多状态影响下基于Bi⁃LSTM网络的锂电池剩余寿命预测方法
2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)（2021）
College of Missile Engineering
Abstract
现有基于深度学习的锂电池剩余寿命（Remaining Useful Life, RUL）预测方法中，锂电池多个内部状态所蕴含的寿命信息未得到充分考虑.鉴于此，提出了一种融合电池容量、阻抗与温度三个内部状态的RUL预测模型.首先，引入双向长短时记忆（Bi-directional Long Short-Term Memory, Bi-LSTM）网络学习三种状态数据的时间相关性.其次，利用dropout技术与Bayesian变分推断技术间的等价性实现了RUL预测结果的不确定性量化，得到了预测结果的95%置信区间与概率密度分布（Probability Density Function, PDF），并分析了不同dropout率对预测不确定性的影响.最后，通过四种不同的深度学习模型框架与两种内部状态输入方案的对比实验，验证了本文方法的有效性.
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Key words
Remaining Useful Life prediction,three time-varying internal states,enhanced Bi-LSTM network,Monte Carlo-Dropout technology,uncertainty analysis
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