An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles

Yong Tian, Chaoren Chen, Bizhong Xia, Wei Sun, Zhihui Xu and Weiwei Zheng
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Yong Tian: Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, The University Town, Shenzhen 518055, Guangdong, China
Chaoren Chen: Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, The University Town, Shenzhen 518055, Guangdong, China
Bizhong Xia: Graduate School at Shenzhen, Tsinghua University, Tsinghua Campus, The University Town, Shenzhen 518055, Guangdong, China
Wei Sun: Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, Guangdong, China
Zhihui Xu: Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, Guangdong, China
Weiwei Zheng: Sunwoda Electronic Co. Ltd., Yihe Road, Baoan District, Shenzhen 518108, Guangdong, China

Energies, 2014, vol. 7, issue 9, 1-18

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.

Keywords: state of charge ( SOC ); adaptive gain nonlinear observer (AGNO); lithium-ion battery (LIB); electric vehicles (EVs) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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