Considering rising pollution as well as fuel expenses, it has now become critical to transition to a sustainable method of transportation. As a result, automakers have begun to spend on research and development in the electric vehicle (EV) industry. The amount of EVs has expanded rapidly in recent years. This is owing to new improved technology, particularly in electric motor engineering, as well as government initiatives to limit the level of environmental impact produced by combustion engines. Because EVs are powered by electricity, implementing their charging stations presents certain complications. In this paper, we have discussed the different types of EVs, such as BEVs, FCEVs, HEVs, PHEVs, and REHEVs. Even though the capacity of many of these electric car models has been substantially enhanced within the past few years, some challenges remain as a selection barrier for several customers. Considering these challenges, we have also implemented a fuzzy AHP-TOPSIS-based unified model to evaluate the different types of EVs. The study’s technical importance is the identification of various evaluation factors, implementation of a unified model for measuring performance, and computation using the fuzzy MCDM technique. The outcomes of the unified model approach also were validated. We concluded that FCEVs are excellent for long journeys, and have the resources to cause minimal disruption.

As of late, Automatic speech recognition has advanced on account of instruments, for example, natural language processing, and deep learning, among others. It is a framework or put in another way, a gadget that changes a raw signal into computer comprehensible text. The genuine creation of speech is comprised of changes in air pressure that outcomes in pressure wave that our ear and cerebrum comprehend. The vocal tract is utilized to deliver a human speech, which is adjusted by teeth, tongue, and lips. Speech recognition alludes to a machine's ability to perceive human speech and transform it into a computer comprehensible text. Speech recognition is a magnificent illustration of good interaction between humans and computers. In this paper, we introduce the process to extricate the feature from the signal utilizing Mel-frequency cepstral coefficients. Mel-frequency cepstral coefficients are a genuinely far wide and proficient methodology for feature extraction from a sound file. This technique improved the speech recognition process and removes the distortion in the voice. In this manuscript we applied the Mel-frequency filtration process to improve speech and remove the background noise. Therefore, the proposed methodology gives better performance in the automated speech recognition system.