Vehicle CO2 Emissions Research Articles

Dynamic Modeling and Control Strategy Optimization of a Volkswagen Crafter Hybrid Electrified Powertrain

This article studies the transformation and assembly process of the Volkswagen (VW) Crafter from conventional to hybrid vehicle of the department of vehicles engineering, University of Debrecen, and uses a computer-aided simulation (CAS) to design the vehicle based on the real measurement data (hardware-in-the-loop, HIL method) obtained from an online CAN bus data measurement platform using MATLAB/Simulink/Simscape and LabVIEW software. The conventional vehicle powered by a 6-speed manual transmission and a 4-stroke, 2.0 Turbocharged Direct Injection Common Rail (TDI CR) Diesel engine and the transformed hybrid electrified powertrain are designed to compare performance. A novel methodology is introduced using Netcan plus 110 devices for the CAN bus analysis of the vehicle’s hybrid version. The acquired raw CAN data is analyzed using LabVIEW and decoded with the help of the database (DBC) file into physical values. A classical proportional integral derivative (PID) controller is utilized in the hybrid powertrain system to manage the vehicle consumption and CO2 emissions. However, the intricate nonlinearities and other external environments could make its performance unsatisfactory. This study develops the energy management strategies (EMSs) on the basis of enhanced proportional integral derivative-based genetic algorithm (GA-PID), and compares with proportional integral-based particle swarm optimization (PSO-PI) and fractional order proportional integral derivative (FOPID) controllers, regulating the vehicle speed, allocating optimal torque and speed to the motor and engine and reducing the fuel and energy consumption and the CO2 emissions. The integral time absolute error (ITAE) is proposed as a fitness function for the optimization. The GA-PID demonstrates superior performance, achieving energy efficiency of 90%, extending the battery pack range from 128.75 km to 185.3281 km and reducing the emissions to 74.79 gCO2/km. It outperforms the PSO-PI and FOPID strategies by consuming less battery and motor energy and achieving higher system efficiency.

Implementation of monitoring and certification with the developed China heavy-duty vehicle energy consumption and carbon emission calculation model

Owing to escalating concerns regarding global warming, there has been a heightened focus on greenhouse gases emitted by vehicles. To effectively monitor and certify the fuel consumption and CO2 emissions of heavy-duty vehicles, this study proposes a calculation model named the China Heavy-Duty Vehicle Energy Consumption and Carbon Emission Calculation Model (CHECM). The CHECM is a simulation tool based on longitudinal dynamics. A classification learner was utilised to obtain shifting strategies, achieving accuracies of 92.9% and 93.5% under regulated driving cycles. A fuel-consumption model was incorporated to predict the transient performance of the engine and transmission. In addition, the Sobol method was used to assess the sensitivities of rolling resistance, air drag and rotational mass conversion coefficients to the driving force and a method was proposed to obtain the road resistance correction factor. The test results of three China-6 heavy-duty vehicles over two regulatory test cycles were obtained and used for model accuracy evaluation. The results showed that the deviations between the measured and calculated fuel consumption were 1.25–3.57%, whereas those between the measured and calculated CO2 emissions using the Chinese World Transient Vehicle Cycle were 3.23–4.16%. The CHECM has the potential to accurately replicate various driving conditions and vehicle configurations, particularly when specific sources of uncertainty are constrained.

Effect of alternative fuels on the emissions of a passenger car under real-world driving conditions: a comparison of biodiesel, gas-to-liquid, coal to liquid.

Fossil fuel energy crisis and environmental pollution have initiated the scientific research on alternative fuels. Biodiesel (B100), gas to liquid (G100), and coal to liquid (C100) are superb selections to be substitutes for conventional diesel. To better investigate the emission characteristics of the alternative fuels mentioned above, a portable emission measurement system (PEMS) was used to carry out this study under real-world driving conditions. Results showed that the driving conditions had a notable effect on the vehicle emissions, the CO, THC, and CO2 emissions were higher under urban condition, and the NOx, PM (particle mass), and PN (particle number) emissions were higher under suburban condition. The expressway condition resulted in lower emissions except for PN due to more nucleation particles emitted. The use of B100, G100, and C100 fuels led to a reduction of more than 50% in the CO emission, especially for the C100, but the reduction effects for the THC were not obvious, and among them, G100 is the most prominent. Higher NOx emission was emitted after using the three fuels, especially for the B100; meanwhile, B100 increased the CO2, but G100 and C100 decreased the CO2 emission compared with D100. The PN emissions reduced by 1-2 orders of magnitude in comparison with those from D100 after using the three alternative fuels, and more than 50% of the PM could be reduced. B100 has the most significant particle reduction effect due to its oxygen-containing property, and it produced an evidently higher proportion of nucleation particles than D100, followed by G100 and C100.

