International Journal of Energy Research

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system’s feasibility.

The choice of mechanical and electrical topologies can affect piezoelectric energy harvesting efficiency, but the problem of achieving high-efficiency energy conversion in energy harvesters stacked as cantilevers has not been perfectly solved. This study focuses on the topology of piezoelectric elements in a stacked vehicle suspension vibration energy harvesting device. Through theoretical analysis, the stress expressions of the excited and driven elements are derived. The stress of the piezoelectric elements is affected by the positions of the connection and excitation points. A stress model was established for a four-piece piezoelectric element connected by a thin light rod in ANASYS. The simulation results show that the average stress in the piezoelectric bending element model is maximum when the excitation and connection points are located at both ends of the free end. Compared with the middle position of the free end of the piezoelectric element, the average stress of the model is increased by 92.904%. Considering the difference in voltage generated by piezoelectric elements, four kinds of electrical topology are designed and analyzed experimentally. When the driven elements are connected in parallel and then connected in series with the excited element, the output power varied the least with the change of load resistance. The system produces a high power and offers a wide selection of load resistors. When the load is 26 kΩ, a single set of four piezoelectric elements produces 86.407 mW of output power.

Efficient steam condensation is crucial for safe nuclear power plant (NPP) operations, preventing pressure buildup, overheating, and the release of radioactive materials. However, the presence of noncondensable gases (NCGs), such as air, nitrogen, hydrogen, or helium, can hinder the condensation process by creating a thermal resistance layer that impedes steam diffusion and condensation on the system’s surface. Maximizing the efficiency of steam condensation requires a thorough grasp of the fundamental processes, theories, advancements, and technical hurdles. Therefore, this work thoroughly addresses these needs, with a particular emphasis on addressing the challenges posed by NCGs by dividing the work into four thematic areas. The first theme relates to a comprehensive examination of pure steam condensation phenomena, which includes an exploration of familiar condensation scenarios and various film condensation types. The second theme examines condensation in the presence of NCGs, their mixture properties, and related theories and modelling of heat and mass transfer. The third theme investigates condensation in NPP by exploring passive cooling systems and condensation phenomena under both natural and forced convection conditions during nuclear accidents, the origin of NCGs in NPP and their transportation aspects. This is followed by experimental work related to condensation scenarios and scale. Finally, the last theme looks upon the recent advancements in computational fluid dynamics (CFD) modelling of wall condensation, system analysis codes coupling with CFD, and the implementation of machine learning (ML) for predicting the condensation HTC. By bridging the gap between fundamental knowledge and practical applications, the four thematic areas presented in this work are aimed at providing a comprehensive foundation for researchers and experts in the field of steam condensation when NCGs are involved. The ultimate objective is to bolster the safety and efficacy of NPP operations by understanding the heat and mass transfer mechanisms while mitigating the risk of catastrophic events.

The development of energy harvesters based on inexpensive inorganic materials has attracted considerable attention to envisage next-generation self-powered electronic devices. In this work, we presented surface modification of ZnO nanorods (NRs) by thermochemical reaction using photoresist (PR) as an etching source. The morphological and microstructural properties of surface-etched ZnO NRs (M: ZnO) were systematically studied in detail through SEM and HRTEM. The morphological results show that the surface-etched NRs possess nanofiber-like porous structures and are penetrated throughout the NRs with high surface area. We fabricated triboelectric nanogenerators (TENG) using M: ZnO NRs with poly (dimethylsiloxane) (PDMS) as negative triboelectric material and mica as positive triboelectric material. The prepared M: ZnO NR TENG successfully delivered an output voltage of up to 20 V and a current density of 3.2 μA cm⁻², which is ∼1.5 times higher than those observed for smooth ZnO NRs, respectively. The prepared M: ZnO NR TENG device can be able to lit 24 red light-emitting diodes (LEDs) as the power source. Finally, to demonstrate the practical applications of M: ZnO NR TENG, it was attached to the human body (elbow, knee, wrist, and heel) and efficiently harvested the energy from daily human activities.

Focusing on mitigating global challenges arising from hydrocarbon-based sources, the integration of cogeneration power plants with solar photovoltaics offers a viable solution. The intermittent nature of renewable resources presents a challenge to the consistent performance of cogeneration systems. To address these issues, this work introduces a novel framework for integrating cogeneration power plants (CGPPs) with solar photovoltaic systems. The key innovation of this research lies in its dual-algorithm approach that seamlessly blends cogeneration power plants with solar photovoltaic. This study proposed an integrated approach, employing the Derivative Log Sigmoid-Woodpecker Mating Algorithm (DLS-WMA) and Optimized Artificial Neural Networks (O-ANN), to combine cogeneration power plants with solar photovoltaics in industrial distribution systems. The methodology is aimed at achieving a cost-effective, efficient system design, enhancing the efficiency of cogeneration power plants, and introducing energy storage batteries for uninterrupted power generation under diverse atmospheric conditions and loads. Additionally, the proposed system includes rechargeable batteries for energy storage to support critical services when the solar plant is offline and the CGPP cannot meet the power demand. The industrial system’s photovoltaic component is tuned using the DLS-WMA for cost minimization and O-ANN for solar irradiance prediction, ensuring continuous power flow by optimizing both the photovoltaic system and the cogeneration power plant (CGPP) system. Real-time datasets are used to compare the results obtained by this new approach with those of the previous state-of-the-art algorithms. The error with O-ANN prediction is 1.2%, compared to 4.1% with the existing WMA-ANN technique, while the cost-benefit with DLS-WMA shows a 9% improvement over the WMA-ANN technique. The experimental outcomes demonstrate the efficiency of this new approach. Collaboration with industry stakeholders and policymakers is crucial for the large-scale deployment of this system, facilitating the adoption of sustainable energy practices in industrial distribution systems.

The increasing emission of carbon dioxide worldwide has emerged as a major global concern in the context of addressing climate change. Converting CO₂ to high-value carbon materials is a promising solution to capture emitted carbon for achieving carbon neutrality. Furthermore, such conversion can provide carbon nanomaterials for key industries, including the lithium battery and fuel cell industries. Here, it is shown that sub-50 nm tangled carbon nanotubes (CNTs) can be synthesized by adjusting the metaborate concentration and the current density through the electrochemical conversion of carbon dioxide in a molten carbonate salt. The metaborate ion concentration affects the product selectivity and carbon morphology, and the current density is strongly related to the particle size of in situ seed catalysts supplied by the dissolution of an Ni-Fe-Cr alloy anode. The optimized process conditions control the nucleation and growth of carbon via a tip-growth mechanism, thereby promoting the formation of sub-50 nm CNTs rather than bulky irregular carbon particles. The Raman and Brunauer–Emmett–Teller analyses showed that the properties of the prepared CNTs depended on the synthetic parameters. This study provides deep insights into the mechanism underlying carbon synthesis through the electrochemical reduction of a molten carbonate salt.