Potential Benefits of Plug-In Hybrid Electric Vehicles for Consumers and Electric Power Utilities

2011 ◽  
Vol 133 (3) ◽  
Author(s):  
Jim B. Himelic ◽  
Frank Kreith
Keyword(s):  
Cost Effectiveness ◽  
Co2 Emissions ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Unit Commitment ◽  
Electric Utility ◽  
Battery Pack ◽  
Hybrid Electric ◽  
Power Utilities ◽  
The Cost

Plug-in hybrid electric vehicles (PHEVs) have the potential of substantially reducing petroleum consumption and vehicular CO2 emissions relative to conventional vehicles. The analysis presented in this article first ascertains the cost-effectiveness of PHEVs from the perspective of the consumer. Then, the potential effects of PHEVs to an electric utility are evaluated by analyzing a simplified hypothetical example. When evaluating the cost-effectiveness of a PHEV, the additional required premium is an important financial parameter to the consumer. An acceptable amount for the additional upfront costs will depend on the future costs of gasoline and the on-board battery pack. The need to replace the on-board battery pack during the assumed vehicle lifetime also affects the allowed premium. A simplified unit commitment and dispatch model was used to determine the costs of energy and the CO2 emissions associated with PHEVs for different charging scenarios. The results show that electricity can be used to charge PHEVs during off-peak hours without an increase in peak demand. In addition, the combined CO2 emissions from the vehicles and the electric generation facilities will be reduced, regardless of the charging strategy.

Potential Benefits of Plug-In Hybrid Electric Vehicles for Consumers and Electric Power Utilities

2008 ◽  
Author(s):  
Jim B. Himelic ◽  
Frank Kreith
Keyword(s):  
Cost Effectiveness ◽  
Co2 Emissions ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Unit Commitment ◽  
Electric Utility ◽  
Battery Pack ◽  
Hybrid Electric ◽  
Power Utilities ◽  
The Cost

Plug-in hybrid electric vehicles (PHEVs) have the potential of substantially reducing petroleum consumption and vehicular CO2 emissions relative to conventional vehicles (CVs). The analysis presented in this article first ascertains the cost-effectiveness of PHEVs from the perspective of the consumer. Then, the potential effects of PHEVs to an electric utility are evaluated by analyzing a simplified hypothetical example. When evaluating the cost effectiveness of a PHEV, the additional required premium is the most important financial parameter to the consumer. An acceptable amount for the additional upfront costs will depend on the future costs of gasoline and the on-board battery pack. The need to replace the on-board battery pack during the assumed vehicle lifetime also affects the allowed premium. A simplified unit commitment and dispatch model was used to determine the costs of energy and the CO2 emissions associated with PHEVs for different charging scenarios. The results show that electricity can be used to charge PHEVs during off-peak hours without an increase in peak demand. In addition, the combined CO2 emissions from the vehicles and the electric generation facilities will be reduced, regardless of the charging strategy.

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.

Unit commitment in power systems with plug-in hybrid electric vehicles

2012 ◽  
Vol 23 (7) ◽  
pp. 1205-1220 ◽  
Author(s):  
Lingrong Lu ◽  
Fushuan Wen ◽  
Gerard Ledwich ◽  
Jiansheng Huang
Keyword(s):  
Power Systems ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Unit Commitment ◽  
Hybrid Electric

A Dynamic Programming Control Strategy for HEV

2012 ◽  
Vol 263-266 ◽  
pp. 541-544 ◽  
Author(s):  
Babici Leandru Corneliu Cezar ◽  
Onea Alexandru
Keyword(s):  
Dynamic Programming ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
The State ◽  
State Of Charge ◽  
Battery Pack ◽  
Matlab Simulink ◽  
Hybrid Electric

Dynamic programming is a very powerful algorithmic paradigm which solves a problem by identifying subproblems and tackling them one by one. First the smallest are solved, and then using their answers, it can be figured out larger ones, until the whole lot of them is solved. This paper presents a control strategy for hybrid electric vehicles, based on the dynamic programming, applied in MATLAB, Simulink environment, using ADVISOR. It was tried this method due to the calculation speed of the suitable torque and speed required from the engine, considering the driver power request (torque and speed), and the state of charge (SOC) of the batteries. Using the fuel converter (FC) fuel map, and the remaining SOC of the battery pack, it was designed an algorithm that will chose at each time the required torque and speed from the first and second source of power.

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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