Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming

Chen, Zheng; Mi, Chris; Xiong, Rui; Xu, Jun; You, Chenwen

doi:10.1016/j.jpowsour.2013.09.085

Cited by 222 publications

(92 citation statements)

References 29 publications

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“…In this paper, GA is applied to estimate the battery parameters to gain the global optimal solution. GA is a stochastic method simulating the revolution of biology [41]. By a series of actions, including elitism selection, crossover and mutation, GA can find the optimal solution with numerous iterations.…”

Section: Parameters Identification Based On Genetic Algorithmmentioning

confidence: 99%

“…The main objective of elitism selection is to pick the optimal offspring; the purpose of crossover is to generate new offspring by combing the performances of the current populations. The goal of mutation is to stochastically change some populations to prevent them from sinking into locally optimal solutions [25,41]. Currently, GA has been widely implemented in industry applications [42], such as automatic control, robotics, image processing, and machine learning.…”

Section: Parameters Identification Based On Genetic Algorithmmentioning

confidence: 99%

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A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

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This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

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Section: Parameters Identification Based On Genetic Algorithmmentioning

confidence: 99%

Section: Parameters Identification Based On Genetic Algorithmmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

Self Cite

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“…In [19,20] the application of the genetic algorithm (GA) was described for the optimization of the control parameters in parallel HEV. The objective function was defined so as to minimize the vehicle engine fuel consumption and emissions with regard to vehicle dynamic requirements.…”

Section: Figurementioning

confidence: 99%

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

2016

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Preliminary noteOne of the major issues in automotive industry is to develop hybrid electric vehicles (HEVs) as the prime candidate for improving fuel efficiency and emissions reduction. This study addresses the impact of an actual drive pattern and control strategy on the conventional and plug-in hybrid electric vehicles (PHEV). In the first step the development procedure of driving cycle for real condition is proposed. Then effects of driving pattern and energy management strategy as the main factors, which strongly affect the fuel consumption and emission of pollutants, are investigated for HEVs and PHEVs. For this purpose fuzzy logic controller which is optimized with genetic algorithm is developed for energy management system. Then vehicle performance is simulated in ADVISOR. Simulation results demonstrate the effectiveness of the approach for reducing the fuel consumption and emissions. Furthermore, simulation results indicate that PHEVs in comparison to HEVs improve fuel efficiency and reduce emissions in real world driving cycle. Keywords: emission; fuel consumption; fuzzy controller; genetic algorithm; hybrid vehicle; plug-in hybrid vehicleIstraživanje potrošnje energije i ispuštanja štetnih plinova kod plug-in i hibridnih električnih vozila Prethodno priopćenje Jedan od osnovnih zadataka u automobilskoj industriji je razvoj hibridnih električnih vozila (HEVs) kao primarnog kandidata za poboljšanje učinkovitosti goriva i smanjenje ispušnih plinova. Ovo se istraživanje bavi učinkom aktualnog modela pogona i upravljanja na konvencionalna i plug-in hibridna električna vozila (PHEV). Najprije se predlaže postupak razvoja pogonskog ciklusa za stvarne uvjete. Tada se za HEVs i PHEVs istražuju učinci pogonskog modela i načina upravljanja potrošnjom energije kao glavnih čimbenika koji bitno djeluju na potrošnju goriva i ispuštanje plinova koji zagađuju okolinu. U tu je svrhu za sustav upravljanja energijom razvijen upravljač neizrazite logike optimiziran genetskim algoritmom. Tada je rad vozila simuliran u ADVISOR-u. Rezultati simulacije pokazuju učinkovitost pristupa kojim se smanjuje potrošnja goriva i količina ispušnih plinova. Nadalje, ti rezultati pokazuju da PHEVs u usporedbi s HEVs poboljšavaju učinkovitost goriva i smanjuju količinu ispušnih plinova u stvarnom pogonu. Ključne riječi: genetički algoritam; hibridno vozilo; ispuštanje plinova; plug-in hibridno vozilo; potrošnja goriva; upravljač neizrazite logike

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“…In recent times, the demand for lithium rechargeable cells with high energy density and high discharging voltage, having application in electric-powered applications such as electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) has gradually increased [1][2][3][4][5][6][7][8][9][10][11]. Half of the price of these electricpowered applications is related to the battery charge.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

Lee¹,

Park²,

Kim³

2016

Journal of Electrical Engineering and Technology

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-Lithium rechargeable cells are used in many industrial applications, because they have high energy density and high power density. For an effective use of these lithium cells, it is essential to build a reliable battery management system (BMS). Therefore, the state of charge (SOC) estimation is one of the most important techniques used in the BMS. An appropriate modeling of the battery characteristics and an accurate algorithm to correct the modeling errors in accordance with the simplified model are required for practical SOC estimation. In order to implement these issues, this approach presents the comparative analysis of the SOC estimation performance using equivalent electrical circuit modeling (EECM) and noise suppression technique (NST) in three representative LiCoO 2 /LiFePO 4 /LiNiMnCoO 2 cells extensively applied in electric vehicles (EVs), hybrid electric vehicles (HEVs) and energy storage system (ESS) applications. Depending on the difference between some EECMs according to the number of RC-ladders and NST, the SOC estimation performances based on the extended Kalman filter (EKF) algorithm are compared. Additionally, in order to increase the accuracy of the EECM of the LiFePO 4 cell, a minor loop trajectory for proper OCV parameterization is applied to the SOC estimation for the comparison of the performances among the compared to SOC estimation performance.

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Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming

Cited by 222 publications

References 29 publications

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells

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Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming

Cited by 222 publications

References 29 publications

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Investigation of energy usage and emissions on plug-in and hybrid electric vehicle

Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO2/LiFePO4/LiNiMnCoO2Cells

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Comparative Analysis of SOC Estimation using EECM and NST in Rechargeable LiCoO₂/LiFePO₄/LiNiMnCoO₂Cells