Analysis of rule-based parameterized control strategy for a HEV Hybrid Electric Vehicle

Hmidi, Mohamed Elhedi; Salem, Ines Ben; Amraoui, Lilia El

doi:10.1109/sta.2019.8717250

Cited by 10 publications

(3 citation statements)

References 20 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 order to compare the energy consumption of different driving cycles, a redefinition must be described using the approach proposed by Duoba and Larsen (1996) and Hmidi et al (2019b), that uses linear interpolation on fuel consumption values. With this method, the vehicle is tested on the specified remote driving cycle to have a zero battery balance at the final driving cycle, Δ SOC = 0 .…”

Section: Metaheuristic Algorithm In This Studymentioning

confidence: 99%

An efficient method for energy management optimization control: Minimizing fuel consumption for hybrid vehicle applications

Hmidi

Salem

Amraoui

2019

Transactions of the Institute of Measurement and Control

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One of the main research areas of the automotive sector is energy consumption and the reduction of pollutant emissions. Therefore, the hybrid electrical vehicle Vehicle electrical hybrid (VEH) is a possible approach to improve the vehicle components or its architecture to maximize system efficiency. This vehicle benefits from levels of consumption and emission of pollutants lower than the thermal vehicle. The problem is then to find the right split power between the two hybrid sources, the internal combustion engine (ICE) and the electrical machines (EM) to minimize fuel consumption. These energy management strategies are the algorithms that achieve instant split power between the two sources in the hybrid electric vehicle (HEV) while controlling the battery state of charge (SOC), in order to improve the fuel economy and optimize the performance HEV. In this research, a parametric study on the impact of optimization of the energy management strategy on the optimal operation of the vehicle is presented. A hybrid vehicle model is described and rules-based energy management strategy is presented. Later these rules are optimized with an optimization algorithm based on Grey Wolf optimizer (GWO). It is a new meta-heuristic based on the population. The results of the simulation make it possible to deduce the impact of this optimal strategy of energy management on fuel consumption and CO2 emissions. The obtained results show that the optimal operation of the vehicle varies according to the best energy management strategies.

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“…As a result, both methods are combines ICE-powered vehicles' high power density with battery-powered vehicles' cost-effectiveness [18]. HEVs have rechargeable batteries, which provide a source of clean and green energy while reducing the demand for fuel [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

Raj¹,

Beno²

2023

Intelligent Automation &Amp; Soft Computing

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Analysis of rule-based parameterized control strategy for a HEV Hybrid Electric Vehicle

Cited by 10 publications

References 20 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

An efficient method for energy management optimization control: Minimizing fuel consumption for hybrid vehicle applications

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

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