Electric forklifts are increasingly adopted in industrial environments due to their energy efficiency, reduced emissions, and lower operating noise compared to combustion alternatives. This paper presents a novel methodology for estimating the transported load weight in electric forklifts based on the output power signal of the DC–DC converter driving the electro-hydraulic lifting system. The proposed method leverages non-intrusive measurements of voltage and current to compute the lifting power, lifting speed, and energy, also allowing the computation of the lifting efficiency. The analysis confirmed that lifting energy is not linearly correlated with transported weight but lifting efficiency can be reasonably approximated as a function of lifting power and lifting speed, subsequently allowing the estimation of the transported mass. Experimental validation using 53 lifting events demonstrated that the methodology can estimate load weight with a reasonable mean absolute percentage error of 10.6% and 6.4% when using linear or multivariable regression analysis, respectively. These results demonstrate that the approach is sufficiently accurate for practical applications such as triggering load warnings when the estimated mass exceeds predefined safety thresholds.

Energy performance in small utility electric vehicles (SUEVs) emerges from coupled interactions among route topology, tire condition, transported load, drivetrain operating point, and battery state. However, integrated experimental analyses of these factors remain scarce, particularly for commercial platforms with limited manufacturer information. This study presents an experimental and physics-based framework for system-level characterization of a commercial SUEV under realistic operating conditions. An onboard acquisition system records DC bus and induction motor voltages and currents, complemented by GNSS measurements. Physics-based observers derived from the motor model reconstruct rotor flux, electromagnetic torque, rotor speed, and converted mechanical power, enabling energy flow analysis and effective transmission ratio identification with reduced instrumentation. Tests were conducted on flat, uphill, and downhill campus routes under four tire pressure levels and two payload conditions. Under baseline conditions, normalized DC energy consumption ranged from 1.13 kWh/100 km on the downhill route to 19.86 kWh/100 km on the uphill route, demonstrating the dominant influence of terrain. Severe tire underinflation increased consumption by 26.0%, 30% and 52.2% on the flat, uphill and downhill routes, respectively. Beyond the specific numerical results, the proposed framework integrates electrical measurements, physics-based modeling, virtual sensing, and energy performance indicators into a unified methodology for characterizing commercial SUEVs under real operating conditions, even when proprietary drivetrain information is unavailable.