论文标题
通过LSTM RNN预测锂离子电池周期寿命
Predicting Li-ion Battery Cycle Life with LSTM RNN
论文作者
论文摘要
有效且准确的剩余使用寿命预测是可靠且安全使用锂离子电池的关键因素。这项工作训练了长期记忆复发性神经网络模型，以从各种周期和电压下排放能力的顺序数据中学习，并作为在不同条件下循环的电池电池的循环寿命预测变量。使用前60-80个周期的实验数据，我们的模型在大约80个样本的测试集上实现了有希望的预测准确性。
Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries. This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages and to work as a cycle life predictor for battery cells cycled under different conditions. Using experimental data of first 60 - 80 cycles, our model achieves promising prediction accuracy on test sets of around 80 samples.
