The policy is framed with the mission to provide an enabling framework through incentives and initiatives for promoting zero emission mobility with societal, economic and environmental considerations at forefront.

This work addresses the multi-objective route planning and charging problem for Battery Electric Vehicles (BEVs).

A Hidden Markov Model (HMM) based approach similar to Machine Learning has been proposed in this paper for identifying houses with EVs.

This paper presents a battery centralized scheduling strategy (BCSS) in the battery swap scenario, significantly reducing BPC and battery charging peak load (BCPL).

This article proposes a battery fault diagnosis method that achieves joint soft SC fault detection and estimation.

In recent years, China has actively promoted the development of new energy vehicles and achieved good results in reducing petroleum energy consumption and environmental pollution. Car ownership has surged year after year.

This study proposes a multi-objective electric vehicle (EV) charging scheduling model, aiming at minimizing both the peak-valley load difference of the power grid and the total charging cost of EV users.

This study proposes an integrated approach incorporating an extended RFM model, a two-stage clustering method, and the Entropy Weight Method to classify EV users. Analysis results demonstrate that 7426 EV users are divided into six groups, namely “high-value users,” “key users to maintain,” “key users to develop,” “potential users,” “new users,” and “lost users”.

The Life Cycle Assessment (LCA) method was used in this study to examine how electrifying the urban bus fleet will affect the environment. Public bus transportation is one of the primary sources of pollution in major urban agglomerations.

As electric vehicles are presently very rare in Brunei, this article seeks to determine the total cost of ownership of internationally popular electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) to put things in perspective for the Brunei market.