Forecasting CO2 emissions of fuel vehicles for an ecological world using ensemble learning, machine learning, and deep learning models.

The continuous increase in carbon dioxide (CO2) emissions from fuel vehicles generates a greenhouse effect in the atmosphere, which has a negative impact on global warming and climate change and raises serious concerns about environmental sustainability. Therefore, research on estimating and reducing vehicle CO2 emissions is crucial in promoting environmental sustainability and reducing greenhouse gas emissions in the atmosphere. This study performed a comparative regression analysis using 18 different regression algorithms based on machine learning, ensemble learning, and deep learning paradigms to evaluate and predict CO2 emissions from fuel vehicles. The performance of each algorithm was evaluated using metrics including R2, Adjusted R2, root mean square error (RMSE), and runtime. The findings revealed that ensemble learning methods have higher prediction accuracy and lower error rates. Ensemble learning algorithms that included Extreme Gradient Boosting (XGB), Random Forest, and Light Gradient-Boosting Machine (LGBM) demonstrated high R2 and low RMSE values. As a result, these ensemble learning-based algorithms were discovered to be the most effective methods of predicting CO2 emissions. Although deep learning models with complex structures, such as the convolutional neural network (CNN), deep neural network (DNN) and gated recurrent unit (GRU), achieved high R2 values, it was discovered that they take longer to train and require more computational resources. The methodology and findings of our research provide a number of important implications for the different stakeholders striving for environmental sustainability and an ecological world.

A multi‐objective sustainable closed‐loop supply chain network problem with hybrid facilities

AbstractA sustainable closed‐loop supply chain network requires conjunctive implementation of reverse logistics in the supply chain, with decisions that consider economic, environmental, and social factors. In real life, the problem needs to be addressed by prioritizing targets or interacting between them to give a range of solutions to the decision maker. In this context, this work proposes a novel multi‐objective sustainable closed‐loop supply chain network problem based on the revised network design model with hybrid recovery centers minimizing (1) the total economic cost, (2) the CO2 emission of vehicles used, and (3) the total obnoxious distance. The latter objective is a novel implementation of the social dimension of a sustainable model. A sensitivity analysis of the multi‐objective model is developed through ANOVA. A dataset of instances was generated to test the model and the solution methods, which are configured with AUGMECON2, a linear programming relaxation implemented to improve the CPU time, and AUGMECON2‐EXTENDED to obtain more solutions to avoid exploring all space of the solution. The results show that an AUGMECON2‐EXTENDED implementation outperforms all the selected performance metrics. These performance metrics include NPS, CPU time, RPOS, QM, and HV. The results show an improvement on average of at least , , , , and , respectively, in those metrics, in comparison to other implementations.

Energy sources as a function of electric vehicle emission: The case of Bosnia and Herzegovina

This paper deals with the analysis of challenges and perspectives of the transition to electric vehicles as a sustainable solution for the transport sector in the context of global energy challenges and the need to reduce negative environmental impacts. With an emphasis on the energy situation in Bosnia and Herzegovina, the paper explores the possibilities of switching to electric vehicles (EVs) and analyses the effects of energy sources on CO2 emissions. The paper highlights the motivation to switch to EVs, driven by the need to reduce greenhouse gas emissions and rely on renewable energy sources. After analysing relevant studies, it is concluded that smaller and lighter electric vehicles have lower CO2 emissions and that the participation of renewable sources in electricity production reduces these emissions. The conducted analysis of the vehicle fleet specifies that the CO2 emissions of electric vehicles are not zero and that they depend on the source of electricity. Furthermore, other factors, such as the production of batteries, also play an important role in the overall environmental impact. Although the motivation to switch to electric vehicles is emphasized to reduce greenhouse gas emissions and use renewable energy sources, it has been shown that the CO2 emissions of electric vehicles (EVs) are not zero and significantly depend on the energy sources. Calculations performed on the vehicle fleet of the Federation of Bosnia and Herzegovina for the year 2021, using Copert as the tool, showed that vehicles driven by fossil fuels emit about 1.6 million tonnes of CO2. In comparison, if all vehicles were replaced with electrical ones, the CO2 emissions would be about 1.15 million tonnes. As for the required electricity to power EVs, it is calculated that the required amount would be about 1,539 GWh per year. This paper acknowledges the presence of emissions associated with battery production, storage, and disposal, as well as vehicles themselves. However, it does not delve into this issue in detail. Future research will aim to address this matter more thoroughly.

