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DOE OSTI2021

Co-Optimization Scheme for Hybrid Electric Vehicles Powertrain and Exhaust Emission Control System Using Future Speed Prediction

Hybrid electric vehicles (HEVs) have been an effective solution for improved vehicle fuel efficiency and reduced emission pollution. In this paper, a co-optimization scheme is proposed to optimize fuel efficiency for HEVs. Here, the proposed optimization scheme uses obtainable future speed prediction as the basis to optimally tune control parameters for the existing powertrain control system. The ramp-up time of the catalyst temperature to reach its light-off level in the exhaust emission system is also considered as an additional optimization constraint to reduce emission. The Toyota Prius Hybrid Simulink model which is an integrated model for a powertrain and exhaust emission system is validated using real data from several real driving cycle scenarios. Then, to simplify the formulation of the proposed algorithm, the model for the optimization for powertrain and exhaust emission systems is represented by a set of equivalent neural network (NN) models learned using the data generated from the well-validated Toyota Prius Hybrid Simulink model. Using NN models, a co-optimization algorithm is established that provides an optimal tuning of some fuel-sensitive powertrain control parameters using future speed prediction, leading to a novel co-optimization algorithm, achieving on average a further 9.22% fuel savings for the Toyota Prius Hybrid Simulink model.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Well‐to‐wheels analysis of greenhouse gas emissions for passenger vehicles in Middle East and North Africa

Battery electric vehicles (BEVs) are widely considered a pathway to achieve low carbon mobility. BEVs emit zero emissions from the tailpipe, but their life cycle carbon reduction compared to gasoline vehicles varies based on primary energy sources, electricity generation, and use efficiency. The Middle East and North Africa (MENA) region is an area rich in fossil fuels, meriting a detailed comparison between the emissions from BEV and other powertrains. We developed a MENA‐specific life cycle model that estimates well‐to‐wheel (WTW) greenhouse gas (GHG) emissions from passenger transport with internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug‐in hybrid electric vehicles, and BEVs. MENA's average WTW GHG emissions for all supply chain steps including combustion emissions from vehicle operation are 767 g/kWh and 84 g CO 2 eq/MJ for electricity and gasoline, respectively, but are highly variable due to heterogeneity in upstream supply chains. The use of hybrid gasoline ICEVs provides the largest emission reduction opportunity for existing vehicle fleets in 9 of the 16 MENA countries. For these nine countries, replacing gasoline ICEVs with HEVs could, on average, reduce country‐level life cycle GHG emissions by 47%. There is a similar emission reduction opportunity for 14 of the 16 MENA countries when normalizing vehicle efficiencies irrespective of the powertrain shares and other trends in existing vehicle fleets. Future scenario analysis shows that BEVs would have the lowest WTW GHG emissions among all powertrains in most MENA countries only if significantly reduced electricity transmission losses and cleaner grid mix are realized, although a high cost of infrastructure developments is expected.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Impact of Regional and Seasonal Characteristics on Battery Electric Vehicle Operational Costs in the U.S.

This study investigates the operational cost competitiveness of battery electric vehicles (BEVs) in the United States, considering regional climates, energy prices, and driving patterns. By comparing BEVs with plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and the alternative use of BEVs and conventional vehicles (Convs), the analysis incorporates thermal dynamometer tests, real-world vehicle miles traveled (VMT), and state-specific energy prices. Using detailed simulations, the study evaluates energy consumption across varying temperatures and driving distances. The findings reveal that, while BEVs remain cost-effective for short trips in moderate climates, PHEVs are more economical for long-range trips and cold environments, due to the excessive cost of using external direct current fast chargers (DCFCs) and reduced BEV efficiency at low temperatures. HEVs are identified as the most cost-efficient option in regions like New England, characterized by high residential electricity prices. These insights are critical for shaping vehicle electrification strategies, particularly under diverse regional and seasonal conditions, and for advancing policies on alternative energy and fuels.

