| 基于支持向量回归的动力电池参数辨识 |
 点此下载全文 |
| 引用本文：张浒，王敏，黄心汉.基于支持向量回归的动力电池参数辨识[J].计算技术与自动化,2014,(3):27-30 |
| 摘要点击次数: 979 |
| 全文下载次数: 43 |
|
|
| 中文摘要:根据铅酸蓄电池在放电过程中内部电化学反应导致外部电特性变化的特点，提出一种基于支持向量机原理的电解液密度辨识模型。利用支持向量机理论非线性回归的特性，简化测量电解液密度的过程，在恶劣环境下检测动力电池的电解液密度更显其优越性。预测实验表明，采用改进的交叉验证预测模型具有泛化能力强、稳定性好的特点，并且在小样本的条件下能达到预期的辨识精度。 |
| 中文关键词:电解液密度 支持向量机 交叉验证 参数辨识 |
| |
| Parameter Identification of Power Battery with Support Vector Regression |
|
|
| Abstract:This paper presents an identification model of electrolyte density, which based on the Support Vector Machine Theory, according to the feature that the chemical reaction of interior electrics leading to the characteristic change of exterior electrics in the discharge process of the lead-acid battery. This model simplifies the process of measuring the electrolyte density by using the nonlinear regression characteristics of the Support Vector Machine Theory, and it works better when measuring the electrolyte density of power battery in severe environment. Prediction experiment shows that the improved cross-validation pridiction model is featured by good generalization capability and stability, and can reach the expected identifying accuracy on small sample. |
| keywords:electrolyte density support vector machine cross validation parameter identification |
| 查看全文 查看/发表评论 下载pdf阅读器 |
次