Central Power Research Institute

The rise in Electric Vehicle (EV) usage has significantly increased the need for high-power fast charging systems that must meet rigorous performance standards. This paper examines the performance of 50 kW grid-connected EV charger. The design of the charger utilizes a dual stage Vienna rectifier at the front end, combined with a bi-directional DC-DC stage, to achieve a high-power factor, minimize total harmonic distortion (THD), and maintain stable operation of the DC bus. Vienna rectifiers are commonly utilized in high-power electric vehicle chargers because of their excellent efficiency greater than 94% and nearly unity power factor. An analysis of 50 kW charger that employs a Vienna rectifier focuses on its output ripple, input signal distortion, and power input factor. Under standard operating conditions, the performance remains equable. However, once the battery charge status surpasses 80%, the notable decline in performance occurs. In this scenario, both ripple and THD increase, and the power factor strays from unity, potentially harming the battery State of Health (SOH) during constant-voltage charging. Based on research results, this paper quantified the implication of current ripple on conversion efficiency in Electric vehicle charger through experimental verification and the results communicate that the current ripples have important influence on EV chargers.

Abstract Dual-functionalised Nafion membranes, obtained via glutaraldehyde crosslinking followed by post-sulfonation (N-GS), were investigated to enhance ionic transport, hydration stability, and chemical durability, which are relevant to polymer electrolyte membrane systems. A combination of FTIR and FT-Raman spectroscopy, mechanical analysis, accelerated oxidative ageing, and terahertz time-domain spectroscopy (THz-TDS) was employed to establish quantitative structure–property relationships governing proton conduction and water-mediated transport. FTIR revealed enhanced hydrophilic character and increased density of proton-exchange sites in N-GS membranes, as evidenced by strengthened O-H (0.1140) and SO 3 - (0.1265) band intensities relative to pristine Nafion. The modified membranes exhibited increased water uptake (28.7%), stable long-term retention (3.6%),moderate swelling (14.6%), and consistent porosity (3.9-5.8% after 16 h), indicating improved hydration control. Mechanical testing demonstrated a balanced combination of stiffness (Young’s modulus ≈81.5 MPa) and toughness (≈21.8 MJ/m 3 ), while peroxide-assisted ageing in 30% H 2 O 2 confirmed enhanced oxidative resistance, with a limited thickness loss of 1.94%. THz-TDS measurements provided direct insight into hydration-state dynamics, yielding an average absorption coefficient (α avg ≈100.1 cm -1 ) and indicating a 1.19% improvement in water retention behaviour compared to unmodified Nafion. These terahertz-derived hydration descriptors were incorporated into an electrochemical transport model, demonstrating that conductivity losses associated with dehydration could be mitigated and, under optimised conditions, converted into performance gains across different membrane thicknesses. The results highlight THz-TDS as a sensitive, operando-capable technique for probing dynamic water-ion interactions in proton-conducting membranes. The dual-functionalisation strategy, combined with terahertz-guided analysis, provides a robust framework for designing ionically efficient and chemically durable Nafion-based membranes for electrochemical energy applications.

Luminescence imaging techniques assume a critical role in the evaluation of the durability and lifespan of solar cells. This study presents a novel empirical approach that capitalizes on simple machine learning-based linear regression (MLLR) to prognosticate the performance of perovskite solar cells (PSCs). Three types of MLLR models were developed: (a) Full width at Half maximum (FWHM)-based MLLR Model, (b) Colour Correlated Temperature (CCT)-based MLLR Model, and (c) Hybrid or FWHM-CCT-based MLLR Model. The proposed FWHM-based MLLR model achieves the best overall performance in fitting all current-voltage (IV) parameters. The CCT-based model, while applicable to all parameters, exhibits slightly lower accuracy compared to the FWHM-based approach; however, considering standalone analysis, CCT-based MLLR model is the best fit for estimation of Voc and FF parameters. This prognostication hinges on quantitatively assessing the FWHM using electroluminescence (EL) spectroscopy data. Notably, this investigation stands out by showcasing the potential of simple yet supervised machine learning in appraising IV characteristics of PSC through EL spectroscopy. Remarkably, the study attains an average predictive accuracy of beyond 90% for IV characteristics via EL spectroscopy facilitated by a supervised machine-learning model using both continuous and discontinuous datasets. The findings of this research underscore the potential of supervised machine learning as an innovative technique for approximating IV curve parameters of PSC, utilizing EL spectroscopy. This advancement holds significant ramifications for fortifying the assessment of PSCs in terms of reliability analysis and manufacturing efficiency.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-93004-6.

Traditional non-isolated DC-DC converters typically operate in either inverting or non-inverting mode, but not both. In this paper, we are proposing a non-isolated DC-DC converter topology capable of seamlessly operating in both inverting and non-inverting modes. The converter is also designed to step up or step down the input voltage, offering greater flexibility for various applications. The proposed topology utilizes five switches, two inductors, and one capacitor to achieve these functionalities. Notably, the configuration of the converter can be dynamically altered between inverting and non-inverting modes during operation, providing enhanced versatility and adaptability. Simulation results validate the performance of the proposed topology, demonstrating its efficiency and ability to handle input conditions while maintaining stable output voltage. This work offers a significant advancement in DC-DC converter design, making it suitable for a wide range of applications where both step-up/step-down conversion and mode flexibility are required.

In the present scenario, the deployment of AMI and the smart grid requires smart energy meters with minimum quality and compliance. Since the meters are interoperable and adhere to the open protocol standards, the vital part of the qualification process is the protocol testing to guarantee the meters' compliance. Even though many proprietary and foreign tools are available for conformance and parameter testing, they have limitations such as non-interoperability and lack of economic viability. The suggested smart meter testing tool is an indigenously developed, interoperable, and economically feasible tool for testing the conformance, functionality, communicability, and security of smart energy meters. The features of the indigenously developed smart meter testing tool are described in this paper.

The worldwide transition to e-mobility is a welcome step towards ensuring a greener planet. Countries across the world are planning to adopt Electric Vehicles (EVs) in a big way which calls for an increased number of charging stations. EV chargers, which are non-linear loads will impact the power quality of the system when connected to the distribution network leading to harmonics, frequency deviation, voltage unbalance, voltage fluctuation etc. Therefore, studies need to be carried out to analyse the effect of EV charging on the distribution system and to recommend possible remedial measures. This paper aims to review the research carried out on the analysis of the impact of EV charging on the distribution network.

Transformer is an important component in the electric grid. Several tests have been defined in relevant standards to ensure reliability of the transformer. Short-circuit test is the one of the critical tests for transformer design verification. Any weakness in design results in the failure of transformer’s short-circuit test. Present paper discusses the thermal performance of the transformer during short-circuit test.Since the transformer experiences excessive amount of current during short-circuit test, transformer winding experiences mainly mechanical stress and thermal stress. Thermal stress shall result in increase in winding temperature. Transformer winding temperature during short-circuit test can be calculated using formulae given in IS/IEC and IEEE. Efforts are made to measure transformer winding temperature during short-circuit test. Comparison is established between calculated and measured transformer winding temperatures during short-circuit test.