Widespread Use Of Electric Vehicles Research Articles

Electrification of Transportation: Policy Framework, Technical Aspects and Challenges in Pakistan - A Case Study

Pakistan faces severe air pollution and high dependency on imported fossil fuels for transportation, urgently needing solutions. Electrification of transportation could address these issues, but Pakistan lacks a clear roadmap for adopting electric vehicles. This paper examines the electrification of transportation in Pakistan, focusing on policies, technical aspects, and standards. It looks at best practices from developed countries and discusses incentives offered by the Pakistani government to boost electric vehicle adoption. The study also explores challenges and opportunities in adopting electric vehicles within existing power systems. A SWOT analysis is included, highlighting strengths, weaknesses, opportunities, and threats related to electric vehicle policies and market integration in Pakistan. By reviewing these factors, the paper provides insights into the obstacles and potential benefits of electric vehicle adoption. It also suggests ways for developing nations like Pakistan to encourage widespread use of electric vehicles, aiming to reduce fossil fuel dependency and meet environmental goals.

Driving the future: How value retention rate shapes electric vehicle adoption

The government has been promoting the adoption of electric vehicles (EVs) in recent decades. However, challenges still exist in encouraging widespread use of EVs, which may impact potential buyers. Aside from the well-known barriers related to purchasing, the rate at which the value of EVs is retained over time may also influence consumers’ decisions. This article presents a model based on cue utilization theory to understand how consumers respond to the value retention rate when considering purchasing an EV. The model also examines how perceived quality and two intrinsic cues - brand reputation and the frequency of new product releases - influence this response. The findings indicate that the value retention rate influences consumers’ assessments and purchase decisions, particularly when the intrinsic cues are absent or insufficient. These findings have implications for government policies, EV manufacturers’ promotion strategies, and operational management.

The adoption of electric vehicle in Thailand with the moderating role of charging infrastructure: an extension of a UTAUT

ABSTRACT Electric vehicles (EVs) have the potential to greatly reduce the amount of gasoline consumed and carbon emissions produced during transportation. Consequently, the Thai government actively advocates for the widespread use of electric vehicles (EVs). This study has examined the factors that influence the adoption of electric vehicles in Thailand, with the moderation of charging infrastructure. The research builds upon the UTAUT2 model and incorporates new elements such as environmental concerns, satisfaction with incentive policies, trust, and the availability of charging infrastructure. Environmental concerns, price value, and hedonic motivation significantly affect use behaviour through trust and behaviour intentions; however, facilitating conditions do not affect them. Habits positively affect use behaviour through behaviour intention, though factors such as effort expectancy and social influence satisfaction with policies showed insignificant results when affecting use behaviour. The moderating effect of charging infrastructure showed an insignificant result.

The effect of water mist nozzle settings on fire suppression of electric vehicles in garages

In recent years, along with the widespread use of electric vehicles (EV), EV fires have occurred frequently. However, few studies have investigated EV fires in confined spaces. To address this gap, this paper aims to explore the different aspects of fire evolution of EVs as well as examining the suppression effect of different water mist (WM) settings on these fires. Multiple simulations are carried out, using the Fire Dynamics Simulator (FDS) simulation tool, considering different numbers and working pressures of WM devices. The results highlighted that the number of WM devices had a more significant effect on the fire. Peak heat release was reduced by 17 % and peak arrival time was delayed by 26 % using 6 MPa with 3 nozzles compared to 0 nozzles. Additionally, it leads to accelerated mixing of the smoke layer with the air layer. Moreover, it was shown that the closer to the fire source in the vertical direction, the greater the degree of peak temperature decrease. In this regard, the maximum reduction in the measured temperature was around 70 %.

Application of improved Wolf pack algorithm in planning and operation of multi-microgrid systems with electric vehicles

