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.
Highlights
With the development of electric vehicles (EVs), portable devices and even smart grids [1,2], battery technology has attracted more and more attention worldwide
There have been plenty of State of Charge (SOC) estimation methods developed in the last decade, which can be cataloged in several types: the ampere-hour method (AHM), the electrochemical method, the artificial intelligence methods, the model based method, etc
It is clear that the rise times of the convergent processes are quite different
Summary
With the development of electric vehicles (EVs), portable devices and even smart grids [1,2], battery technology has attracted more and more attention worldwide. As one of the key parameters of a battery, the state of charge (SOC) is one of the main study topics of battery technology. Unlike the voltage and the current of the battery, the SOC cannot be measured directly. Proper estimation methods should be utilized to obtain the SOC of a battery. There have been plenty of SOC estimation methods developed in the last decade, which can be cataloged in several types: the ampere-hour method (AHM), the electrochemical method, the artificial intelligence methods, the model based method, etc. J.; Mi, C.; Cao, B.; Deng, J.; Chen, Z.; Li, S. The state of charge estimation of lithium-ion batteries based on a proportional integral observer. An online state of charge estimation method with reduced prior battery testing information.
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