scholarly journals A deep neural network based model for the prediction of hybrid electric vehicles carbon dioxide emissions

Energy and AI ◽  
2021 ◽  
Vol 5 ◽  
pp. 100073
Author(s):  
Claudio Maino ◽  
Daniela Misul ◽  
Alessandro Di Mauro ◽  
Ezio Spessa
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Electric Vehicles ◽  
Carbon Dioxide Emissions ◽  
Deep Neural Network ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric

Cost Optimization of a Plug-In Hybrid Electric Vehicle

2008 ◽  
Author(s):  
Sam Golbuff ◽  
Elizabeth D. Kelly ◽  
Samuel V. Shelton
Keyword(s):  
Carbon Dioxide ◽  
Electric Vehicles ◽  
Carbon Dioxide Emissions ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Argonne National Laboratory ◽  
Battery Pack ◽  
Gasoline Consumption ◽  
Hybrid Electric ◽  
The Cost

In order to decrease the use of petroleum and release of greenhouse gases such as carbon dioxide, the efficiency of transportation vehicles must be increased. One way to increase vehicle efficiency is by extending the electric-only operation of hybrid electric vehicles through the addition of batteries that can be charged using grid electricity. These plug-in hybrid electric vehicles (PHEVs) are currently being developed for introduction into the U.S. market. As with any consumer good, cost is an important design metric. This study optimizes a PHEV design for a mid-size, gasoline-powered passenger vehicle in terms of cost. Three types of batteries, Pb-acid, NiMH, and Li-ion, and three all-electric ranges of 10, 20, and 40 miles (16.1, 32.2, and 64.4 km) were examined. System modeling was performed using Powertrain Systems Analysis Toolkit (PSAT), an Argonne National Laboratory-developed tool. Performance constraints such as acceleration, sustained grade ability, and top speed were met by all systems. The societal impact of the least cost optimum system was quantified in terms of reduced carbon emissions and gasoline consumption. All of the cost optimal designs (one for each combination of all-electric distance and battery type) demonstrated more than a 60% reduction in gasoline consumption and 45% reduction in CO2 emissions, including the emissions generated from producing the electricity used to charge the battery pack, as compared with an average car in the current U.S. fleet. The least cost design for each all-electric range consisted of a Pb-acid design, including a necessary battery replacement of the battery pack twice during the 15 year assumed life. Due to the cost of the battery packs, the 10-mile all-electric range proved to be the least costly. Also, this system saved the most carbon dioxide emissions, a 53% reduction. The most fuel savings came from the PHEV40 system, yielding an 80% reduction in gasoline consumption.

scholarly journals Demographic, Social, Economic, and Regional Factors Affecting the Diffusion of Hybrid Electric Vehicles in Japan

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2130
Author(s):  
Ken’ichi Matsumoto ◽  
Yui Nakamine ◽  
Sunyong Eom ◽  
Hideki Kato
Keyword(s):  
Electric Vehicles ◽  
Urban Areas ◽  
Carbon Dioxide Emissions ◽  
Regional Differences ◽  
Fixed Effects ◽  
Hybrid Electric Vehicles ◽  
Alternative Fuel Vehicles ◽  
Factors Affecting ◽  
Hybrid Electric ◽  
The Right

The transportation sector is a major contributor to carbon dioxide emissions, and the resulting climate change. The diffusion of alternative fuel vehicles, including hybrid electric vehicles (HEV), is an important solution for these issues. This study aimed to evaluate the factors affecting the ownership ratio of HEVs, particularly passenger vehicles, and the regional differences in the purchase of HEVs in Japan. This study performed a fixed-effects regression analysis with panel data for 47 prefectures during the period 2005–2015 to evaluate the factors affecting the HEV ownership ratio and conducted three cluster analyses to investigate the regional differences in diffusion in terms of price categories, body types, and drive systems of HEVs. Some demographic and social factors were found to affect the ownership ratio in Japan, whereas economic factors, including prefecture-level subsidies for purchasing HEVs, were not. Regarding regional differences, prefectures in urban areas with higher income levels tend to purchase more expensive and large-sized HEVs. These results suggest that a strategy to sell the right vehicle to the right person and region is essential for further promoting HEVs in Japan.

A neural network-based ECMS for optimized energy management of plug-in hybrid electric vehicles

Energy ◽  
2021 ◽  
pp. 122727
Author(s):  
Zhihang Chen ◽  
Yonggang Liu ◽  
Yuanjian Zhang ◽  
Zhenzhen Lei ◽  
Zheng Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric

scholarly journals Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 202 ◽  
Author(s):  
Lu Han ◽  
Xiaohong Jiao ◽  
Zhao Zhang
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Electric Vehicles ◽  
Recurrent Neural Network ◽  
Hybrid Electric Vehicles ◽  
Management Control ◽  
Weight Factor ◽  
Traffic Data ◽  
Hybrid Electric ◽  
The Cost

A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.

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