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RISS 인기검색어
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- 저자 : Huirong Jiao (검색결과 2 건)
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State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer
Zhao, Yulan,Yun, Haitao,Liu, Shude,Jiao, Huirong,Wang, Chengzhen The Korean Institute of Power Electronics 2014 JOURNAL OF POWER ELECTRONICS Vol.14 No.5
Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor-capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.
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State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer
Yulan Zhao,Haitao Yun,Shude Liu,Huirong Jiao,Chengzhen Wang 전력전자학회 2014 JOURNAL OF POWER ELECTRONICS Vol.14 No.5
Lithium-ion batteries are widely used in hybrid and pure electric vehicles. State-of-charge (SOC) estimation is a fundamental issue in vehicle power train control and battery management systems. This study proposes a novel model-based SOC estimation method that applies closed-loop state observer theory and a comprehensive battery model. The state-space model of lithium-ion battery is developed based on a three-order resistor?capacitor equivalent circuit model. The least square algorithm is used to identify model parameters. A multi-state closed-loop state observer is designed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model. Battery SOC can then be estimated based on the corresponding relationship between battery OCV and SOC. Finally, practical driving tests that use two types of typical driving cycle are performed to verify the proposed SOC estimation method. Test results prove that the proposed estimation method is reasonably accurate and exhibits accuracy in estimating SOC within 2% under different driving cycles.
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