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Research on the optimal charging strategy for Li-Ion batteries based on multi-objective optimization

Research on the optimal charging strategy for Li-Ion batteries based on multi-objective optimization
Research on the optimal charging strategy for Li-Ion batteries based on multi-objective optimization
Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users’ charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.
EV charging; Li-ion batteries; multi-objective optimization; equivalent circuit model (ECM); MOPSO algorithm; multistage constant current charging
1996-1073
Min, Haitao
80e81d9c-43ed-4048-aa50-75c32f84830b
Sun, Weiyi
0a3813d7-1427-446a-8125-3e2bdd9d58e8
Li, Xinyong
d0cd6339-0c15-40d3-9627-16f18882ff94
Guo, Dongni
24425f22-8e37-4172-92f2-c82b30afd5d2
Yu, Yuanbin
40b8a3d1-d0cd-4e55-8888-15466bc4c20f
Zhu, Tao
2333524f-f55e-4069-85b9-82d89277efc4
Zhao, Zhongmin
976a83a8-edb7-4b07-9cda-b3f267b2d701
Min, Haitao
80e81d9c-43ed-4048-aa50-75c32f84830b
Sun, Weiyi
0a3813d7-1427-446a-8125-3e2bdd9d58e8
Li, Xinyong
d0cd6339-0c15-40d3-9627-16f18882ff94
Guo, Dongni
24425f22-8e37-4172-92f2-c82b30afd5d2
Yu, Yuanbin
40b8a3d1-d0cd-4e55-8888-15466bc4c20f
Zhu, Tao
2333524f-f55e-4069-85b9-82d89277efc4
Zhao, Zhongmin
976a83a8-edb7-4b07-9cda-b3f267b2d701

Min, Haitao, Sun, Weiyi, Li, Xinyong, Guo, Dongni, Yu, Yuanbin, Zhu, Tao and Zhao, Zhongmin (2017) Research on the optimal charging strategy for Li-Ion batteries based on multi-objective optimization. Energies, 10 (5), [709]. (doi:10.3390/en10050709).

Record type: Article

Abstract

Charging performance affects the commercial application of electric vehicles (EVs) significantly. This paper presents an optimal charging strategy for Li-ion batteries based on the voltage-based multistage constant current (VMCC) charging strategy. In order to satisfy the different charging demands of the EV users for charging time, charged capacity and energy loss, the multi-objective particle swarm optimization (MOPSO) algorithm is employed and the influences of charging stage number, charging cut-off voltage and weight factors of different charging goals are analyzed. Comparison experiments of the proposed charging strategy and the traditional normal and fast charging strategies are carried out. The experimental results demonstrate that the traditional normal and fast charging strategies can only satisfy a small range of EV users’ charging demand well while the proposed charging strategy can satisfy the whole range of the charging demand well. The relative increase in charging performance of the proposed charging strategy can reach more than 80% when compared to the normal and fast charging dependently.

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More information

Accepted/In Press date: 12 May 2017
e-pub ahead of print date: 17 May 2017
Published date: May 2017
Keywords: EV charging; Li-ion batteries; multi-objective optimization; equivalent circuit model (ECM); MOPSO algorithm; multistage constant current charging

Identifiers

Local EPrints ID: 418767
URI: http://eprints.soton.ac.uk/id/eprint/418767
ISSN: 1996-1073
PURE UUID: 81ce1cfe-7bb4-4225-b2ef-f8486918374d

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Date deposited: 21 Mar 2018 17:30
Last modified: 15 Mar 2024 18:48

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Contributors

Author: Haitao Min
Author: Weiyi Sun
Author: Xinyong Li
Author: Dongni Guo
Author: Yuanbin Yu
Author: Tao Zhu
Author: Zhongmin Zhao

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