scholarly journals Discussão sobre a utilização dos \'fatores de ajuste de consumo de combustível\' do programa brasileiro de etiquetagem veicular em veículos híbridos: estudo de caso com o veículo Toyota Prius

Author(s):  
Renato Romio
Keyword(s):  
2018 ◽  
Vol 79 (2) ◽  
pp. 16-21
Author(s):  
Takashi Uehara ◽  
Shinji Ichikawa
Keyword(s):  

Author(s):  
Keyu Chen ◽  
Rochdi Trigui ◽  
Alain Bouscayrol ◽  
Emmanuel Vinot ◽  
Walter Lhomme ◽  
...  

Author(s):  
Jeffrey D. Wishart ◽  
Yuliang Zhou ◽  
Zuomin Dong

Hybrid vehicle technology is beginning to make a significant mark in the automotive industry, most notably by the Toyota Prius THS-II and its one-mode technology, but also by two-mode architectures recently introduced. GM-Allison, Renault, and the Timken Company have attempted to capitalize on the advantages over simpler series and parallel architectures that the series-parallel configuration confers on the Prius while also improving the design by allowing the powertrain configuration to physically shift and operate in two different modes depending on the driving load. This work provides an overview of the state-of-the-art in two-mode hybrid vehicle architectures, and demonstrates the performance of this technology in comparison to the market-leading Toyota Prius one-mode hybrid vehicle technology and conventional ICE technology. Simulations in the NREL ADVISOR® software compare the performances of the one- and two-mode architectures against a parallel-full design and the ICE baseline for four different drive cycles and a vehicle with varying weight that simulates a commercial vehicle application. A configuration that is a variation of those designed by GM-Allison was chosen as the representative of the two-mode architectures. The performance metric was fuel economy. The fuel economy was measured over the course of the drive cycles: (1) Urban Dynamometer Driving Schedule for Heavy Duty Vehicles (UDDSHDV); (2) New York City Truck (NYCT); (3) City-Suburban Heavy Vehicle Route (CSHVR); and (4) Highway Fuel Economy Test (HWFET). The vehicle model uses a module developed in-house for a Kenworth T400 truck with a payload that varies from empty to completely full. The results demonstrate that the two-mode architecture provides significantly improved performance to that of the conventional non-hybrid design and comparable performance to that of the parallel-full hybrid design. Furthermore, the one-mode design is shown to be sub-optimal for this vehicle type. Development and optimization of the control strategy, which is the direction of the current research, should allow for additional improvement in fuel economy; optimization of vehicular components could result in improvements in acceleration ability, gradeability, and top speed performance, which lags behind the performance capabilities of the conventional powertrain vehicle in these metrics. The study confirms that two-mode architecture presents unique advantages for constantly changing driving cycles and vehicle payloads and represents the future of hybrid vehicle technology.


Eng ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 592-607
Author(s):  
Zhemin Hu ◽  
Ramin Tafazzoli Mehrjardi ◽  
Lin Lai ◽  
Mehrdad Ehsani

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.


2021 ◽  
Author(s):  
Nadia Sultana

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.


Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3940 ◽  
Author(s):  
Alexander de Winter ◽  
Simone Baldi

This work is meant to report on activities at TU Delft on the design and implementation of a path-following system for an autonomous Toyota Prius. The design encompasses: finding the vehicle parameters for the actual vehicle to be used for control design; lateral and longitudinal controllers for steering and acceleration, respectively. The implementation covers the real-time aspects via LabVIEW from National Instruments and the real-life tests. The deployment of the system was enabled by a Spatial Dual Global Positioning System (GPS) system providing more accuracy than the regular GPS. The results discussed in this work represent the first autonomous tests on the Toyota Prius at TU Delft, and we expect the proposed system to be a benchmark against which to test more advanced solutions. The tests show that the system is able to perform in real-time while satisfying comfort and trajectory tracking requirements: in particular, the tracking error was within 16 cm, which is compatible with the 13 cm precision of the Spatial Dual GPS, whereas the longitudinal and lateral acceleration are within comfort levels as defined by available experimental studies.


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