Abstract With the rapid growth of renewable energy sources and the widespread use of electric vehicles (EVs), the planning and operation problems of multiple microgrids (MMGs) have become more complex and diverse. This paper develop an MMG model with multiple renewable energy sources and small-scale EVs, aiming to maximize the use of renewable energy sources and realize the charging demand of EVs, and highlighting the potential role of EVs in MMGs. In addition, the paper underscores the indispensable role of measurement technology in microgrids and the impetus that microgrid development provides for advancements in measurement technology. To this end, this paper proposes an improved Wolf pack algorithm (IWPA) based on the standard Wolf Pack Algorithm (WPA) with a spiral search approach and chaotic updating of individuals to improve the global search capability of the algorithm and the complexity of solving the scheduling problem. Through simulation experiments on ten standard test functions and examples, it is verified that the IWPA algorithm improves the search accuracy by 2.8%–6.8% and 13.9%–18.3% in the worst and best cases, respectively, in comparison with other algorithms, and it also has a faster convergence speed. Meanwhile, this paper proposes a load interval pricing strategy for the shortcomings of time-of-use pricing strategy and traditional real-time pricing strategy, which is simulated under grid-connected operation, isolated grid operation, and multi-microgrid cooperative operation modes, and the simulation results of the arithmetic example show that this strategy can effectively reduce carbon emissions, and IWPA can effectively coordinate renewable energy, EVs, and other energy resources to achieve efficient energy management of MMGs and supply-demand balance.

Design of an Electric Vehicle Charging System Consisting of PV and Fuel Cell for Historical and Tourist Regions

One of the most important problems in the widespread use of electric vehicles is the lack of charging infrastructure. Especially in tourist areas where historical buildings are located, the installation of a power grid for the installation of electric vehicle charging stations or generating electrical energy by installing renewable energy production systems such as large-sized PV (photovoltaic) and wind turbines poses a problem because it causes the deterioration of the historical texture. Considering the need for renewable energy sources in the transportation sector, our aim in this study is to model an electric vehicle charging station using PVPS (photovoltaic power system) and FC (fuel cell) power systems by using irradiation and temperature data from historical regions. This designed charging station model performs electric vehicle charging, meeting the energy demand of a house and hydrogen production by feeding the electrolyzer with the surplus energy from producing electrical energy with the PVPS during the daytime. At night, when there is no solar radiation, electric vehicle charging and residential energy demand are met with an FC power system. One of the most important advantages of this system is the use of hydrogen storage instead of a battery system for energy storage and the conversion of hydrogen into electrical energy with an FC. Unlike other studies, in our study, fossil energy sources such as diesel generators are not included for the stable operation of the system. The system in this study may need hydrogen refueling in unfavorable climatic conditions and the energy storage capacity is limited by the hydrogen fuel tank capacity.

Optimization of LiNiCoMnO2 Cathode Material Synthesis Using Polyvinyl Alcohol Solution Method for Improved Lithium-Ion Batteries.

The growing need for lithium-ion batteries, fueled by the widespread use of electric vehicles (EVs) and portable electronic devices, requires high energy density and safety. The cathode material Li1-x(NiyCozMn1-y-z)O2 (NCM) shows promise, but attaining high efficiency necessitates optimization of both composition and manufacturing methods. Polycrystalline LiNiCoMnO2 powders were synthesized and assessed in this investigation using a polyvinyl alcohol (PVA) solution method. The study examined different synthesis conditions, such as the PVA to metal ions ratio and the molecular weight of PVA, to assess their influence on powder characteristics. Electrochemical analysis indicated that cathode materials synthesized with a relatively high quantity of PVA with a molecular weight of 98,000 exhibited the highest discharge capacity of 170.34 mAh/g and a high lithium-ion diffusion coefficient of 1.19 × 10-9 cm2/s. Moreover, decreasing the PVA content, irrespective of its molecular weight, led to the production of powders with reduced surface areas and increased pore sizes. The adjustments of PVA during synthesis resulted in pre-sintering observed during the synthesis process, which had an impact on the long-term stability of batteries. The electrodes produced from the synthesized powders had a positive impact on the insertion and extraction of Li+ ions, thereby improving the electrochemical performance of the batteries. This study reveals that cathode materials synthesized with a high quantity of PVA with a molecular weight of 98,000 exhibited the highest discharge capacity of 170.34 mAh/g and a high lithium-ion diffusion coefficient of 1.19 × 10-9 cm2/s. The findings underscore the significance of optimizing methods for synthesizing PVA-based materials to enhance the electrochemical properties of NCM cathode materials, contributing to the advancement of lithium-ion battery technology. The findings underscore the significance of optimizing methods for synthesizing PVA-based materials and their influence on the electrochemical properties of NCM cathode materials. This contributes to the continuous progress in lithium-ion battery technology.

