Vehicle Electrification: Status and Issues

Microscopic series plug-in hybrid electric vehicle energy consumption model: Model development and validation

Author(s): Muehlegger, Erich; Rapson, David | Abstract: This research project explores the plug-in electric vehicle (PEV) market, including both Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), and the sociodemographic characteristics of purchasing households. The authors use detailed micro-level data on PEV purchase records to answer two primary research questions. Their results confirm that low-income households exhibit a lower share of PEV purchases than they do for conventional, internal combustion engine (ICE) vehicles. Households with annual income less than $50,000 comprise 33 percent of ICE purchases and only 14 percent of PEVS. By comparison, high-income households earning more than $150,000 annually comprise only 12 percent of ICE purchases and 35 percent of PEV purchases over their sample period. Similarly, unsurprising patterns can be seen across ethnicities. For example, non-Hispanic Whites represent 41 percent of ICE purchases but 55 percent of PEV purchases, as compared to Hispanics (38 percent of ICE and 10 percent of PEVs) and African Americans (3 percent of ICEs and 2 percent of PEVS). These differences naturally raise questions about barriers to PEV adoption among low-income and minority ethnic populations. By comparing outcomes in the ICE, hybrid, and PEV markets across income and ethnic groups, the authors are able to test whether price discrimination and barriers to market access are higher in PEV markets for low-income and minority ethnic groups. The authors find that, overall, they are not, although there are mixed results for the used PEV market. In general, non-white, low-income populations face higher prices in the used PEV market, relative to a baseline, than they do in the new PEV market. While some people travel farther to buy used PEVs than they do to buy used ICE vehicles, there is not a pattern that would indicate systematic discrimination (e.g. Hispanics travel farther to buy used PHEVs but less far to buy used BEVs). While the authors admit that their empirical approach cannot control for all potential vehicle composition effects, the authors view their results as being most consistent with a market that provides access to all ethnicities and income groups.View the NCST Project Webpage

Are the electric vehicles more sustainable than the conventional ones? Influences of the assumptions and modeling approaches in the case of typical cars in China

The government policies and cost benefits led to the surge in penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs) into both public and private sectors, challenging the electric grid's potential to maintain its stability and efficiently serve the purpose. The penetration of PHEVs brings technological advancements and environmental benefits to the traditional transportation and electrical power systems. Designing of appropriate charging methods to satisfy PHEV owners helps to improve system performance and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a coordinated charging algorithm to charge maximum number of PHEVs with the existing infrastructure at a Charging Station (CS) located in residential locality. The stochastic driving patterns of PHEVs are studied and different charging strategies are developed. The Optimized charging scheme is obtained for American Electric Power utility distribution feeder modeled in OpenDSS. Further, PHEV customers are modeled into price responsive consumers capable of participating in Demand Response programs to economically charge their vehicles and render support to the stabilization of power system. Consumer behavior modeling has been done by developing extensive demand-price elasticity matrices (PEMs) which are utilized to calculate the level of demand response.;This thesis also proposes a concept of converting the existing Personal Rapid Transit (PRT) system from guideway power rail propelling vehicles to Battery Electric Vehicles (BEVs) by developing a novel routing algorithm based on the data provided by West Virginia University's Department of Transportation and Parking. This turns into a feasibility analysis of implementing new EV technologies in electrification of public transportation. The routing algorithm involves Mixed Integer Linear Programming (MILP) Optimization resulting in hourly schedule of all the vehicles satisfying the passenger demand. The effect of charging load of BEVs on the electrical distribution system is analyzed by performing a time series simulation on AEP distribution feeder.;Additionally, the impact of PHEV charging on power market prices i.e. Locational Marginal Prices (LMP) in a distribution microgid scenario is presented. Locational marginal pricing is a market based wholesale pricing approach adopted by six out of ten national electricity markets. The uncoordinated charging of EVs results in unplanned peak demand causing system changes and fluctuation of LMP. The stabilizing effect of coordinated charging scheme is demonstrated on IEEE 13 Node test system.

Routing a mix of conventional, plug-in hybrid, and electric vehicles

The rising concerns over global climate change and depleting fossil fuel reserves are two of the main reasons for the ongoing efforts towards the electrification of the transportation sector. While greenhouse gases (GHGs) emissions from other sectors are generally falling, emissions from the road transport have increased over the past few decades, with both full electric vehicles (FEVs) and plug-in hybrid electric vehicles (PHEVs) being recognized as potential alternatives to combat climate change and reduce GHG emissions. However, wide-spread integration of FEVs and PHEVs will substantially increase the load on the power system which will eventually affect the reliability of existing power systems. In this paper, a probabilistic model for integrating FEVs and PHEVs with existing power grids is proposed that incorporates important FEV and PHEV characteristics, such as battery capacity, charge depleting distance, and charging rates. In addition, user behavior is taken into account through time of recharging, arrival and departure times, and daily miles driven. Furthermore, different charging strategies, i.e., opportunistic charging and controlled charging with and without vehicle-to-grid (V2G) scheme have been considered to evaluate the impact of FEVs and PHEVs on the composite power system. IEEE-RTS-79 system is used to examine the proposed probabilistic technique considering different FEV and PHEV penetration levels as well as charging strategies. Simulation results show that even a relatively low penetration level of FEVs or PHEVs might have a significant impact on the system reliability unless a proper charging and/or discharging schemes are utilized.

