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

Hong, Wanshi; Chakraborty, Indrasis; Wang, Hong; Tao, Gang

doi:10.1109/tiv.2021.3049296

Cited by 20 publications

(8 citation statements)

References 28 publications

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“…where P is the value of the vehicle at year y, given by equation (11), f own is the frequency of tax imposition in a year, b own is the tax coefficient, and P th is a threshold value for ownership tax imposition. For Mexico City, the trends and values of the coefficients for the vehicle emissions control and the public road taxes are given in figure 4, while P th = 250000 $MXN, according to governmental information [30].…”

Section: Taxationmentioning

confidence: 99%

“…In turn, the depreciated value of the vehicle is computed with equation (11), whereas the accumulated taxes, at month m of year y, are calculated as follows:…”

Section: Numeric Calculationsmentioning

confidence: 99%

“…One remarkable example is the emergence of hybrid electric vehicles (HEVs), which combine traditional internal combustion engines with electric motors, for propulsion. The HEVs have proven to be highly efficient and have gained popularity, due to their ability to reduce fuel consumption and emissions [11][12][13]. The introduction of hybrid electric vehicles has marked a significant milestone in the past three decades, playing a crucial role in the continuous evolution and commercialization of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Electric or gasoline: a simple model to decide when buying a new vehicle

Ledesma-Alonso,

Becerra-Nuñez

2024

Environ. Res. Commun.

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In this work, a simple methodology to follow the behavior of motorized urban vehicles, from the point of view of personal finances, is presented. Including the acquisition of a new vehicle, the analysis considers the driving distance per week, the average speed, the time spent at rest due to traffic conditions, the evolution of gasoline and electric energy prices, maintenance and services, and local taxes. Herein, two low-range compact vehicles were chosen and compared: one powered by combustion of gasoline, and the other by electric energy stored in batteries. Historical data and trend projections, according to inflation and prices evolution, are taken into consideration. The developed model may help to select adequately a new vehicle, according to the user's needs. A good choice depends strongly on the usage and traffic conditions, the electric vehicle being suitable for large weekly driving distances and heavy traffic, whereas the gasoline vehicle is preferred for short distances and light traffic. The expenses of the vehicles are compared through time, with different scenarios envisaged according to the user's resolution to keep the vehicle for the entire lifespan or to sell it quickly.

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Section: Taxationmentioning

confidence: 99%

“…In turn, the depreciated value of the vehicle is computed with equation (11), whereas the accumulated taxes, at month m of year y, are calculated as follows:…”

Section: Numeric Calculationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Electric or gasoline: a simple model to decide when buying a new vehicle

Ledesma-Alonso,

Becerra-Nuñez

2024

Environ. Res. Commun.

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“…T HE past decade has witnessed the significant breakthroughs on autonomous driving with artificial intelligence methods [2], [3], leading to numerous applications in transportation, including improving traffic safety [4]- [6], reducing traffic congestion [7], [8], minimizing air pollution [9], [10], and enhancing traffic efficiency [11]- [13]. Object detection is a critical component of autonomous driving, which relies on computer vision and artificial intelligence techniques to understand driving scenarios [2], [14].…”

Section: Introductionmentioning

confidence: 99%

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

et al. 2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Typically, object detection methods for autonomous driving that rely on supervised learning make the assumption of a consistent feature distribution between the training and testing data, however such assumption may fail in different weather conditions. Due to the domain gap, a detection model trained under clear weather may not perform well in foggy and rainy conditions. Overcoming detection bottlenecks in foggy and rainy weather is a real challenge for autonomous vehicles deployed in the wild. To bridge the domain gap and improve the performance of object detectionin foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection. The adaptations at both the image-level and objectlevel are intended to minimize the differences in image style and object appearance between domains. Furthermore, in order to improve the model's performance on challenging examples, we introduce a novel adversarial gradient reversal layer that conducts adversarial mining on difficult instances in addition to domain adaptation. Additionally, we suggest generating an auxiliary domain through data augmentation to enforce a new domain-level metric regularization. Experimental findings on public V2V benchmark exhibit a substantial enhancement in object detection specifically for foggy and rainy driving scenarios.

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“…This results in an abrupt increase in the demand for electricity on the energy generating hubs. To contemplate this situation, a viable solution is needed to avoid energy mismatch of energy integrated systems [23].…”

Section: Introduction a Backgroundmentioning

confidence: 99%

A Dynamic Optimal Scheduling Strategy for Multi-Charging Scenarios of Plug-in-Electric Vehicles Over a Smart Grid

et al. 2023

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Green transportation has become our top priority due to the depletion of the earth's natural resources and rising pollutant emission levels. Plug-in electric vehicles (PEVs) are seen as a solution to the problem because they are more cost and environment friendly. Due to rapid industrialization and government incentives for zero-emission transportation, a significant challenge is also constituted in power grids by the self-interested nature of PEVs, with the asymmetry of information between the charging power demand and supply sides. In this paper, we propose an optimal strategy in industrial energy management system, based on evolutionary computing, to characterize different charging situations. The proposed approach considers stochastic, off-peak, peak, and electric power research institute charging scenarios for attaining the vehicleto-grid capacity in terms of optimal cost and demand. An extensive scheduling of charging cases is studied in order to avoid power outages or scenarios in which there is a significant supply-demand mismatch. Furthermore, the proposed scheme model also reduces the greenhouse gases emission from generation side to build a sustainable generation infrastructure, which maximizes the utility of fuel-based energy production in the presence of certain nonlinear constraints. The simulation analysis demonstrates that PEVs can be charged and discharged in a systematic manner. The participation of transferable load through the proposed methodology can significantly reduce the economic costs, pollutant impacts, efficiency, and security of power grid operation.INDEX TERMS Energy emission dispatch (EED), industrial energy management system (IEMS), plugin electric vehicle (PEV), plug-in electric vehicle charging coordination (PEVCC), vehicle-to-grid (V2G), valve-point loading effect (VLE)

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

Cited by 20 publications

References 28 publications

Electric or gasoline: a simple model to decide when buying a new vehicle

Electric or gasoline: a simple model to decide when buying a new vehicle

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

A Dynamic Optimal Scheduling Strategy for Multi-Charging Scenarios of Plug-in-Electric Vehicles Over a Smart Grid

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