Vilma Alves Oliveira

In this paper, a real-time optimized sigmoid-based energy management system (EMS) is proposed for a fuel cell hybrid vehicle (FCHV). The power demand is dynamically shared among the fuel cell, a lithium-ion battery (LIB), and an ultracapacitor. The sigmoid function is employed to regulate the full cell operation, ensuring higher efficiency. A real-time particle swarm optimization (RT-PSO) algorithm defines both the power distribution and the ultracapacitor frequency response in a multi-objective framework that accounts for source degradation, fuel consumption, and power losses, without relying on prior knowledge of the driving cycle. To reduce computational complexity, the optimization is executed at fixed time steps. Numerical results demonstrate the enhanced performance and real-time feasibility of the proposed strategy, increasing LIB lifetime by factors of 1.41 and 1.74 compared to global and non-optimized strategies, respectively, while reducing operational costs by 16.5% and 27.4%. These results confirm that the proposed EMS is a suitable real-time solution with optimal performance and low computational burden.

Accurate pressure prediction is essential for predictive control in variable-rate agricultural sprayers, but changes in pump command, valve opening, and intermittent nozzle activation modify the hydraulic resistance and reduce the validity of fixed AutoRegressive with eXogenous input (ARX) models. This work evaluates hydraulic-regime-dependent ARX pressure models for a Smart Weed Sprayer and proposes a fixed-denominator adaptive-numerator structure for control-oriented prediction. An experimental dataset with 84 tests was collected under different pump duty cycles, valve openings, and active-nozzle configurations. The hydraulic regime was characterized by the equivalent resistance index ρ = log10(KTeq). Local ARX models showed good accuracy within their identification regimes, but poor portability across operating conditions, with the mean Root Mean Squared Error (RMSE) increasing from 0.4492 in diagonal validation cases to 2.2377 in off-diagonal transfers. The analysis showed that the denominator polynomial was not robustly related to ρ, while the numerator coefficients were more sensitive to hydraulic changes but not sufficiently explained by direct scheduling alone. Therefore, the proposed model keeps a nominal denominator A0(z⁻¹) fixed and adapts the numerator B(z⁻¹, k) online using Recursive Least Squares (RLS). Compared with fixed, scheduled, lookup-table, and oracle benchmarks, the RLS model reduced the free-run mean RMSE from 6.6580 to 1.4762, corresponding to a 77.8% reduction. These results show that online error-driven adaptation provides more robust pressure prediction than fixed or directly scheduled ARX models across changing hydraulic regimes.

Precise flow rate control in agricultural sprayers is critical for variable-rate application, but is challenged by nonlinear dynamics, dead zones, and model variations due to operational changes and nozzle switching. This work aims to develop an efficient control strategy capable of handling these challenges while ensuring compliance with operational constraints. To this end, a generalized predictive controller (GPC) integrated with a dynamic output-linearization scheme is proposed. The dynamic linearization plays a key role to enable adaptive control under practical operating conditions, without requiring a detailed characterization of the hydraulic system. To support this framework, a simplified Kalman filter is employed, to estimate, in real time, the total equivalent fluidic resistance of the system. Experimental validation was conducted using an agricultural sprayer development system, demonstrating superior performance compared to traditional methods, with improved accuracy and adaptability in the presence of disturbances. The proposed output linearizing fuzzy GPC controller (L-FGPC) effectively handles nonlinearities and operational constraints, offering a practical solution for advanced agricultural spraying systems.

This article proposes an optimized real-time clustering strategy for state-of-charge (SoC) balancing in DC microgrids (MGs) integrating multiple battery energy storage systems (BESSs). The method employs a neighbor-to-neighbor communication framework, where clusters are dynamically formed based on local load conditions and the state-of-health of the storage units. Within each cluster, consensus-based SoC balancing is achieved through sigmoid functions, ensuring smooth control actions without discontinuities. In addition, bus voltages are locally regulated by phase-lag compensators, activated after DC-link voltage restoration, thereby enhancing system stability. The effectiveness of the proposed approach is validated through theoretical analysis, numerical simulations, and experimental evaluations. Results demonstrate that the strategy reduces line resistance power losses by up to 4.65% when compared to global SoC balancing schemes reported in the literature. Furthermore, it achieves an operational cost reduction of up to 11.89%, corresponding to savings of ${\$}$0.46 per hour, while extending BESS lifetime up to 10.08%. The approach also exhibits resilience under adverse conditions, maintaining reliable performance in the presence of communication failures and BESS disconnections.

The Undergraduate Committee of the São Carlos School of Engineering (EESC-USP) has created two optative tutorial classes to guide and welcome new students from the 10 undergraduate engineering programs offered. With practical activities and motivational and informative lectures, the objective is to bring to students the academic, sociocultural and scientific dimensions that the university can offer during their studies along with opportunities to be engaged in extracurricular projects. Academic Tutoring I is provided to new students in the first semester, starting in 2022. Academic Tutoring II is offered in the second semester, starting in 2023, and was created to continue supporting first-year students at EESC. This article presents the proposal for academic tutoring, considering the main performance issues that students currently face, such as the increase in anxiety and depression among young people and the problems arising from the isolation caused by Covid-19. The results of new students' acceptance of the tutorial classes were obtained through qualitative and quantitative research and also student reports. It was evident that the subjects provided a better welcome and encouraged discussions about competencies and skills in the context of diversity and inclusion.