%0 Journal Article
%T 基于改进相关向量机的锂电池寿命预测方法<br>Life prediction method of lithium battery based on improved relevance vector machine
%A 王春雷
%A 赵琦
%A 秦孝丽
%A 冯文全
%J 北京航空航天大学学报
%D 2018
%R 10.13700/j.bh.1001-5965.2018.0181
%X 摘要 锂电池具有轻便安全、循环寿命长和安全性能好等优点，作为一个被广泛应用的储能电源，锂电池健康管理和寿命预测是国内外研究的热点。建立锂电池寿命预测方法和模型，基于实验历史数据，建立电池衰减模型从而对整个电池的工作状态进行评估，及时对设备进行维护和替换，以确保电池工作的稳定。对相关向量机（RVM）的核函数进行了组合改进，优化了RVM的性能，减小了锂电池寿命预测的偏差度，提高了预测精度。<br>Abstract：Lithium batteries have the advantages of light weight and safety, long cycle life, and good safety performance. As a widely-used energy storage power supply, lithium battery health management and life prediction are hot topics both at home and abroad. Lithium battery life assessment methods and prediction models were established. Battery decay models were established based on experimental historical data to evaluate the working status of the entire battery, and the equipment was maintained and replaced in time to ensure stable battery operation. In this paper, the kernel function of the relevance vector machine (RVM) was mainly improved, the performance of the relevance vector machine was optimized, the lithium battery life prediction bias was reduced, and the prediction accuracy was improved.
%K 锂电池
%K 剩余寿命
%K 预测
%K 相关向量机(RVM)
%K MATLAB&lt
%K br&gt
%K lithium battery
%K remaining useful life
%K prediction
%K relevance vector machine (RVM)
%K MATLAB
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14597.shtml