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AbstractAs an energy-efficient transportation innovation, hybrid vehicles (HVs) have the potential to minimize carbon emissions and address environmental issues. The study is novel as it presents significant theoretical contributions within the theory of planned behavior (TPB) framework. Firstly, it integrates the perceived seriousness of environmental problems as a predictor, enhancing understanding of individual responses to environmental concerns. Secondly, it expands the TPB model by introducing willingness to pay for HVs as a moderator, acknowledging the economic aspect of adopting environmentally friendly technologies. These additions enrich the TPB framework, offering insights into the interplay between psychological determinants and economic considerations in behavior change. The study therefore aims to extend the TPB by incorporating two key elements: perceived seriousness of environmental issues and willingness to pay for HVs. We find that perceived seriousness of problems in the environmental has positive impact on attitude toward adoption of HVs, subjective norms, perceived behavioral control, intentions to adopt and actual use of HVs. TPB constructs have positive impact on intentions to adopt and actually use HVs. Moreover, intentions to adopt HVs also positively influence the actual use of HVs. Moreover, the results show that the serial mediations are also in play, whereby perceived seriousness of environmental issues influences TPB constructs, which influence intentions to adopt HV which in turn influence the actual use of HVs. We also find that willing to pay for HVs plays an enhancing role as a moderator in the TPB model. [ABSTRACT FROM AUTHOR]

In recent years, the adoption of alternative fuels and hybrid vehicles has necessitated radical changes to meet the energy needs of the automotive sector. Hybrid electric vehicles are used as a perfect step between traditional fossil fueled internal combustion engines and fully electric vehicles. Deeply related to this, complicated industry and manufacturers need simulation programs to reduce both the cost of test sets and to use time with the highest efficiency. Before the production, especially in automotive sector, the simulation and prototyping procedure is playing an indispensable role. For this study, the AVL-Cruise simulation tool was chosen. Due to its capability to keep up with evolving technological structures, this program can be easily implemented in hybrid vehicle simulations. In this study, a hybrid engine model was created based on a 1.5 L, four-cylinder gasoline engine and a 10 kW electric motor. Energy analysis, based on the variations in the CVT and AMT transmissions of a hybrid motor, was conducted under three different driving cycles and compared in detail. Additionally, energetic balance like exergy is executed for this model. Sankey diagrams were employed to illustrate the differences and energy consumptions of the vehicle based on the selected modeling criteria. As a result, the CVT is slightly better behaviors than AMT both fuel consumption (2.5–4%), emission reducing (1.5–4.6%) and energetic balances (3.6–5%). • Hybrid vehicles energy analyses are determined with Sankey diagrams. • Effects of CVT and AMT transmission to HEVs energetic outputs were compared. • Effects of various driving cycles on HEV components were discussed detailed. • CVT is slightly better than AMT with reducing the fuel consumption and emissions. • For this study, CVT is more energetic efficiency with 5% improvement vs AMT. [ABSTRACT FROM AUTHOR]

The increasing diversity of vehicle type holdings and growing demand for BEVs and PHEVs have serious policy implications for travel demand and air pollution. Consequently, it is important to accurately predict or estimate the preference for vehicle holdings of households as well as the vehicle miles traveled by vehicle body- and fuel-type to project future VMT changes and mobile source emission levels. Leveraging the 2019 California Vehicle Survey data, this report presents the application of a utility-based model for multiple discreteness that combines multiple vehicle types with usage in an integrated model, specifically the MDCEV model. The model results suggest the important effects of household demographics, residence location, and built environment factors on vehicle body type and powertrain choice and usage. Further the predictions associated with changes inbuilt environment factors like population density can inform the design of land-use and transportation policies to influence household vehicle holdings and usage that can in turn impact travel demand and air quality issues in California.View the NCST Project Webpage

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs' development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. [ABSTRACT FROM AUTHOR]

Using a unique panel dataset on air pollution and car sales from thirty major cities in China across three years, we estimate the impact of air pollution on the adoption of hybrid and electric vehicles. To identify the causal effect, we use three sets of interactive fixed effects to control for a large group of potential confounders and an instrument for the air pollution variable. Our results show that worse air quality leads consumers to purchase more hybrid and electric cars, and the effect is larger in cities with higher incomes. These findings are consistent with predictions from the social preferences theory and point out new directions for strategies to promote the adoption of hybrid and electric cars. [ABSTRACT FROM AUTHOR]

In the era of rapid technological advancement and evolving industrial landscapes, embracing the concept of the factory of the future (FoF) is crucial for companies seeking to optimize efficiency, enhance productivity, and stay sustainable. This case study explores the concept of the FoF and its role in driving the energy transition and digital transformation within the automotive sector. By embracing advancements in technology and innovation, these factories aim to establish a smart, sustainable, inclusive, and resilient growth framework. The shift towards hybrid and electric vehicles necessitates significant adjustments in vehicle components and production processes. To achieve this, the adoption of lighter materials becomes imperative, and new technologies such as additive manufacturing (AM) and artificial intelligence (AI) are being adopted, facilitating enhanced efficiency and innovation within the factory environment. An important aspect of this paradigm involves the development and utilization of a modular, affordable, safe human–robot interaction and highly performant intelligent robot. The introduction of this intelligent robot aims to foster a higher degree of automation and efficiency through collaborative human–robot environments on the factory floor and production lines, specifically tailored to the automotive sector. By combining the strengths of human and robotic capabilities, the future factory aims to revolutionize manufacturing processes, ultimately driving the automotive industry towards a more sustainable and technologically advanced future. This study explores the implementation of automation and the initial strides toward transitioning from Industry 4.0 to 5.0, focusing on three recognized, large, and automotive companies operating in the north of Portugal. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) significantly reduce energy consumption and emissions from on-road operations and help create more sustainable transportation environment by reducing emissions from the entire well-to wheel energy cycle. Differences between hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), and battery electric vehicles (BEV) users is an important element in understanding potential impacts on travel demand and vehicle adoption, the fact that these vehicles may be adopted into households that undertake very different vehicle activities and energy usage patterns has not been a primary focus in the literature. This study differentiates between HEV, PHEV, and BEV users across three factors: owner household socio-demographic attributes, household daily travel patterns, and household energy usage profiles. The analyses examine factors that appear to influence users’ preferences towards specific EV types and how the selection of different EV types potentially relates to household socio demographics and daily travel patterns. The 2019 Puget Sound Regional Council travel survey data set serves as the main analytical dataset. Influential factors identified as significant through statistical approaches are employed as variables for developing a two-phase choice model for determining potential EV-purchasing households and their choice of specific EV type. As EVs continue to capture increasing market share over time, these research findings and the resulting vehicle type choice model are expected to significantly improve future travel demand model development, allowing activity-based travel demand models to assign specific vehicles to specific households and then to individual trips in planning scenario analysis.View the NCST Project Webpage