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Applied energy 388, 125530 (2025). doi:10.1016/j.apenergy.2025.125530 Published by Elsevier Science, Amsterdam [u.a.]

Abstract As an alternative to thermoelectric generators, heat engines show great interest thanks to their ability to convert temperature spatial gradient into time-domain temperature variations or vibrations. To this end, MultiPhysic Memory Alloys (MPMAs), combining shape memory characteristics with ferromagnetic properties, provide significant attractive characteristics such as sharp transition with reduced hysteresis as well as magnetic properties enabled by heating, thus allowing easier device development and implementation. In this study, we report the development of a heat engine for small-scale energy harvesting where the MPMA transfers its heat to a pyroelectric element that provides thermal to electrical energy conversion, yielding a more direct energy conversion path compared to conventional electromechanical heat engines. Furthermore, thermally decoupling the pyroelectric element from the MPMA allows a faster cooling of the latter, accounting for higher variation frequency. Compared to the use of electromagnetic transduction through a coil attached to the moving MPMA, this approach is shown to provide 3 to 9 times more power density (according to considered volume), with theoretical potential gains from 8 to 25 with the use of nonlinear electrical interfaces.

Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field's characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks to assimilate diverse sensor data types. This includes line-of-sight wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier-Stokes equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model's ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework's source code is openly accessible.

Abstract In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system’s energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole system with the proposed strategy based on the collected real-world driving data. Furthermore, processor-in-the-loop tests verify that the proposed strategy can achieve a much higher convergence rate and a better performance in terms of the minimization of both energy loss and aging cost compared with state-of-the-art learning-based strategies.

There is an intrinsic value in higher integration of multi-carrier energy systems (especially gas and electricity), to increase operational flexibility in the electricity system and to improve allocation of resources in gas and electricity networks. The integration of different energy carrier markets is challenging due to the existence of physical and economic dependencies between the different energy carriers. We propose in this paper an integrated day-ahead multi-carrier gas, electricity and heat market clearing which includes new types of orders and constraints on these orders to represent techno-economic constraints of conversion and storage technologies. We prove that the proposed market clearing gives rise to competitive equilibria. In addition, we propose two decentralised clearing algorithms which differ in how the decomposition of the underlying centralised clearing optimisation problem is performed. This has implications in terms of the involved agents and their mutual information exchange. It is proven that they yield solutions equivalent to the centralised market clearing under a mild assumption of sufficient number of iterations. We argue that such an integrated multi-carrier energy market mitigates (spot) market risks faced by market participants and enables better spot pricing of the different energy carriers. The results show that conversion/storage technology owners would suffer from losses and/or opportunity costs, if they were obliged to only use elementary orders. For the test cases considered in this article, sum of losses and opportunity costs could reach up to 13,000 EUR/day and 9,000 EUR/day respectively, compared with the case where conversion and storage orders are used.

Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation -driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the eval-uation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention. Funding Agencies|Youth Innovation Team of Shaanxi Universities ?; Special ConstructionFund for Key Disciplines of Shaanxi Provincial Higher Education; Natural Science Basic Research Plan in Shaanxi Province of China [2022JQ-37]; Shaanxi Provincial Education Department [22JK0567]; Project of National Natural Science Foundation of China [62271390, 51905399]; Postgraduate Innovation Fund of Xian University of Posts and Telecommunications [CXJJDL2022012]