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Complex, non-linear systems with diverse consumption, generation, and storage technologies require advanced energy management by considering several factors: resource availability, energy efficiency, economic viability, consumption costs, and installation feasibility. To address these challenges, this paper integrates artificial intelligence into a real grid-connected household with photovoltaic systems, consumption curves, and a potential storage system applying demand response. The first approach incorporates neural networks for generation prediction and genetic algorithms for demand optimization. The second approach integrates optimization and storage, significantly contributing to the feasibility study of storage implementation post-initial optimization; combining such technologies, the synergy between demand-side optimization and energy storage is evaluated by employing commonly used scientific tools, such as MATLAB for modeling and applying artificial intelligence and HOMER for economic analysis. The methodology and results show these approaches' achievements since few studies comprehensively address household power management, considering aspects to minimize costs and maximize efficiency. Results show minimum nRMSEs in the forecast of 8.80, together with regression values up to 0.99594. The study reports cost reductions of up to 69 % and 100 % in worst-case periods for systems without and with batteries, respectively; additionally, it reports increasing self-consumption of renewables by 76 % and 99 %, respectively.

This research paper presents an in-depth development and investigation of a solar-based energy system incorporating thermal energy storage to produce electricity, heat, fresh water, and hydrogen to cover the needs of a community for better sustainability. The proposed integrated energy system utilizes a concentrated solar plant to generate process heat, offering a novel approach to address the growing demand for diverse energy outputs. The assessment of the integrated system employs the first and second laws of thermodynamics, focusing on both energy and exergy analyses. A dynamic analysis, using real-world data where available, is conducted to ensure alignment between energy demand and supply. Two distinctive case studies are presented to highlight the versatility of the proposed system. In case study 1, the concentrated solar power plant focuses only on electricity production. The calculations are made based on the assumption that 6% of the total energy emitted by the sun is stored. The total work of the system is found to be 316,466.00 kWh. Additionally, as a result of the 24-hour analysis, case study 1 exhibits outstanding performance, reaching total energy and exergy efficiency of 31.66% and 33.36%, respectively. Emphasizing community electricity needs and incorporating thermal energy storage for enhanced flexibility help contribute to a tailored and successful energy production strategy. Case study 2 examines the integration of electricity generation with heating and cooling systems, as well as addressing the production of hydrogen and clean water. The total power generation, heating load, and cooling load capacities are obtained as 1,299,247.95, 531,588.54, and 555,708.16 kW/day, respectively. The system's high-power output facilitates daily desalination processes and hydrogen production, producing 29,018.10 kg of hydrogen and 369,013.52 m3of clean water in a single day. As a result of the performance analysis, case study 2 provides the overall energy and exergy efficiencies of 20.68% and 16.87%, respectively. This holistic approach effectively addresses society's energy needs in line with sustainability goals. The parametric studies provide valuable insights into the system's resilience and applicability in diverse real-world scenarios, supporting ongoing development efforts. Beyond reducing carbon emissions and improving air quality, the integrated energy system's long-term benefits make it a promising solution for achieving greener and more resilient communities, acknowledging the need to overcome challenges in its implementation.

Standalone microgrids using Photovoltaic (PV) systems might be a feasible alternative for powering off-grid populations. However, this form of application necessitates the use of energy storage systems (ESS) to control the intermittent nature of PV production. This paper proposes a novel energy management strategy (EMS) based on Artificial Neural Network (ANN) for controlling a DC microgrid using a hybrid energy storage system (HESS). The HESS connects to the DC Microgrid using a bidirectional converter (BC), that enables energy exchange between the battery and supercapacitor (SC). The effectiveness of the proposed approach in relation to the traditional control method is based on performance metrics such as overshoot and settling time. The proposed ANN control attains 18 % overshoot while step increases in PV generation and attains 21 % overshoot while decreasing PV generation. The results show that the proposed control method ensures continuous power supply from the PV system by optimising load balancing under various operating conditions, maintaining state-of-charge (SoC), and improving the voltage profile.

