Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization

Xiao Yang, Long Chen, Xing Xu, Wei Wang, Qiling Xu, Yuzhen Lin and Zhiguang Zhou
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Xiao Yang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
Long Chen: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
Xing Xu: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
Wei Wang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
Qiling Xu: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
Yuzhen Lin: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
Zhiguang Zhou: New Energy Development Department Powertrain Technology Center, Chery Automobile Co., Ltd., Wuhu 241009, Anhui, China

Energies, 2017, vol. 10, issue 11, 1-16

Abstract: The dynamic characteristics of power batteries directly affect the performance of electric vehicles, and the mathematical model is the basis for the design of a battery management system (BMS).Based on the electrode-averaged model of a lithium-ion battery, in view of the solid phase lithium-ion diffusion equation, the electrochemical model is simplified through the finite difference method. By analyzing the characteristics of the model and the type of parameters, the solid state diffusion kinetics are separated, and then the cascade parameter identifications are implemented with Particle Swarm Optimization. Eventually, the validity of the electrochemical model and the accuracy of model parameters are verified through 0.2–2 C multi-rates battery discharge tests of cell and road simulation tests of a micro pure electric vehicle under New European Driving Cycle (NEDC) conditions. The results show that the estimated parameters can guarantee the output accuracy. In the test of cell, the voltage deviation of discharge is generally less than 0.1 V except the end; in road simulation test, the output is close to the actual value at low speed with the error around ±0.03 V, and at high speed around ±0.08 V.

Keywords: lithium-ion battery; electrochemical model; particle swarm optimization; parameter identification (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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