Safer lithium-ion batteries realized by electrolyte with thermoresponsive poly(vinylidene fluoride-co-trifluoroethylene)

The increasingly widespread use of electric vehicles has necessitated the development of high-energy batteries capable of operating under harsh conditions, e.g., at elevated temperatures and high charge/discharge rates. Hence, considerable attention has been focused on enhancing the safety of lithium-ion batteries (LIBs). Herein, a thermoresponsive polymer, poly(vinylidene fluoride-co-trifluoroethylene) with 75 mol% vinylidene fluoride and 25 mol% trifluoroethylene (TrFE-25), is employed as an electrolyte additive to enhance the thermal stability of LIBs (LiCoO2/graphite full cells). Under thermal-abuse conditions, i.e., above a certain threshold temperature, the TrFE-25-containing electrolyte turns into a gel, thereby abruptly increasing of the electrolyte viscosity and interfacial resistance. The gel layers block ion transport, suppress exothermic reactions between the electrodes and electrolyte, and hinder separator shrinkage via adhesive bonding to the anode and cathode, thus preventing internal short-circuiting and enhancing battery safety. According to the results of cycling tests, the addition of TrFE-25 to the baseline electrolyte has no negative effects under normal conditions. Therefore, the proposed methodology holds promise for improving the safety of LIBs, particularly those with flammable carbonate-based electrolytes, and paves the way for the widespread use of these batteries in electric vehicles.

Incorporating nickel foam with nano-encapsulated phase change material and water emulsion for battery thermal management: Coupling CFD and machine learning

In recent years, the rise of machine learning (ML) has prompted researchers to expand the datasets required for optimizing and designing thermal systems. Also, the development and widespread use of electric vehicles (EVs) have surged significantly. However, one of the major challenges associated with EVs is the efficient cooling of Lithium-ion batteries (LIBs). Therefore, the exploration of innovative cooling methods can contribute greatly to the rapidly growing electric vehicle industry. This study focused on investigating the impact of embedding a nickel porous medium around a single 38,120 LiFeO4 cell. To conduct the study, the LIB, along with the nickel porous medium, was placed inside a duct that received a flow of water and Nano-encapsulated phase change materials (NEPCMs). The results obtained from the study indicate that embedding nickel porous media around the LIB led to a significant decrease in the maximum temperature of LIB, more than 40 C, and a remarkable increase in pressure drop more than 100 times. Additionally, it was observed that the decrease in porosity from 1 to 0.97 had a more pronounced effect on pressure drop and the maximum temperature of the LIB's surface, compared to the decrease from 0.97 to 0.95.

Single-Phase Robust Charger with High Power Quality for Electric Vehicle Application

The widespread use of electric vehicles (EVs) necessitates the development of a robust charging infrastructure. The goal of this research paper is to create a high-quality, single-phase charger for electric cars. The purpose of this design is to provide a charger that is both fast and efficient but also protects against power quality problems including harmonics, power factor correction, and voltage variations. A single-phase–single-stage improved power quality EV charger for small and medium power applications has been designed and simulated. A single DC-DC converter is utilized for constant charging and improved power quality operation. The charger presented exhibits improved power quality as sinusoidal current is drawn from the utility grid supply with the total harmonic distortion (THD) in compliance with the IEEE 519 and IEC 61000-3-2 standards. In order to charge the battery of an electric vehicle, most two-stage converters first use a boost converter for power factor correction (PFC), and then they use a DC-DC converter that can accept any input voltage. These two-stage conversions are inefficient and use more parts than necessary. This work proposes a PFC converter based on a single-stage switching inductor Cuk converter, which has the advantages of high step-down gain, low current stress, high efficiency, and a small number of components. The suggested converter’s operational analysis and design equations are performed in continuous current mode. The proposed converter is the subject of extensive mathematical modelling, analysis, simulation, and experimentation presented in this study. With both constant voltage (CV) and constant current (CC) loads, the proposed converter’s performance is analysed with regard to power quality indices like voltage total harmonic distortion (THD), current THD, and total power factor. Additionally, in CV mode and CC mode, the suggested converter’s dynamic performance with battery charging is evaluated in relation to the extensive supply changes. The developed charger presents a reliable and efficient solution for EV charging, fostering the transition to a cleaner and more sustainable transportation system.

