The ever-growing global concern on climate change caused due to vehicular greenhouse gas emission coupled with the depletion of natural resources is driving global economies towards the adoption of alternate fuel technology. Electric vehicles (EV) are positioned as an alternate green and clean technology which potentially can enable the efficient transition to sustainable low-carbon emission transportation system and preservation of natural scare resources. Despite announcing favorable policy measures to encourage EV adoption, the multiplicity of potential barriers with mutual interaction has resisted its penetration in several countries. Though researchers have identified the barriers, but the question "How EV barriers mutually interact among themselves?" has remained largely unanswered in empirical research. Unpacking the relationship within barriers will empower manufacturers, policymakers in strategic planning, and devising suitable measures in controlling the barriers. A hybrid two-phased multi-criterion decision making (MCDM) tools are applied. Firstly, quantitatively BWM (Best-Worst Method) is applied in ranking and prioritizing the important barriers/sub-barriers. The obtained sub-barriers are then analyzed to establish a mutual relationship using interpretive structural modelling (ISM). This study has been conducted for the Indian EV context with a focus on technological, infrastructural, financial, behavioural, and external barriers. Ranking and prioritization of EV barriers provides a framework for decision-makers to focus on high-priority barriers/sub-barriers in addressing them through preferential resource allocation. The strength of the relationship among barriers to EV adoption was established based on corresponding driving and dependence power. The research finding suggests that EV barriers such as performance and range, the total cost of ownership, shortage of charging infrastructure, lack of consumer awareness about EV technology are critically influential in driving EV adoption. Our research contributes to building an improved understanding of the multifaceted nature of EV barriers and its inter-dependencies in policy and decision making. • The mutual relationship among the barriers in adoption of Electric Vehicle (EV) is explored. • An integrated two-phased sequential multi-criterion decision making (MCDM) tools comprising of Best-Worst Method (BWM) and Interpretive Structural Modeling (ISM) is applied. • The study has been conducted for the Indian EV context with a focus on technological, infrastructural, financial, behavioral, and external barriers. • The strength of the relationship among the barriers of EV adoption is structured based on the relative driving and dependence power. [ABSTRACT FROM AUTHOR]