Kim, Kyung-Ho (ORCID:0009000450851732)↗

DOE OSTI2024

Online characterization of primary and secondary emissions of particulate matter and acidic molecules from a modern fleet of city buses

The potential impact of transitioning from conventional fossil fuel to a non-fossil-fuel vehicle fleet was investigated by measuring primary emissions via extractive sampling of bus plumes and assessing secondary mass formation using the Gothenburg Potential Aerosol Mass (Go:PAM) reactor from 76 in-use transit buses. Online chemical characterization of gaseous and particulate emissions from these buses was conducted using chemical ionization mass spectrometry (CIMS) with acetate as the reagent ion, coupled with the Filter Inlet for Gases and AEROsols (FIGAERO). Acetate reagent ion chemistry selectively ionizes acidic compounds, including organic and inorganic acids, as well as nitrated and sulfated organics. A significant reduction (48 %–98 %) in fresh particle emissions was observed in buses utilizing compressed natural gas (CNG), biodiesels like rapeseed methyl ester (RME) and hydrotreated vegetable oil (HVO), and hybrid-electric HVO (HVO HEV ) compared to diesel (DSL). However, secondary particle formation from photooxidation of emissions was substantial across all the fuel types. The median ratio of particle mass emission factors of aged to fresh emissions increased in the following order: DSL buses at 4.0, HVO buses at 6.7, HVO HEV buses at 10.5, RME buses at 10.8, and CNG buses at 84. Of the compounds that can be identified by CIMS, fresh gaseous emissions from all Euro V/EEV (Enhanced Environmentally friendly Vehicle) buses, regardless of fuel type, were dominated by nitrogen-containing compounds such as nitrous acid (HONO), nitric acid (HNO 3 ), and isocyanic acid (HNCO), alongside small monoacids (C 1 –C 3 ). Notably, the emission of nitrogen-containing compounds was lower in Euro VI buses equipped with more advanced emission control technologies. Secondary gaseous organic acids correlated strongly with gaseous HNO 3 signals (R 2 =0.85–0.99) in Go:PAM, but their moderate to weak correlations with post-photooxidation secondary particle mass suggest that they are not reliable tracers of secondary organic aerosol formation from bus exhaust. Our study highlights that non-regulated compounds and secondary pollutant formation, not currently addressed in legislation, are crucial considerations in the evaluation of environmental impacts of future fuel and engine technology shifts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1992

Electric and hybrid electric vehicle study utilizing a time-stepping simulation

The applicability of NASA's advanced power technologies to electric and hybrid vehicles was assessed using a time-stepping computer simulation to model electric and hybrid vehicles operating over the Federal Urban Driving Schedule (FUDS). Both the energy and power demands of the FUDS were taken into account and vehicle economy, range, and performance were addressed simultaneously. Results indicate that a hybrid electric vehicle (HEV) configured with a flywheel buffer energy storage device and a free-piston Stirling convertor fulfills the emissions, fuel economy, range, and performance requirements that would make it acceptable to the consumer. It is noted that an assessment to determine which of the candidate technologies are suited for the HEV application has yet to be made. A proper assessment should take into account the fuel economy and range, along with the driveability and total emissions produced.

Schreiber, Jeffrey G.↗

NASA NTRS2010

Improved Low-Temperature Performance of Li-Ion Cells Using New Electrolytes

As part of the continuing efforts to develop advanced electrolytes to improve the performance of lithium-ion cells, especially at low temperatures, a number of electrolyte formulations have been developed that result in improved low-temperature performance (down to 60 C) of 26650 A123Systems commercial lithium-ion cells. The cell type/design, in which the new technology has been demonstrated, has found wide application in the commercial sector (i.e., these cells are currently being used in commercial portable power tools). In addition, the technology is actively being considered for hybrid electric vehicle (HEV) and electric vehicle (EV) applications. In current work, a number of low-temperature electrolytes have been developed based on advances involving lithium hexafluorophosphate-based solutions in carbonate and carbonate + ester solvent blends, which have been further optimized in the context of the technology and targeted applications. The approaches employed, which include the use of ternary mixtures of carbonates, the use of ester co-solvents [e.g., methyl butyrate (MB)], and optimized lithium salt concentrations (e.g., LiPF6), were compared with the commercial baseline electrolyte, as well as an electrolyte being actively considered for DoE HEV applications and previously developed by a commercial enterprise, namely LiPF6 in ethylene carbonate (EC) + ethyl methyl carbonate (EMC)(30:70%).

