Numerical Investigation on Fuzzy Logic Control Energy Management Strategy of Parallel Hybrid Electric Vehicle

Ma, Kunqi; Wang, Zaizhou; Hai, Liu; Yu, Hanzhengnan; Wei, Changyin

doi:10.1016/j.egypro.2019.02.016

Cited by 35 publications

(17 citation statements)

References 7 publications

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“…One of the main concerns in the area of HEV design is to determine proper power split between the ICE and EM for a given power demand by the vehicle's supervisory controller [1,2]. The HEV supervisory control strategies can be categorized into three groups: (i) *Corresponding Author Email: saadat@sharif.edu (M. Saadat Foumani) the approaches based on global optimization techniques, for instance Dynamic Programming [3][4][5] and PSO algorithm [6][7][8], (ii) the techniques with the instantaneous optimization of an overall FC function which is calculated by converting the electric energy consumption to an equivalent FC [9][10][11], and (iii) the methods on the basis of some predefined rules, such as Electric Assist Control Strategy (EACS) [12,13] and fuzzy logic [14,15]. It is notable that the first group cannot be employed as real-time controller because it requires priori knowledge of the vehicle's driving cycle, and also, is very time consuming [16,17].…”

Section: Nomenclaturementioning

confidence: 99%

A New Framework for Advancement of Power Management Strategies in Hybrid Electric Vehicles

Delkhosh

Foumani

Azad

2020

IJE

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Power management strategies play a key role in the design process of hybrid electric vehicles. Electric Assist Control Strategy (EACS) is one of the popular power management strategies for hybrid electric vehicles (HEVs). The present investigation proposes a new framework to advance the EACS. Dynamic Programming method is applied to an HEV model in several drive cycles, and as a result, some optimal operating regions are found. The obtained regions are almost distinct, and consequently, some threshold lines can be defined to separate them. The obtained threshold lines are used to eliminate some parameters of the EACS to reduce its sensitivity to the driving behavior. It is shown that by applying the mentioned modification, the sensitivity of the EACS decreases without a significant increase in the HEV's FC. All in all, our findings indicate the effectiveness of the proposed methodology to improve the EACS strategy for HEV supervisory control applications.

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Section: Nomenclaturementioning

confidence: 99%

A New Framework for Advancement of Power Management Strategies in Hybrid Electric Vehicles

Delkhosh

Foumani

Azad

2020

IJE

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“…In Ma et al [10], a dual input and single output fuzzy logic was established, and fuel consumption was reduced by 13.3% and 4.5% for new European driving cycle and worldwide harmonised light-duty vehicles test cycles, respectively. In Guo et al [11], DP and state of charge (SoC) constraint-based EMS in a plug-in HEV (PHEV) was proposed.…”

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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“…The cooperative braking control strategy mainly includes two types of parallel and series, and the schematic diagram is shown in Figure 1. For the parallel control strategy, motor braking and friction braking are independent of each other [7][8][9]. Motor braking is additionally superimposed on the drive shaft while keeping the conventional front and rear axle friction braking ratio unchanged.…”

Section: Introductionmentioning

confidence: 99%

Research on Cooperative Braking Control Algorithm Based on Nonlinear Model Prediction

Liang

Cheng

Zhao

et al. 2021

WEVJ

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To address the coordinated distribution of motor braking and friction braking for the regenerative braking system, a cooperative braking algorithm based on nonlinear model predictive control (NMPC) is proposed, with braking energy recovery power, tire slip rate, and motor torque variation as the optimization objectives, and online optimization of the coordinated distribution of motor braking and friction braking. Using the offline model built in Matlab/Simulink, the cooperative braking algorithm is tested for energy efficiency and braking safety. The results show that when based on World Light Vehicle Test Cycle (WLTC), the energy recovery rate can reach 30.4%, and with a single high braking intensity, the braking safety can still be ensured.

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Numerical Investigation on Fuzzy Logic Control Energy Management Strategy of Parallel Hybrid Electric Vehicle

Cited by 35 publications

References 7 publications

A New Framework for Advancement of Power Management Strategies in Hybrid Electric Vehicles

A New Framework for Advancement of Power Management Strategies in Hybrid Electric Vehicles

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

Research on Cooperative Braking Control Algorithm Based on Nonlinear Model Prediction

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