The authors examined technological and geographical relatedness between these patent codes, before classifying cities in four different groups according to their patent scores.

The authors propose a model predictive control framework for scheduling the charging of residential electric vehicles to account for the uncertainties associated.

This paper presents a bilevel stochastic optimization model for EV charging navigation, considering uncertainties, and proposes a real-time EV charging navigation method using the hierarchical enhanced deep Q network (HEDQN).

This work reviews the design process and real-time implementation of EMSs tailored for DM-BEVs, starting from the fundamental concepts of two-motor coupling.

This study investigates the use of electric waste collection trucks for mixed and selectively collected waste in Poland.

This article presents a current analysis of various EMSs proposed in the literature. It highlights the shift towards integrating machine learning and artificial intelligence (AI) breakthroughs in EMSs development.

This study suggests a unique approach to managing the energy consumption of electric vehicles while analyzing security utilizing blockchain machine learning (ML) algorithms.

This paper proposes a two-stage framework for managing electric vehicle charging stations, addressing challenges such as uncertainty, pricing mechanisms, and network coupling.

This paper analyzes the impact of multiple scenarios on the utility’s financials using Revenue and Tariff Analysis for Electric Utilities (RATE) model. The RATE modeling tool can provide actionable insights into the impact of various decisions on overall demand–supply scenarios, the energy balance, and revenue gaps.

The study uses discrete choice models with workplace, leisure place, and highway as location alternatives to explore potential electric vehicle buyers' public charging preferences.