Li-ion-Battery State-of-charge diagnosis-prognosis based on Lebesgue-sampling equivalent-circuit-model

Description:

Reference #: 01527

The University of South Carolina is offering licensing opportunities for Li-ion-Battery State-of-charge diagnosis-prognosis based on Lebesgue-sampling equivalent-circuit-model.

Background:

Traditional SOC estimation and prediction is mainly based on the electrochemical model (EM) or ECM of Li-battery. The EM method has a high computation cost and ECM cannot simulate behavior accurately with the effect of battery degradation.

Invention Description:

The innovation identifies the parameters of ECM by fitting a series of selected OCV (open circuit voltage) points with consideration of the nonlinearity of the terminal voltage, greatly decreases the time cost of obtaining OCV, and enable accurate SOC diagnostics and prognostics during the whole life service of the battery. In addition, the SOC diagnosis and prognosis are conducted in Lebesgue sampling framework, which further greatly reduces the computation cost and uncertainty accumulation, without sacrificing the accuracy of SOC diagnosis and prognosis.

Potential Applications:

The battery is a critical component of industrial systems, electric cars, and aerospace areas. The Li-battery has a bright future, and it will change our world because of its high energy density, voltage capacity, and lower self-discharge rate than other rechargeable batteries.

Advantages and Benefits:

The purposed method overcomes the drawback of the traditional parameter identification and OCV acquirement method. Moreover, it greatly decreases the computation cost and uncertainty accumulation, while significantly improving the efficiency, simplifying the process, and improving the accuracy.

Patent Information:
Category(s):
Energy
For Information, Contact:
Technology Commercialization
University of South Carolina
technology@sc.edu
Inventors:
Bin Zhang
Enhui Liu
Keywords:
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