Analysis of the carbon footprint of electric vehicles in relation to their life cycle

In order to determine the environmental impact of electric vehicles (EVs), a comparative analysis was made between the CO2 emissions of internal combustion vehicles (ICEs) during their entire life cycle and the emissions of electric vehicles. This study sought to have a more realistic perspective. For this purpose, the life cycle of both types of vehicles was divided into three phases, calculating the kg of CO2 emitted during each of them. The results showed the significant difference in CO2 emissions generated by both types of vehicles, with ICVs generating 32,492 kg of CO2 more than EVs with NCM lithium-ion batteries and 32,403.7 kg of CO2 for those with LFP batteries. Once the analysis was done, it was determined that EVs are an appropriate response to the environmental crisis that the world is currently facing.

Evolving preferences in sustainable transportation: a comparative analysis of consumer segments for electric vehicles across Europe

PurposeThis study aims to explore and segment consumer preferences for electric and hybrid vehicles in Germany, Sweden, the Netherlands and Turkey, focusing on understanding the various factors that influence consumer decisions in these markets.Design/methodology/approachUsing latent class analysis (LCA) on data collected through online surveys and discrete choice experiments, this research categorizes consumers into distinct segments. The approach allows for a nuanced understanding of how various factors such as income level, fuel cost, age, CO2 emissions, purchase price, vehicle range, policy policies and environmental concerns interact with shape consumer preferences.FindingsThe analysis uncovers significant heterogeneity in consumer preferences for electric and hybrid vehicles across Germany, Sweden, the Netherlands and Turkey, revealing four key segments: “Eco-Driven Innovators,” “Value-Focused Pragmatists,” “Tech-Savvy Early Adopters” and “Reluctant Traditionalists.” “Eco-Driven Innovators” prioritize environmental benefits and are less sensitive to price, demonstrating a strong inclination toward vehicle CO2 emissions and policy policies. “Value-Focused Pragmatists” weigh economic factors heavily, showing a sharp interest in fuel costs and purchase prices but are open to considering electric and hybrid vehicles if they present clear long-term savings. Technology-savvy early adopters are attracted by the latest technological advancements in vehicles, regardless of the type, and are motivated by factors beyond just environmental concerns or cost savings. Lastly, “Reluctant Traditionalists” exhibit minimal interest in electric and hybrid vehicles due to concerns over charging infrastructure and upfront costs. This detailed segmentation illustrates the diverse motivations and barriers influencing consumer choices, from governmental policies and environmental concerns to individual financial considerations and technological appeal.Originality/valueThis study stands out for its pioneering application of LCA to dissect the complexity of consumer preferences for electric and hybrid vehicles, a methodological approach not widely used in this research domain. Using LCA, the authors are able to uncover nuanced consumer segments, each with distinct preferences and motivations, providing a depth of insight into market dynamics that traditional analysis methods may overlook. This approach enables a more granular understanding of how diverse factors – ranging from environmental concerns to economic considerations and technological attributes – interact to shape consumer choices in different countries. The findings not only fill a critical gap in the existing literature by mapping the intricate landscape of consumer preferences, but also offer a novel perspective on strategizing market interventions. Therefore, the application of LCA enriches the discourse on sustainable transportation, offering stakeholders, manufacturers, policymakers and researchers – a refined toolkit for navigating the evolving market dynamics and fostering the adoption of electric and hybrid vehicles.

Impact of Engine Inertia on P2 Mild HEV Fuel Consumption

The energy management system (EMS) of a hybrid electric vehicle (HEV) is an algorithm that determines the power split between the electrical and thermal paths. It defines the operating state of the power sources, i.e., the electric motor (EM) and the internal combustion engine (ICE). It is therefore one of the main factors that can significantly influence the fuel consumption and performance of hybrid vehicles. In the transmission path, the power generated by the ICE is in part employed to accelerate the rotating components of the powertrain, such as the crankshaft, flywheel, gears, and shafts. The main inertial components are the crankshaft and the flywheel. This additional power is significant during high-intensity acceleration. Therefore, the actual engine operation is different from that required by the power split unit. This study focuses on exploring the influence of engine inertia on HEV fuel consumption by developing a controller based on an equivalent consumption minimisation strategy (ECMS) that considers crankshaft and flywheel inertia. The optimal solution obtained by the ECMS controller is refined by incorporating the inertia effect of the main rotating components of the engine into the cost function. This reduces the engine operation during high inertial torque transient phases, resulting in a decrease in vehicle CO2 emissions by 2.34, 2.22, and 1.13 g/km for the UDDS, US06, and WLTC driving cycles, respectively.

