Model predictive control as the cloud control strategy for a battery thermal management system

Publisher Copyright: © 2025The fast charges are critical in supporting long-distance travels with battery based electric vehicles and alleviating range anxiety. Nonetheless, the battery heat generation under fast charge damages the battery if the battery thermal management system (BTMS) is not managed correctly. This paper proposes an innovative cloud control strategy for the BTMS that optimizes the electric consumption of the BTMS while managing the battery temperature under extreme events such as fast charges. The optimized control strategy is based on the concepts of model predictive control, which provides the optimized BTMS operation in the next 60 minutes. To do so, firstly, the different components that influence the optimization problem have been modeled. The models are the electro-thermal battery system model, the battery management system model, the energy control unit model and the BTMS model. Secondly, a model predictive control algorithm has been designed to avoid hard constraint violation along with an optimization approach that allows black-box model execution. Finally, the approach has been structured to be integrated as a functional mock-up into a digital twin application to solve current micro-controller computational limitations. The developed cloud control strategy shows a decrease of 1ºC on the maximum achieved temperature at fast charge events.Peer reviewe

Exploring the application of quantum technologies to industrial and real-world use cases

Publisher Copyright: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage quantum computing to solve real-world problems in fields such as machine learning, optimization, and material simulation, using revolutionary quantum methods and machines. All this progress has been achieved even while being immersed in the noisy intermediate-scale quantum era, characterized by the current devices’ inability to process medium-scale complex problems efficiently. Consequently, there has been a surge of interest in quantum algorithms in various fields. Multiple factors have played a role in this extraordinary development, with three being particularly noteworthy: (i) the development of larger devices with enhanced interconnections between their constituent qubits, (ii) the development of specialized frameworks, and (iii) the existence of well-known or ready-to-use hybrid schemes that simplify the method development process. In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and optimization in the coming years

Model-predictive control with admittance matrix estimation for the optimal power sharing in isolated DC microgrids

Publisher Copyright: © 2024 Elsevier B.V.Direct current (DC) microgrids are relevant in modern energy systems due to their high efficiency, simplified architecture, and capability of direct integration of distributed resources (DERs). A proportional power-sharing is essential in these grids to balance the power injections according to the capability of each DER. However, the intermittency of DERs, load variations, and the non-linear nature of the model are major challenges. Therefore, this paper proposes a non-linear model predictive control (MPC) alongside an estimator for the nodal admittance matrix. By using MPC, it is possible to achieve optimal operation, considering voltage and power constraints. Moreover, the estimator enables the consideration of load variations with a reduced number of measurements. The robustness of the proposed control strategy is evaluated both in simulations and through a Power-Hardware-in-the-loop (PHIL) implementation. Radial and meshed microgrids were tested with different numbers of nodes. These results validate the practical feasibility and performance of the proposed approach.Peer reviewe

Hydrogen Station Model Design Using Functional Mock-Up Units and Metaheuristics Optimization

Publisher Copyright: © The Author(s) 2025.Hydrogen-powered heavy-duty vehicles will transform the logistics landscape, but their extensive adoption presents substantial challenges. Matching hydrogen demand with supply, scaling up infrastructure, controlling carbon emissions targets, and integrating with renewable energy sources are significant obstacles to overcome. This paper addresses these challenges by modeling a hydrogen station for heavy-duty vehicle fleets using Matlab-Simulink software. The hydrogen station components proposed are individually modeled: (1) the electrolyzer model generates hydrogen and oxygen by electrolysis consuming water and electricity; (2) the hydrogen reformer model generates hydrogen and carbon dioxide through steam methane reforming or ethanol reforming; (3) the hydrogen storage tank; and (4) carbon capture and storage. These models were compiled into functional mock-up units (FMU) to facilitate further exploration. This paper incorporates metaheuristic optimization techniques to address the design complexities and enhance the performance of hydrogen stations under various operating conditions. Multiple optimization objectives have been considered, including reducing carbon emissions and reducing the total monetary cost. Furthermore, several critical constraints are integrated to ensure realistic scenarios. These constraints include the accumulated hydrogen production that meets daily demand and the limitations in resource consumption. Finally, the combination of the FMU approach with metaheuristics techniques demonstrates the potential for the optimal hydrogen infrastructure design.Peer reviewe

Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A data-morphology-based counterfactual generation method for trustworthy artificial intelligence

Publisher Copyright: © 2025 Elsevier Inc.Explainable Artificial Intelligence (XAI) is a pivotal research domain aimed at clarifying AI systems, particularly those considered “black boxes” due to their complex, opaque nature. XAI seeks to make these AI systems more understandable and trustworthy, providing insight into their decision-making processes. By producing clear and comprehensible explanations, XAI enables users, practitioners, and stakeholders to trust a model's decisions. This work analyses the value of data morphology strategies in generating counterfactual explanations. It introduces the Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF) method, a model-agnostic counterfactual generator that leverages data morphology to estimate a model's decision boundaries. The ONB-MACF method constructs hyperspheres in the data space whose covered points share a class, mapping the decision boundary. Counterfactuals are then generated by incrementally adjusting an instance's attributes towards the nearest alternate-class hypersphere, crossing the decision boundary with minimal modifications. By design, the ONB-MACF method generates feasible and sparse counterfactuals that follow the data distribution. Our comprehensive benchmark from a double perspective (quantitative and qualitative) shows that the ONB-MACF method outperforms existing state-of-the-art counterfactual generation methods across multiple quality metrics on diverse tabular datasets. This supports our hypothesis, showcasing the potential of data-morphology-based explainability strategies for trustworthy AI.Peer reviewe

Reviewing numerical studies on sensible thermal energy storage in cementitious composites: report of the RILEM TC 299-TES

