A Robust Algorithm for State-of-Charge Estimation With Gain Optimization

The charging and discharging procedure of a battery is a typical electrochemical process, which can be modeled as a dynamic system. State of charge (SoC) is a commonly used measure to quantify the charge stored in the battery in relation to its full capacity. Recent efforts of optimizing battery performance require more accurate SoC information. The noise in sensor readings makes the estimation even more challenging, especially in battery-operated systems where the supply voltage of the sensor keeps changing. Traditionally used methods of Coulomb counting and extended Kalman filter suffer from the accumulation of noise and common phenomenon of biased noise, respectively. The traditional approach of dealing with ever-increasing demand for accuracy is to develop more complicated and sophisticated solutions, which generally require special models. A key challenge in the adoption of such systems is the inherent requirement of specialized knowledge and hit-and-trial-based tuning. In this paper, we explore a new dimension from the perspective of a self-tuning algorithm, which can provide accurate SoC estimation without error accumulation by creating a negative feedback loop and enhancing its strength to penalize the estimation error. Specifically, we propose a novel method, which uses a battery model and a conservative filter with a strong feedback, which guarantees that worst-case amplification of noise is minimized. We capitalize on the battery model for data fusion of current and voltage signals for SoC estimation. To compute the best parameters, we formulate the linear matrix inequality conditions, which are optimally solved using open-source tools. This approach also features a low computational expense during estimation, which can be used in real-time applications. Thorough mathematical proofs, as well as detailed experimental results, are provided, which highlight the advantages of the proposed method over traditional techniques.