Smart, Marshall C.↗

DOE OSTI2021

Development and Evaluation of Velocity Predictive Optimal Energy Management Strategies in Intelligent and Connected Hybrid Electric Vehicles

In this study, a thorough and definitive evaluation of Predictive Optimal Energy Management Strategy (POEMS) applications in connected vehicles using 10 to 20 s predicted velocity is conducted for a Hybrid Electric Vehicle (HEV). The presented methodology includes synchronous datasets gathered in Fort Collins, Colorado using a test vehicle equipped with sensors to measure ego vehicle position and motion and that of surrounding objects as well as receive Infrastructure to Vehicle (I2V) information. These datasets are utilized to compare the effect of different signal categories on prediction fidelity for different prediction horizons within a POEMS framework. Multiple artificial intelligence (AI) and machine learning (ML) algorithms use the collected data to output future vehicle velocity prediction models. The effects of different combinations of signals and different models on prediction fidelity in various prediction windows are explored. All of these combinations are ultimately addressed where the rubber meets the road: fuel economy (FE) enabled from POEMS. FE optimization is performed using Model Predictive Control (MPC) with a Dynamic Programming (DP) optimizer. FE improvements from MPC control at various prediction time horizons are compared to that of full-cycle DP. All FE results are determined using high-fidelity simulations of an Autonomie 2010 Toyota Prius model. The full-cycle DP POEMS provides the theoretical upper limit on fuel economy (FE) improvement achievable with POEMS but is not currently practical for real-world implementation. Perfect prediction MPC (PP-MPC) represents the upper limit of FE improvement practically achievable with POEMS. Real-Prediction MPC (RP-MPC) can provide nearly equivalent FE improvement when used with high-fidelity predictions. Constant-Velocity MPC (CV-MPC) uses a constant speed prediction and serves as a “null” POEMS. Results showed that RP-MPC, enabled by high-fidelity ego future speed prediction, led to significant FE improvement over baseline nearly matching that of PP-MPC.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2019

Battery Thermal Characterization

A well-designed thermal management system is critical to the life and performance of electric-drive vehicles (EDVs) hybrids (HEVs), plug-in hybrids (PHEVs), and all-electric vehicles (EVs). Temperature and temperature uniformity both significantly affect the performance, lifespan, and safety of vehicle energy storage devices. NREL conducts thermal management research and development (R&D) to optimize battery performance and extend useful life. A recognized leader in battery thermal analysis and characterization, NREL evaluates electrical and thermal performance of battery cells, modules, and packs, full energy storage systems, and the interaction of these systems with other vehicle components. The lab's performance assessments factor in the design of the thermal management system, the thermal behavior of the cell, battery lifespan, and safety of the energy storage system, as well as full integration into a vehicle. The lithium-ion (Li-ion) batteries found in most of today's electric-drive vehicles are smaller and more lightweight than previous nickel-metal hydride (NiMH) technology, but they are also more sensitive to overheating, overcharging, and extreme spikes in temperature known as thermal runaway. High temperatures diminish battery life and increase battery replacement costs, while low temperatures decrease battery power and capacity, all of which impact vehicle range, performance, and affordability. NREL's breakthrough research is focused on reducing thermal barriers to achieve more uniform temperatures. NREL uses its R&D 100 Award-winning Isothermal Battery Calorimeters, the only instruments in the world capable of such precise thermal measurements, for much of this research. Calorimeter readings are paired with thermal imaging of batteries using infrared cameras to evaluate battery temperature distribution. In addition, NREL is working with industry to develop computer-aided engineering software tools to optimize thermal management of batteries. NREL's analysis for the U.S. Advanced Battery Consortium (USABC) show that optimized thermal management can increase battery power by more than 10%. Without proper thermal management, an EDV battery can last more than 10 years in a temperate climate, like in Minnesota, but only 7 years in a hot climate, such as in Arizona. In extreme instances, battery overheating can pose safety hazards, including fires. Performance: Maintaining desired temperature ranges is critical in optimizing EDV battery storage capacity, lifespan, and performance, and eventually impacts cost and consumer acceptance. NREL's breakthrough research focuses on achieving more uniform temperatures while a vehicle is in motion and actively generating heat, as well as when it is parked and exposed to ambient temperatures over prolonged periods of time. Safety: Most energy storage systems deliver maximum performance at higher temperatures. However, this extreme heat can overload systems and create dangerous conditions. NREL evaluation helps to more effectively regulate thermal characteristics, providing greater safeguards and stronger performance. NREL has invented a device that emulates internal short circuits in batteries to better understand how to prevent future failures and related hazards.

