锂动力电池老化特性研究与循环寿命预测
ging characters and cycle-life predictions of Li-ion cells
动力电池健康状态与循环寿命是电池监测与维护的关键。针对锂离子电池寿命的研究热点，本文首先搭建了循环寿命实验平台并采集数据，然后根据实验重点分析了动力电池的老化特性，并对锂离子电池的寿命进行了预测。最后对预测采用的BP神经网络和SVM算法的效果进行了评价；实验结果表明，文中提出的预测方法能够有效的利用于锂离子电池寿命预测中，在工程实际上有较高的利用价值。
he state of health and cycle life predictions of power battery are the key to battery monitoring and maintenance. For the puepose of solving this problem, this paper firstly set up battery experimental platform and collects data, then analysed aging characters according to the data, predicts battery remaining useful life. Finally, this paper evaluated the results of BP algorithm and SVM algorithm that used in the predictions. The results indicated that it is efficient to use this method in the cycle life predictions of Li-ion cells; it has high value in engineering.
张承慧、崔纳新、黄海
能源动力工业经济电工技术概论自动化技术、自动化技术设备
锂离子电池寿命预测神经网络支持向量机
Li-ion battery Cycle-life predictions Artificial Neural Network Support Vector Machine
张承慧,崔纳新,黄海.锂动力电池老化特性研究与循环寿命预测[EB/OL].(2016-05-26)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/201605-1229.点此复制
评论