Researchers at a leading university have developed a novel approach to enhance the accuracy of lithium-ion (Li-ion) battery parameter identification, a critical aspect of battery management and prediction. The team, led by experts in energy systems and data analysis, has addressed long-standing challenges posed by traditional discrete-time methods.
The new continuous-time approach allows for direct estimation of battery parameters from sampled data, circumventing the discretization errors inherent in converting continuous-time models into discrete-time ones. This advancement is particularly significant for the energy sector, as accurate battery modeling is essential for optimizing energy storage systems, electric vehicles, and grid applications.
One of the standout features of this research is the joint identification of the open-circuit voltage (OCV) and the state of charge (SOC) relation of the battery without the need for offline OCV tests. By modeling the OCV-SOC curve as a cubic B-spline, the researchers achieved a high-fidelity representation of the OCV curve, which is crucial for precise battery state estimation.
The method involves solving a rank and L1 regularized least squares problem to jointly identify battery parameters and the OCV-SOC relation from the battery’s dynamic data. This approach has been validated using both simulated and real-life data, demonstrating its effectiveness and potential for practical applications.
For the energy sector, this research offers a promising avenue for improving battery management systems, enhancing the reliability and efficiency of energy storage solutions. Accurate parameter identification can lead to better predictions of battery behavior, prolonging battery life and optimizing performance in various applications.
This article is based on research available at arXiv.