Researchers from the University of Michigan, including Feng Guo, Luis D. Couto, Khiem Trad, Guangdi Hu, and Mohammadhosein Safari, have developed a new method to improve state of charge (SOC) estimation in lithium iron phosphate (LFP) batteries. Their work, published in the journal IEEE Transactions on Control Systems Technology, addresses a significant challenge in the energy industry: accurately estimating the remaining charge in these widely used batteries.
LFP batteries are known for their safety, long cycle life, and cost-effectiveness, making them popular in various applications, from electric vehicles to renewable energy storage. However, their relatively flat open-circuit voltage (OCV) characteristic makes it difficult to accurately estimate their state of charge, which is crucial for optimal battery management and energy efficiency.
The researchers developed a novel approach called the residual bias compensation dual extended Kalman filter (RBC-DEKF). This method uses two filters: one to estimate the battery’s electrochemical states using a control-oriented parameter-grouped single particle model with thermal effects, and another to estimate a residual bias that continuously corrects the voltage observation equation. This dual-filter structure decouples residual bias estimation from electrochemical state estimation, unlike conventional methods that treat the bias as an augmented state within a single filter.
The researchers validated their method on an LFP cell from a public dataset under three different operating conditions: US06 at 0°C, DST at 25°C, and FUDS at 50°C. They found that their approach significantly reduced the average SOC root mean square error (RMSE) from 3.75% to 0.20% and the voltage RMSE between the filtered model voltage and the measured voltage from 32.8 mV to 0.8 mV. The improvement was most evident in the mid-SOC range, where the OCV-SOC curve is flat.
This research has practical applications for the energy industry. Accurate SOC estimation is crucial for battery management systems, which optimize battery performance, extend battery life, and ensure safety. Improved SOC estimation can lead to more efficient use of battery storage systems, better integration of renewable energy sources, and enhanced performance of electric vehicles. The RBC-DEKF method could be particularly beneficial for LFP batteries, which are widely used in these applications.
The researchers’ work highlights the importance of advanced control strategies in improving battery performance and efficiency. As the demand for energy storage solutions continues to grow, innovative approaches like the RBC-DEKF method will play a crucial role in optimizing battery management and enhancing the overall performance of energy storage systems.
This article is based on research available at arXiv.