New generation steels for light weight vehicle safety related applications

Abstract Vehicle lightening efforts, low fuel consumption and low CO2 emission have become an important target in the automotive industry without compromising safety and comfort. By reducing vehicle weights, fuel consumption and CO2 emissions are significantly reduced. In this context, the next generation steels are constantly being developed in terms of high strength, forming, weld ability, low cost in matters such as vehicle lightening and impact safety. In this study, side-impact performance of the next generation steels that can be used for the body B-Pillar part as part of vehicle lightening was examined. In finite element analysis, Usibor 1500, TWIP 980, TRIP 800, DP 1000 materials and three different metal sheet thicknesses (1.3–1.5–1.8 mm) were used for the B-Pillar part. All results obtained were compared in terms of energy absorption ability, formability, weld ability, cost and weight.

Shared pooled mobility essential complement to decarbonize China’s transport sector until 2060

Greenhouse gas emission reduction in the passenger transport sector is a main challenge for China’s climate mitigation agenda. Electrification and shared mobility provide encouraging options for carbon emissions reduction in road transport. Based on an integrated scenario-based assessment framework, a provincial-level projection is made for vehicle growth and CO2 emissions in China under shared socioeconomic pathways (SSPs). This work illustrates how passenger car electrification and sharing contribute to China’s “30·60” climate goals (peaking of CO2 emissions by 2030 and carbon neutrality by 2060). The results demonstrate that China is en route to achieving the goal of a 2030 carbon peak (1.0Gt CO2) under current conditions, and could reach peak emissions around 2026 with optimistic growth in EVs and shared mobility. Compared with no policy action, the single EV policy (shifting from ICEVs to EVs) can reduce 71% of emissions by 2060, thus narrowing but not closing the mitigation gap to carbon neutrality in passenger cars (302 Mt CO2). Shared mobility can provide further emission reduction support, reducing emissions by 83% in 2060. Comprehensive climate actions (including electrification, sharing mobility to reduce car use, and improving vehicle efficiency and fuel carbon intensity) are needed to achieve deep decarbonization to net-zero by 2060 in the passenger transport sector.

Effective Modeling of CO2 Emissions for Light-Duty Vehicles: Linear and Non-Linear Models with Feature Selection

Predictive modeling is important for assessing and reducing energy consumption and CO2 emissions of light-duty vehicles (LDVs). However, LDV emission datasets have not been fully analyzed, and the rich features of the data pose challenges in prediction. This study aims to conduct a comprehensive analysis of the CO2 emission data for LDVs and investigate key prediction model characteristics for the data. Vehicle features in the data are analyzed for their correlations and impact on emissions and fuel consumption. Linear and non-linear models with feature selection are assessed for accuracy and consistency in prediction. The main behaviors of the predictive models are analyzed with respect to vehicle data. The results show that the linear models can achieve good prediction performance comparable to that of nonlinear models and provide superior interpretability and reliability. The non-linear generalized additive models exhibit enhanced accuracy but display varying performance with model and parameter choices. The results verify the strong impact of fuel consumption and powertrain attributes on emissions and their substantial influence on the prediction models. The paper uncovers crucial relationships between vehicle features and CO2 emissions from LDVs. These findings provide insights for model and parameter selections for effective and reliable prediction of vehicle emissions and fuel consumption.

