Lithium-ion battery health management based on single particle model

Description:

Reference #: 01494

The University of South Carolina is offering licensing opportunities for Lithium-ion battery health management based on single particle model

Background:

A single particle model is used in simulating the behavior of lithium-ion battery. Particle swarm optimization is used to identify the parameters of the single particle model. Single particle is implemented in the LS and Bayesian estimation framework to estimate the battery state of charge and state of health online. Due to integration with Lebesgue sampling, an event-based sampling system where signal is sampled only when they pass certain requirements, the computation is significantly reduced. This enables deployment of the proposed approach on low-cost hardware such as embedded systems or microprocessors.

Invention Description:

The single particle model can accurately describe the behavior of lithium-ion batteries. The proposed SP model in LS-based Bayesian approach combines the accurate SP model and the low computational cost of LS. The proposed approach presents a novel accurate SOC and SOH estimation approach for lithium-ion batteries with better performance and higher computational efficiency.

Potential Applications:

Lithium-ion batteries are widely used in many applications due to the advantage in high energy density, high cycle life, low self-discharge and less weight. With the development of new energy industry, lithium-ion batteries can be found in electric vehicles, energy storage systems, solar power system, etc. Accurate and reliable lithium-ion battery SOC and SOC estimation has a significant effect on the safety and reliability of systems. Besides, it can ensure the efficient operation of the systems.

Advantages and Benefits:

The single particle model can accurately describe the behavior of lithium-ion batteries. The proposed SP model in LS-based Bayesian approach combines the accurate SP model and the low computational cost of LS. The proposed approach presents a novel accurate SOC and SOH estimation approach for lithium-ion batteries with better performance and higher computational efficiency.

Patent Information:
For Information, Contact:
Technology Commercialization
University of South Carolina
technology@sc.edu
Inventors:
Guangxing Niu
Bin Zhang
Keywords:
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