Optimal Hybridization of Conventional ICE Vehicles

Most commercially available hybrid electric vehicle (HEV) drivetrains are made of small internal combustion (IC) engines and large electric drives to improve fuel economy. They usually have higher cost than the conventional IC-engine-based vehicles because of the high costs of the electric drives. This paper proposes a hybridized powertrain composed of the original full-size engine of the vehicle and a universally optimum size parallel electric drive. The dynamic programming (DP) algorithm was used to obtain the sensitivity of the maximum miles per gallon (MPG) values versus the power rating of the electric drive. This sensitivity was then analyzed to determine the optimal window of the electric drive power ratings. This was proven to be universal for all passenger cars of various masses and engine powers. The fuel economy and vehicle performance of this HEV was compared with those of the 2019 Toyota Corolla, a conventional IC-engine-based vehicle, and the 2019 Toyota Prius, a commercially available HEV. The results showed that the proposed universally optimized HEV powertrain achieved better fuel economy and vehicle performance than both the original ICE and HEV vehicles, at low additional vehicle cost.

Inerrogating Variables Affecting Consumers' EV Purchasing Decision

This paper takes a multi-step approach to answer the research question “What are the factors that affect the consumers’ EV purchasing decision-making process and how do they affect it?” In order to answer this question, this paper studies consumer data from the last 15 years. Using Hierarchical cluster analysis, this paper shows how the importance of the factors changes over time. A predictive model has been developed using Ethnographic Decision tree Modeling (EDTM) for the decision-making process of the owners of the 4 top selling EV. The top selling EVs includes models of Nissan Leaf, Tesla, Chevy Volt, and Toyota Prius, from year 2009 to 2014. This EDTM model indicates that while consumers prefer variables such as gas requirement, performance and mile coverage over other variables when deciding to purchase an EV, when given several options of EV they consider other variable such as the environment, brand and country of vehicle production to be more important.

Inerrogating Variables Affecting Consumers' EV Purchasing Decision

This paper takes a multi-step approach to answer the research question “What are the factors that affect the consumers’ EV purchasing decision-making process and how do they affect it?” In order to answer this question, this paper studies consumer data from the last 15 years. Using Hierarchical cluster analysis, this paper shows how the importance of the factors changes over time. A predictive model has been developed using Ethnographic Decision tree Modeling (EDTM) for the decision-making process of the owners of the 4 top selling EV. The top selling EVs includes models of Nissan Leaf, Tesla, Chevy Volt, and Toyota Prius, from year 2009 to 2014. This EDTM model indicates that while consumers prefer variables such as gas requirement, performance and mile coverage over other variables when deciding to purchase an EV, when given several options of EV they consider other variable such as the environment, brand and country of vehicle production to be more important.

Emission Implications of Plug-in Hybrid Electric Vehicles Through an Empirical Exploration of Engine Starts

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.

Understanding Real-World Variability of Hybrid Electric Vehicle Fuel Economy

The variability of fuel economy (FE) is of significant importance as that of average FE to realize FE benefits of hybrid electric vehicles (HEVs) consistently by all users in the real world. Over the years, majority of the research has been focused on improving average FE overlooking the variability. Although in recent years few studies have been focused on the reduction of FE variability, no study has been concentrated to understand why certain design has lower FE variability as that of others. This article provides a detailed analysis to decipher the reasons for the FE variability in the real world. This study considered the optimum designs based on two established design optimization methodologies considering Toyota Prius non-plug-in hybrid as a base vehicle. This study analyses the impacts of the parameters of driving patterns and the operation of powertrains on FE variability. The study explains that comparatively bigger internal combustion engine (ICE) in combination with the optimum sizes of generator motor and battery could lead to lower FE variability in the real world due to lesser time of operation of ICE to charge the battery.