Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis

Bingxiang Sun; Jiuchun Jiang; Fangdan Zheng; Wei Zhao; Bor Yann Liaw; Haijun Ruan; Zhiqiang Han; Weige Zhang

doi:10.1016/j.jpowsour.2015.01.106

Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis

Bingxiang Sun
, Jiuchun Jiang
, Fangdan Zheng
, Wei Zhao
, Bor Yann Liaw
, Haijun Ruan
, Zhiqiang Han
, Weige Zhang

Beijing Jiaotong University
Electric Power Research Institute of Guangdong Power Grid Corporation
University of Hawaii at Manoa

Research output: Contribution to journal › Article › peer-review

77 Citations (Scopus)

Abstract

The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. Here, we used a unique hybrid approach to enable complex SOH estimations. The approach hybridizes the Delphi method known for its simplicity and effectiveness in applying weighting factors for complicated decision-making and the grey relational grade analysis (GRGA) for multi-factor optimization. Six critical factors were used in the consideration for SOH estimation: peak power at 30% state-of-charge (SOC), capacity, the voltage drop at 30% SOC with a C/3 pulse, the temperature rises at the end of discharge and charge at 1C; respectively, and the open circuit voltage at the end of charge after 1-h rest. The weighting of these factors for SOH estimation was scored by the 'experts' in the Delphi method, indicating the influencing power of each factor on SOH. The parameters for these factors expressing the battery state variations are optimized by GRGA. Eight battery cells were used to illustrate the principle and methodology to estimate the SOH by this hybrid approach, and the results were compared with those based on capacity and power capability. The contrast among different SOH estimations is discussed.

Original language	English
Pages (from-to)	146-157
Number of pages	12
Journal	Journal of Power Sources
Volume	282
Early online date	2 Feb 2015
DOIs	https://doi.org/10.1016/j.jpowsour.2015.01.106
Publication status	Published - 15 May 2015
Externally published	Yes

Funder

This work is supported by the National High Technology Research and Development Program of China (Grant No. 2012AA050211).

Funding

This work is supported by the National High Technology Research and Development Program of China (Grant No. 2012AA050211 ). The authors would like to thank Tony Yip and Xiaoqing Zhang of Beijing Jiaotong University for their fruitful discussions on the research direction, manuscript preparation, and verification of the data. The authors would also like to thank the panel of experts: Xinhong Li, Zidong Wang, Zhengyao Liu, Zhenpo Wang, Feng Wen, Hongyu Guo, Yuqi Tong, Jinling Zhang and Xin Shi for their contributions to the selection of the indexes and the discussions on the parameterization to generate the weighting coefficients for the power batteries.

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

State-of-health (SOH)
Power batteries
Delphi method
Grey relational grade analysis
Battery management systems
Power capability

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology
Physical and Theoretical Chemistry
Electrical and Electronic Engineering

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10.1016/j.jpowsour.2015.01.106

Cite this

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title = "Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis",

abstract = "The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. Here, we used a unique hybrid approach to enable complex SOH estimations. The approach hybridizes the Delphi method known for its simplicity and effectiveness in applying weighting factors for complicated decision-making and the grey relational grade analysis (GRGA) for multi-factor optimization. Six critical factors were used in the consideration for SOH estimation: peak power at 30\% state-of-charge (SOC), capacity, the voltage drop at 30\% SOC with a C/3 pulse, the temperature rises at the end of discharge and charge at 1C; respectively, and the open circuit voltage at the end of charge after 1-h rest. The weighting of these factors for SOH estimation was scored by the 'experts' in the Delphi method, indicating the influencing power of each factor on SOH. The parameters for these factors expressing the battery state variations are optimized by GRGA. Eight battery cells were used to illustrate the principle and methodology to estimate the SOH by this hybrid approach, and the results were compared with those based on capacity and power capability. The contrast among different SOH estimations is discussed.",

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AU - Sun, Bingxiang

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AU - Ruan, Haijun

AU - Han, Zhiqiang

AU - Zhang, Weige

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N2 - The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. Here, we used a unique hybrid approach to enable complex SOH estimations. The approach hybridizes the Delphi method known for its simplicity and effectiveness in applying weighting factors for complicated decision-making and the grey relational grade analysis (GRGA) for multi-factor optimization. Six critical factors were used in the consideration for SOH estimation: peak power at 30% state-of-charge (SOC), capacity, the voltage drop at 30% SOC with a C/3 pulse, the temperature rises at the end of discharge and charge at 1C; respectively, and the open circuit voltage at the end of charge after 1-h rest. The weighting of these factors for SOH estimation was scored by the 'experts' in the Delphi method, indicating the influencing power of each factor on SOH. The parameters for these factors expressing the battery state variations are optimized by GRGA. Eight battery cells were used to illustrate the principle and methodology to estimate the SOH by this hybrid approach, and the results were compared with those based on capacity and power capability. The contrast among different SOH estimations is discussed.

AB - The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. Here, we used a unique hybrid approach to enable complex SOH estimations. The approach hybridizes the Delphi method known for its simplicity and effectiveness in applying weighting factors for complicated decision-making and the grey relational grade analysis (GRGA) for multi-factor optimization. Six critical factors were used in the consideration for SOH estimation: peak power at 30% state-of-charge (SOC), capacity, the voltage drop at 30% SOC with a C/3 pulse, the temperature rises at the end of discharge and charge at 1C; respectively, and the open circuit voltage at the end of charge after 1-h rest. The weighting of these factors for SOH estimation was scored by the 'experts' in the Delphi method, indicating the influencing power of each factor on SOH. The parameters for these factors expressing the battery state variations are optimized by GRGA. Eight battery cells were used to illustrate the principle and methodology to estimate the SOH by this hybrid approach, and the results were compared with those based on capacity and power capability. The contrast among different SOH estimations is discussed.

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KW - Delphi method

KW - Grey relational grade analysis

KW - Battery management systems

KW - Power capability

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JF - Journal of Power Sources

ER -

Practical state of health estimation of power batteries based on Delphi method and grey relational grade analysis

Abstract

Funder

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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