论文标题
使用复发性神经网络在车辆到网格应用中对锂离子电池的健康状况估算，以学习降解应力因素的影响
State of Health Estimation of Lithium-Ion Batteries in Vehicle-to-Grid Applications Using Recurrent Neural Networks for Learning the Impact of Degradation Stress Factors
论文作者
论文摘要
这项工作提出了一个有效的健康指标状态，以指示基于长期短期记忆（LSTM）复发性神经网络（RNN）和滑动窗口的锂离子电池降解。开发的LSTM RNN能够捕获电池降解应力因素降解的细胞容量的基本长期依赖性。当有足够的培训数据时，学习绩效是可靠的，如果为培训提供了价值超过1.15年的数据，则误差<5％。
This work presents an effective state of health indicator to indicate lithium-ion battery degradation based on a long short-term memory (LSTM) recurrent neural network (RNN) coupled with a sliding-window. The developed LSTM RNN is able to capture the underlying long-term dependencies of degraded cell capacity on battery degradation stress factors. The learning performance was robust when there was sufficient training data, with an error of < 5% if more than 1.15 years worth of data was supplied for training.