This study provides a comprehensive lifecycle analysis (LCA), or cradle-to-grave (C2G) analysis, of the cost and greenhouse gas (GHG) emissions of a variety of vehicle-fuel pathways, as well as the levelized cost of driving (LCD) and cost of avoided GHG emissions. This study also estimates the technology readiness levels (TRLs) of key fuel and vehicle technologies along the pathways. The C2G analysis spans a full portfolio of midsize light-duty vehicles (LDVs), including conventional internal combustion engine vehicles (ICEVs), flexible fuel vehicles (FFVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicles (FCEVs). In evaluating the vehicle-fuel combinations, this study considers both low-volume and high-volume “CURRENT TECHNOLOGY” cases (nominally 2015) and a high-volume “FUTURE TECHNOLOGY” lower-carbon case (nominally 2025–2030). For the CURRENT TECHNOLOGY case, low-volume vehicle and fuel production pathways are examined to determine costs in the near term.

This study employed the Life Cycle Assessment (LCA) method to evaluate the greenhouse gas (GHG) emissions of passenger vehicles across 31 provinces in China. It examined the potential of Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) in significantly reducing emissions compared to Internal Combustion Engine Vehicles (ICEVs). The findings revealed that HEVs, PHEVs, and BEVs substantially lower emissions under the national average electricity GHG emissions coefficient, with PHEVs being the most effective, reducing emissions by over 30%. However, disparities exist at the provincial level due to varying electricity structures, with BEVs not fully effective in reducing emissions in about ten provinces, including Liaoning. The study further categorized provinces into six groups using the K-means method to guide the promotion of different types of New Energy Vehicles (NEVs). Sensitivity analysis highlights the critical role of reducing electricity emission coefficients and adjusting PHEV's utility factor in achieving emission reductions. The study also notes that battery replacements in PHEVs and BEVs during their lifecycle could impede emission reduction in most provinces. To maximize the environmental benefits of NEVs, there is a need to move to a greener electricity mix. At the same time, strategies for promoting NEVs should vary for different provinces.

This paper aims to characterize the engine start activity profiles and emission potential of various plug-in hybrid electric vehicle (PHEV) models by examining the characteristics associated with engine starts, identifying the travel conditions that trigger engine starts, and determining the frequency of different types of engine starts. The study analyzed on-road vehicle data from six PHEV models: Toyota Prius Plug-in, Ford C-Max Energi, Ford C-Max Fusion, Toyota Prius Prime, Chrysler Pacifica, and Chevrolet Volt. An analysis on travel conditions before engine starts revealed that low state-of-charge is the dominant engine start trigger for PHEVs with high all-electric range whereas high vehicle power requirement is the most critical trigger for PHEVs with low all-electric range. For PHEVs with mid-range capabilities, several vehicle specifications, ranging from peak electric motor power to curb weight, could be engine start determinants. A strong inverse correlation exists between battery capacity and the annual frequency of engine starts but this relationship does not hold for cold and high-power cold starts. Both the low and the high battery capacity PHEVs logged fewer cold starts than the mid-sized battery vehicles, indicating that there could be a fundamental tradeoff between engine start emissions and fuel displacement for PHEVs to a certain degree. Despite this tradeoff, all PHEV models in the study logged fewer cold starts than comparable conventional internal combustion engine vehicles, performing the same trips. Ultimately, long-range PHEVs with high battery capacity are found to be ideal for both curbing start emissions and reducing fuel use.

Plug-in hybrid electric vehicle (PHEV) presents the new trend of clean energy vehicle development due to their advantage of all electric driving, allowable external charging, lower emissions, and lower petroleum fuel consumption, comparing to the maturing HEV. Applications of advanced plug-in hybrid powertrain system technology to different type of vehicles lead to the need to identify the key characteristics of these different vehicular applications, and to develop more systematic and effective powertrain design methods. In this work two different types of PHEV powertrain architecture were investigated: a) pre- trans series-parallel multi-regime plug-in hybrid electric commercial vehicle (SPMR-PHEV), and b) post-trans parallel plug-in hybrid electric formula racing car. Model-based design (MBD) methods were used for powertrain system modelling of the two PHEV applications. With the powertrain system models developed using MATLAB Simulink and dSPACE Automotive Simulation Models (ASM), the powertrain configurations, control strategies and key features were investigated. The simulation results on standard driving cycles for each model using different rule-based control strategies were compared. The differences and key features of two types of vehicle in design and calibration were presented and analyzed.

