Researchers at the University of Connecticut, led by Yang Wang, have introduced a novel approach to enhance the accuracy of lithium-ion (Li-ion) battery parameter identification, a critical aspect of battery management and prediction systems. Their work, titled “Continuous-Time System Identification and OCV Reconstruction of Li-ion Batteries via Regularized Least Squares,” addresses the limitations of existing discrete-time methods that struggle with the fast-slow dynamics of batteries and the estimation of physical parameters.
The team’s innovative solution lies in developing a continuous-time approach that directly estimates battery parameters from sampled data, circumventing the discretization errors inherent in converting continuous-time models to discrete-time ones. This method not only improves the accuracy of parameter identification but also enables the joint estimation of the open-circuit voltage (OCV) and state of charge (SOC) relation without the need for offline OCV tests. By modeling the OCV-SOC curve as a cubic B-spline, the researchers achieve a high-fidelity representation of the OCV curve, which is crucial for battery management systems.
The relevance of this research to the energy sector is substantial. Accurate battery parameter identification is essential for optimizing battery performance, extending lifespan, and ensuring safety. As Li-ion batteries continue to play a pivotal role in renewable energy storage and electric vehicles, advancements in battery management technologies, such as the one proposed by Wang and colleagues, are vital for improving energy efficiency and reliability. The researchers’ continuous-time approach, validated through simulated and real-life data, offers a promising solution for enhancing the precision and effectiveness of battery management systems, ultimately contributing to the broader adoption and integration of renewable energy sources.
This article is based on research available at arXiv.