## 1 Introduction
Both studies highlight the importance of considering the overall center of gravity changes to maintain operational safety. The work described in [17] created a virtual prototype based on the longitudinal and lateral stability of an AGV forklift to estimate the positions of the center of gravity within a predefined safety zone. To prevent instability during the forklift operation, a real-time estimation method for the detection of changes in the height of the center of gravity is described in [18]. In [19] the authors evaluate a prototype to implement a detection system and a center of gravity correction mechanism to prevent loss of forklift balance. Considering the structural characteristics of counterbalanced forklifts, a research area focuses on the development of anti-rollover control strategies. These strategies are designed to enhance the stability and active safety of counterbalanced forklifts based on roll energy classifications [20], stable roll partitions [21], and model predictive control (MPC) [22], to name a few. Operational safety has also been improved by the development of autonomous forklifts capable of replacing human interaction in tasks requiring transport of hazardous objects or work in unknown environments [23; 24; 25]. Advances in computer vision and deep learning have also enabled highly accurate pallet detection and manipulation, facilitating autonomous or semi-autonomous operation [26]. Complementarily, indoor localization techniques combining UWB and IMU sensors have been proposed to enhance real-time traceability and route optimization [27]. The previous paragraphs indicatethat one of the primary concerns in the study of forklifts is their dynamic stability for safe operation. According to the Occupational Safety and Health Administration (OSHA), not exceeding the rated load capacity is crucial for ensuring safety, energy efficiency, and the durability of system components during load handling [28]. However in practical scenarios, the exact weight of the transported load is often unknown, so it is necessary to develop accurate methods for estimating this operational parameter. To the best of the authors' knowledge, no publicly available research has focused on load estimation in forklift trucks, although there are some commercial patents addressing this critical issue. The patent [29] estimates the load carried by measuring the changes in the rolling circumference of the wheels caused by deformation under load by adding speed and distance sensors. In [30], electrical signals measured during the operation of a pallet truck are used to estimate the weight of the lifted load as a linear function of the energy consumed during a predefined time interval. This literature review highlights both the importance of adopting electric forklifts to achieve sustainable operations and the significance of accurately determining forklift load weight not only for preventing accidents but also for enabling more effective vehicle control, thereby enhancing both operational safety and overall efficiency. This paper proposes a methodology for estimating the transported load using electrical variables supplied by the DC-DC converter to the DC motor that drives the hydraulic pump of the forklift lifting system. The results shows a mean absolute percentage error (MAPE) of 10.6% when comparing measured and estimated load (by linear regression) for 53 experimental tests. When multivariable regression is applied, the MAPE is reduced to 6.4%. These results demonstrate the efficacy of the proposed methodology by estimating load weight with a reasonable error sufficient for practical use cases. The approach provides a cost-effective and non-intrusive alternative to conventional load sensors while opening future research in the field of industrial automation, energy optimization, and safe operation of industrial electric forklifts.