Machine Learning Based Predictive Modelling of Vehicle CO2 Emissions: An Extensive Examination of Automobile Features

There is a growing interest in discovering novel and inventive methods to decrease the emission of carbon dioxide (CO2) from diverse sources, mostly due to concerns about safeguarding the environment and mitigating the adverse effects of greenhouse gas emissions, especially in the automotive sector. This study article explores the fascinating potential of predicting the quantity of CO2 generated by autos using machine learning (ML) based methodologies. In order to create a system that can predict carbon dioxide emissions depending on a number of factors, we are investigating machine learning. Significant details about the car, such as its construction, kind, number of cylindrical objects, horsepower, gearbox type, fuel type, and fuel consumption, are included in the dataset that forms the basis of our investigation. This project's main objective is to create accurate and dependable prediction models for calculating vehicle-generated CO2 emissions. Legislators that deal with the environment, corporations, and consumers could find great value in these models. We use a range of machine learning (ML) techniques, such as ensemble techniques, neural networks, decision trees, and regression algorithms, to accomplish this goal. This study assesses the prediction accuracy, adaptability to different situations, and data utilisation of several techniques. To determine which predictive models are most suited for predicting CO2 emissions within the limitations of the given information, we employ a comprehensive evaluation process. Additionally, we look at how different feature subsets affect prediction accuracy, providing an indication of the relative significance of different vehicle parameters in relation to CO2 emissions. Our results advance the field of predictive emissions models and provide insight on the primary drivers of vehicle-related greenhouse gas emissions.

The impact of real driving on vehicle CO2 emissions

On-road (real) driving of a conventional vehicle having free gear-shift strategy and driving style, non-zero positive altitude gain, and uncontrolled ambient conditions is expected to produce different emissions values, including CO2, in respect to those produced in a controlled laboratory experiment following a standardized velocity profile.This paper aims to investigate the impact of driving style characterized by driving dynamics on CO2 emissions of a passenger car equipped with an internal combustion engine. The study is based on recorded on-road trips of a passenger car that are postprocessed using the EU real driving calculation procedures.Selected segment trips of urban, rural and motorway driving are used as input for a detailed vehicle model developed in LMS Imagine. Lab AMESim in order to assess the impact of real driving on vehicle CO2 emissions.

Application of Deep Learning Initiatives for CO2 Emissions Forecasting

This work models and forecasts vehicle CO2 emissions, a major source of atmospheric changes and climate disruptions, using cutting-edge artificial intelligence. The CO2 emission by vehicle dataset from Kaggle, which includes several features such vehicle class, engine size, cylinder transmission, fuel type, fuel consumption, city, highway, comb, and CO2 emissions, was used to build the model. To predict CO2 emissions, a hybrid model (CNN-LSTM-MLP) was developed based on long short-term memory network (LSTM), convolution neural network (CNN), and multi-layer perceptron (MLP). The proposed model shows superior results compared with CNN, MLP, LSTM, Light Gradient Boosting Machine (LGBM) Regressor, support vector machine (SVM), Linear Regression, and Random Forest.

Prospects of Passenger Vehicles in China to Meet Dual Carbon Goals and Bottleneck of Critical Materials from a Fleet Evolution Perspective

China has pledged to peak its CO2 emissions by 2030 and achieve carbon neutrality by 2060. To meet these goals, China needs to accelerate the electrification of passenger vehicles. However, the rapid development of electric vehicles may impact the supply of critical raw materials, which may hinder the low-carbon transition. Therefore, the impact of vehicle electrification on CO2 emissions and the corresponding bottlenecks in the supply of critical raw materials should be systematically considered. In this study, we developed the China Automotive Fleet CO2 Model (CAFCM) to simulate a mixed-technology passenger vehicle fleet evolution. We further assessed the impact of energy and CO2 emissions and evaluated the demand for critical battery materials. We designed three scenarios with different powertrain type penetration rates to depict the potential uncertainty. The results showed that (1) the CO2 emissions of passenger vehicles in both the operation stage and the fuel cycle can peak before 2030; (2) achieving the dual carbon goals will lead to a rapid increase in the demand for critical raw materials for batteries and lead to potential supply risks, especially for cobalt, with the cumulative demand for cobalt for new energy passenger vehicles in China being 5.7 to 7.3 times larger than China’s total cobalt reserves; and (3) the potential amount of critical material recycled from retired power batteries will rapidly increase but will not be able to substantially alleviate the demand for critical materials before 2035. China’s new energy vehicle promotion policies and key resource supply risks must be systematically coordinated under the dual carbon goals.

