New Physics Model Predicts Electric Truck Energy Use Across Rough Terrain

Heavy electric trucks running across India face complex route challenges that require precise energy calculations to make zero-emission freight economically viable. A new physics-based energy model successfully predicts how much power commercial trucks consume across diverse landscapes, turning chaotic route data into reliable planning tools.

Logistics expenses currently account for 14 percent of India’s gross domestic product while driving up emissions through high fuel consumption. Electrifying the nation’s commercial fleet offers a path to lower operating costs, but traditional transportation planning relies on rough estimates that fail to capture steep mountain grades, fluctuating payloads, and heavy traffic. To solve this, researchers built an integrated energy framework combining vehicle dynamics with real-world operating data from 26 distinct routes across the country.

How Physics Outperformed Machine Learning in Data-Scarce Freight Corridors

The research team tested their physics-driven equation against standard machine learning regression techniques using the exact same dataset of 207 route-vehicle combinations. The study, published in Re-Generation in Transport, reveals that the physics model achieved a strong predictive capability with a coefficient of determination, or R-squared, of 0.813. By contrast, pure data-driven regression approaches scored only 0.28, while polynomial and random forest models suffered from severe overfitting with negative cross-validated scores.

The model calculates total energy consumption by breaking it down into traction force, auxiliary systems, and mechanical losses. Traction force itself accounts for rolling resistance, aerodynamic drag, gravitational pull on slopes, and acceleration. Route elevation profiles were pulled at 30-meter spatial resolution using the Google Maps Elevation API, while vehicle performance benchmarks were cross-referenced against real-world fleet telemetry.

Why Truck Loading Does Not Increase Energy Consumption at a One-to-One Ratio

The analysis examined seven distinct truck segments ranging from 14-tonne light commercial vehicles used in cities to 55-tonne ultra-heavy trucks deployed in mining operations. When testing loading scenarios between 80 percent and 110 percent capacity, the authors discovered a fascinating sub-proportional energy increase, yielding a loading elasticity between 0.75 and 0.78.

• For 19-tonne medium trucks shifting from 80 to 100 percent load, a 25 percent weight increase resulted in an 18.8 percent rise in energy use.
• For 40-tonne heavy trucks moving from 100 to 110 percent load, a 10 percent weight increase caused a 7.8 percent rise in energy consumption.

These values below 1.0 prove that weight additions do not scale linearly with power demands, meaning intelligent load management can unlock hidden efficiencies. Furthermore, mining circuits demonstrated unique dynamics where downhill travel used extra weight to boost regenerative braking recovery by up to 15 percent.

What These Findings Mean for Fleet Owners and Infrastructure Planners

Applying this modeling framework to India’s massive 7.5 million truck fleet highlights potential annual energy savings between 45 and 67 terawatt-hours. Accurate route prediction cuts vehicle payback periods down to two to three years instead of seven to ten years under poor planning. The authors note that while the core physics equations are universal, the parameters must be recalibrated with local road data when applied to other emerging markets.

The research was published in Re-Generation in Transport on January 1, 2027.