Optimal Control Strategy for Hybrid Electric Vehicle Powertrain

Al-Aawar, N.; Arkadan, A.A.

doi:10.1109/jestpe.2014.2323019

Cited by 20 publications

(9 citation statements)

References 18 publications

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“…In case of very stiff shafts, the torque T A of the A machine is obtained from Eq. (20) for the steady state operation of the ICE, namely for € h E ¼ 0. Hence, the hybrid controller calculates the difference between the command torque for the vehicle T dr and the command torque from the A machine T Ar as command torque T Br to the B machine.…”

Section: Hybrid Controllermentioning

confidence: 99%

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Modeling, control, and performance of a novel architecture of hybrid electric powertrain system

Yi¹,

Epureanu²,

Hong

et al. 2016

Applied Energy

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Section: Hybrid Controllermentioning

confidence: 99%

“…Al-Aawar and Arkadan have designed an optimal control strategy which maximizes fuel economy by using a mathematical search algorithm, and maximizes drivability by using a fuzzy logic algorithm [20]. Hou et al have developed a control strategy based on estimating trip information acquired using statistical methods [21].…”

Section: Introductionmentioning

confidence: 99%

Modeling, control, and performance of a novel architecture of hybrid electric powertrain system

Yi¹,

Epureanu²,

Hong

et al. 2016

Applied Energy

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“…An EMS based on the genetic algorithm (GA) was proposed in Zhang et al [26]. In Al-Aawar and Arkadan [27], an EMS based on a mathematical search algorithm to reduce fuel consumption and provide required torque was proposed to improve the performance of the vehicle. A fuzzy logic algorithm was also used to ensure good drivability (comfort) and ICE efficiency was reported to increase by 17%.…”

Section: Introductionmentioning

confidence: 99%

Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles

Singh

Bansal

Singh

2021

IET Electrical Systems in Transportation

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The most viable option to achieve the goals of saving energy and protecting the environment is to replace conventional vehicles with hybrid electric vehicles (HEVs). In HEVs, the operational characteristics of an internal combustion engine (ICE) and an electric motor (EM) are different from each other and thus require an adaptive control strategy to achieve higher fuel economy along with smooth operation and better performance of the vehicle. An energy management control strategy is proposed for an HEV based on an adaptive network-based fuzzy inference system (ANFIS). The proposed adaptive equivalent consumption minimisation strategy decides the power to be drawn from ICE and EM based on input parameters such as the speed of the vehicle, the state of charge of the battery, the EM torque and the ICE torque. The whole system is simulated in an advanced vehicle simulator tool. The proposed non-linear controller has also been tested for real-time behaviour using a field-programmable gate array-based MicroLabBox hardware controller to compare its performance against existing controllers. The authors compared the fuel economy obtained using the proposed method with several other methods available in the literature. The comparison clearly reveals that the proposed ANFIS-based method results in better optimization of energy and hence offers better fuel economy. The urban dynamometer driving schedule has been employed for this analysis.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Difficulty in availability and remoteness makes conveyance of sustainable energy sources costly. Grid extension is also not cost effective [1]. Sustainable source are the optimal outcome to provide power in remote zones.…”

Section: Introductionmentioning

confidence: 99%

Some findings of Hybrid Sustainable Energy System for Standalone and Grid Connected Operation: Types, Storage Options, Trends for Research and Control Strategies

Kumar¹,

Mishra²,

Ranjan³

2019

IJITEE

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This article presents some critical findings of various issue concerning to HSES like types, storage, trends for research and control strategies of HSES. Various interconnection techniques for HSES are presented. It has been made a short review of Mathematical model for renewable energy sources. Conclusion and result of control strategies work of HSES, Storage techniques of HSES for energy flow management is presented. Finally, it has been made a summary of the future analysis, challenges in techniques and improvements of HSES based power generation techniques for Isolated and grid connected applications

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Optimal Control Strategy for Hybrid Electric Vehicle Powertrain

Cited by 20 publications

References 18 publications

Modeling, control, and performance of a novel architecture of hybrid electric powertrain system

Modeling, control, and performance of a novel architecture of hybrid electric powertrain system

Development of an adaptive neuro‐fuzzy inference system–based equivalent consumption minimisation strategy to improve fuel economy in hybrid electric vehicles

Some findings of Hybrid Sustainable Energy System for Standalone and Grid Connected Operation: Types, Storage Options, Trends for Research and Control Strategies

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