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This paper presents the design of an extended Kalman filter (EKF) as a state-of-charge (SoC) estimator for a lithium–titanate (LTO) battery cell. The design is based on a Thevenin model of the equivalent battery electrical circuit with two parallel resistive-capacitive (RC) elements for the emulation of electrolyte polarization effects. The effectiveness of the proposed SoC estimator is verified through computer simulations on the comprehensive nonlinear battery simulation model featuring battery parameter vs. SoC variations, which is subject to highly dynamic loading regimes.

The purpose of this study is to enhance control strategies for selective catalytic reduction (SCR) and ammonia slip catalyst (ASC) systems, aiming to effectively reduce NOx emissions from automotive engines during realistic driving cycles. Despite the effectiveness of these after-treatment systems (ATS), their dynamic and non-linear characteristics present significant challenges in achieving precise control. Therefore, this research proposes a hybrid approach that combines backward induction (BI) as the primary optimization technique with model predictive control (MPC) framework for real-time application. The article introduces a reduced-state control-oriented model of the SCR + ASC system, which is embedded into the BI algorithm to calculate optimal control actions within a finite horizon. Additionally, it is proposed an alternative approach for adapting the grid of model states within the BI algorithm, effectively reducing the computational cost. This adjustment enables the algorithm to operate in real-time with near-optimal results, as confirmed by experimental validation. Lastly, the study explores how different degrees of knowledge regarding system disturbances impact the strategy’s performance, examining three distinct scenarios: constant prediction horizon, probabilistic description, and full knowledge of the prediction horizon.

AbstractFly ash is currently a problem in different companies, mainly in thermoelectric plants, which must follow a production and consumption model that involves reusing, renewing, and recycling, thus contributing to the circular economy. This work aims to evaluate the reactivity and compressive strength of different types of ashes in concrete monoliths. For this purpose, sugarcane bagasse (SCBA), bituminous coal (BC), and untreated hazardous waste (RUD) were evaluated as replacements for cement. Monoliths and mortars have been manufactured in different mixtures, taken to a curing room, and their compressive strength and reactivity have been determined at different times (up to 28 days for the monoliths and up to 90 for the mortars), mainly in order to check the formation of calcium silicate hydrate (CSH) gels. In the monolith tests, the best performances have been with SCBA and BC, which have been used for the manufacture of the mortars. On day 56, the behavior of the replacement of 30% fly ash (15% BC:15% SCBA) presents a type H mortar behavior, with better results than the control. By day 90, all replacements had the same resistance as M mortars and even higher resistances than the control. This demonstrates the feasibility of its use in the production of Portland cement for the manufacture of low‐performance inputs. This implies the possible reduction of impacts, both in waste disposal sites and in emissions caused by the construction industry, thus contributing to the circular economy.

Due to resource constraints in wireless sensor networks (WSNs), energy consumption and networks' lifetime are considered significant challenges. Because sensors have a tiny battery and cannot be charged again. In WSN, collected data is usually transferred to the Base station (BS) directly or hop-by-hop. Therefore, load balancing and routing are one of the main issues in the WSN. This paper proposes a new routing scheme with load-balancing capability using the Markov Model (MM) and the Artificial Bee Colony (ABC) algorithm. LEACH algorithm is used to maintain load balancing between Cluster Heads (CHs). Then the Markov Model and the Artificial Bee Colony (MMABC) algorithm were used to find the best candidate nodes of each cluster to be turned into a CH. The simulation results in MATLAB software demonstrated that the proposed method surpasses the compared methods in terms of energy efficiency, number of alive nodes, and the number of delivered packets to BS and CH. European Union [861219]; Marie Curie Actions (MSCA) [861219] Funding Source: Marie Curie Actions (MSCA) This study has been conducted under the project Mobility and Training for beyond 5G ecosystems (MOTOR5G)'. The project has received funding from the European Union's Horizon 2020 program under the Marie Sklodowska Curie Actions (MSCA) Innovative Training Network (ITN) having grant agreement No. 861219.

Third-generation biofuels are currently a promising alternative for energy production, since electricity prices have risen sharply and the search for alternative energy sources to fossil fuels has become a necessity. The use of the anaerobic digestion process for the production of biomethane from microalgae has been studied over the years and has reached a certain degree of maturity, becoming the most straightforward way to obtain energy from microalgae without the need for a previous drying or extraction phase, thus eliminating the costs associated with these operations. Microalgae also show a high potential to grow in different media, including wastewater, as well as a high capacity for nutrient absorption and carbon dioxide fixation, which aid in decarbonization. These characteristics, combined with biomethane production, can contribute to achieving a satisfactory bioenergy production in a circular economy model. Peer reviewed 2 Tablas