Publisher Copyright: © The Author(s) 2024.Concrete has emerged as a promising solid-based sensible heat storage (SHS) material due to its favorable balance of thermal properties, cost-effectiveness, non-toxicity, and widespread availability. This state-of-the-art review examines the applications of concrete-based SHS across diverse domains, including buildings, concentrated solar power systems, and industrial power generation. It also investigates the thermal properties of concrete relevant for SHS applications and explores the design considerations for concrete SHS systems and reviews the current research landscape and the role of numerical modeling and simulation techniques in optimizing the performance of concrete SHS systems. Various computational methods, such as transient modeling, finite element method (FEM), computational fluid dynamics, and simplified lumped capacitance models, have been employed to analyze and enhance the design of these systems. As research and development continue in this field, several future trends are anticipated.Peer reviewe

Improvement opportunities on fatigue and corrosion behaviors in offshore fastener threads combining a maraging steel skin with a class 10.9 32CRB4 core

Publisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.The new generation of large offshore wind turbines (+ 15 MW) will require an improvement in the bolted joints used in these turbines. From the material perspective, this improvement will involve the development of fasteners that combine both corrosion-resistant properties and enhanced high-cycle fatigue performance, reducing the frequency of maintenance in the installations. This work explores the possibility of using L-DED (laser-directed energy deposition) technology to create hybrid fasteners consisting of a high-toughness 10.9 class carbon steel core and a 1.5 mm maraging steel coating that provides good resistance to marine corrosion. C300, 15–5 PH, and 17–4 PH maraging steel grades were tested. The results show that, in addition to corrosion protection, the 15–5 PH and 17–4 PH coatings improve high cycle fatigue performance compared to the fastener fully made in 32CrB4 used as a reference. Fifty percent of the coated fasteners tested in this work were fatigue runouts after 5 × 106 cycles, while none of the reference specimens reached such condition. The average fatigue life in the coated specimens was around 3.5 × 106 cycles, 3 times higher than the reference (average fatigue life slightly higher than 1 × 106 cycles). Another advantage to consider is that the application of the coating via L-DED does not impose limitations on the subsequent turning and thread-rolling operations, nor does it require additional finishing steps.Peer reviewe

Antimicrobial Activity of Lignin-Based Alkyd Coatings Containing Soft Hop Resins and Thymol

Publisher Copyright: © 2025 by the authors.The growing concern over the transmission of pathogens, particularly in high-risk environments such as healthcare facilities and public spaces, necessitates the development of effective and sustainable antimicrobial solutions. Traditional coatings often rely on metals, which despite their efficacy, pose significant environmental and economic challenges. This study explores the potential of bio-based alkyd resins, supplemented with natural antimicrobial bioadditives, as an eco-friendly alternative to traditional antibacterial and antiviral coatings. Specifically, alkyd formulations incorporating thymol and soft resins extracted from hops were evaluated for antimicrobial and antiviral efficacy, following ISO standards (ISO 22196:2007 and ISO 21702:2019, respectively). The coating formulations showed significant activity against Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus), and Influenza A (H3N2) virus, proving their potential for mitigating pathogen spread. These bio-based coatings not only reduce reliance on harmful chemicals but also align with circular economy principles by repurposing industrial by-products. This innovative approach represents a significant step toward greener antimicrobial technologies, with broad applications in healthcare, public infrastructure, and beyond, especially considering the rising zoonotic disease outbreaks.Peer reviewe

A machine learning approach for the efficient estimation of ground-level air temperature in urban areas

Publisher Copyright: © 2025 Elsevier B.V.The increasingly populated cities of the 21st Century face the challenge of being sustainable and resilient spaces for their inhabitants. However, climate change, among other problems, makes these objectives difficult to achieve. The Urban Heat Island phenomenon that occurs in cities, increasing their thermal stress, is one of the stumbling blocks to achieve a more sustainable city. The ability to estimate temperatures with a high degree of accuracy allows for the identification of the highest priority areas in cities where urban improvements need to be made to reduce thermal discomfort. In this work we posit that image-to-image deep neural networks (DNNs) can effectively correlate spatial and meteorological variables of an urban area with street-level air temperature. To this end, we introduce a novel DNN-based model leveraging a U-Net architecture to tackle this modeling task. We evaluate the proposed model through experiments in a use case focused on the city of Bilbao, Spain. Our method achieves regression performance metrics comparable to those of the numerical model it was trained against, with mean absolute error values below 2°C and a Pearson correlation close to 1. Additionally, it demonstrates strong regression performance against true temperature values recorded by on-site weather stations, enhancing the precision of estimates produced by numerical models. These results confirm that DNNs offer a fast and computationally efficient alternative for the data-driven estimation of ground-level air temperature.Peer reviewe

Multi-Assignment Scheduler: A New Behavioral Cloning Method for the Job-Shop Scheduling Problem

Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Recent advances in applying deep learning methods to address complex scheduling problems have highlighted their potential in learning dispatching rules. However, most studies have predominantly focused on deep reinforcement learning (DRL). This paper introduces a novel methodology aimed at learning dispatching policies for the job-shop scheduling problem (JSSP) by employing behavioral cloning and graph neural networks. By leveraging optimal solutions for the training phase, our approach sidesteps the need for exhaustive exploration of the solution space, thereby enhancing performance compared to DRL methods proposed in the literature. Additionally, we introduce a novel modelling of the JSSP with the aim of improving efficiency in terms of solving an instance in real time. This involves two key aspects: firstly, the creation of an action space that allows our policy to assign multiple operations to machines within a single action, substantially reducing the frequency of model usage; and secondly, the definition of a state space that only includes significant operations. We evaluated our methodology using a widely recognized open JSSP benchmark, comparing it against four state-of-the-art DRL methods and an enhanced metaheuristic approach, demonstrating superior performance.Peer reviewe