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.

This study addresses the environmental problems stemming from greenhouse gas (GHG) emissions from public transportation in the cities of Chillán and Chillán Viejo. Specifically, it analyzes emissions from fossil-fuel-powered buses, which contribute to climate change and negatively affect people’s quality of life. Given this situation, the need to reduce these emissions and move towards more sustainable mobility systems is recognized. The main objective of this research is to estimate the CO₂-equivalent reduction that could be achieved by replacing fossil-fuel-powered public transportation with electric vehicles in the aforementioned cities. To this end, the ISO 14064 methodology is used, subject to specific scope and limitations. This study reviews key aspects, including current environmental legislation, Chile’s international commitments regarding climate change, and the state of emissions in the transportation sector, to illustrate the current state of electromobility in Chile. Finally, the impact of the transition to electromobility in public passenger transport by bus is quantified, resulting in a reduction of 9429 tons of CO₂ equivalent emissions, equivalent to 63.4% compared to the 2023 bus fleet, considering the average emission factor of the national electricity system for 2023. The proposal consists of implementing a 100% electric bus public transport system, highlighting its advantages in reducing emissions, improving energy efficiency, improving air quality, and reducing noise pollution. However, this requires a significant financial investment of approximately USD 57 million, excluding public charging stations for electric buses. Furthermore, public policies offering means of accelerating the transition of public transport in Chile are analyzed.

Heavy-duty freight transport remains one of the most difficult sectors to decarbonize due to its high energy demand, long-distance operation, and strong dependence on diesel fuel. In Chile, more than 90% of heavy trucks operate with diesel engines, contributing significantly to greenhouse gas emissions and local air pollutants. At the same time, Chile has favorable conditions for the development of green hydrogen due to its world-class solar and wind resources. This study presents a technical and economic assessment of green hydrogen fuel cell trucks recently introduced in Chile, comparing their operational performance with conventional diesel freight trucks. A techno-economic framework based on total cost of ownership, fuel consumption, operational range, fleet utilization, and hydrogen price scenarios was developed using information reported in public studies and official Chilean strategic documents. The results indicate that hydrogen trucks are technically suitable for long-haul and intensive-duty operations due to their rapid refueling capability and high operational autonomy. However, economic competitiveness remains strongly dependent on hydrogen price, fleet scale, infrastructure utilization, and vehicle capital cost. Under current market conditions, diesel trucks preserve cost advantages, while hydrogen trucks become increasingly competitive as hydrogen prices approach long-term target values and annual mileage increases. Chile’s renewable resource base positions the country as a strategic candidate for early adoption in mining, logistics corridors, and captive fleets. The study concludes that hydrogen freight transport can become a realistic decarbonization pathway if accompanied by targeted public policies, infrastructure deployment, and industrial scale-up.

This paper presents an initial estimate of the energy demand and charging power requirements for the initial replacement of internal combustion public transport buses by battery electric buses in Chillán and Chillán Viejo, Ñuble Region, Chile. The analysis is based on an operational baseline derived from route lengths, service frequencies, departures, and fleet data, combined with official energy and transport demand projections. A case study is conducted for the introduction of 10 battery electric buses on Line 13 of public transport, comparing 100% overnight depot charging and 80% overnight charging complemented by opportunity charging. Results show that the initial 10-bus deployment would require an installed depot charging power between 300 and 450 kW, depending on the charging strategy, with an annual delivered energy of 1149.75 MWh. Long-term scaling scenarios suggest that bus-dedicated charging infrastructure could require between 4.52 MW and 22.35 MW by 2050. Although the numerical results are specific to the case study, the main contribution of this study lies in an operationally grounded planning framework that links the sizing of pilot routes with long-term charging demand scenarios and charging infrastructure projections, providing a transferable basis for preliminary electric bus planning in other mid-size or emerging regions facing similar infrastructure constraints.

This study proposes and evaluates a data-driven framework for short-term System Marginal Price (SMP) forecasting in the Chilean National Electric System (NES), a power system characterized by high penetration of variable renewable generation and persistent transmission congestion. Using publicly available hourly operational data for 2024, multiple machine learning regressors including Linear Regression (base case), Bayesian Ridge, Automatic Relevance Determination, Decision Trees, Random Forests, and Support Vector Regression are implemented under a node-specific modeling strategy. Two alternative approaches for predictor selection are compared: a system-wide methodology that exploits lagged SMP information from all network nodes; and a spatially filtered methodology that restricts SMP inputs to correlated subsystems identified through nodal correlation analysis. Model robustness is explicitly assessed by reserving January and July as out-of-sample test periods, capturing contrasting summer and winter operating conditions. Forecasting performance is analyzed for representative nodes located in the northern, central, and southern zones of the NES, which exhibit markedly different congestion levels and generation mixes. Results indicate that non-linear and ensemble models, particularly Random Forest and Support Vector Regression, provide the most accurate forecasts in well-connected areas, achieving mean absolute errors close to 10 USD/MWh. In contrast, forecast errors increase substantially in highly congested southern zones, reflecting the structural influence of transmission constraints on price formation. While average performance differences between M1 and M2 are modest, a paired Wilcoxon signed-rank test reveals statistically significant improvements with M2 in highly congested zones, where M2 yields lower absolute errors for most models, despite relying on fewer inputs. These findings highlight the importance of congestion-aware feature selection for reliable price forecasting in renewable-intensive systems.

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.