scholarly journals Investigation of a Planetary Differential for Use as a Continuously Variable Transmission

2014 ◽  
Author(s):  
Austin B. Randall ◽  
Robert H. Todd
Keyword(s):  
Control Systems ◽  
Electric Vehicles ◽  
Efficient Solution ◽  
Hybrid Electric Vehicles ◽  
Electronic Control ◽  
Low Emission ◽  
Variable Transmission ◽  
Hybrid Electric ◽  
The Cost ◽  
Future Work

With gas prices and pollution on the rise, the production of electric and hybrid-electric vehicles has been a focus of all major automobile manufacturers. The further advancement of this technology must not only continue to focus on fuel-efficient, low-emission vehicles, but also decrease their cost to make them more available and enticing to the general public. Results from this research include one potential solution to reduce the cost of electric and hybrid-electric vehicles. An exploration of the functionality of a Planetary Differential (PD) has shown that it can simplify expensive and complex electronic control systems for electric and hybrid-electric vehicles. This research is a furtherance of work performed previously and its main purpose is to discover a more efficient solution than what was already tested under similar circumstances. Recommendations for future work and implementation of the PD in electric and hybrid-electric vehicles are presented herein.

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.

scholarly journals Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2074 ◽  
Author(s):  
Hangyang Li ◽  
Yunshan Zhou ◽  
Huanjian Xiong ◽  
Bing Fu ◽  
Zhiliang Huang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Speed Ratio ◽  
Driving Pattern ◽  
Variable Transmission ◽  
Equivalent Factor ◽  
Hybrid Electric

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

scholarly journals Design Optimization of an Electric Variable Transmission for Hybrid Electric Vehicles

Energies ◽  
2018 ◽  
Vol 11 (5) ◽  
pp. 1118 ◽  
Author(s):  
Qiwei Xu ◽  
Jing Sun ◽  
Wenjuan Wang ◽  
Yunqi Mao ◽  
Shumei Cui
Keyword(s):  
Design Optimization ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Variable Transmission ◽  
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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