Comparative analysis of CO2 emissions of electric ride-hailing vehicles over conventional gasoline personal vehicles

Ride-hailing services have become popular due to their convenience, affordability, and accessibility. Increasing cost of car ownership, traffic congestion and parking shortages are fostering this global market growth. Though four-wheeler ride-hailing vehicles are fuel efficient, researchers have determined that the emission reductions from fuel efficiency alone cannot compensate for the additional emissions caused by "deadheading," the issue of driving without passengers on ride-hailing trips. We suggest that electric vehicle-based ride-hailing services have the potential to reduce utilization CO2 emissions, which are emissions released while using (driving) the vehicle. However, skeptics challenging this hypothesis cite CO2 released during electricity generation (carbon intensity) needed for recharging batteries and the inherent issue of deadheading in ride-hailing services. We compared ride-hailing vehicles to gasoline-powered passenger cars, the largest source of carbon emissions within the transportation sector. In many cases, ride-hailing vehicles are electric and have newer models, so they emit less CO2 per kilometer. We conducted a quantitative analysis, using a mathematical model to estimate the utilization of CO2 emissions for electric vehicles per ride-hailing trip kilometer, considering carbon intensity of electricity generation, electric vehicle fuel efficiency, and deadheading. We compared our results with average estimated utilization CO2 emissions to gasoline-powered passenger cars. Additionally, we performed a sensitivity analysis, which involved adjusting the input variables within plausible ranges to assess their impact on our model results. According to our analysis, despite deadheading, improved vehicle fuel efficiency and cleaner electricity generation result in lower CO2 emissions for electric ride-hailing vehicles than gasoline personal vehicles.

On data-induced CO[formula omitted] emissions of vehicle automation: An overlooked emission source

CO2 emission of vehicles and its influence on climate change is a widely discussed topic already for many years. New CO2 emission norms for vehicles have been introduced based on the propulsion of the vehicle, to reduce future CO2 emissions. Automated vehicles (AVs) have potential in reducing emissions by optimizing routes and speed profiles. However, they also generate extra emissions due to large data involved. Whether the norms can be met with these extra data-induced emissions of AVs remains an open question. This paper provides an approach to determine the CO2 emissions of these data related aspects. The approach dissects data-induced emissions stemming from energy consumption of the sensing components, the computing platform, disks inside the vehicle, wireless communication networks and data centers. We apply the approach to estimate CO2 emissions for varying scenarios of technology composition and energy mix. Sensitivity analysis shows that the energy intensity of wireless communication networks and the data transmission rate from vehicle to data center have the strongest influence on the resulting CO2 emissions. The energy mix also significantly affects whether the norms can be met. For high amounts of data transmission, compliance with the norms seem to be difficult in most scenarios. We recommend that the energy consumption of wireless communication networks and data transmission from vehicle to data center should be further optimized. Future work should focus on empirical evidence to validate/falsify the key assumptions in this paper, which will lead to a more accurate estimate of automation-induced emissions.

Characterizing CO2 and NOx emission of vehicles crossing toll stations in highway

Electronic toll collection (ETC) has been designed and implemented to improve traffic efficiency and reduce traffic-related air pollution. However, the real-world impact of ETC on these factors is currently under-qualified. With this consideration, we attempted to reveal the effect of ETC on traffic efficiency and traffic pollution using portable emission measurement systems. Firstly, in contrast to the manual toll collection (MTC), ETC offer lower emissions and time savings, and there exist a quadratic relationship between emission rates and vehicle specific power. Secondly, vehicles in acceleration state emit more pollutants than other situations such as deceleration, steady cruising and idling in both ETC and MTC lanes. Thirdly, ETC lanes show 34.25% and 13.63% lower CO2 and NOx emissions, respectively, along with a 46.55% timesaving compared to MTC lanes. Finally, the study identifies engine load percent as the primary factor impacting emissions, accounting for 60–88% of the variance.

Comparing the CO2 emissions of three type of Vehicles

In the quest for sustainable transportation, this research delves deep into the lifecycle CO2 emissions of Internal Combustion Engine Vehicles (ICEVs), Electric Vehicles (EVs), and Plug-in Hybrid Electric Vehicles (PHEVs), unveiling surprising intricacies. While ICEVs maintain a predictable emission pattern, the story for EVs and PHEVs is far more captivating, with emissions dancing to the rhythm of regional energy mixes, grid demands, and charging nuances. This study not only deciphers the complexities of vehicular emissions but also paints a vivid picture of the transformative potential of cleaner energy grids. As the narrative of EVs and PHEVs unfolds, the research spotlights the pivotal role they could play in sculpting a greener transportation future. Beyond mere data, this exploration beckons readers into a compelling journey from the heart of our current transportation landscape to the promising horizons of sustainable mobility. Dive in to discover the challenges, opportunities, and innovations that could redefine our relationship with the road.