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 inJournal of power sources Vol. 246; pp. 667 - 678
Main Authors Chen, Xiaopeng, Shen, Weixiang, Cao, Zhenwei, Kapoor, Ajay
Format Journal Article
LanguageEnglish
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.
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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