25 ENERGY STORAGE↗

DOE OSTI2026

Design and optimization of processes for recovering rare earth elements from end‐of‐life permanent magnets

Recovery of rare earth elements (REEs) from end-of-life (EOL) products represents a strategic opportunity to strengthen the domestic supply chain for rare earth elements. This work presents a superstructure-based optimization framework for finding the most economical processing pathway for different EOL rare earth permanent magnets (REPMs). The framework evaluates state-of-the-art technologies across four processing stages—disassembly, demagnetization, leaching and extraction, and precipitation and calcination—using net present value (NPV) maximization and cost of recovery (COR) minimization objectives. A novel bottom-up costing framework for hydrogen decrepitation is also introduced. Two feedstocks were considered: REPMs from EOL hard disk drives (HDDs), and electric and hybrid electric vehicles (EVs and HEVs). While HDD recycling proved unprofitable due to limited feedstock availability, EVs/HEVs were profitable across a range of parameters and cost estimates. Therefore, our findings suggest that the proposed EOL EV/HEV recycling process may be economical and is worthy of further investigation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Ni/Li Disordering in Layered Transition Metal Oxide: Electrochemical Impact, Origin, and Control

Lithium-ion batteries (LIBs) not only power most of today’s hybrid electric vehicles (HEV) and electric vehicles (EV) but also are considered as a very promising system for grid-level storage. Large-scale applications for LIBs require substantial improvement in energy density, cost, and lifetime. Layered Li(NixMnyCoz)O2 (NMC, x+y+z=1) materials from solid-solution approaches to LiMO2 (M = Ni, Mn, Co, etc) are the most promising candidates for their high reversible capacity, better environmental compatibility, and lower cost compared with the traditional LiCoO2. In order to further boost Li storage capacity, a great deal of attention has been directed toward developing Ni-rich NMCs. However, structural disorder as a result of Ni/Li exchange in octahedral sites becomes a critical issue when Ni content increases to high values, as it leads to a detrimental effect on Li diffusivity, cycling stability, first-cycle efficiency, and overall electrode performance. Increasing efforts have been dedicated to improving the electrochemical performance of NMC materials via reduction of cationic mixing. Therefore, it is important to summarize this research field and provide in-depth insight into impact of Ni/Li disorder on electrochemical characteristics in layered NMC materials and its origin to accelerate the future development of NMC materials with high performance. In this Account, we start by analyzing the impact of Ni/Li disorder on electrochemical characteristics in layered NMC materials. The antisite Ni in the Li layer can limit the rate performance by impeding the Li-ion transport. It will also degrade the cycling stability by inducing the anisotropic stress in the bulk structure. Nevertheless, the antisite Ni-ions don’t always bring drawbacks to the electrochemical performance, some studies including our works found that it can improve the thermal stability and the cycling structure stability of Ni-rich NMC materials. We next discussed the driving forces and the kinetic advantages accounting for the Ni/Li exchange and summarized that the steric effect of cation size and the magnetic interactions between TM cations are the two main driving forces to promote the Ni/Li exchange during synthesis procedure and the electrochemical cycling, and the low energy barrier of Ni2+ migration from the 3a site in TM layer to 3b site in Li layer further provides the kinetic advantage. Based on above understandings, we reviewed the progress made to control the Ni/Li disorder and summarized three main ways: (i) suppressing the driving force from the steric effect by ion exchange; (ii) tuning the magnetic interaction by cationic substitution; (iii) kinetic controlling of Ni migration. Finally, our brief outlooks on the future development of controlling the Ni/Li disorder in NMC materials are provided. It is believed that this Account will provide significant understanding and inspirations toward developing high-performance NMC materials with controlled Ni/Li disorder.