This paper presents power management and control of standalone PV and battery integrated hybrid system using high gain converter suitable for the DC Microgrid applications. The necessity of high gain DC-DC converter is raised due to generation of low output voltage of PV system. The attractive features of high gain converter are high voltage gain, continuous input current and suitability for integration of multiple input sources. The perturb and observe algorithm is used for tracing the maximum power point from solar and intermittency of solar is compensated by energy storage element battery. In the suggested hybrid system, the battery charging is carried out from DC bus voltage through buck converter and discharging to the load through high gain DC-DC converter. In the proposed work, Simulink model of proposed system rated for 1.2 kW PV module, 120 V, 42 Ah battery is developed using Simulink of MATLAB software and results are obtained under different operating conditions. Scaled experimental model of proposed hybrid system is implemented in laboratory environment using STM32F407VGT6 microcontroller and results are validated.

With green development driving carbon neutrality, lithium-ion batteries with natural advantages have taken over the majority of the market. The safe management of movable and storable lithium batteries is critical. Aiming at the fact that the sudden changes in remaining useful life of lithium-ion batteries seriously affects the prediction by the existing methods, this paper proposes a strong tracking filter based on a compensated adaptive model, which is able to accurately predict the sudden changes. Firstly, a higher-order state model is established based on the random-walking state of the remaining useful life, and the sudden changes are compensated into the measurement model to form a compensated adaptive model. At the same time, the Multi-dimensional Taylor network is used to identify high-order parameters in real time. Secondly, a long and short-term memory network is utilized to assist in the prediction of the innovation during the filter update. Finally, using the compensated adaptive model and the innovation obtained, the remaining useful life is effectively predicted by the high-order strong tracking filter. The proposed method is validated by simulation on accessible datasets. Using RMSE as the indicator, the performance improvement compared to single PF and LSTM is at least 21.77%. Furthermore, compared to methods proposed in recent years, the improvement is at least 44.90% on the CALCAE dataset.

As the latest technology to promote the transformation of the new energy automobile industry, cell-to-pack (CTP) batteries have attracted unprecedented attention. However, there is a lack of research on the vented gases at the module level. In this paper, the thermal runaway propagation (TRP) behavior and vented gas characteristics of large modules of cell-to-pack batteries in closed space are studied experimentally. Results indicated that the total quantity of vented gas from module during thermal runaway (TR) process was 31.01 mol. The main components of vented gas are H2, CO2, CO, C2H4, and CH4. Of these, H2, CO2and CO accounted for 32.85 %, 28.00 % and 25.25 % respectively. In addition, the calculated explosion limit of vented gases was 6.27 %–54.14 %. Therefore, the vented gas is prone to explosion, causing serious accidents. Compared with the open space system, the TRP temperature of the battery module in the closed nitrogen space system was higher and the mass loss was smaller. This study can provide reference for the prevention of TRP in large battery modules, and for evaluating the fire hazard of powered vehicles.

The utilisation of solid electrolytes in the manufacturing of high-energy density and very safe solid state lithium batteries exhibits significant promise. Due to its excellent machinability and flexibility, the composite solid electrolyte has drawn increasing attention. However, it was unable to develop further due to the organic phase's chemical incompatibility with the inorganic phase. In this paper, a type of composite solid electrolyte reinforced by supramolecular self-assembly material is therefore presented. PVDF-HFP serves as the polymer matrix, whereas βCD-MSN is the inorganic filler. In addition to creating a suitable organic-inorganic interface, the abundant hydroxyl groups in βCD-MSN can strongly interact with the -F group of PVDF-HFP to degrade the conductive chain segment and increase the lithium ion conductivity (2.71 × 10−5S/cm). At the same time, βCD-MSN also has the advantages of high specific surface area and active site, which can inhibit the movement of anions in the solid electrolyte to a certain extent, and obtain a high lithium ion migration number (0.65). Based on the above advantages, we assembled a solid state symmetric lithium battery containing βCD-MSN and a lithium iron phosphate battery, which showed good electrochemical performance and stability at room temperature. Different from previous studies, composite solid electrolytes designed by supramolecular self-assembly materials can balance the relationship between ionic conductivity and mechanical strength of solid electrolytes to a certain extent. This will provide new insights for subsequent researchers to design a new generation of composite solid electrolytes.

