• Energy Research
• 2025-2025close
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With the rapid development of electric vehicles, the accuracy requirement for lithium-ion battery state feedback is increasing. However, traditional algorithms cannot achieve the desired accuracy. For this purpose, this article focuses on the ternary lithium-ion battery as the research object to achieve high-precision feedback. The results show that the proposed second-order resistor capacitance-partnership for a new generation vehicle (RC-PNGV) equivalent circuit model for ternary lithium-ion batteries and gradually decaying memory recursive least squares method improve the online parameter identification accuracy of battery equivalent models, successfully reducing the overall precision error of State of charge (SOC) estimation from 7.99 % to only 0.35 %. The improved particle swarm optimization algorithm-adaptive dual extended Kalman filter method effectively improves the accuracy and stability of joint estimation of SOC and SOE for ternary lithium-ion batteries. The error in joint estimation is reduced from 3.16 % to 0.89 %, demonstrating that the improved algorithm has high precision, adaptability, and correction capability. This study uses the improved particle swarm optimization - adaptive dual extended Kalman filter algorithm to research the joint estimation of SOC and SOE for lithium-ion batteries, aiming to provide efficient state feedback for batteries to ensure their operational efficiency and safety.

Data availability: Data will be made available on request. An earlier version of this paper was presented during the 36th International Conference on Efficiency, Cost, Optimisation, Simulation and Environmental Impact of Energy Systems (ECOS 2023) held in Las Palmas de Gran Canaria, Spain, 25–30 June 2023. This paper studies portfolios of electricity- and hydrogen-driven heat pumps, electricity- and hydrogen-driven boilers and thermal energy storage technologies from an energy system perspective. Thermodynamic and component-costing models of heating and cooling technologies are integrated into a whole-energy system cost optimisation model to determine configurations of heating and cooling systems that minimise the overall system cost. Case studies focus on two archetypal systems (North and South) that differ in terms of heating and cooling demand and availability profiles of solar and wind generation. Modelling results suggest that optimal capacities for heating and cooling technologies vary significantly depending on system properties. Between 83 % and 100 % of low-carbon heat is supplied by electric heat pump technologies, with the rest contributed by electric or hydrogen boilers, supplemented by heat storage. Air-to-air electric heat pumps emerge as a significant contributor to both heating and cooling, although their contribution may be constrained by the compatibility with existing heating systems and the inability to provide hot water. Nevertheless, they are found to be a useful supplementary source of space heating that can displace between 20 and 33 GWth of other heating technologies compared to the case where they do not contribute to space heating. The research presented in this paper has been supported by the UK Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/R045518/1] (IDLES Programme).

Data availability: Data will be made available on request. Industry sector within the European Union (EU) accounts for approximately 25 % of final energy use, where the steel industry accounts for 10 % of the total energy consumption in the industry sector. The steel industry and similar process industries are facing significant challenges to reduce their greenhouse gas emissions due to recent climate change legislations. One method to achieve this, is via the implementation of waste heat recovery systems. The paper presented focuses on a steel plant located in Slovenia, where significant amounts of thermal energy are lost through exhaust gases from a natural gas furnace. The novel multi-sink gravity-assisted Heat Pipe Heat Exchanger (HPHE) aims to recover and reuse waste heat and generates two useful heat sinks. The novel HPHE consists of air and water heat sink sections with an average energy recovery efficiency of 47 %. The recovered energy from the air section provides preheated combustion air to the burners, whereas the recovered energy from the water section opens the possibility for district heating. The thermosyphons in the exhaust-air section were arranged in a counterflow arrangement with Dowtherm A and distilled water as the working fluid, whereas the exhaust-water sections were arranged in a crossflow, with distilled water as the working fluid. The novel HPHE features a bypass, allowing complete flexibility for the end user to deactivate the exhaust-water section. To ensure replicability of the HPHE, a theoretical model has been developed and validated through experimental results, the model exhibited a good agreement with the results within an error of 15 %. Both air and water sections recovered 1677 MWh and 753 MWh annually, operating at 8050 and 5750 h respectively. The implementation of the HPHE equates to an overall reduction in CO2 emissions of 334 tCO2 per annum. Moreover, the unit highlights a benchmark for the technology due to its readiness within industry due to a reported Return on Investment (ROI) of under 10 months. The presented work is part of HEAT PIPE TECHNOLOGY FOR THERMAL ENERGY RECOVERY IN INDUSTRIAL APPLICATIONS — ETEKINA project. The project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement NO 768772. “Heat pipe technology for thermal energy recovery in industrial applications” ( https://www.etekina.eu/, H2020-EE-2017-PPP- 768772).

Data availability: The data that has been used is confidential. Demand response (DR) based on the time-of-use (TOU) electricity price is an effective method for addressing the source‒load mismatch in microgrids by improving the load curve on the user side, thereby improving source‒load matching. However, the degree to which users respond to DR strategies is not only influenced by economic factors but also closely related to psychological factors. Therefore, considering the TOU electricity prices on both the generation side and the load side, this paper presents an optimization strategy for the bidirectional TOU electricity price for multi-microgrids (MMGs) coupled with multilevel games. First, the DR model based on the endowment effect is constructed with close attention to the influence of psychological factors on user behavior in the context of electric energy trading in an MMG system. A bidirectional TOU electricity pricing incentive mechanism is designed that simultaneously targets both power producers and users, promoting the active participation of various stakeholders in scheduling within MMG systems. Second, a multilevel differential game model is established, which takes power producers, microgrid operators (MGOs), and microgrid users as the main actors, couples a noncooperative game and a leader–follower game, achieves game balance by optimizing the bidirectional TOU electricity price, and makes appropriate decisions. Finally, the case study results demonstrate that the proposed strategy can optimize energy management, reduce the system's operating cost and the user's power consumption cost, and improve the power producers' economic benefit and user satisfaction. This work was supported in part by the National Natural Science Foundation of China under Grant 62233006.