an adaptive gain nonlinear observer for state of charge estimation of lithium-ion batteries in electric vehicles

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ID: 141284
2014
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Abstract
The state of charge (SOC) is important for the safety and reliability of battery operation since it indicates the remaining capacity of a battery. However, it is difficult to get an accurate value of SOC, because the SOC cannot be directly measured by a sensor. In this paper, an adaptive gain nonlinear observer (AGNO) for SOC estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs) is proposed. The second-order resistor–capacitor (2RC) equivalent circuit model is used to simulate the dynamic behaviors of a LIB, based on which the state equations are derived to design the AGNO for SOC estimation. The model parameters are identified using the exponential-function fitting method. The sixth-order polynomial function is used to describe the highly nonlinear relationship between the open circuit voltage (OCV) and the SOC. The convergence of the proposed AGNO is proved using the Lyapunov stability theory. Two typical driving cycles, including the New European Driving Cycle (NEDC) and Federal Urban Driving Schedule (FUDS) are adopted to evaluate the performance of the AGNO by comparing with the unscented Kalman filter (UKF) algorithm. The experimental results show that the AGNO has better performance than the UKF algorithm in terms of reducing the computation cost, improving the estimation accuracy and enhancing the convergence ability.
Reference Key
tian2014energiesan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yong Tian;Chaoren Chen;Bizhong Xia;Wei Sun;Zhihui Xu;Weiwei Zheng
Journal acs combinatorial science
Year 2014
DOI
10.3390/en7095995
URL
http://www.mdpi.com/1996-1073/7/9/5995
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