Zheng, Jiaxin↗

DOE OSTI2024

Battery-powered bargains? Assessing electric vehicle resale value in the United States

Abstract The resale market will play a critical role in expanding plug-in electric vehicle (PEV) adoption to middle- and lower-income households. Understanding PEV depreciation trends in comparison to those of conventional gasoline vehicles (CVs) is critical for assessing PEV affordability and informing relevant policy, such as subsidies for used PEVs. We deliver comprehensive, high-resolution estimates of value retention rates at the make-model level in the United States for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and CVs using a database of 9 million used cars listed online between 2016 and 2022. While BEVs and PHEVs have depreciated at faster rates than CVs, this trend is changing, with newer model year BEVs and those with larger ranges have significantly higher retention rates than older model years with smaller ranges. Tesla BEVs are a notable exception following the opposite trend, with earlier model years holding their value better than newer model years. Subsidized BEVs in the new market are associated with lower prices for the same model in the resale market, with the $7500 federal subsidy translating to a 3% lower resale price on average. Finally, disruptions from the COVID-19 pandemic have affected affordability across all vehicles, with mean listing prices rising 37% and 39% for CVs and BEVs, respectively, from January 2020 to March 2022 in inflation-adjusted 2019 dollars.

Environmental Sciences & Ecology↗

DOE OSTI2019

Parameter Co-optimization for Hybrid Electric Vehicles Powertrain System Leveraging V2V/V2I Information

A parameter tuning based co-optimization scheme for the hybrid electric vehicles (HEV) powertrain system is designed to maximize the fuel efficiency. The optimization controlled input parameters are chosen based on sensitivity study of powertrain control parameters. The vehicle to vehicle (V2V) and vehicle to infrastructure information is another optimization input, to have the driving conditions taking in to considerations for maximizing fuel efficiency. The catalyst temperature is considered as an additional constraint as the speed to reach light-off temperature should not decrease during optimized operation. Neural network is used to develop a simplified yet equivalent model for the optimization problem model. We have achieved an average of 9.22% fuel savings for a random driving cycle on a Toyota Prius test model.

V2V, Cooptimization↗

DOE OSTI2020

System-Level Thermal Management and Reliability of Automotive Electronics: Goals and Opportunities Using Phase-Change Materials

Abstract Electronic packaging for automotive applications are at particular risk of thermomechanical failure due to the naturally harsh conditions it is exposed to. With the rise of electric and hybrid electric vehicles (EVs and HEVs), combined with a desire to miniaturize, the challenge of removing enough heat from electronic devices in automotive vehicles is evolving. This paper closely examines the new challenges in thermal management in various driving environments and aims to classify each existing cooling method in terms of performance. Particular focus is placed upon emerging solutions regarded to hold great potential, such as phase-change materials (PCMs). PCMs have been regarded for some time as a means of transferring heat quickly away from the region with the electronic components and are widely regarded as a possible means of carrying out cooling in large scale from small areas, because of their high latent heat of fusion, high specific heat, temperature stability, and small volume change during phase change, etc. They have already been utilized as a method of passive cooling in electronics in various ways, but their adoption in automotive power electronics, such as in traction inverters, has yet to be fulfilled. A brief discussion is made on some of the potential areas of application and challenges relating to more widespread adoption of PCMs, with reference to a case study using computational model of a commercially available power module used in automotive applications.