In end milling of difficult-to-cut material such as Ti-6Al-4V, thermal wear is one of the main reasons leading to short tool life. High temperature occurred at the cutting zone during cutting process advances adhesion wear and diffusion wear at rake face, accelerates thermal plastic deformation. Furthermore, high temperature could also accelerate flank wear and promote adhesion wear at flank face. Consequently, machining precision of tool will degrade. Ultra-high pressure coolant (UHPC) is one of the well-known cooling/lubrication methods to solve this problem. In this paper, effect of UHPC on end mill flank face have been studied. To accomplish this, we designed an end mill with flank face coolant supplied from a close distance. The influence of coolant nozzle's direction has been studied by computational fluid dynamics (CFD) simulations. The result explains how the direction of the coolant nozzle impact on flow condition on the tool surface. The end mill was manufactured based on simulations by additive manufacturing. Experimental results including flank wear, surface roughness and cutting temperature demonstrate advantages of UHPC in end milling compared to dry cutting and flood coolant. The micro morphology of the worn areas on flank face and rake face further supports the advantages of UHPC.

With the increasing use of titanium alloys, it is essential to improve cutting efficiency. However, there are various problems such as high noise, strong vibration and difficulty to control the surface finish quality in actual cutting process, which can be more serious with the increase of machining parameters. In order to obtain better machining quality and higher efficiency, the influence of cutting parameters and cutting fluid on the milling process of TA2 pure titanium under two cutting conditions that use different cutting fluid is firstly studied in this paper. The correlation between the surface roughness (SR) of the workpiece and multiple metrics which milling noise (MN) and milling vibration (MV) is further studied on the basis of experimental data. In this paper, a series of parameters that have a strong correlation with SR are selected. Then, prediction models based on Least Squares Method (LSM) and Response Surface Methodology (RSM) are established. Finally, established models are compared to find ways to improve prediction accuracy. The experimental results indicate that compared with water-based cutting fluid, oil-based cutting fluid can make TA2 workpiece obtain better and more stable cutting performance most of the time; MV has a more significant correlation with SR and is therefore more suitable for establishing prediction model; The accuracy of the prediction model that takes the multi-factor coupling effect into consideration has been generally improved and the average optimization degree of the Improved Particle Swarm Optimization (IPSO) algorithm for LSM is 2.91 %, which has good optimization performance.

In this paper, we described full penetration laser welding (FPLW), a process capable of achieving single-pass welding of thick-section steel. This work summarized a welding process window encompassing various typical formations, including those well-formed and collapse defects. Furthermore, collapse defects were categorized as “collapse formation with backside humping” and “collapse formation with underfill”. High-speed camera (HSC) images reveal that the formation mechanisms of these two defects are attributed to the accumulation of liquid metal at the rear edge of the bottom molten pool and the ejection of a large amount of liquid metal from the weld in a spatter form, respectively. A computational fluid dynamics (CFD) numerical simulation model well-aligned with the experimental results has been established. The analysis of the molten pool flow behavior and keyhole evolution revealed that during well-formed welding, the keyhole status in the welding process exhibits a periodic closure-collapse mode. This periodic mode can instantaneously release the pressure at the bottom of the keyhole, inhibiting the excessive accumulation of liquid metal at the bottom of the weld and thus inhibiting the formation of collapse defects. The cooling rate in the upper part of the weld exceeds that in the middle and lower parts, primarily resulting in the formation of fine acicular ferrite and martensite. The increased tensile strength exhibited by the welded joint relative to the base material predominantly emanates from the mechanisms of grain boundary reinforcement and dislocation strengthening, precisely caused by the refined microstructure.