摘要
针对目前的剩余寿命预测(RUL)方法存在模型适应性差及预测不准确等问题,提出多尺度深度神经网络的锂电池健康退化预测模型.通过经验模态分解(EEMD)方法和相关性分析(CA),将采集到的锂电池能量数据分解为主趋势数据和波动数据;采用深度置信网络(DBN)和长短期记忆网络(LSTM),分别对主趋势与波动数据进行建模;将DBN与LSTM预测结果进行有效集成,得到锂电池的健康预测结果.实验结果表明,利用该方法能够有效地对锂电池的健康趋势进行拟合,得到准确的RUL预测结果,性能优于其他典型的预测方法.
A health degradation prediction model of lithium batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current remaining useful life(RUL) prediction methods. The collected energy data of lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition(EEMD) and correlation analysis(CA). Then deep belief network(DBN) and long short-term memory(LSTM) were used to model the main trend and fluctuation data respectively. The predicting outcomes of DBN and LSTM were effectively integrated to obtain the health predicted results of lithium-ion battery. The experimental results show that the method can effectively fit the health trend of lithium-ion batteries and obtain accurate RUL prediction results. The performance of the method is better than other typical prediction methods.
作者
胡天中
余建波
HU Tian-zhong;YU Jian-bo(School of Mechanical Engineering, Tongji University, Shanghai 201804, China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2019年第10期1852-1864,共13页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(51375290,71777173)
中央高校基本科研业务费资助项目
上海科委创新科技行动计划资助项目(17511109204)
