A Novel Architecture of Multimode Hybrid Powertrains for Fuel Efficiency and Sizing Optimization

Kwon, Hyukjoon; Choi, Yeongil; Choi, Woo Jung; Lee, Seung-Wook

doi:10.1109/access.2021.3139029

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…A novel hybrid vehicle system, named Dual Split Hybrid System (DSHS), includes four different driving modes [3]. Fig.…”

Section: System Descriptionmentioning

confidence: 99%

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Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization

Kwon

2022

IEEE Access

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In this study, a control map generation strategy for hybrid electric vehicles based on machine learning (ML) with optimization data was studied using a multimode hybrid electric vehicle. The optimization data from dynamic programming were used to produce the control maps by employing different ML methods, including Gaussian naïve Bayes, linear discriminant analysis, decision tree, k-nearest neighbors, and support vector machine. Since control map domains separated into several domains can exhibit unrealistic control behavior during engine on-off and hybrid mode shift processes, control maps separated into the same number of domains were used for the simulation study among the different ML methods. The demand torque and power maps by ML training were used for simulations of representative driving test cycles from the Environmental Protection Agency. The results with ML methods indicate that operating domains in the torque and power maps were separated for different driving modes, while they were not clearly separated in the results of DP optimization. Further, the results with the control maps for demand power exhibited slightly improved fuel efficiency compared to the maps for demand torque. This study is meaningful because a control map generation strategy based on ML was not only studied in order to observe the possibility of utilizing DP optimization results for real vehicles, but different types of ML methods were also analyzed and discussed to find appropriate methods for vehicle control map generation in terms of demand torque and power operating points.

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“…A novel hybrid vehicle system, named Dual Split Hybrid System (DSHS), includes four different driving modes [3]. Fig.…”

Section: System Descriptionmentioning

confidence: 99%

“…The hybrid system used in this study is the same as in the reference [3]. The simulation conditions are chosen from EPA fuel economy test data for Toyota Prius Prime with the model year of 2020.…”

Section: B System Modelingmentioning

confidence: 99%

Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization

Kwon

2022

IEEE Access

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“…On the same powertrain, it can have parallel modes, series modes, pure electric modes and fixed-gear modes at the same time. And having a number of operating modes makes it possible to fully realize the potential of the powertrain [32]. Using clutches can achieve multiple operating modes to improve powertrain operation flexibility and efficiency at the expense of higher complexity [33].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Novel Multimode Powertrain for a PHEV Using Two Electric Machines

Zou,

Zhang,

Liu

et al. 2024

IEEE Access

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A new dual-motor plug-in hybrid system has been developed in this paper. The system has a variety of operating modes such as "pure electric drive", "series hybrid drive", "parallel hybrid drive", and "pure oil drive", which can maximize fuel efficiency according to the driving conditions of the vehicle and the needs of the driver. By adding high-power on-board chargers and high-capacity lithium-ion battery packs to the system, more electric driving can be achieved. This paper introduces the parameters and performance indexes of the main components of the system, establishes the simulation model of the powertrain, formulates the control strategy, and analyzes the power performance and fuel economy of the system through simulation. The simulation results are as follows: the maximum speed of the vehicle can reach 180.82km/h; the acceleration time of 100 km/h is 7.72s; in the WLTC driving cycle, the fuel consumption of the hybrid power system is 7.648L/100km; the electric driving range is 156.63 km. The simulation results show that the powertrain can reduce fuel consumption while maintaining sufficient power performance, which proves the feasibility and fuel economy potential of the scheme.

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Multimode dual-motor electric vehicle system for eco and dynamic driving

Kwon

Choi

et al. 2023

Results in Engineering

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A Novel Architecture of Multimode Hybrid Powertrains for Fuel Efficiency and Sizing Optimization

Cited by 6 publications

References 29 publications

Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization

Control Map Generation Strategy for Hybrid Electric Vehicles Based on Machine Learning With Energy Optimization

Design and Analysis of a Novel Multimode Powertrain for a PHEV Using Two Electric Machines

Multimode dual-motor electric vehicle system for eco and dynamic driving

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