Electric vehicles adoption challenges in Oman: a comprehensive assessment and future prospects for sustainable cities

The rising popularity of Electric Vehicles (EVs), fuelled by technological advancements and supportive government policies, presents a promising solution to reduce carbon emissions from conventional cars. This study explores public perceptions of obstacles to Electric Vehicle (EV) adoption in Oman and assesses EVs' impact on city sustainability. Over 300 car consumers in Muscat participated in a survey, facilitated through Microsoft Forms, and distributed via email and social media platforms like WhatsApp, Facebook, LinkedIn, and Instagram. Data analysis utilized descriptive statistics and Structural Equation Modelling (SEM). The study finds that the main obstacles to EV adoption in Oman include a lack of public charging infrastructure, limited EV knowledge, and concerns about available EV models. Safety issues, financial constraints, and low public awareness of climate change also hinder adoption. Structural SEM results show positive correlations between cost-effectiveness, charging infrastructure availability, climate change awareness, and intention to adopt EVs. However, safety concerns negatively impact EVs purchase intention. Regarding the impact of EVs on Oman's cities, respondents generally believe in the potential for EVs to improve air quality, enhance urban living conditions, and mitigate noise and air pollution. However, the respondents were also cautious about the widespread use of EVs due to the high infrastructure investment costs and energy consumption. Therefore, this study recommends targeted interventions like awareness campaigns, better charging infrastructure, and adoption incentives. These insights are vital for policymakers and stakeholders aiming to promote sustainable EV adoption in Oman and beyond.

Locating and sizing charging station in multi-period to promote electric vehicles adoption in urban areas

The layout of public fast charging stations (CSs) often fails to meet the charging demand of electric vehicle (EV) users, resulting in charging anxiety. To address this issue and promote the widespread use of EVs in urban areas, this paper presents a multi-period locating and sizing optimization model under dynamic demand. The model incorporates GPS trajectory extraction data to identify potential charging demand points and considers the mobile load characteristics of EVs for fusion modeling. A multi-period change formula is proposed to reflect the interaction between public fast CSs and EV market penetration, incorporating charging opportunity and waiting time satisfaction as measures of charging service quality. To solve the model, a heuristic algorithm combining genetic algorithm (GA) and k-medoids clustering algorithm is designed. We apply the model to an actual case of Shenzhen and find that increasing investment in charging infrastructure can boost EV market penetration in the later planning period. Moreover, the gap between charging opportunity and waiting time satisfaction tends to narrow as the planning progresses. Our findings highlight the importance of optimizing the location and size of public fast CSs to improve the charging service quality and promote the adoption of EVs.

Design and Implementation of a Wireless Power Transfer System for Electric Vehicles

Wireless power transfer (WPT) systems, which have been around for decades, have recently become very popular with the widespread use of electric vehicles (EVs). In this study, an inductive coupling WPT system with a series–series compensation topology was designed and implemented for use in EVs. Initially, a 3D Maxwell (ANSYS Electromagnetics Suite 18) model of the system was generated. The impact of individual parameters on the coupling coefficient was analyzed through systematic variations in each parameter’s values. As a result, a system with a higher coupling coefficient was obtained. Using this system, three distinct load cases were investigated for their efficiency in the Simplorer (ANSYS Electromagnetics Suite 18) circuit. Subsequently, a prototype of the system was constructed, and the experimental results were compared with the model’s results. This study shows that both the output power and the efficiency of the system increase as the load resistance increases. The results obtained in this study are anticipated to offer valuable insights for the enhancement of WPT system design.

EV Integration-Oriented DC Conversion of AC Low-Voltage Distribution Networks and the Associated Adaptive Control Strategy

Driven by carbon-neutral targets and transportation electrification, the widespread use of electric vehicles (EVs) has become an irreversible trend. However, low-voltage distribution networks (LVDNs) can face several challenges under the high-penetration EV integration, including overloads, voltage violations and unbalance issues. In this paper, an EV integration-oriented DC conversion scheme and the associated adaptive control method for LVDNs is proposed, aiming at releasing more capacity for EVs and collaborative improvement of the above issues caused by EV charging. Based on analyzing the influence of different charging piles connected to the AC and DC LVDNs, a DC conversion scheme for three-phase four-wire LVDNs under high-penetration EVs is proposed. The voltage source converter (VSC) control strategies aimed at alleviating the overload of the DTs, voltage violations, and three-phase unbalance are designed separately based on the modified three-phase four-wire voltage sensitivity. Then, a coordinated adaptive control strategy of on-load tap changer and VSCs is proposed considering the simultaneous occurrence of multiple power quality issues in hybrid AC/DC LVDNs. Case study verifies the effectiveness of the proposed DC conversion and adaptive control methods, by which the maximum EV penetration of the hybrid AC/DC LVDN is increased from 85% to 215% compared with the AC LVDN.

