Optimization and Application for Hydraulic Electric Hybrid Vehicle

Hwang, Hsiu-Ying; Lan, Tian-Syung; Chen, Jia-Shiun

doi:10.3390/en13020322

Cited by 22 publications

(13 citation statements)

References 14 publications

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“…Hwang et al reported on "Optimization and Application for Hydraulic Electric Hybrid Vehicle" [1]. In this research, the rule-based control strategy was implemented as the energy distribution management strategy first, and then the genetic algorithm was utilized to conduct global optimization strategy analysis.…”

Section: The Topic Of Energies and Its Applicationsmentioning

confidence: 99%

Special Issue on Selected Papers from IEEE ICKII 2019

2020

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This Special Issue on “Selected papers from IEEE ICKII 2019” selected 13 excellent papers from 260 papers presented in IEEE ICKII 2019 on topics in energies. The fields include: energy fundamentals, energy sources and energy carriers, energy exploration, intermediate and final energy use, energy conversion systems, and energy research and development. The main goal of this Special Isue is to discover new scientific knowledge relevant to the topic of energies.

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Section: The Topic Of Energies and Its Applicationsmentioning

confidence: 99%

Special Issue on Selected Papers from IEEE ICKII 2019

2020

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“…Among them, the rule-based EMS, which is easy to implement and has good robustness as well as low development cost, is the first control strategy applied to hybrid electric vehicles. [2][3][4] It is mainly based on engineering experience or optimization algorithm to make a series of rules for determining the trigger threshold value of different working paths. In practice, the current operating mode should be selected based on the data collected in real-time.…”

Section: Introductionmentioning

confidence: 99%

MPGA-based-ECMS for energy optimization of a hybrid electric city bus with dual planetary gear

Yang

Tan

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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To improve the fuel economy and reduce the exhaust emissions of a hybrid electric city bus (HECB) with dual planetary gear, a vehicle model is proposed based on the coupling mechanism between engine and battery motor in the gear. Then, two kinds of adaptive equivalent consumption minimization strategy (ECMS) algorithms based on fuzzy proportional-integral (PI) controller: Fuzzy PA-ECMS and Fuzzy MPGA-ECMS (MGPA: multiple population genetic algorithm), are established to improve the control effect of ECMS with equivalent factor (EF) as the core. Firstly, an approximate expression of optimal EF is derived based on the Pontryagin’s minimum principle (PMP). Subsequently, the deviation between the reference state of charge (SOC) and the actual SOC and the corresponding variation rate are calculated, which are combined with the fuzzy logic controller to adjust the EF. Finally, the driving style is introduced to rectify the trajectory of EF. According to different driving conditions (different initial values of SOC), the PI parameters of EF are optimized offline by multiple population genetic algorithm. Meanwhile, the interval of PI parameters is continuously optimized by the fuzzy controller. Then, simulation experiments are conducted to verify the efficacy of the two proposed algorithms. The simulation results show that compared to the conventional bus and rule-based control strategy, the proposed Fuzzy PA-ECMS algorithm can improve the fuel economy of bus by 41.70% and 5.29%, respectively. Further, compared to Fuzzy PA-ECMS, Fuzzy MPGA-ECMS algorithm can improve the fuel economy of bus by 0.3%.

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“…Rule-based strategies are generally known as the summary of engineers' long-term working experience. Typical representatives of these schemes include the thermostat method, power following method, engine optimal working point strategy and the fuzzy method [4]- [6]. Further investigations revealed that this method has superior characteristics, including reasonable real-time performance, simple and effective algorithm and simple implementation.…”

Section: Introductionmentioning

confidence: 99%