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首页> 外文会议>2014 International Conference on Mechatronics and Control >Least squares support vector machine based lithium battery capacity prediction
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Least squares support vector machine based lithium battery capacity prediction

机译：基于最小二乘支持向量机的锂电池容量预测

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摘要

The capacity character of lithium-ion battery is one of the most important performance parameters, which need to be accurate measurement for the safety and efficiency usage. In this paper, the regularity that battery capacity parameter changes with working temperature and charge or discharge rate has been analyzed, and the least squares support vector machine based battery capacity prediction method has been proposed for LiFePO battery. Furthermore, the lithium-ion battery capacity estimation experiments are carried out for both charging and discharging process, and the related results illustrate that the proposed method is able to give an accurate and efficient estimation of the corresponding battery capacity parameter in the presumed range of working temperature and charge or discharge rate.
机译：锂离子电池的容量特性是最重要的性能参数之一，需要对其进行准确测量以确保安全性和效率。本文分析了电池容量参数随工作温度和充放电速率变化的规律，提出了基于最小二乘支持向量机的LiFePO电池容量预测方法。此外，针对充电和放电过程进行了锂离子电池容量估计实验，相关结果表明，该方法能够在假定的工作范围内准确，有效地估计相应的电池容量参数。温度和充放电速率。

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