Heuristic Algorithms for Heterogeneous and Multi-Trip Electric Vehicle Routing Problem with Pickup and Delivery

In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches.

Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

Fundamentals of the recycling of spent lithium-ion batteries.

This review discusses the critical role of fundamentals of battery recycling in addressing the challenges posed by the increasing number of spent lithium-ion batteries (LIBs) due to the widespread use of electric vehicles and portable electronics, by providing the theoretical basis and technical support for recycling spent LIBs, including battery classification, ultrasonic flaw detection, pretreatment (e.g., discharging, mechanical crushing, and physical separation), electrolyte recovery, direct regeneration, and theoretical calculations and simulations. Physical chemistry principles are essential for achieving effective separation of different components through methods like screening, magnetic separation, and flotation. Electrolyte recovery involves separation and purification of electrolytes through advanced physical and chemical techniques. Direct regeneration technology restores the structure of electrode materials at the microscopic scale, requiring precise control of the physical state and crystal structure of the material. Physical processes such as phase changes, solubility, and diffusion are fundamental to techniques like solid-state sintering, eutectic-salt treatment, and hydrothermal methods. Theoretical calculations and simulations help predict the behaviour of materials during recycling, guiding process optimization. This review provides insights into understanding and improving the recycling process, emphasizing the central role of physical chemistry principles in addressing environmental and energy issues. It is valuable for promoting innovation in spent LIB recycling processes and is expected to stimulate interest among researchers and manufacturers.

Electrochemistry-Based Diagnosis of Li Plating for Fast-Charging Li-Ion Batteries

Recent innovations in battery technologies have facilitated the development and widespread use of electric vehicles (EVs), by increasing the energy density of batteries while lowering their cost. Despite the rapid growth of the EV market, the long charging time of lithium-ion batteries (LIBs) remains as a major obstacle for expanding customer acceptance of EVs. Typically, graphite-based anodes in the state-of-the-art LIBs suffer from Li plating under fast-charging conditions, which causes severe performance degradations and early cell failure of batteries. To prevent the Li plating-induced failure, extensive research efforts have been devoted to developing diagnostic methods capable of detecting Li plating. The electrochemical analysis of Li plating enables non-destructive, in operando detection without complicated experimental procedures or special equipment.Herein, we present a non-destructive method for detecting Li plating during fast-charge cycling by analyzing current transients during the constant-voltage charging step, based on electrochemical battery models. Three-dimensional (3D) and pseudo-two-dimensional (P2D) battery models are employed to predict the electrochemical behavior of full cells under fast-charging conditions. 3D model enables the evaluation of local electrochemical properties, such as local reaction current densities and electrode potentials, which are largely affected by the microstructure of the electrode. Furthermore, P2D models are employed to investigate the effect of changes in microstructural properties (e.g., porosity and fraction of active surface of the graphite anode) on electrochemical behavior of full cells.For 3D modeling, the 3D Voronoi tessellation and Catmull-Clark algorithm are employed to construct the model geometry of graphite anodes. 3D modeling results predict that upon fast-charging, the charge-transfer potential on the top surface of graphite anode rapidly drops below 0 V vs. Li/Li+, triggering Li plating. To extract the electrochemical signals of Li plating, the fast-charging behaviors of full cells are investigated by using the P2D model with a partially reversible Li plating–stripping reaction. In specific, the constant-current and constant-voltage (CC-CV) charging process of the full cells with graphite anodes is simulated with Li plating-induced degradation modes; reduction of (i) interparticle pores, (ii) active surface area for Li intercalation reaction, and (iii) cyclable Li amount. Modeling results demonstrate that an inflection point appears in the current transient curve, and it shows a positive shift when the Li plating-induced degradation occurs. Electrochemical tests and microstructural analyses further confirm the validity of proposed electrochemical detection of Li plating. This study provides an effective strategy to design electrochemical methods for Li plating detection during fast-charging batteries based on electrochemistry-based models.

Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm

Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system. In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoT-based smart charging system for scheduling V2G connections for hybrid electrical vehicles. It allows for more precise and effective control and greater efficiency by enabling the system to learn from its surroundings.