characteristics, not generic parameters

• CAN-BMS-aware: aligned with ENVO's CAN-enabled Battery Management System, which tracks both voltage and amp-hours in real time

• Buyer-friendly outputs: built for riders planning a commute or weekend ride, not only engineers

• Maintained by ENVO: updated as new models, motor configurations, and battery packs ship

Conclusion  

By integrating theoretical modeling (Method 1), archived simulation work via Grin (Method 2), and our current proprietary in-house simulator (Method 3), ENVO provides customers with a more comprehensive and accurate understanding of how their eBike will perform in real conditions. While Method 1 serves as a useful benchmark, the Trip Simulator allows for a highly customizable and precise prediction tailored to specific regions, routes, and riding styles.

For users and engineers interested in conducting their own performance analysis, ENVO now provides its own proprietary tool — purpose-built for the ENVO lineup — at:
https://envodrive.com/pages/envo-ebike-trip-simulator (The original Grin-based ebikes.ca trip simulator remains available as a generic-purpose reference.)

BONUS TIP:

New ENVO bikes are now equipped with a CAN-enabled Battery Management System (BMS), allowing for significantly more accurate range estimation. Unlike traditional eBikes that rely solely on voltage to estimate state of charge (SOC)—a method that can fluctuate under load and lead to misleading predictions—ENVO’s CAN system continuously monitors both voltage (V) and amp-hours (Ah) in real time. This dual-parameter tracking provides a much clearer picture of actual battery capacity and energy consumption, enabling smarter, more reliable range calculations and enhancing the overall riding experience.