## 5 Conclusions  
This work presents a novel methodology for estimating the transported load in electric forklifts by analyzing the electrical power signals of the lifting motor within an electro-hydraulic system. The proposed approach relies on measuring the electrical energy, median power, and average speed during lifting operations, from which the lifting efficiency and potential energy are estimated, ultimately allowing inference of the transported mass. Experimental validation using 53 lifting events demonstrated that the methodology can estimate load weight with a reasonable error sufficient for practical use cases, such as the activation of warnings when the estimated load exceeds safety thresholds, providing a cost-effective and non-intrusive alternative to conventional load sensors. The results also highlight the importance of selecting robust signal descriptors--such as the median power--over conventional mean values, particularly in the presence of \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Pressure-Sensor-Based Method** & **Load-Cell-Based Method** & **Power-Based Method (This Work)** \\ \hline
**Measurement** & High accuracy due to direct measurement of hydraulic pressure & Very high accuracy; direct force measurement & Moderate accuracy; depends on correlation between power and efficiency \\ \hline
**Installation Complexity** & Requires invasive installation into the hydraulic circuit; may require fluid disconnection & Requires mechanical integration into the lifting mechanism or forks & Non-intrusive; could use existing voltage and current signals \\ \hline
**System Integration** & Needs additional hardware, wiring, and calibration procedures & May require structural adaptation and dedicated interface circuits & Easily integrates with existing forklift control/monitoring systems \\ \hline
**Cost and Maintenance** & Higher cost due to sensor exposure to hydraulic environment and periodic recalibration & High cost; sensitive to mechanical wear & Low cost; no extra sensors required \\ \hline
**Scalability and Replicability** & Limited by system-specific hydraulic architecture & Limited by forklift model and structural variations & Highly scalable and replicable across forklift models with electric drive \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between conventional load estimation methods and the proposed power-based approach. asymmetric data distribution. Moreover, 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. The integration of average lifting speed as an additional explanatory variable in a second-order regression model significantly improved the estimation performance compared to the single-variable linear approach. By combining electrical and kinematic information, the multidimensional model captured more accurately the dynamics of the lifting process, particularly under higher load conditions. This enhancement was reflected in a higher adjusted coefficient of determination (\(R_{\text{adj}}^{2}=0.9326\)), confirming that the inclusion of kinematic variables can substantially improve the reliability of load estimation without requiring additional instrumentation. This research lays the foundation for a line of investigation in intelligent monitoring and control of electro-hydraulic systems. Future work will focus on improving estimation accuracy by incorporating additional non-invasive variables, such as instantaneous lifting speed or hydraulic pressure, and by integrating physical models and observers to enhance system robustness and adaptability under varying operating conditions. Conceptualization, G.R. and S.V. ; methodology, G.R. and J.P.A. ; software J.P.A. and C.M. ; validation, J.P.A., C.M. and G.R. ; formal analysis, G.R., S.V., R.L. and R.B. ; investigation, G.R., J.P.A. and S.V. ; writing--original draft preparation, G.R., J.P.A., R.L., S.V. and R.B.