Electric vehicles (EVs) are one of the best replacements of conventional vehicles due to environmental friendly nature. The poor State of Charge (SOC in %) control and large settling time & peak overshoot of speed are the few research gap which need to be address due to continuous degradation of battery. The paper demonstrates the conceptual design and implementation of a solar-powered electric vehicle that uses soft computing methods for smart control. The implementation of a dynamic solar-powered electric vehicle charging stations combining smart control & soft computation methods requires careful, optimal design, charging the economy, & regular upkeep. Nonetheless, it can offer an environmentally friendly, long-term EV charging option and perhaps reduce continuing operational costs. Because electric vehicles emit no pollutants, this work also assists global efforts to reduce emissions of greenhouse gases and combat climate change. In this paper, electric vehicle charging has been assessed at various voltage levels. Subsequently, an electric vehicle is controlled by DC motor. In addition to state of charging of electric vehicle which is measured in terms of state of charging, the speed of electric vehicle is analyzed. In order to attain the smooth operation of speed and SOC, an objective function has been developed. Further, the objective function has been controlled by using artificial neural network and genetic algorithm. It is observed that SOC (%) shows better and smoother performance with GA in comparison to ANN and existing methods. In addition to this, settling time and peak overshoot of the speed has been improved a lot with GA (2.5 sec, 2%) and ANN (3.1 sec, 2.7%).

EVMCSDLT: Electric vehicle mobile charging system using distributed ledger technology.

Tribological failure mechanisms and mitigation strategies for bevel gear differentials in electric vehicles

“Electric vehicles do not work in the winter”: Exploring the role of misperceptions and socio-psychological factors in zero-emission vehicle adoption

Frequency regulation of an interconnected renewable rich power system with electric vehicles using tilt multistage PIDF controller approach

Event-triggered observer-based load frequency control for cyber–physical power systems with electric vehicles under hybrid attacks

External pressure effects on thermal runaway in prismatic LiFePO4 batteries: Mechanistic insights for safer battery systems in electric vehicles

Active DC to DC converter based battery charge balancing systems from renewable energy by using electric vehicle

Optimization design of integrated module for thermal management system in electric vehicle

Examining green brand approaches in promoting new electric vehicles

Optimization of three-layer staggered liquid cooling system for high-rate charging of large cylindrical battery module in electric vehicles

Driving sustainable transportation: analyzing consumer behavior toward used electric vehicles

Addressing voltage regulation challenges in low voltage distribution networks with high renewable energy and electrical vehicles: A critical review

The growth of electric vehicles (EVs) raises new challenges for electricity systems. We implement a field experiment to assess the effect of time-of-use (TOU) pricing and managed charging on EV charging behavior. We find that while TOU pricing is effective at shifting EV charging into off-peak hours, it unintentionally induces new and larger “shadow peaks” of simultaneous charging. These shadow peaks lead to greater exceedance of local capacity constraints and advance the need for distribution network upgrades. In contrast, centrally managed charging solves the coordination problem, reducing transformer capacity requirements, and is well tolerated by consumers in our setting. (JEL C93, D91, L62, L94, Q42)

Thermal management of lithium-ion battery packs in electric vehicles using an innovative heat sink design

Impact analysis of renewable energy resources and electric vehicles in hybrid power systems

Robust hybrid Neural–Kalman filter for real-time supercapacitor state-of-charge estimation in electric vehicles

Policy Implications of Net-Zero Emissions: A Multi-Model Analysis of United States Emissions and Energy System Impacts.

Abstract Lithium (Li) is a critical element driving the transition toward a decarbonized environment by enabling sustainable energy storage and use in modern infrastructure. Over the past decades, the widespread exploitation of electronic devices and electric vehicles (EVs) has significantly driven global demand for Li. Although Li is primarily extracted from ore deposits, the increasing depletion of mineral resources has shifted focus toward other sources, such as seawater and salt lake brines. Several methods have been employed to recover Li from saline water (brine and seawater) at laboratory and pilot scales, which can be categorized into conventional and direct lithium extraction (DLE) approaches. Conventionally, the lime‐soda evaporation method has been widely applied for extracting Li from brine; however, this approach limits brine with low Mg/Li ratios and low efficiency. On the other hand, this review focuses on the DLE approaches, particularly electrochemical techniques, via electrolysis, electrodialysis, and capacitive dialysis for Li recovery. The advancements in the synthesis of working electrode materials (i.e., lithium iron phosphate [LiFePO 4 ]), electrode modifications, and membrane modifications for enhancing Li recovery and selectivity are highlighted. Current challenges and future perspectives, particularly on scaling these innovations from bench‐scale to industrial applications, and the technical challenges relating to the process scale of electrochemical extraction approaches are discussed, together with future research directions. In short, this review provides useful insights into the potential of electrochemical approaches as sustainable, effective, and cost‐efficient processes that facilitate the discovery of novel approaches to satisfy the growing worldwide need for Li.

The topological advancements in twin rotor axial flux induction motors (TRAxFIMs) have spurred the interest in performance optimization and control strategies for electric vehicle (EV) applications in particular. This paper investigates for the enhanced performance of multi-level inverters (MLIs) fed TRAxFIMs with different advanced control techniques. The performance evaluation is done under variable speed conditions at constant torque and vice versa. The TRAxFIMs offer unique advantages like high power density, high efficiency and most suitable for EV applications. The performance analysis of MLIs fed TRAxFIM has been carried out with proportional-integral (PI), fuzzy controllers, and artificial neural network (ANN) controllers. The PI controller provides a conventional control approach, while the fuzzy and ANN controllers serve as advanced control strategies. The integration of MLIs and advanced control techniques with TRAxFIMs aims to enhance dynamic response, stability and efficiency. The proposed control strategies are evaluated through extensive MATLAB simulations and the potential of MLIs fed TRAxFIMs is emphasized for EV applications.