Driving cycles were identified as one of the core sources that contribute to develop the powertrain for vehicle. Plug-in hybrid electric vehicles (PHEVs) are the future transport for next generation. Compared to conventional internal combustion engine vehicle, hybrid and electric vehicle can improve fuel economy and reduce green house gases. This paper describes a development of Kuala Terengganu driving cycle for Universiti Malaysia Terengganu PHEV. Car speed-time data along the two selected fixed route is obtained by using on-board technique which is Global Positioning System, GPS. The developed driving cycle contains a 1050s speed time series, with a distance of 2.17 km, and an average and a maximum speed of 20.67 km/h and 61.47 km/h, respectively. The results obtained from this analysis are within reasonable range and satisfactory.

The objective of this paper is to help state agencies better understand the impact of electric and hybrid vehicles on the Highway Trust Fund and to develop a method for estimating proper annual registration fees for electric vehicles (EVs). In this study, a comprehensive literature review was conducted to summarize the background on electric and hybrid vehicles, current national and state policies and incentives, the trend of EV market in the U.S., and registration fees on electric and hybrid vehicles. As electric and hybrid vehicles do not contribute to fuel excise tax revenue, to compensate the lost tax revenues, some states charge additional annual registration fees to EV owners. To help the legislators determine the proper annual fees, a method was developed to assess the additional registration fees for EVs and plug-in hybrid electric vehicles (PHEVs) in Alabama. The collected data include number of registered electric and hybrid vehicles, fuel tax per gallon, and annual average mileage traveled by electric and hybrid vehicles in Alabama. The results of this study served as a key reference in the Rebuild Alabama Act that proposed an annual registration fee of $200 and $100 for EVs and PHEVs, respectively, which is effective since January 2020. The method in this study can be applied to other states for developing policies on registration fees for EVs and PHEVs to offset the fuel excise tax revenue loss.

Electric vehicles (EVs) are viewed as a key pathway to reducing greenhouse gas emissions. However, their environmental benefits depend on whether their vehicle kilometers traveled (VKT) substitute for travel by internal combustion engine vehicles (ICEVs) rather than generating additional travel demand. This review synthesizes evidence from 34 empirical studies examining VKT by battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), comparing them with ICEVs and identifying the factors that shape EV usage. Across studies, BEVs and PHEVs generally travel fewer kilometers per day than ICEVs, with BEVs showing the lowest VKT levels. Regional differences are evident: BEVs in Europe typically record higher VKTs than those in the US. VKT patterns are influenced by a combination of socio-demographic, spatial, and vehicle-related factors, as well as EV-specific elements such as charging access and ownership type. Leased and company EVs typically travel more kilometers than privately owned vehicles, and greater access to charging is generally associated with higher electric VKT. Most studies included in this review draw on data from the early phases of EV adoption and focus on a limited set of regions. As a result, current findings may not fully capture emerging VKT patterns as EV markets scale and diversify. Future research should incorporate more recent and geographically varied datasets to track evolving travel behaviors and better assess how EVs are reshaping mobility and sustainability outcomes.

Electric vehicles are anticipated to be essential components of future energy systems, as they possess the capability to assimilate surplus energy generated by renewable sources. With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), conventional internal combustion engine (ICE)-based vehicles are expected to be gradually phased out, thereby decreasing greenhouse gases and reliance on foreign oil. Intensive research and development efforts across the globe are currently concentrated on developing effective PHEV charging solutions that can efficiently cater to the charging needs of PHEVs, while simultaneously minimizing their detrimental effects on the power infrastructure. Efficient PHEV charging strategies and technologies are necessary to overcome the obstacles presented. Forecasting PHEV charging loads provides a solution by enabling energy delivery to power systems based on anticipated future loads. We have developed a novel approach, utilizing machine learning methods, for accurately forecasting PHEV charging loads at charging stations across three phases of powering (smart, non-cooperative, and cooperative). The proposed Q-learning method outperforms conventional AI techniques, such as recurrent neural and artificial neural networks, in accurately forecasting PHEV loads for various charging scenarios. The findings indicate that the Q-learning method effectively predicts PHEV loads in three scenarios: smart, non-cooperative, and cooperative. Compared to the ANN and RNN models, the forecast precision of the QL model is higher by 31.2% and 40.7%, respectively. The Keras open-source set was utilized to simulate three different approaches and evaluate the efficacy and worth of the suggested Q-learning technique.

Plug-in hybrid electric vehicles (PHEVs) connect two energy systems—the electric power network and the transportation sector—in ways that could provide significant public health benefits. To begin, it is important to...