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Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting

Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting
Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting
The hybrid energy storage system (HESS) composed of batteries and supercapacitors (SCs) is a dual energy storage technology that can compensate for the shortcomings of a single energy storage technology acting alone. The energy management of HESS splits the power and energy demands from the electric vehicle (EV) to the battery and SC and thus is vital to EV propulsion. This paper presents an online energy management strategy (EMS) that optimises the operating costs of battery-SC HESS and can be adaptive to real-time EV driving conditions. We analyse the optimal offline benchmarks to guide online EMS design and propose the adaptive online EMS with variable perception horizon based on both neural network and rule-based techniques. Compared with existing research, the proposed EMS features reduced complexity, flexible perception and intelligent rulemaking. Case study results show that the proposed variable perception horizon and neural network fitting can improve EMS optimality compared with the conventional methods in existing research. The proposed EMS can realise more than 97% cost optimisation efficacy of offline benchmarks. By the proposed EMS, this paper is expected to provide a practical and effective energy management approach for the battery-SC HESS to reduce costs in EV applications.
cost optimisation, electric vehicle, energy management, hybrid energy storage system, neural network fitting, variable perception horizon
0306-2619
Zhu, Tao
2333524f-f55e-4069-85b9-82d89277efc4
Wills, Richard
60b7c98f-eced-4b11-aad9-fd2484e26c2c
Lot, Roberto
ceb0ca9c-6211-4051-a7b8-90fd6f0a6d78
Ruan, Haijun
70da465d-de16-4607-a4eb-4aaf6a87f34e
Jiang, Zhihao
838f9a9c-3664-4495-8223-98e81ebd81fa
Zhu, Tao
2333524f-f55e-4069-85b9-82d89277efc4
Wills, Richard
60b7c98f-eced-4b11-aad9-fd2484e26c2c
Lot, Roberto
ceb0ca9c-6211-4051-a7b8-90fd6f0a6d78
Ruan, Haijun
70da465d-de16-4607-a4eb-4aaf6a87f34e
Jiang, Zhihao
838f9a9c-3664-4495-8223-98e81ebd81fa

Zhu, Tao, Wills, Richard, Lot, Roberto, Ruan, Haijun and Jiang, Zhihao (2021) Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting. Applied Energy - Elsevier, 292, [116932]. (doi:10.1016/j.apenergy.2021.116932).

Record type: Article

Abstract

The hybrid energy storage system (HESS) composed of batteries and supercapacitors (SCs) is a dual energy storage technology that can compensate for the shortcomings of a single energy storage technology acting alone. The energy management of HESS splits the power and energy demands from the electric vehicle (EV) to the battery and SC and thus is vital to EV propulsion. This paper presents an online energy management strategy (EMS) that optimises the operating costs of battery-SC HESS and can be adaptive to real-time EV driving conditions. We analyse the optimal offline benchmarks to guide online EMS design and propose the adaptive online EMS with variable perception horizon based on both neural network and rule-based techniques. Compared with existing research, the proposed EMS features reduced complexity, flexible perception and intelligent rulemaking. Case study results show that the proposed variable perception horizon and neural network fitting can improve EMS optimality compared with the conventional methods in existing research. The proposed EMS can realise more than 97% cost optimisation efficacy of offline benchmarks. By the proposed EMS, this paper is expected to provide a practical and effective energy management approach for the battery-SC HESS to reduce costs in EV applications.

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More information

Accepted/In Press date: 3 April 2021
e-pub ahead of print date: 14 April 2021
Published date: 15 June 2021
Keywords: cost optimisation, electric vehicle, energy management, hybrid energy storage system, neural network fitting, variable perception horizon

Identifiers

Local EPrints ID: 448541
URI: http://eprints.soton.ac.uk/id/eprint/448541
ISSN: 0306-2619
PURE UUID: c9831a02-fe42-46bf-bac8-ac543f19db09
ORCID for Richard Wills: ORCID iD orcid.org/0000-0002-4805-7589
ORCID for Roberto Lot: ORCID iD orcid.org/0000-0001-5022-5724

Catalogue record

Date deposited: 26 Apr 2021 17:07
Last modified: 26 Nov 2021 03:03

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Contributors

Author: Tao Zhu
Author: Richard Wills ORCID iD
Author: Roberto Lot ORCID iD
Author: Haijun Ruan
Author: Zhihao Jiang

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