## 4 Discussion  
The proposed methodology for estimating the transported load in electric forklifts using electrical variables from the lifting motor has demonstrated promising results, with an MAPE of 10.6% in the case of linear regression and 6.4% in the case of multivariable regression. However, the estimation error exhibits significant variability across the tested events. This variability highlights the need for further refinement to improve the consistency and reliability of the estimations under different operational conditions. It is important to emphasize that this study represents an initial step toward a novel research direction focused on sensorless load estimation in electro-hydraulic lifting systems. Unlike traditional approaches that rely on invasive, high-cost, and fragile load sensors installed on the forks, this method offers a non-intrusive and scalable alternative based solely on power signal analysis. It is also important to mention certain considerations in the present study regarding battery and ambient temperature conditions, which may introduce variability in the results. It is well known that temperature affects the battery's state of charge (SOC) and, consequently, the available voltage level. However, this is not expected to significantly impact the performance of the proposed estimation method. Although SOC variations do influence battery voltage, our methodology does not rely on voltage as a standalone variable. Instead, it uses electrical power--calculated as the product of voltage and current--as the primary input. For a given power demand from the lifting load, a decrease in SOC may lower the voltage, but this is inherently compensated by a corresponding increase in current. While increased current (and temperature) may slightly reduce the efficiency of the electric motor, the dominant energy losses in electro-hydraulic systems are typically mechanical rather than electrical. Therefore, the estimation methodology can be considered sufficiently robust to temperature and SOC fluctuations. Additionally, the estimation accuracy may be influenced by operator behavior, such as inconsistent lifting speeds or partial load lifts, which introduce variability in power demand that is not necessarily related to the transported mass. While our method assumes quasi-static lifting conditions to maintain a strong correlation between electrical power and lifted weight, deviations from this assumption--such as fast or interrupted lifting maneuvers--can degrade estimation performance. Although the current level of accuracy does not support high-precision applications, it is sufficient for enabling practical functionalities such as the activation of load warnings. For instance, the system could generate alerts when the estimated load exceeds a defined threshold--such as 75% of the forklift's nominal capacity--helping prevent unsafe operating conditions and improving operator awareness without requiring additional instrumentation. It should be noted that the second-order multivariable model significantly reduces estimation error under higher load conditions, where accurate weight monitoring is most critical. This makes the approach particularly valuable for enhancing operational safety during heavy-duty tasks. Additionally, Table 4 provides a comparison between the proposed power-based method and traditional pressure-sensor-based load estimation techniques. The table outlines key trade-offs in terms of measurement accuracy, system complexity, and ease of integration, highlighting the practical advantages of the proposed approach in real-world applications. Future work may focus on enhancing the accuracy of the proposed method by incorporating additional variables such as instantaneous lifting speed and hydraulic pressure. These variables can be obtained through non-invasive means and would offer complementary insights to more precisely characterize the dynamic behavior of the lifting system, enabling the integration of both electrical and kinematic effects.