Engineering↗

DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗

DOE OSTI2024

Multiphysics Co-Optimization Design and Analysis of Double-Side Cooled Silicon Carbide-Based Power Module: Preprint

With the rapid growth of Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs), much more rigorous design targets have been set for automotive power electronics, including high power density, high reliability, and low cost. Novel power module and inverter technologies based on wide bandgap (WEG) semiconductors have been developed to meet these design targets, while providing optimal power semiconductor operating temperature and promising thermomechanical performance. Compared with conventional cooling techniques which are normally applied only on one side of power module, double-side cooling approach is now believed to be the solution to enable high power density and low thermal resistance of WEG semiconductor-based power electronics. In this work, we develop a three-phase power module that is double-sided cooled using dielectric fluid jet impingement. In each phase, four silicon carbide (SiC) power semiconductors are bonded to copper busbars without electrical insulation layers. A finite element analysis (FEA) model is created for thermal and thermomechanical analysis. Based on FEA modeling results, we select particular dimensions for a parametric study to optimize thermal and mechanical performance. Using a multi-objective genetic algorithm (MOGA)-based optimization method, we have minimized the maximum junction temperature and thermal stresses within the power module. The multiphysics co-optimization approach has enabled an efficient design process of power modules with greatly reduced computational cost, as compared to conventional processes that rely on exhaustive numerical simulations and iterations.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

RouteE-Powertrain [SWR-19-19]

RouteE-Powertrain is a tool for predicting energy usage over a set of road links. RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions. Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models. https://pypi.org/project/nrel.routee.powertrain/ pip install nrel.routee.powertrain

Holden, Jacob↗

DOE OSTI2026

Battery Performance and Cost Model (BatPaC) Version 6.0

SF-26-016 The Battery Performance and Cost model (BatPaC) is a calculation method based on Microsoft Excel spreadsheets that has been developed at Argonne for estimating the performance and manufacturing cost of lithium-ion batteries for electric-drive vehicles including hybrid-electrics (HEV), plug-in hybrids (PHEVs) and pure electrics. BatPaC was first developed in 2007, was subsequently peer reviewed, and it has served Argonne researchers and the greater battery community in studying the impact of material properties on performance at the pack level. BatPaC has been updated and re-released multiple times since its original public release in 2011. This current version is BatPaC 6.0, which contains additional functionality needed to handle advances in automotive batteries, like the use of lithium metal and silicon anodes and the need to accommodate cell expansion and apply high levels of pressure.

KNEHR, KEVIN [Argonne National Laboratory (ANL), A↗

DOE OSTI2026

Substitution or Shared Utilization? Intrahousehold Vehicle Use in Mixed-Powertrain Households

While previous research has focused heavily on understanding the factors deriving alternative fuel vehicle adoption rates, there remains a significant gap in understanding how households distribute mileage across different powertrains. This study utilizes data from the 2022 Next Generation National Household Travel Survey to investigate vehicle miles traveled within a sample of 150 plug-in electric vehicle (PEV)-owning households (in which at least one battery electric vehicle is present), characterizing how different powertrains are integrated into daily mobility. Leveraging a Seemingly Unrelated Regression (SUR) framework the study jointly models the utilization of PEVs, hybrid electric vehicles (HEV), and internal combustion engine vehicles (ICEVs) while accounting for household-level substitution effects. The results provide evidence of an asymmetric substitution effect. In households with mixed-powertrain configurations, the ICEV captures a substantially higher share of household miles (compared with the PEV), acting as a utility sponge. Conversely, the model identifies specific socioeconomic and geographic cohorts that prioritize PEV as the primary household workhorse, indicating a systematic sorting effect. Although the sample size limits broader generalizability, these findings suggest that PEVs are used for frequent, specific routine-intensive roles, whereas the ICEV remains a specialized utility vehicle. These insights highlight distinct intrahousehold vehicle use behaviors that are often obscured by aggregate fleetwide statistics.

25 ENERGY STORAGE↗