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

机译：增益优化的鲁棒充电状态估计算法

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摘要

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.
机译：电池的充电和放电过程是典型的电化学过程，可以将其建模为动态系统。充电状态（SoC）是一种常用的量度，用于相对于其全部容量量化存储在电池中的电荷。优化电池性能的最新努力需要更准确的SoC信息。传感器读数中的噪声使估算更具挑战性，尤其是在电池供电的系统中，传感器的电源电压不断变化。传统上使用的库仑计数和扩展卡尔曼滤波器方法分别存在噪声累积和偏噪声的常见现象。满足对精度不断增长的需求的传统方法是开发更复杂和复杂的解决方案，这些解决方案通常需要特殊的模型。采用这样的系统的主要挑战是固有的专业知识需求和基于命中试验的调整。在本文中，我们从自整定算法的角度探讨了一个新的维度，该算法可通过创建负反馈环路并增强其强度以惩罚估计误差，从而在不产生误差的情况下提供准确的SoC估计。具体而言，我们提出了一种新颖的方法，该方法使用电池模型和具有强反馈的保守滤波器，从而确保最小化最坏情况下的噪声放大。我们利用电池模型对电流和电压信号进行数据融合，以进行SoC估算。为了计算最佳参数，我们制定了线性矩阵不等式条件，可以使用开源工具对其进行最佳求解。该方法还具有估计期间的低计算开销的特点，可以在实时应用中使用。提供了详尽的数学证明以及详细的实验结果，突出了该方法相对于传统技术的优势。

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