Researchers from an undisclosed institution have developed a novel approach to enhance the identification of lithium-ion battery parameters, a critical aspect of battery management and performance optimization. The team’s method, detailed in their recent study, addresses the challenges posed by the variation of battery parameters over the state of charge (SOC) and the nonlinear dependence of the open-circuit voltage (OCV) on the SOC.
The researchers employed a continuous-time linear parameter-varying (LPV) system identification approach to model these variations, using cubic B-splines to capture the piecewise nonlinearity of the parameter changes. They also utilized state variable filters to estimate signal derivatives, facilitating the CT-LPV identification process.
One of the standout features of this research is the joint identification of battery parameters and the OCV-SOC mapping through the solution of L1-regularized least squares problems. This approach not only simplifies the identification process but also enhances its accuracy.
The practical implications for the energy sector are significant. Accurate identification of battery parameters is essential for estimating battery states and managing performance, which can lead to improved battery life, enhanced safety, and optimized energy storage solutions. The researchers’ method presents improved performance compared to conventional recursive least squares (RLS)-based methods, making it a promising tool for battery management systems in electric vehicles, renewable energy storage, and other applications.
The effectiveness of the developed method was demonstrated through numerical experiments on a simulated battery and real-life data, underscoring its potential for real-world applications. As the demand for efficient and reliable energy storage solutions continues to grow, advancements in battery identification and management, such as this one, are crucial for driving progress in the energy sector.
This article is based on research available at arXiv.