A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles
In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model...
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Published in | Journal of power sources Vol. 246; pp. 667 - 678 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier
2014
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Abstract | In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban. |
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AbstractList | In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode observer (ASGSMO) has been presented. To design the ASGSMO for the SOC estimation, the state equations based on a battery equivalent circuit model (BECM) are derived to represent dynamic behaviours of a battery. Comparing with a conventional sliding mode observer, the ASGSMO has a capability of minimising chattering levels in the SOC estimation by using the self-adjusted switching gain while maintaining the characteristics of being able to compensate modelling errors caused by the parameter variations of the BECM. Lyapunov stability theory is adopted to prove the error convergence of the ASGSMO for the SOC estimation. The lithium-polymer battery (LiPB) is utilised to conduct experiments for determining the parameters of the BECM and verifying the effectiveness of the proposed ASGSMO in various discharge current profiles including EV driving conditions in both city and suburban. |
Author | ZHENWEI CAO XIAOPENG CHEN WEIXIANG SHEN AJAY KAPOOR |
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Keywords | Lithium ion batteries Adaptive switching gain sliding mode observer Sliding mode Variable structure system Alkaline storage battery Battery management system Secondary cell Electric vehicle Switching Lithium battery Battery equivalent circuit model Lithium-polymer battery Equivalent circuit State of charge Electrical impedance Electrochemical impedance spectroscopy |
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Snippet | In this paper, a novel approach for battery state of charge (SOC) estimation in electric vehicles (EVs) based on an adaptive switching gain sliding mode... |
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SubjectTerms | Applied sciences Charge Direct energy conversion and energy accumulation Electric batteries Electric charge Electric vehicles Electrical engineering. Electrical power engineering Electrical power engineering Electrochemical conversion: primary and secondary batteries, fuel cells Exact sciences and technology Gain Ground, air and sea transportation, marine construction Lithium batteries Mathematical models Observers Road transportation and traffic Sliding mode Switching |
Title | A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles |
URI | https://search.proquest.com/docview/1475558340 https://search.proquest.com/docview/1513479957 https://search.proquest.com/docview/1671454128 |
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