A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System

Jiao Li; Lijun Zhang; Longzhi Yang; Bo Wang

doi:10.1109/icarm58088.2023.10218833

A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System

Jiao Li, Lijun Zhang, Longzhi Yang, Bo Wang

School of Computer Science

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

1 Citation (Scopus)

Abstract

This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time.

Original language	English
Title of host publication	International Conference on Advanced Robotics and Mechatronics (ICARM)
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	285-290
Number of pages	6
ISBN (Electronic)	9798350300178, 9798350300161
ISBN (Print)	9798350300185
DOIs	https://doi.org/10.1109/icarm58088.2023.10218833
Publication status	Published - 8 Jul 2023

UN SDGs

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

SDG 7 Affordable and Clean Energy

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10.1109/icarm58088.2023.10218833

Cite this

@inproceedings{137fba3323bc4f6f84e33fe40623d6d6,

title = "A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System",

abstract = "This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time.",

author = "Jiao Li and Lijun Zhang and Longzhi Yang and Bo Wang",

note = "Funding information: This research is supported in part by the National Natural Science Foundation of China (grant number: 62203177), the Department of Science and Technology of Hubei Province, China (grant number: 2022EHB015), and the UK Royal Academy of Engineering Transforming Systems through Partnership programme (grant number: TSP2021\textbackslash{}100016).",

year = "2023",

month = jul,

day = "8",

doi = "10.1109/icarm58088.2023.10218833",

language = "English",

isbn = "9798350300185",

pages = "285--290",

booktitle = "International Conference on Advanced Robotics and Mechatronics (ICARM)",

publisher = "IEEE",

address = "United States",

}

A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System. / Li, Jiao; Zhang, Lijun; Yang, Longzhi et al.
International Conference on Advanced Robotics and Mechatronics (ICARM). Piscataway, NJ, USA: IEEE, 2023. p. 285-290.

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

TY - GEN

T1 - A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System

AU - Li, Jiao

AU - Zhang, Lijun

AU - Yang, Longzhi

AU - Wang, Bo

N1 - Funding information: This research is supported in part by the National Natural Science Foundation of China (grant number: 62203177), the Department of Science and Technology of Hubei Province, China (grant number: 2022EHB015), and the UK Royal Academy of Engineering Transforming Systems through Partnership programme (grant number: TSP2021\100016).

PY - 2023/7/8

Y1 - 2023/7/8

N2 - This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time.

AB - This paper presents a real-time power split strategy for a battery-supercapacitor hybrid energy storage system. The objective of the proposed strategy is to alleviate battery degradation through effective supercapacitor utilization. The proposed strategy employs an adaptive neural fuzzy inference system, which is capable of learning a power split strategy from offline optimization results and distributing power in real-time. The performance of the strategy proposed and a low pass filtering technique is compared through simulations. The results demonstrate that the proposed method can effectively reduce battery capacity loss and perform well under unknown driving cycles in real time.

UR - https://www.scopus.com/pages/publications/85171551094

U2 - 10.1109/icarm58088.2023.10218833

DO - 10.1109/icarm58088.2023.10218833

M3 - Conference contribution

SN - 9798350300185

SP - 285

EP - 290

BT - International Conference on Advanced Robotics and Mechatronics (ICARM)

PB - IEEE

CY - Piscataway, NJ, USA

ER -

A Power Split Strategy for a Vehicular Hybrid Energy Storage System Using Adaptive Neuro Fuzzy Inference System

Abstract

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