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Energies 2014, 7(8), 5065-5082; doi:10.3390/en7085065

Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries

1
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
2
Department of Electrical and Computer Engineering, University of Michigan, Dearborn, MI 48128, USA
*
Authors to whom correspondence should be addressed.
Received: 16 April 2014 / Revised: 1 August 2014 / Accepted: 5 August 2014 / Published: 8 August 2014
(This article belongs to the Special Issue Electrochemical Energy Storage—Battery and Capacitor)
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Abstract

Four model-based State of Charge (SOC) estimation methods for lithium-ion (Li-ion) batteries are studied and evaluated in this paper. Different from existing literatures, this work evaluates different aspects of the SOC estimation, such as the estimation error distribution, the estimation rise time, the estimation time consumption, etc. The equivalent model of the battery is introduced and the state function of the model is deduced. The four model-based SOC estimation methods are analyzed first. Simulations and experiments are then established to evaluate the four methods. The urban dynamometer driving schedule (UDDS) current profiles are applied to simulate the drive situations of an electrified vehicle, and a genetic algorithm is utilized to identify the model parameters to find the optimal parameters of the model of the Li-ion battery. The simulations with and without disturbance are carried out and the results are analyzed. A battery test workbench is established and a Li-ion battery is applied to test the hardware in a loop experiment. Experimental results are plotted and analyzed according to the four aspects to evaluate the four model-based SOC estimation methods. View Full-Text
Keywords: model-based estimation; state of charge (SOC); battery management system (BMS); Luenberger observer; Kalman filter; sliding mode observer; proportional integral observer model-based estimation; state of charge (SOC); battery management system (BMS); Luenberger observer; Kalman filter; sliding mode observer; proportional integral observer
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Zou, Z.; Xu, J.; Mi, C.; Cao, B.; Chen, Z. Evaluation of Model Based State of Charge Estimation Methods for Lithium-Ion Batteries. Energies 2014, 7, 5065-5082.

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