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Energies 2017, 10(11), 1811; https://doi.org/10.3390/en10111811

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

1
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
2
Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, Jiangsu, China
3
New Energy Development Department Powertrain Technology Center, Chery Automobile Co., Ltd., Wuhu 241009, Anhui, China
*
Author to whom correspondence should be addressed.
Received: 23 October 2017 / Revised: 6 November 2017 / Accepted: 7 November 2017 / Published: 9 November 2017
(This article belongs to the Section Energy Sources)
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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. View Full-Text
Keywords: lithium-ion battery; electrochemical model; particle swarm optimization; parameter identification lithium-ion battery; electrochemical model; particle swarm optimization; parameter identification
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Yang, X.; Chen, L.; Xu, X.; Wang, W.; Xu, Q.; Lin, Y.; Zhou, Z. Parameter Identification of Electrochemical Model for Vehicular Lithium-Ion Battery Based on Particle Swarm Optimization. Energies 2017, 10, 1811.

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