This infographic shows the EV Market Scenario in India for the year 2020.

This infographic shows the Electric 2-Wheeler sales for the year 2018-19.

This article explored the development of EV charging stations in Thailand between 2015 and 2020. This research aimed to study the main players and examine their goals, strategies, and operations in the EV charging business as well as the key issues that these charging operators have encountered in developing charging stations.

The contribution of this paper is to categorize the state-of-the-art position estimation approaches and advancements in sensorless permanent magnet traction systems of electric vehicles.

Under the regional technical assistance programme NDC-TIA, the study “Status quo analysis of various segments of Electric mobility and low carbon passenger road transport in India” was undertaken.

A method for the monitoring and warning of electric vehicle charging faults based on a battery model is proposed in this paper.

This paper proposes a method to experimentally investigate the influence of the HVAC system on the energy consumption and thermal comfort in a 12 m e-bus. An appropriate thermal comfort model is identified and the required climatic input parameters are selected and measured with self-developed sensor stations.

The Nationally Determined Contribution – Transport Initiative for Asia (NDC-TIA) is a regional initiative funded by the International Climate Initiative (IKI) of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU).

This paper provides a systematic and comprehensive review of available scientific papers to investigate the social perspective and social aspect of EVs. This research provides new insight and reveals EVs social sustainability research focus defined and structured by studying papers included in WoS published in the last five years.

. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior.