Colloidal suspensions of nanoparticles in a base fluid, known as nanofluids, have gained attention as a promising green technology with a lot of promise to address issues with sustainability and energy efficiency in a variety of industries. The main features and uses of nanofluids as a sustainable solution are summarized in this paper. Due to their high thermal conductivity and high surface area to volume ratio, nanoparticles give off exceptional thermal and heat transmission capabilities. They are a desirable option for boosting the effectiveness of heat exchange systems, such as refrigeration, air conditioning, and cooling in electronic equipment, due to their improved qualities. Higher heat transfer rates and lower energy consumption can be achieved by using nanofluids as coolants or heat transfer fluids, which lowers greenhouse gas emissions and energy expenditures. Nanofluids have also found use in the realm of renewable energy, where they can improve the performance of geothermal and solar thermal collectors. The capture and conversion of renewable energy sources can be greatly enhanced by using nanofluids as working fluids in these systems, helping to create a greener and more sustainable energy landscape. Additionally, environmental cleanup and pollution management could benefit from the use of nanofluids. They are appropriate for uses including wastewater treatment, oil spill cleanup, and air purification because of their special features that allow for effective heat transfer and pollutant absorption. Furthermore, nanofluids can significantly contribute to lowering the amount of water and energy used in industrial operations, thus advancing sustainability objectives. The numerous uses of nanofluids as a green technology are highlighted in this paper, with an emphasis on their potential to improve energy efficiency, lessen environmental impact, and contribute to a more sustainable future. As this area of study and development develops, nanofluids will be in a position to play a crucial part in resolving the urgent problems of our day.

The purpose of this study is to assess the potential improvement in fuel economy and performance of a fuel-cell series hybrid electric vehicle (FCHEV) in combination with a supercapacitor (SC) by using a hybrid power management (HPM) strategy. A combination of the extended Kalman filter (EKF) and traditional coulomb counting (CC) is used to conduct for estimating the SC state of charge. The combined Environmental Protection Agency (EPA) test cycles for a city and highway are considered in order to evaluate the fuel economy under realistic driving conditions. The HPM is able to deliver a maximum speed of 177 km/h, an enhanced fuel economy of 93.38 km/kg, 0–100 km/h acceleration in 9.0 seconds, a hydrogen consumption of 0.303 kg and a cruising range of 500 km for a FCHEV with a weight of 2180 kg. To place these figures in context, they are compared with the equivalent calculated figures for a 2017 Toyota Mirai fuel cell Electric Vehicle (FCEV).

Machine learned fault detection approaches are being increasingly used for fault detection in distribution grid. However, the performance of the models can be improved by customizing the models. In this regard, a customised artificial neural network (CANN) for fault detection in a distribution grid is proposed in this paper. The proposed work develops a CANN that combines the “up‐pyramid” and “down‐pyramid” model of ANN into a “custom‐pyramid” model. As a result, the same model can be used both for determining the types of fault as well as its location. The data needed to train the model has been taken from a reconfigured IEEE‐33 bus distribution system developed in Typhoon HIL real‐time simulator. Spectral‐kurtosis is utilized for extraction of features of the faulted transient signals which are used as input data to develop the CANN. The result showcases that the reduction of input features reduces computational complexity without compromising its accuracy. The proposed model classifies fault location with an accuracy of 95.43%. The proposed method also identifies fault type with an accuracy of 96.08%. Several test cases have been developed to test the method. The method proved to be able to perform in most of the cases.

The rise in thermal runaway events within electric vehicle (EV) battery systems requires anticipatory models to predict critical safety failures during operation. This investigation develops a multi-physics digital twin framework that links electrochemical, thermal, and structural domains to replicate the internal dynamics of lithium-ion packs in both normal and faulted modes. Coupled simulations distributed among MATLAB 2024a, Python 3.12-powered three-dimensional visualizers, and COMSOL 6.3-style multi-domain solvers supply refined spatial resolution of temperature, stress, and ion concentration profiles. While the digital twin architecture is designed to accommodate different battery chemistries and pack configurations, the numerical results reported in this study correspond specifically to a lithium NMC-based 4S3P cylindrical cell module. Quantitative benchmarks show that the digital twin identifies incipient thermal deviation with 97.4% classification accuracy (area under the curve, AUC = 0.98), anticipates failure onset within a temporal margin of ±6 s, and depicts spatial heat propagation through three-dimensional isothermal surface sweeps surpassing 120 °C. Mechanical models predict casing strain concentrations of 142 MPa, approaching polymer yield strength under stress load perturbations. A unified operator dashboard delivers diagnostic and prognostic feedback with feedback intervals under 1 s, state-of-health (SoH) variance quantified by a root-mean-square error of 0.027, and mission-critical alerts transmitting with a mean latency of 276.4 ms. Together, these results position digital twins as both diagnostic archives and predictive safety envelopes in the evolution of next-generation EV architectures.

Induction motors are typically promoted in industrial applications by adopting energy‐efficient power‐electronic drive technology. Multilevel inverters (MLI) have been widely recognized in recent days for high‐power, medium‐voltage‐efficient drives. There has been vital interest in forming novel multilevel inverters with reduced switching elements. The newly proposed reduced‐switch five‐level inverter topology extends with fewer switches, low dv/dt stress, high efficiency, and so on, over the formal multilevel inverter topologies. The multilevel inverter's reputation is greatly affected due to several faults on switching elements and complex switching sequences. In this paper, a novel fault identification process is evaluated in both healthy and faulty conditions using discrete‐wavelet transform analysis. The discrete wavelet transform utilizes the multi‐resolution analysis with a feature extraction methodology acquired for fault identification over the classical methods. A novel fault identification scheme is implemented on reduced‐switch five‐level MLI topology using the Matlab/Simulink platform to increase the drive system's reliability. The effectiveness of simulation outcomes is illustrated with proper comparisons. The proposed topology's hardware model is implemented using a dSPACE DS1103 real‐time digital controller and the results of the experiment are presented.