Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms

Ima Essiet; Yanxia Sun

doi:10.1109/ICECET52533.2021.9698563

Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms

Ima Essiet
, Yanxia Sun

Electrical and Electronic Engineering Science

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

Electric vehicles (EVs) with vehicle-to-grid (V2G) capability are a promising source of alternative and renewable energy for the existing power grid. However, it is important to prioritize the health of the EV battery since it is the most expensive single component of the vehicle. In this paper, we present a multiobjective function consisting of grid and EV battery parameters which are optimized using a recently proposed evolutionary algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). The EV aggregator model consists of 5 vehicle clusters connected at various electrical distances within the IEEE 33-node system, and each cluster contains 300 EVs. Battery parameters for the Nissan Leaf 2019 model EV are considered in the simulation. The performance of E-dyNSGA-III is also compared with other well-known nature-inspired algorithms such as hybrid spider monkey algorithm (HSMA), raccoon family optimization algorithm (RFOA) and artificial bee colony (ABC) algorithm. Simulation results indicate that E-dyNSGA-III performs better than the three other algorithms.

Original language	English
Title of host publication	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665442312
DOIs	https://doi.org/10.1109/ICECET52533.2021.9698563
Publication status	Published - 2021
Event	2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 - Cape Town, South Africa Duration: 9 Dec 2021 → 10 Dec 2021

Publication series

Name	International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

Conference

Conference	2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021
Country/Territory	South Africa
City	Cape Town
Period	9/12/21 → 10/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

aggregator
electric vehicle
state-of-charge
vehicle-to-grid

ASJC Scopus subject areas

Electrical and Electronic Engineering
General Computer Science
Energy Engineering and Power Technology

Access to Document

10.1109/ICECET52533.2021.9698563

Cite this

Essiet, I., & Sun, Y. (2021). Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECET52533.2021.9698563

@inproceedings{c9e62df3d1f24093a56b3c26a3a6fddf,

title = "Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms",

abstract = "Electric vehicles (EVs) with vehicle-to-grid (V2G) capability are a promising source of alternative and renewable energy for the existing power grid. However, it is important to prioritize the health of the EV battery since it is the most expensive single component of the vehicle. In this paper, we present a multiobjective function consisting of grid and EV battery parameters which are optimized using a recently proposed evolutionary algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). The EV aggregator model consists of 5 vehicle clusters connected at various electrical distances within the IEEE 33-node system, and each cluster contains 300 EVs. Battery parameters for the Nissan Leaf 2019 model EV are considered in the simulation. The performance of E-dyNSGA-III is also compared with other well-known nature-inspired algorithms such as hybrid spider monkey algorithm (HSMA), raccoon family optimization algorithm (RFOA) and artificial bee colony (ABC) algorithm. Simulation results indicate that E-dyNSGA-III performs better than the three other algorithms.",

keywords = "aggregator, electric vehicle, state-of-charge, vehicle-to-grid",

author = "Ima Essiet and Yanxia Sun",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021 ; Conference date: 09-12-2021 Through 10-12-2021",

year = "2021",

doi = "10.1109/ICECET52533.2021.9698563",

language = "English",

series = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021",

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}

Essiet, I & Sun, Y 2021, Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms. in International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, Institute of Electrical and Electronics Engineers Inc., 2021 International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021, Cape Town, South Africa, 9/12/21. https://doi.org/10.1109/ICECET52533.2021.9698563

Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms. / Essiet, Ima; Sun, Yanxia.
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms

AU - Essiet, Ima

AU - Sun, Yanxia

PY - 2021

Y1 - 2021

N2 - Electric vehicles (EVs) with vehicle-to-grid (V2G) capability are a promising source of alternative and renewable energy for the existing power grid. However, it is important to prioritize the health of the EV battery since it is the most expensive single component of the vehicle. In this paper, we present a multiobjective function consisting of grid and EV battery parameters which are optimized using a recently proposed evolutionary algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). The EV aggregator model consists of 5 vehicle clusters connected at various electrical distances within the IEEE 33-node system, and each cluster contains 300 EVs. Battery parameters for the Nissan Leaf 2019 model EV are considered in the simulation. The performance of E-dyNSGA-III is also compared with other well-known nature-inspired algorithms such as hybrid spider monkey algorithm (HSMA), raccoon family optimization algorithm (RFOA) and artificial bee colony (ABC) algorithm. Simulation results indicate that E-dyNSGA-III performs better than the three other algorithms.

AB - Electric vehicles (EVs) with vehicle-to-grid (V2G) capability are a promising source of alternative and renewable energy for the existing power grid. However, it is important to prioritize the health of the EV battery since it is the most expensive single component of the vehicle. In this paper, we present a multiobjective function consisting of grid and EV battery parameters which are optimized using a recently proposed evolutionary algorithm called enhanced dynamic non-dominated sorting genetic algorithm III (E-dyNSGA-III). The EV aggregator model consists of 5 vehicle clusters connected at various electrical distances within the IEEE 33-node system, and each cluster contains 300 EVs. Battery parameters for the Nissan Leaf 2019 model EV are considered in the simulation. The performance of E-dyNSGA-III is also compared with other well-known nature-inspired algorithms such as hybrid spider monkey algorithm (HSMA), raccoon family optimization algorithm (RFOA) and artificial bee colony (ABC) algorithm. Simulation results indicate that E-dyNSGA-III performs better than the three other algorithms.

KW - aggregator

KW - electric vehicle

KW - state-of-charge

KW - vehicle-to-grid

UR - https://www.scopus.com/pages/publications/85127039328

U2 - 10.1109/ICECET52533.2021.9698563

DO - 10.1109/ICECET52533.2021.9698563

M3 - Conference contribution

AN - SCOPUS:85127039328

T3 - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

BT - International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Essiet I, Sun Y. Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms. In International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021). doi: 10.1109/ICECET52533.2021.9698563

Parameter Optimization of EV Battery SOC Model with Aggregator Using Evolutionary Algorithms

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

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