应用于无人机的电池建模回归方法比较

Comparison of battery modeling regression methods for application to unmanned aerial vehicles

Jon Ander Martin, Justin N. Ouwerkerk, Anthony P. Lamping, Kelly Cohen

An effective battery prognostics method is fundamental for any application in which batteries have a critical role, such as in unmanned aerial vehicles. Given the batteries' variable nature, effectively predicting their End of Discharge or End of Life can become a difficult task. Therefore, developing an accurate and efficient model becomes a key step of this problem. The framework provided by traditional modeling techniques usually leads to inaccurate results, so newer state-of-the-art methodologies are needed to successfully build a model from a dataset. This paper compares the accuracy and time performance of three existing methods: a maximum likelihood optimal Support Vector Machine, a Bayesian Relevance Vector Machine, and a Fuzzy Inference System. Through this research, we aim to implement a real-time battery prognostics system in an Unmanned Aerial Vehicle. The three methods are used to model a Lithium-ion (Li-ion) battery's discharge curve while accounting for the State of Health of the battery for the estimation of voltage. This paper compares the accuracy and time performance of a maximum likelihood optimal Support Vector Machine, a Bayesian Relevance Vector Machine, and a Fuzzy Inference System for the modeling of Lithium-ion (Li-ion) batteries' discharge curve. Moreover, the model accounts for the State of Health of the battery for the estimation of voltage. We show that the three methodologies are valid for the modeling of the discharge curve with similar accuracy values. The Relevance Vector Machine proves to be the most computationally efficient method.

传统建模技术提供的框架通常会导致不准确的结果，因此需要更新的最先进的方法来成功地从数据集中建立一个模型。本文比较了三种现有方法的准确性和时间性能：最大似然最优支持向量机、贝叶斯相关性向量机和模糊推理系统。通过这项研究，我们的目标是在无人驾驶飞行器中实现一个实时的电池预知系统。这三种方法被用来对锂离子电池的放电曲线进行建模，同时考虑到电池的健康状态来估计电压。

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